Diabetes

Computer technology and artificial intelligence (AI) have come a long way in several decades:

• Between 1971 and 1996, access to the Medline database was primarily limited to university libraries and other institutions; in 1997, the database became universally available online as PubMed.¹
• In 2004, the President of the United States issued an executive order that launched a 10-year plan to put electronic health records (EHRs) in place nationwide; EHRs are now employed in nearly 9 of 10 (85.9%) medical offices.²

Over time, numerous online resources sprouted as well, including DxPlain, UpToDate, and Clinical Key, to name a few. These digital tools were impressive for their time, but many of them are now considered “old-school” AI-enabled clinical decision support.

In the past 2 to 3 years, innovative clinicians and technologists have pushed medicine into a new era that takes advantage of machine learning (ML)-enhanced diagnostic aids, software systems that predict disease progression, and advanced clinical pathways to help individualize treatment. Enthusiastic early adopters believe these resources are transforming patient care—although skeptics remain unconvinced, cautioning that they have yet to prove their worth in everyday clinical practice.

In this review, we first analyze the strengths and weaknesses of evidence supporting these tools, then propose a potential role for them in family medicine.

Machine learning takes on retinopathy

The term “artificial intelligence” has been with us for longer than a half century.³ In the broadest sense, AI refers to any computer system capable of automating a process usually performed manually by humans. But the latest innovations in AI take advantage of a subset of AI called “machine learning”: the ability of software systems to learn new functionality or insights on their own, without additional programming from human data engineers. Case in point: A software platform has been developed that is capable of diagnosing or screening for diabetic retinopathy without the involvement of an experienced ophthalmologist.

A software platform has been developed that is capable of diagnosing or screening for diabetic retinopathy without the involvement of an experienced ophthalmologist.

The landmark study that started clinicians and health care executives thinking seriously about the potential role of ML in medical practice was spearheaded by ­Varun Gulshan, PhD, at Google, and associates from several medical schools.⁴ Gulshan used an artificial neural network designed to mimic the functions of the human nervous system to analyze more than 128,000 retinal images, looking for evidence of diabetic retinopathy. (See “Deciphering artificial neural networks,” for an explanation of how such networks function.⁵) The algorithm they employed was compared with the diagnostic skills of several board-certified ophthalmologists.

[polldaddy:10453606]

Continue to: Deciperhing artificial neural networks

Using an area-under-the-receiver operating curve (AUROC) as a metric, and choosing an operating point for high specificity, the algorithm generated sensitivity of 87% and 90.3% and specificity of 98.1% and 98.5% for 2 validation data sets for detecting referable retinopathy, as defined by a panel of at least 7 ophthalmologists. When AUROC was set for high sensitivity, the algorithm generated sensitivity of 97.5% and 96.1% and specificity of 93.4% and 93.9% for the 2 data sets.

These results are impressive, but the researchers used a retrospective approach in their analysis. A prospective analysis would provide stronger evidence.

That shortcoming was addressed by a pivotal clinical trial that convinced the US Food and Drug Administration (FDA) to approve the technology. Michael Abramoff, MD, PhD, at the University of Iowa Department of Ophthalmology and Visual Sciences and his associates⁶ conducted a prospective study that compared the gold standard for detecting retinopathy, the Fundus Photograph Reading Center (of the University of Wisconsin School of Medicine and Public Health), to an ML-based algorithm, the commercialized IDx-DR. The IDx-DR is a software system that is used in combination with a fundal camera to capture retinal images. The researchers found that “the AI system exceeded all pre-specified superiority endpoints at sensitivity of 87.2% ... [and] specificity of 90.7% ....”

Continue to: The FDA clearance statement...

The FDA clearance statement for this technology⁷ limits its use, emphasizing that it is intended only as a screening tool, not a stand-alone diagnostic system. Because ­IDx-DR is being used in primary care, the FDA states that patients who have a positive result should be referred to an eye care professional. The technology is contraindicated in patients who have a history of laser treatment, surgery, or injection in the eye or who have any of the following: persistent vision loss, blurred vision, floaters, previously diagnosed macular edema, severe nonproliferative retinopathy, proliferative retinopathy, radiation retinopathy, and retinal vein occlusion. It is also not intended for pregnant patients because their eye disease often progresses rapidly.

A large-scale validation study performed on data from Kaiser Permanente Northwest found that it is possible to estimate a person's risk of colorectal cancer by using age, gender, and complete blood count.

Additional caveats to keep in mind when evaluating this new technology include that, although the software can help detect retinopathy, it does not address other key issues for this patient population, including cataracts and glaucoma. The cost of the new technology also requires attention: Software must be used in conjunction with a specific retinal camera, the Topcon TRC-NW400, which is expensive (new, as much as $20,000).

IMAGE: ©GETTY IMAGES

Speaking of cost: Health care providers and insurers still question whether implementing AI-enabled systems is cost-­effective. It is too early to say definitively how AI and machine learning will have an impact on health care expenditures, because the most promising technological systems have yet to be fully implemented in hospitals and medical practices nationwide. Projections by Forbes suggest that private investment in health care AI will reach $6.6 billion by 2021; on a more confident note, an Accenture analysis predicts that the best possible application of AI might save the health care sector $150 billion annually by 2026.⁸

What role might this diabetic retinopathy technology play in family medicine? Physicians are constantly advising patients who have diabetes about the need to have a regular ophthalmic examination to check for early signs of retinopathy—advice that is often ignored. The American Academy of Ophthalmology points out that “6 out of 10 people with diabetes skip a sight-saving exam.”⁹ When a patient is screened with this type of device and found to be at high risk of eye disease, however, the advice to see an eye-care specialist might carry more weight.

Screening colonoscopy: Improving patient incentives

No responsible physician doubts the value of screening colonoscopy in patients 50 years and older, but many patients have yet to realize that the procedure just might save their life. Is there a way to incentivize resistant patients to have a colonoscopy performed? An ML-based software system that only requires access to a few readily available parameters might be the needed impetus for many patients.

Continue to: A large-scale validation...

A large-scale validation study performed on data from Kaiser Permanente Northwest found that it is possible to estimate a person’s risk of colorectal cancer by using age, gender, and complete blood count.¹⁰ This retrospective investigation analyzed more than 17,000 Kaiser Permanente patients, including 900 who already had colorectal cancer. The analysis generated a risk score for patients who did not have the malignancy to gauge their likelihood of developing it. The algorithms were more sensitive for detecting tumors of the cecum and ascending colon, and less sensitive for detection of tumors of the transverse and sigmoid colon and rectum.

To provide more definitive evidence to support the value of the software platform, a prospective study was subsequently conducted on more than 79,000 patients who had initially declined to undergo colorectal screening. The platform, called ColonFlag, was used to detect 688 patients at highest risk, who were then offered screening colonoscopy. In this subgroup, 254 agreed to the procedure; ColonFlag identified 19 malignancies (7.5%) among patients within the Maccabi Health System (Israel), and 15 more in patients outside that health system.¹¹ (In the United States, the same program is known as LGI Flag and has been cleared by the FDA.)

Although ColonFlag has the potential to reduce the incidence of colorectal cancer, other evidence-based screening modalities are highlighted in US Preventive Services Task Force guidelines, including the guaiac-based fecal occult blood test and the fecal immunochemical test.¹²

Beyond screening to applications in managing disease

The complex etiology of sepsis makes the condition difficult to treat. That complexity has also led to disagreement on the best course of management. Using an ML algorithm called an “Artificial Intelligence Clinician,” Komorowski and associates¹³ extracted data from a large data set from 2 nonoverlapping intensive care unit databases collected from US adults.The researchers’ analysis suggested a list of 48 variables that likely influence sepsis outcomes, including:

• demographics,
• Elixhauser premorbid status,
• vital signs,
• clinical laboratory data,
• intravenous fluids given, and
• vasopressors administered.

Komorowski and co-workers concluded that “… mortality was lowest in patients for whom clinicians’ actual doses matched the AI decisions. Our model provides individualized and clinically interpretable treatment decisions for sepsis that could improve patient outcomes.”

A randomized clinical trial has found that an ML program that uses only 6 common clinical markers—blood pressure, heart rate, temperature, respiratory rate, peripheral capillary oxygen saturation (SpO₂), and age—can improve clinical outcomes in patients with severe sepsis.¹⁴ The alerts generated by the algorithm were used to guide treatment. Average length of stay was 13 days in controls, compared with 10.3 days in those evaluated with the ML algorithm. The algorithm was also associated with a 12.4% drop in in-­hospital mortality.

Continue to: Addressing challenges, tapping resources

Advances in the management of diabetic retinopathy, colorectal cancer, and sepsis are the tip of the AI iceberg. There are now ML programs to distinguish melanoma from benign nevi; to improve insulin dosing for patients with type 1 diabetes; to predict which hospital patients are most likely to end up in the intensive care unit; and to mitigate the opioid epidemic.

An ML Web page on the JAMA Network (https://sites.jamanetwork.com/machine-learning/) features a long list of published research studies, reviews, and opinion papers suggesting that the future of medicine is closely tied to innovative developments in this area. This Web page also addresses the potential use of ML in detecting lymph node metastases in breast cancer, the need to temper AI with human intelligence, the role of AI in clinical decision support, and more.

The JAMA Network also discusses a few of the challenges that still need to be overcome in developing ML tools for clinical medicine—challenges that you will want to be cognizant of as you evaluate new research in the field.

Black-box dilemma. A challenge that technologists face as they introduce new programs that have the potential to improve diagnosis, treatment, and prognosis is a phenomenon called the “black-box dilemma,” which refers to the complex data science, advanced statistics, and mathematical equations that underpin ML algorithms. These complexities make it difficult to explain the mechanism of action upon which software is based, which, in turn, makes many clinicians skeptical about its worth.

A randomized clinical trial has found that an ML program that uses only 6 common clinical markers can improve clinical outcomes in patients with severe sepsis.

For example, the neural networks that are the backbone of the retinopathy algorithm discussed earlier might seem like voodoo science to those unfamiliar with the technology. It’s fortunate that several technology-savvy physicians have mastered these digital tools and have the teaching skills to explain them in plain-English tutorials. One such tutorial, “Understanding How Machine Learning Works,” is posted on the JAMA Network (https://sites.­jamanetwork.com/machine-learning/#multimedia). A more basic explanation was included in a recent Public Broadcasting System “Nova” episode, viewable at www.youtube.com/watch?v=xS2G0oolHpo.

Continue to: Limited analysis

Limited analysis. Another problem that plagues many ML-based algorithms is that they have been tested on only a single data set. (Typically, a data set refers to a collection of clinical parameters from a patient population.) For example, researchers developing an algorithm might collect their data from a single health care system.

Several investigators have addressed this shortcoming by testing their software on 2 completely independent patient populations. Banda and colleagues¹⁵ recently developed a software platform to improve the detection rate in familial hypercholesterolemia, a significant cause of premature cardiovascular disease and death that affects approximately 1 of every 250 people. Despite the urgency of identifying the disorder and providing potentially lifesaving treatment, only 10% of patients receive an accurate diagnosis.¹⁶ Banda and colleagues developed a deep-learning algorithm that is far more effective than the traditional screening approach now in use.

To address the generalizability of the algorithm, it was tested on EHR data from 2 independent health care systems: Stanford Health Care and Geisinger Health System. In Stanford patients, the positive predictive value of the algorithm was 88%, with a sensitivity of 75%; it identified 84% of affected patients at the highest probability threshold. In Geisinger patients, the classifier generated a positive predictive value of 85%.

The future of these technologies

AI and ML are not panaceas that will revolutionize medicine in the near future. Likewise, the digital tools discussed in this article are not going to solve multiple complex medical problems addressed during a single office visit. But physicians who ignore mounting evidence that supports these emerging technologies will be left behind by more forward-thinking colleagues.

The best possible application of AI might save the health care sector $150 billion annually by 2026, according to an economic analysis.

A recent commentary in Gastroenterology¹⁷ sums up the situation best: “It is now too conservative to suggest that CADe [computer-assisted detection] and CADx [computer-assisted diagnosis] carry the potential to revolutionize colonoscopy. The artificial intelligence revolution has already begun.”

CORRESPONDENCE
Paul Cerrato, MA, [email protected], [email protected]. John Halamka, MD, MS, [email protected].

Computer technology and artificial intelligence (AI) have come a long way in several decades:

• Between 1971 and 1996, access to the Medline database was primarily limited to university libraries and other institutions; in 1997, the database became universally available online as PubMed.¹
• In 2004, the President of the United States issued an executive order that launched a 10-year plan to put electronic health records (EHRs) in place nationwide; EHRs are now employed in nearly 9 of 10 (85.9%) medical offices.²

Over time, numerous online resources sprouted as well, including DxPlain, UpToDate, and Clinical Key, to name a few. These digital tools were impressive for their time, but many of them are now considered “old-school” AI-enabled clinical decision support.

In the past 2 to 3 years, innovative clinicians and technologists have pushed medicine into a new era that takes advantage of machine learning (ML)-enhanced diagnostic aids, software systems that predict disease progression, and advanced clinical pathways to help individualize treatment. Enthusiastic early adopters believe these resources are transforming patient care—although skeptics remain unconvinced, cautioning that they have yet to prove their worth in everyday clinical practice.

In this review, we first analyze the strengths and weaknesses of evidence supporting these tools, then propose a potential role for them in family medicine.

Machine learning takes on retinopathy

The term “artificial intelligence” has been with us for longer than a half century.³ In the broadest sense, AI refers to any computer system capable of automating a process usually performed manually by humans. But the latest innovations in AI take advantage of a subset of AI called “machine learning”: the ability of software systems to learn new functionality or insights on their own, without additional programming from human data engineers. Case in point: A software platform has been developed that is capable of diagnosing or screening for diabetic retinopathy without the involvement of an experienced ophthalmologist.

A software platform has been developed that is capable of diagnosing or screening for diabetic retinopathy without the involvement of an experienced ophthalmologist.

The landmark study that started clinicians and health care executives thinking seriously about the potential role of ML in medical practice was spearheaded by ­Varun Gulshan, PhD, at Google, and associates from several medical schools.⁴ Gulshan used an artificial neural network designed to mimic the functions of the human nervous system to analyze more than 128,000 retinal images, looking for evidence of diabetic retinopathy. (See “Deciphering artificial neural networks,” for an explanation of how such networks function.⁵) The algorithm they employed was compared with the diagnostic skills of several board-certified ophthalmologists.

[polldaddy:10453606]

Continue to: Deciperhing artificial neural networks

Using an area-under-the-receiver operating curve (AUROC) as a metric, and choosing an operating point for high specificity, the algorithm generated sensitivity of 87% and 90.3% and specificity of 98.1% and 98.5% for 2 validation data sets for detecting referable retinopathy, as defined by a panel of at least 7 ophthalmologists. When AUROC was set for high sensitivity, the algorithm generated sensitivity of 97.5% and 96.1% and specificity of 93.4% and 93.9% for the 2 data sets.

These results are impressive, but the researchers used a retrospective approach in their analysis. A prospective analysis would provide stronger evidence.

That shortcoming was addressed by a pivotal clinical trial that convinced the US Food and Drug Administration (FDA) to approve the technology. Michael Abramoff, MD, PhD, at the University of Iowa Department of Ophthalmology and Visual Sciences and his associates⁶ conducted a prospective study that compared the gold standard for detecting retinopathy, the Fundus Photograph Reading Center (of the University of Wisconsin School of Medicine and Public Health), to an ML-based algorithm, the commercialized IDx-DR. The IDx-DR is a software system that is used in combination with a fundal camera to capture retinal images. The researchers found that “the AI system exceeded all pre-specified superiority endpoints at sensitivity of 87.2% ... [and] specificity of 90.7% ....”

Continue to: The FDA clearance statement...

The FDA clearance statement for this technology⁷ limits its use, emphasizing that it is intended only as a screening tool, not a stand-alone diagnostic system. Because ­IDx-DR is being used in primary care, the FDA states that patients who have a positive result should be referred to an eye care professional. The technology is contraindicated in patients who have a history of laser treatment, surgery, or injection in the eye or who have any of the following: persistent vision loss, blurred vision, floaters, previously diagnosed macular edema, severe nonproliferative retinopathy, proliferative retinopathy, radiation retinopathy, and retinal vein occlusion. It is also not intended for pregnant patients because their eye disease often progresses rapidly.

A large-scale validation study performed on data from Kaiser Permanente Northwest found that it is possible to estimate a person's risk of colorectal cancer by using age, gender, and complete blood count.

Additional caveats to keep in mind when evaluating this new technology include that, although the software can help detect retinopathy, it does not address other key issues for this patient population, including cataracts and glaucoma. The cost of the new technology also requires attention: Software must be used in conjunction with a specific retinal camera, the Topcon TRC-NW400, which is expensive (new, as much as $20,000).

IMAGE: ©GETTY IMAGES

Speaking of cost: Health care providers and insurers still question whether implementing AI-enabled systems is cost-­effective. It is too early to say definitively how AI and machine learning will have an impact on health care expenditures, because the most promising technological systems have yet to be fully implemented in hospitals and medical practices nationwide. Projections by Forbes suggest that private investment in health care AI will reach $6.6 billion by 2021; on a more confident note, an Accenture analysis predicts that the best possible application of AI might save the health care sector $150 billion annually by 2026.⁸

What role might this diabetic retinopathy technology play in family medicine? Physicians are constantly advising patients who have diabetes about the need to have a regular ophthalmic examination to check for early signs of retinopathy—advice that is often ignored. The American Academy of Ophthalmology points out that “6 out of 10 people with diabetes skip a sight-saving exam.”⁹ When a patient is screened with this type of device and found to be at high risk of eye disease, however, the advice to see an eye-care specialist might carry more weight.

Screening colonoscopy: Improving patient incentives

No responsible physician doubts the value of screening colonoscopy in patients 50 years and older, but many patients have yet to realize that the procedure just might save their life. Is there a way to incentivize resistant patients to have a colonoscopy performed? An ML-based software system that only requires access to a few readily available parameters might be the needed impetus for many patients.

Continue to: A large-scale validation...

A large-scale validation study performed on data from Kaiser Permanente Northwest found that it is possible to estimate a person’s risk of colorectal cancer by using age, gender, and complete blood count.¹⁰ This retrospective investigation analyzed more than 17,000 Kaiser Permanente patients, including 900 who already had colorectal cancer. The analysis generated a risk score for patients who did not have the malignancy to gauge their likelihood of developing it. The algorithms were more sensitive for detecting tumors of the cecum and ascending colon, and less sensitive for detection of tumors of the transverse and sigmoid colon and rectum.

To provide more definitive evidence to support the value of the software platform, a prospective study was subsequently conducted on more than 79,000 patients who had initially declined to undergo colorectal screening. The platform, called ColonFlag, was used to detect 688 patients at highest risk, who were then offered screening colonoscopy. In this subgroup, 254 agreed to the procedure; ColonFlag identified 19 malignancies (7.5%) among patients within the Maccabi Health System (Israel), and 15 more in patients outside that health system.¹¹ (In the United States, the same program is known as LGI Flag and has been cleared by the FDA.)

Although ColonFlag has the potential to reduce the incidence of colorectal cancer, other evidence-based screening modalities are highlighted in US Preventive Services Task Force guidelines, including the guaiac-based fecal occult blood test and the fecal immunochemical test.¹²

Beyond screening to applications in managing disease

The complex etiology of sepsis makes the condition difficult to treat. That complexity has also led to disagreement on the best course of management. Using an ML algorithm called an “Artificial Intelligence Clinician,” Komorowski and associates¹³ extracted data from a large data set from 2 nonoverlapping intensive care unit databases collected from US adults.The researchers’ analysis suggested a list of 48 variables that likely influence sepsis outcomes, including:

• demographics,
• Elixhauser premorbid status,
• vital signs,
• clinical laboratory data,
• intravenous fluids given, and
• vasopressors administered.

Komorowski and co-workers concluded that “… mortality was lowest in patients for whom clinicians’ actual doses matched the AI decisions. Our model provides individualized and clinically interpretable treatment decisions for sepsis that could improve patient outcomes.”

A randomized clinical trial has found that an ML program that uses only 6 common clinical markers—blood pressure, heart rate, temperature, respiratory rate, peripheral capillary oxygen saturation (SpO₂), and age—can improve clinical outcomes in patients with severe sepsis.¹⁴ The alerts generated by the algorithm were used to guide treatment. Average length of stay was 13 days in controls, compared with 10.3 days in those evaluated with the ML algorithm. The algorithm was also associated with a 12.4% drop in in-­hospital mortality.

Continue to: Addressing challenges, tapping resources

Advances in the management of diabetic retinopathy, colorectal cancer, and sepsis are the tip of the AI iceberg. There are now ML programs to distinguish melanoma from benign nevi; to improve insulin dosing for patients with type 1 diabetes; to predict which hospital patients are most likely to end up in the intensive care unit; and to mitigate the opioid epidemic.

An ML Web page on the JAMA Network (https://sites.jamanetwork.com/machine-learning/) features a long list of published research studies, reviews, and opinion papers suggesting that the future of medicine is closely tied to innovative developments in this area. This Web page also addresses the potential use of ML in detecting lymph node metastases in breast cancer, the need to temper AI with human intelligence, the role of AI in clinical decision support, and more.

The JAMA Network also discusses a few of the challenges that still need to be overcome in developing ML tools for clinical medicine—challenges that you will want to be cognizant of as you evaluate new research in the field.

Black-box dilemma. A challenge that technologists face as they introduce new programs that have the potential to improve diagnosis, treatment, and prognosis is a phenomenon called the “black-box dilemma,” which refers to the complex data science, advanced statistics, and mathematical equations that underpin ML algorithms. These complexities make it difficult to explain the mechanism of action upon which software is based, which, in turn, makes many clinicians skeptical about its worth.

A randomized clinical trial has found that an ML program that uses only 6 common clinical markers can improve clinical outcomes in patients with severe sepsis.

For example, the neural networks that are the backbone of the retinopathy algorithm discussed earlier might seem like voodoo science to those unfamiliar with the technology. It’s fortunate that several technology-savvy physicians have mastered these digital tools and have the teaching skills to explain them in plain-English tutorials. One such tutorial, “Understanding How Machine Learning Works,” is posted on the JAMA Network (https://sites.­jamanetwork.com/machine-learning/#multimedia). A more basic explanation was included in a recent Public Broadcasting System “Nova” episode, viewable at www.youtube.com/watch?v=xS2G0oolHpo.

Continue to: Limited analysis

Limited analysis. Another problem that plagues many ML-based algorithms is that they have been tested on only a single data set. (Typically, a data set refers to a collection of clinical parameters from a patient population.) For example, researchers developing an algorithm might collect their data from a single health care system.

Several investigators have addressed this shortcoming by testing their software on 2 completely independent patient populations. Banda and colleagues¹⁵ recently developed a software platform to improve the detection rate in familial hypercholesterolemia, a significant cause of premature cardiovascular disease and death that affects approximately 1 of every 250 people. Despite the urgency of identifying the disorder and providing potentially lifesaving treatment, only 10% of patients receive an accurate diagnosis.¹⁶ Banda and colleagues developed a deep-learning algorithm that is far more effective than the traditional screening approach now in use.

To address the generalizability of the algorithm, it was tested on EHR data from 2 independent health care systems: Stanford Health Care and Geisinger Health System. In Stanford patients, the positive predictive value of the algorithm was 88%, with a sensitivity of 75%; it identified 84% of affected patients at the highest probability threshold. In Geisinger patients, the classifier generated a positive predictive value of 85%.

The future of these technologies

AI and ML are not panaceas that will revolutionize medicine in the near future. Likewise, the digital tools discussed in this article are not going to solve multiple complex medical problems addressed during a single office visit. But physicians who ignore mounting evidence that supports these emerging technologies will be left behind by more forward-thinking colleagues.

The best possible application of AI might save the health care sector $150 billion annually by 2026, according to an economic analysis.

A recent commentary in Gastroenterology¹⁷ sums up the situation best: “It is now too conservative to suggest that CADe [computer-assisted detection] and CADx [computer-assisted diagnosis] carry the potential to revolutionize colonoscopy. The artificial intelligence revolution has already begun.”

CORRESPONDENCE
Paul Cerrato, MA, [email protected], [email protected]. John Halamka, MD, MS, [email protected].

Computer technology and artificial intelligence (AI) have come a long way in several decades:

• Between 1971 and 1996, access to the Medline database was primarily limited to university libraries and other institutions; in 1997, the database became universally available online as PubMed.¹
• In 2004, the President of the United States issued an executive order that launched a 10-year plan to put electronic health records (EHRs) in place nationwide; EHRs are now employed in nearly 9 of 10 (85.9%) medical offices.²

Over time, numerous online resources sprouted as well, including DxPlain, UpToDate, and Clinical Key, to name a few. These digital tools were impressive for their time, but many of them are now considered “old-school” AI-enabled clinical decision support.

In the past 2 to 3 years, innovative clinicians and technologists have pushed medicine into a new era that takes advantage of machine learning (ML)-enhanced diagnostic aids, software systems that predict disease progression, and advanced clinical pathways to help individualize treatment. Enthusiastic early adopters believe these resources are transforming patient care—although skeptics remain unconvinced, cautioning that they have yet to prove their worth in everyday clinical practice.

In this review, we first analyze the strengths and weaknesses of evidence supporting these tools, then propose a potential role for them in family medicine.

Machine learning takes on retinopathy

The term “artificial intelligence” has been with us for longer than a half century.³ In the broadest sense, AI refers to any computer system capable of automating a process usually performed manually by humans. But the latest innovations in AI take advantage of a subset of AI called “machine learning”: the ability of software systems to learn new functionality or insights on their own, without additional programming from human data engineers. Case in point: A software platform has been developed that is capable of diagnosing or screening for diabetic retinopathy without the involvement of an experienced ophthalmologist.

A software platform has been developed that is capable of diagnosing or screening for diabetic retinopathy without the involvement of an experienced ophthalmologist.

The landmark study that started clinicians and health care executives thinking seriously about the potential role of ML in medical practice was spearheaded by ­Varun Gulshan, PhD, at Google, and associates from several medical schools.⁴ Gulshan used an artificial neural network designed to mimic the functions of the human nervous system to analyze more than 128,000 retinal images, looking for evidence of diabetic retinopathy. (See “Deciphering artificial neural networks,” for an explanation of how such networks function.⁵) The algorithm they employed was compared with the diagnostic skills of several board-certified ophthalmologists.

[polldaddy:10453606]

Continue to: Deciperhing artificial neural networks

Using an area-under-the-receiver operating curve (AUROC) as a metric, and choosing an operating point for high specificity, the algorithm generated sensitivity of 87% and 90.3% and specificity of 98.1% and 98.5% for 2 validation data sets for detecting referable retinopathy, as defined by a panel of at least 7 ophthalmologists. When AUROC was set for high sensitivity, the algorithm generated sensitivity of 97.5% and 96.1% and specificity of 93.4% and 93.9% for the 2 data sets.

These results are impressive, but the researchers used a retrospective approach in their analysis. A prospective analysis would provide stronger evidence.

That shortcoming was addressed by a pivotal clinical trial that convinced the US Food and Drug Administration (FDA) to approve the technology. Michael Abramoff, MD, PhD, at the University of Iowa Department of Ophthalmology and Visual Sciences and his associates⁶ conducted a prospective study that compared the gold standard for detecting retinopathy, the Fundus Photograph Reading Center (of the University of Wisconsin School of Medicine and Public Health), to an ML-based algorithm, the commercialized IDx-DR. The IDx-DR is a software system that is used in combination with a fundal camera to capture retinal images. The researchers found that “the AI system exceeded all pre-specified superiority endpoints at sensitivity of 87.2% ... [and] specificity of 90.7% ....”

Continue to: The FDA clearance statement...

The FDA clearance statement for this technology⁷ limits its use, emphasizing that it is intended only as a screening tool, not a stand-alone diagnostic system. Because ­IDx-DR is being used in primary care, the FDA states that patients who have a positive result should be referred to an eye care professional. The technology is contraindicated in patients who have a history of laser treatment, surgery, or injection in the eye or who have any of the following: persistent vision loss, blurred vision, floaters, previously diagnosed macular edema, severe nonproliferative retinopathy, proliferative retinopathy, radiation retinopathy, and retinal vein occlusion. It is also not intended for pregnant patients because their eye disease often progresses rapidly.

A large-scale validation study performed on data from Kaiser Permanente Northwest found that it is possible to estimate a person's risk of colorectal cancer by using age, gender, and complete blood count.

Additional caveats to keep in mind when evaluating this new technology include that, although the software can help detect retinopathy, it does not address other key issues for this patient population, including cataracts and glaucoma. The cost of the new technology also requires attention: Software must be used in conjunction with a specific retinal camera, the Topcon TRC-NW400, which is expensive (new, as much as $20,000).

IMAGE: ©GETTY IMAGES

Speaking of cost: Health care providers and insurers still question whether implementing AI-enabled systems is cost-­effective. It is too early to say definitively how AI and machine learning will have an impact on health care expenditures, because the most promising technological systems have yet to be fully implemented in hospitals and medical practices nationwide. Projections by Forbes suggest that private investment in health care AI will reach $6.6 billion by 2021; on a more confident note, an Accenture analysis predicts that the best possible application of AI might save the health care sector $150 billion annually by 2026.⁸

What role might this diabetic retinopathy technology play in family medicine? Physicians are constantly advising patients who have diabetes about the need to have a regular ophthalmic examination to check for early signs of retinopathy—advice that is often ignored. The American Academy of Ophthalmology points out that “6 out of 10 people with diabetes skip a sight-saving exam.”⁹ When a patient is screened with this type of device and found to be at high risk of eye disease, however, the advice to see an eye-care specialist might carry more weight.

Screening colonoscopy: Improving patient incentives

No responsible physician doubts the value of screening colonoscopy in patients 50 years and older, but many patients have yet to realize that the procedure just might save their life. Is there a way to incentivize resistant patients to have a colonoscopy performed? An ML-based software system that only requires access to a few readily available parameters might be the needed impetus for many patients.

Continue to: A large-scale validation...

A large-scale validation study performed on data from Kaiser Permanente Northwest found that it is possible to estimate a person’s risk of colorectal cancer by using age, gender, and complete blood count.¹⁰ This retrospective investigation analyzed more than 17,000 Kaiser Permanente patients, including 900 who already had colorectal cancer. The analysis generated a risk score for patients who did not have the malignancy to gauge their likelihood of developing it. The algorithms were more sensitive for detecting tumors of the cecum and ascending colon, and less sensitive for detection of tumors of the transverse and sigmoid colon and rectum.

To provide more definitive evidence to support the value of the software platform, a prospective study was subsequently conducted on more than 79,000 patients who had initially declined to undergo colorectal screening. The platform, called ColonFlag, was used to detect 688 patients at highest risk, who were then offered screening colonoscopy. In this subgroup, 254 agreed to the procedure; ColonFlag identified 19 malignancies (7.5%) among patients within the Maccabi Health System (Israel), and 15 more in patients outside that health system.¹¹ (In the United States, the same program is known as LGI Flag and has been cleared by the FDA.)

Although ColonFlag has the potential to reduce the incidence of colorectal cancer, other evidence-based screening modalities are highlighted in US Preventive Services Task Force guidelines, including the guaiac-based fecal occult blood test and the fecal immunochemical test.¹²

Beyond screening to applications in managing disease

The complex etiology of sepsis makes the condition difficult to treat. That complexity has also led to disagreement on the best course of management. Using an ML algorithm called an “Artificial Intelligence Clinician,” Komorowski and associates¹³ extracted data from a large data set from 2 nonoverlapping intensive care unit databases collected from US adults.The researchers’ analysis suggested a list of 48 variables that likely influence sepsis outcomes, including:

• demographics,
• Elixhauser premorbid status,
• vital signs,
• clinical laboratory data,
• intravenous fluids given, and
• vasopressors administered.

Komorowski and co-workers concluded that “… mortality was lowest in patients for whom clinicians’ actual doses matched the AI decisions. Our model provides individualized and clinically interpretable treatment decisions for sepsis that could improve patient outcomes.”

A randomized clinical trial has found that an ML program that uses only 6 common clinical markers—blood pressure, heart rate, temperature, respiratory rate, peripheral capillary oxygen saturation (SpO₂), and age—can improve clinical outcomes in patients with severe sepsis.¹⁴ The alerts generated by the algorithm were used to guide treatment. Average length of stay was 13 days in controls, compared with 10.3 days in those evaluated with the ML algorithm. The algorithm was also associated with a 12.4% drop in in-­hospital mortality.

Continue to: Addressing challenges, tapping resources

Advances in the management of diabetic retinopathy, colorectal cancer, and sepsis are the tip of the AI iceberg. There are now ML programs to distinguish melanoma from benign nevi; to improve insulin dosing for patients with type 1 diabetes; to predict which hospital patients are most likely to end up in the intensive care unit; and to mitigate the opioid epidemic.

An ML Web page on the JAMA Network (https://sites.jamanetwork.com/machine-learning/) features a long list of published research studies, reviews, and opinion papers suggesting that the future of medicine is closely tied to innovative developments in this area. This Web page also addresses the potential use of ML in detecting lymph node metastases in breast cancer, the need to temper AI with human intelligence, the role of AI in clinical decision support, and more.

The JAMA Network also discusses a few of the challenges that still need to be overcome in developing ML tools for clinical medicine—challenges that you will want to be cognizant of as you evaluate new research in the field.

Black-box dilemma. A challenge that technologists face as they introduce new programs that have the potential to improve diagnosis, treatment, and prognosis is a phenomenon called the “black-box dilemma,” which refers to the complex data science, advanced statistics, and mathematical equations that underpin ML algorithms. These complexities make it difficult to explain the mechanism of action upon which software is based, which, in turn, makes many clinicians skeptical about its worth.

A randomized clinical trial has found that an ML program that uses only 6 common clinical markers can improve clinical outcomes in patients with severe sepsis.

For example, the neural networks that are the backbone of the retinopathy algorithm discussed earlier might seem like voodoo science to those unfamiliar with the technology. It’s fortunate that several technology-savvy physicians have mastered these digital tools and have the teaching skills to explain them in plain-English tutorials. One such tutorial, “Understanding How Machine Learning Works,” is posted on the JAMA Network (https://sites.­jamanetwork.com/machine-learning/#multimedia). A more basic explanation was included in a recent Public Broadcasting System “Nova” episode, viewable at www.youtube.com/watch?v=xS2G0oolHpo.

Continue to: Limited analysis

Limited analysis. Another problem that plagues many ML-based algorithms is that they have been tested on only a single data set. (Typically, a data set refers to a collection of clinical parameters from a patient population.) For example, researchers developing an algorithm might collect their data from a single health care system.

Several investigators have addressed this shortcoming by testing their software on 2 completely independent patient populations. Banda and colleagues¹⁵ recently developed a software platform to improve the detection rate in familial hypercholesterolemia, a significant cause of premature cardiovascular disease and death that affects approximately 1 of every 250 people. Despite the urgency of identifying the disorder and providing potentially lifesaving treatment, only 10% of patients receive an accurate diagnosis.¹⁶ Banda and colleagues developed a deep-learning algorithm that is far more effective than the traditional screening approach now in use.

To address the generalizability of the algorithm, it was tested on EHR data from 2 independent health care systems: Stanford Health Care and Geisinger Health System. In Stanford patients, the positive predictive value of the algorithm was 88%, with a sensitivity of 75%; it identified 84% of affected patients at the highest probability threshold. In Geisinger patients, the classifier generated a positive predictive value of 85%.

The future of these technologies

AI and ML are not panaceas that will revolutionize medicine in the near future. Likewise, the digital tools discussed in this article are not going to solve multiple complex medical problems addressed during a single office visit. But physicians who ignore mounting evidence that supports these emerging technologies will be left behind by more forward-thinking colleagues.

The best possible application of AI might save the health care sector $150 billion annually by 2026, according to an economic analysis.

A recent commentary in Gastroenterology¹⁷ sums up the situation best: “It is now too conservative to suggest that CADe [computer-assisted detection] and CADx [computer-assisted diagnosis] carry the potential to revolutionize colonoscopy. The artificial intelligence revolution has already begun.”

CORRESPONDENCE
Paul Cerrato, MA, [email protected], [email protected]. John Halamka, MD, MS, [email protected].

BARCELONA – People diagnosed with type 2 diabetes when they are 18-39 years old have significantly higher cardiometabolic risk burden, compared with older people, according to the results of a large study from the United Kingdom presented at the annual meeting of the European Association for the Study of Diabetes.

Sara Freeman/MDedge News

Patients in that younger age group were found to have higher glycated hemoglobin (HbA_1c) levels, along with higher levels of low-density lipoprotein cholesterol and higher body weight.

“We wanted to evaluate the population-level trend in the incidence of young-onset type 2 diabetes in the United Kingdom, compared with later-onset diabetes,” said senior study author Sanjoy Paul, PhD, the director of the Melbourne EpiCentre at the University of Melbourne at a press briefing during the meeting.

Other aims of the study were to compare temporal trends in the incidence of atherosclerotic cardiovascular disease in younger and older patients with type 2 diabetes, and to see how being “high risk” at diagnosis affected patients’ risk of ASCVD and subsequent risk of death.

High-risk status was defined as having at least two of the risk factors for ASCVD – smoking, high systolic blood pressure, high low-density lipoprotein cholesterol, or chronic kidney disease.

The investigators searched a nationally representative sample of primary care electronic medical records from The Health Improvement Network (THIN) database to find incident cases of type 2 diabetes that occurred between 2000 and 2017, with a total of 370,854 cases identified.

At diagnosis of type 2 diabetes, 8% of the sample (n = 29,678) was aged 18-39 years; 15% (n = 56,798), 40-49 years; 25% (n = 93,698), 50-59 years; 29% (n = 107,261), 60-69 years; and 23% (n = 83,419), 70-79 years. Follow-up was just more than 6 years.

Baseline HbA_1c in the respective age groups was 8.6%, 8.4%, 8.1%, 7.8%, and 7.6%, with more than 55% of patients in the two youngest age groups having an HbA_1c of 7.5% or higher, compared with 34%-47% in the three oldest age groups.

The percentage of patients with a high LDL cholesterol value (2.6 mmol/L or higher in those without ASCVD, and 1.8 or higher in those with ASCVD) was 71%, 75%, 74%, 69%, and 65%, from the youngest to oldest age groups. A respective 71%, 70%, 66%, 57%, and 44% of the patients had a body mass index of 35 kg/m² or higher.

Few younger patients had ASCVD at diagnosis (2% of the 18-39 age group; 6% of the 40-49 group), with higher rates in the older age groups (13% of the 50-59 group; 23% of the 60-69 group; and 33% of the 70-79 group).

The percentage of patients considered to be at high risk of ASCVD at diagnosis was 23%, 37%, 45%, 50%, and 53%, respectively, across the five age groups.

Although high systolic blood pressure (SBP; 130 mmHg in those with ASCVD, 140 mmHg in those without) was more common in the older age groups (52% at 50-59 years; 60% at 60-69 years, and 64% at 70-79 years,) a substantial proportion of the younger patients also had a high SBP (27% at 18-39 years and 41% at 40-49 years).

Sara Freeman/MDedge News

SOURCE: Koye D et al. EASD 2019, Abstract 82.
 

BARCELONA – People diagnosed with type 2 diabetes when they are 18-39 years old have significantly higher cardiometabolic risk burden, compared with older people, according to the results of a large study from the United Kingdom presented at the annual meeting of the European Association for the Study of Diabetes.

Sara Freeman/MDedge News

Patients in that younger age group were found to have higher glycated hemoglobin (HbA_1c) levels, along with higher levels of low-density lipoprotein cholesterol and higher body weight.

“We wanted to evaluate the population-level trend in the incidence of young-onset type 2 diabetes in the United Kingdom, compared with later-onset diabetes,” said senior study author Sanjoy Paul, PhD, the director of the Melbourne EpiCentre at the University of Melbourne at a press briefing during the meeting.

Other aims of the study were to compare temporal trends in the incidence of atherosclerotic cardiovascular disease in younger and older patients with type 2 diabetes, and to see how being “high risk” at diagnosis affected patients’ risk of ASCVD and subsequent risk of death.

High-risk status was defined as having at least two of the risk factors for ASCVD – smoking, high systolic blood pressure, high low-density lipoprotein cholesterol, or chronic kidney disease.

The investigators searched a nationally representative sample of primary care electronic medical records from The Health Improvement Network (THIN) database to find incident cases of type 2 diabetes that occurred between 2000 and 2017, with a total of 370,854 cases identified.

At diagnosis of type 2 diabetes, 8% of the sample (n = 29,678) was aged 18-39 years; 15% (n = 56,798), 40-49 years; 25% (n = 93,698), 50-59 years; 29% (n = 107,261), 60-69 years; and 23% (n = 83,419), 70-79 years. Follow-up was just more than 6 years.

Baseline HbA_1c in the respective age groups was 8.6%, 8.4%, 8.1%, 7.8%, and 7.6%, with more than 55% of patients in the two youngest age groups having an HbA_1c of 7.5% or higher, compared with 34%-47% in the three oldest age groups.

The percentage of patients with a high LDL cholesterol value (2.6 mmol/L or higher in those without ASCVD, and 1.8 or higher in those with ASCVD) was 71%, 75%, 74%, 69%, and 65%, from the youngest to oldest age groups. A respective 71%, 70%, 66%, 57%, and 44% of the patients had a body mass index of 35 kg/m² or higher.

Few younger patients had ASCVD at diagnosis (2% of the 18-39 age group; 6% of the 40-49 group), with higher rates in the older age groups (13% of the 50-59 group; 23% of the 60-69 group; and 33% of the 70-79 group).

The percentage of patients considered to be at high risk of ASCVD at diagnosis was 23%, 37%, 45%, 50%, and 53%, respectively, across the five age groups.

Although high systolic blood pressure (SBP; 130 mmHg in those with ASCVD, 140 mmHg in those without) was more common in the older age groups (52% at 50-59 years; 60% at 60-69 years, and 64% at 70-79 years,) a substantial proportion of the younger patients also had a high SBP (27% at 18-39 years and 41% at 40-49 years).

Sara Freeman/MDedge News

SOURCE: Koye D et al. EASD 2019, Abstract 82.
 

BARCELONA – People diagnosed with type 2 diabetes when they are 18-39 years old have significantly higher cardiometabolic risk burden, compared with older people, according to the results of a large study from the United Kingdom presented at the annual meeting of the European Association for the Study of Diabetes.

Sara Freeman/MDedge News

Patients in that younger age group were found to have higher glycated hemoglobin (HbA_1c) levels, along with higher levels of low-density lipoprotein cholesterol and higher body weight.

“We wanted to evaluate the population-level trend in the incidence of young-onset type 2 diabetes in the United Kingdom, compared with later-onset diabetes,” said senior study author Sanjoy Paul, PhD, the director of the Melbourne EpiCentre at the University of Melbourne at a press briefing during the meeting.

Other aims of the study were to compare temporal trends in the incidence of atherosclerotic cardiovascular disease in younger and older patients with type 2 diabetes, and to see how being “high risk” at diagnosis affected patients’ risk of ASCVD and subsequent risk of death.

High-risk status was defined as having at least two of the risk factors for ASCVD – smoking, high systolic blood pressure, high low-density lipoprotein cholesterol, or chronic kidney disease.

The investigators searched a nationally representative sample of primary care electronic medical records from The Health Improvement Network (THIN) database to find incident cases of type 2 diabetes that occurred between 2000 and 2017, with a total of 370,854 cases identified.

At diagnosis of type 2 diabetes, 8% of the sample (n = 29,678) was aged 18-39 years; 15% (n = 56,798), 40-49 years; 25% (n = 93,698), 50-59 years; 29% (n = 107,261), 60-69 years; and 23% (n = 83,419), 70-79 years. Follow-up was just more than 6 years.

Baseline HbA_1c in the respective age groups was 8.6%, 8.4%, 8.1%, 7.8%, and 7.6%, with more than 55% of patients in the two youngest age groups having an HbA_1c of 7.5% or higher, compared with 34%-47% in the three oldest age groups.

The percentage of patients with a high LDL cholesterol value (2.6 mmol/L or higher in those without ASCVD, and 1.8 or higher in those with ASCVD) was 71%, 75%, 74%, 69%, and 65%, from the youngest to oldest age groups. A respective 71%, 70%, 66%, 57%, and 44% of the patients had a body mass index of 35 kg/m² or higher.

Few younger patients had ASCVD at diagnosis (2% of the 18-39 age group; 6% of the 40-49 group), with higher rates in the older age groups (13% of the 50-59 group; 23% of the 60-69 group; and 33% of the 70-79 group).

The percentage of patients considered to be at high risk of ASCVD at diagnosis was 23%, 37%, 45%, 50%, and 53%, respectively, across the five age groups.

Although high systolic blood pressure (SBP; 130 mmHg in those with ASCVD, 140 mmHg in those without) was more common in the older age groups (52% at 50-59 years; 60% at 60-69 years, and 64% at 70-79 years,) a substantial proportion of the younger patients also had a high SBP (27% at 18-39 years and 41% at 40-49 years).

Sara Freeman/MDedge News

SOURCE: Koye D et al. EASD 2019, Abstract 82.
 

The association between type 2 diabetes (T2D) and cardiovascular (CV) disease is well-established:

• Type 2 diabetes approximately doubles the risk of coronary artery disease, stroke, and peripheral arterial disease, independent of conventional risk factors¹
• CV disease is the leading cause of morbidity and mortality in patients with T2D
• CV disease is the largest contributor to direct and indirect costs of the health care of patients who have T2D.²

In recent years, new classes of agents for treating T2D have been introduced (TABLE 1). Prior to 2008, the US Food and Drug Administration (FDA) approved drugs in those new classes based simply on their effectiveness in reducing the blood glucose level. Concerns about the CV safety of specific drugs (eg, rosiglitazone, muraglitazar) emerged from a number of trials, suggesting that these agents might increase the risk of CV events.^3,4

All glucose-lowering medications used to treat type 2 diabetes are not equally effective in reducing CV complications.

Consequently, in 2008, the FDA issued guidance to the pharmaceutical industry: Preapproval and postapproval trials of all new antidiabetic drugs must now assess potential excess CV risk.⁵ CV outcomes trials (CVOTs), performed in accordance with FDA guidelines, have therefore become the focus of evaluating novel treatment options. In most CVOTs, combined primary CV endpoints have included CV mortality, nonfatal myocardial infarction (MI), and nonfatal stroke—taken together, what is known as the composite of these 3 major adverse CV events, or MACE-3.

To date, 15 CVOTs have been completed, assessing 3 novel classes of antihyperglycemic agents:

• dipeptidyl peptidase-4 (DPP-4) inhibitors
• glucagon-like peptide-1 (GLP-1) receptor agonists
• sodium–glucose cotransporter-2 (SGLT-2) inhibitors.

None of these trials identified any increased incidence of MACE; 7 found CV benefit. This review summarizes what the CVOTs revealed about these antihyperglycemic agents and their ability to yield a reduction in MACE and a decrease in all-cause mortality in patients with T2D and elevated CV disease risk. Armed with this information, you will have the tools you need to offer patients with T2D CV benefit while managing their primary disease.

Cardiovascular outcomes trials: DPP-4 inhibitors

Four trials. Trials of DPP-4 inhibitors that have been completed and reported are of saxagliptin (SAVOR-TIMI 53⁶), alogliptin (EXAMINE⁷), sitagliptin (TECOS⁸), and linagliptin (CARMELINA⁹); others are in progress. In general, researchers enrolled patients at high risk of CV events, although inclusion criteria varied substantially. Consistently, these studies demonstrated that DPP-4 inhibition neither increased nor decreased (ie, were noninferior) the 3-point MACE (SAVOR-TIMI 53 noninferiority, P < .001; EXAMINE, P < .001; TECOS, P < .001).

Continue to: Rather than improve...

Rather than improve CV outcomes, there was some evidence that DPP-4 inhibitors might be associated with an increased risk of hospitalization for heart failure (HHF). In the SAVOR-TIMI 53 trial, patients randomized to saxagliptin had a 0.7% absolute increase in risk of HHF (P = .98).⁶ In the EXAMINE trial, patients treated with alogliptin showed a nonsignificant trend for HHF.¹⁰ In both the TECOS and CARMELINA trials, no difference was recorded in the rate of HHF.^8,9,11 Subsequent meta-analysis that summarized the risk of HHF in CVOTs with DPP-4 inhibitors indicated a nonsignificant trend to increased risk.¹²

It’s likely that the CV benefits result from mechanisms other than a reduction in the serum glucose level, given the short time frame of the studies and the magnitude of the CV benefit.

From these trials alone, it appears that DPP-4 inhibitors are unlikely to provide CV benefit. Data from additional trials are needed to evaluate the possible association between these medications and heart failure (HF). However, largely as a result of the findings from SAVOR-TIMI 53 and EXAMINE, the FDA issued a Drug Safety Communication in April 2016, adding warnings about HF to the labeling of saxagliptin and alogliptin.¹³

CARMELINA was designed to also evaluate kidney outcomes in patients with T2D. As with other DPP-4 inhibitor trials, the primary aim was to establish noninferiority, compared with placebo, for time to MACE-3 (P < .001). Secondary outcomes were defined as time to first occurrence of end-stage renal disease, death due to renal failure, and sustained decrease from baseline of ≥ 40% in the estimated glomerular filtration rate. The incidence of the secondary kidney composite results was not significantly different between groups randomized to linagliptin or placebo.⁹

Cardiovascular outcomes trials: GLP-1 receptor agonists

ELIXA. The CV safety of GLP-1 receptor agonists has been evaluated in several randomized clinical trials. The Evaluation of Lixisenatide in Acute Coronary Syndrome (ELIXA) trial was the first¹⁴: Lixisenatide was studied in 6068 patients with recent hospitalization for acute coronary syndrome. Lixisenatide therapy was neutral with regard to CV outcomes, which met the primary endpoint: noninferiority to placebo (P < .001). There was no increase in either HF or HHF.

Continue to: LEADER

LEADER. The Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial (LEADER) evaluated long-term effects of liraglutide, compared to placebo, on CV events in patients with T2D.¹⁵ It was a multicenter, double-blind, placebocontrolled study that followed 9340 participants, most (81%) of whom had established CV disease, over 5 years. LEADER is considered a landmark study because it was the first large CVOT to show significant benefit for a GLP-1 receptor agonist.

Liraglutide demonstrated reductions in first occurrence of death from CV causes, nonfatal MI or nonfatal stroke, overall CV mortality, and all-cause mortality. The composite MACE-3 showed a relative risk reduction (RRR) of 13%, equivalent to an absolute risk reduction (ARR) of 1.9% (noninferiority, P < .001; superiority, P < .01). The RRR was 22% for death from CV causes, with an ARR of 1.3% (P = .007); the RRR for death from any cause was 15%, with an ARR of 1.4% (P = .02).

In addition, there was a lower rate of nephropathy (1.5 events for every 100 patient–years in the liraglutide group [P = .003], compared with 1.9 events every 100 patient–years in the placebo group).¹⁵

Results clearly demonstrated benefit. No significant difference was seen in the liraglutide rate of HHF, compared to the rate in the placebo group.

SUSTAIN-6. Evidence for the CV benefit of GLP-1 receptor agonists was also demonstrated in the phase 3 Trial to Evaluate Cardiovascular and Other Long-term Outcomes With Semaglutide in Subjects With Type 2 Diabetes (SUSTAIN-6).¹⁶ This was a study of 3297 patients with T2D at high risk of CV disease and with a mean hemoglobin A_1c (HbA_1c) value of 8.7%, 83% of whom had established CV disease. Patients were randomized to semaglutide or placebo. Note: SUSTAIN-6 was a noninferiority safety study; as such, it was not actually designed to assess or establish superiority.

Continue to: The incidence of MACE-3...

The incidence of MACE-3 was significantly reduced among patients treated with semaglutide (P = .02) after median followup of 2.1 years. The expanded composite outcome (death from CV causes, nonfatal MI, nonfatal stroke, coronary revascularization, or hospitalization for unstable angina or HF), also showed a significant reduction with semaglutide (P = .002), compared with placebo. There was no difference in the overall hospitalization rate or rate of death from any cause.

EXSCEL. The Exenatide Study of Cardiovascular Event Lowering trial (EXSCEL)^17,18 was a phase III/IV, double-blind, pragmatic placebo-controlled study of 14,752 patients at any level of CV risk, for a median 3.2 years. The study population was intentionally more diverse than in earlier GLP-1 receptor agonist studies. The researchers hypothesized that patients at increased risk of MACE would experience a comparatively greater relative treatment benefit with exenatide than those at lower risk. That did not prove to be the case.

EXSCEL did confirm noninferiority compared with placebo (P < .001), but once-weekly exenatide resulted in a nonsignificant reduction in major adverse CV events, and a trend for RRR in all-cause mortality (RRR = 14%; ARR = 1% [P = .06]).

HARMONY OUTCOMES. The Albiglutide and Cardiovascular Outcomes in Patients With Type 2 Diabetes and Cardiovascular Disease study (HARMONY OUTCOMES)¹⁹ was a double-blind, randomized, placebocontrolled trial conducted at 610 sites across 28 countries. The study investigated albiglutide, 30 to 50 mg once weekly, compared with placebo. It included 9463 patients ages ≥ 40 years with T2D who had an HbA_1c > 7% (median value, 8.7%) and established CV disease. Patients were evaluated for a median 1.6 years.

Albiglutide reduced the risk of CV causes of death, nonfatal MI, and nonfatal stroke by an RRR of 22%, (ARR, 2%) (noninferiority, P < .0001; superiority, P < .0006).

Continue to: REWIND

REWIND. The Researching Cardiovascular Events with a Weekly INcretin in Diabetes trial (REWIND),²⁰ the most recently completed GLP-1 receptor agonist CVOT (presented at the 2019 American Diabetes Association [ADA] Conference in June and published simultaneously in The Lancet), was a multicenter, randomized, double-blind placebo-controlled trial designed to assess the effect of weekly dulaglutide, 1.5 mg, compared with placebo, in 9901 participants enrolled at 371 sites in 24 countries. Mean patient age was 66.2 years, with women constituting 4589 (46.3%) of participants.

REWIND was distinct from other CVOTs in several ways:

• Other CVOTs were designed to show noninferiority compared with placebo regarding CV events; REWIND was designed to establish superiority
• In contrast to trials of other GLP-1 receptor agonists, in which most patients had established CV disease, only 31% of REWIND participants had a history of CV disease or a prior CV event (although 69% did have CV risk factors without underlying disease)
• REWIND was much longer (median follow-up, 5.4 years) than other GLP-1 receptor agonist trials (median follow-up, 1.5 to 3.8 years).

In REWIND, the primary composite outcome of MACE-3 occurred in 12% of participants assigned to dulaglutide, compared with 13.1% assigned to placebo (P = .026). This equated to 2.4 events for every 100 person– years on dulaglutide, compared with 2.7 events for every 100 person–years on placebo. There was a consistent effect on all MACE-3 components, although the greatest reductions were observed in nonfatal stroke (P = .017). Overall risk reduction was the same for primary and secondary prevention cohorts (P = .97), as well as in patients with either an HbA_1c value < 7.2% or ≥ 7.2% (P = .75). Risk reduction was consistent across age, sex, duration of T2D, and body mass index.

Dulaglutide did not significantly affect the incidence of all-cause mortality, heart failure, revascularization, or hospital admission. Forty-seven percent of patients taking dulaglutide reported gastrointestinal adverse effects (P = .0001).

Cases of bullous pemphigoid have been reported after initiation of DPP-4 inhibitor therapy.

In a separate analysis of secondary outcomes, ²¹ dulaglutide reduced the composite renal outcomes of new-onset macroalbuminuria (P = .0001); decline of ≥ 30% in the estimated glomerular filtration rate (P = .066); and chronic renal replacement therapy (P = .39). Investigators estimated that 1 composite renal outcome event would be prevented for every 31 patients treated with dulaglutide for a median 5.4 years.

Continue to: Cardiovascular outcomes trials...

Cardiovascular outcomes trials: SGLT-2 inhibitors

EMPA-REG OUTCOME. The Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes trial (EMPA-REG OUTCOME) was also a landmark study because it was the first dedicated CVOT to show that an antihyperglycemic agent 1) decreased CV mortality and all-cause mortality, and 2) reduced HHF in patients with T2D and established CV disease.22 In this trial, 7020 patients with T2D who were at high risk of CV events were randomized and treated with empagliflozin, 10 or 25 mg, or placebo, in addition to standard care, and were followed for a median 2.6 years.

In October, the FDA approved dapaglifozin to reduce the risk of hospitalization for heart failure in adults with T2D and established CV disease.

Compared with placebo, empagliflozin resulted in an RRR of 14% (ARR, 1.6%) in the primary endpoint of CV death, nonfatal MI, and stroke, confirming study drug superiority (P = .04). When compared with placebo, the empagliflozin group had an RRR of 38% in CV mortality, (ARR < 2.2%) (P < .001); an RRR of 35% in HHF (ARR, 1.4%) (P = .002); and an RRR of 32% (ARR, 2.6%) in death from any cause (P < .001).

CANVAS. The Canagliflozin Cardiovascular Assessment Study (CANVAS) integrated 2 multicenter, placebo-controlled, randomized trials with 10,142 participants and a mean follow-up of 3.6 years.²³ Patients were randomized to receive canagliflozin (100-300 mg/d) or placebo. Approximately two-thirds of patients had a history of CV disease (therefore representing secondary prevention); one-third had CV risk factors only (primary prevention).

In CANVAS, patients receiving canagliflozin had a risk reduction in MACE-3, establishing superiority compared with placebo (P < .001). There was also a significant reduction in progression of albuminuria (P < .05). Superiority was not shown for the secondary outcome of death from any cause. Canagliflozin had no effect on the primary endpoint (MACE-3) in the subgroup of participants who did not have a history of CV disease. Similar to what was found with empagliflozin in EMPA-REG OUTCOME, CANVAS participants had a reduced risk of HHF.

Continue to: Patients on canagliflozin...

Patients on canagliflozin unexpectedly had an increased incidence of amputations (6.3 participants, compared with 3.4 participants, for every 1000 patient–years). This finding led to a black box warning for canagliflozin about the risk of lower-limb amputation.

DECLARE-TIMI 58. The Dapagliflozin Effect of Cardiovascular Events-Thrombolysis in Myocardial Infarction 58 trial (DECLARETIMI 58) was the largest SGLT-2 inhibitor outcomes trial to date, enrolling 17,160 patients with T2D who also had established CV disease or multiple risk factors for atherosclerotic CV disease. The trial compared dapagliflozin, 10 mg/d, and placebo, following patients for a median 4.2 years.²⁴ Unlike CANVAS and EMPA-REG OUTCOME, DECLARE-TIMI 58 included CV death and HHF as primary outcomes, in addition to MACE-3.

Dapagliflozin was noninferior to placebo with regard to MACE-3. However, its use did result in a lower rate of CV death and HHF by an RRR of 17% (ARR, 1.9%). Risk reduction was greatest in patients with HF who had a reduced ejection fraction (ARR = 9.2%).²⁵

In October, the FDA approved dapagliflozin to reduce the risk of HHF in adults with T2D and established CV disease or multiple CV risk factors. Before initiating the drug, physicians should evaluate the patient's renal function and monitor periodically.

Meta-analyses of SGLT-2 inhibitors

Systematic review. Usman et al released a meta-analysis in 2018 that included 35 randomized, placebo-controlled trials (including EMPA-REG OUTCOME, CANVAS, and DECLARE-TIMI 58) that had assessed the use of SGLT-2 inhibitors in nearly 35,000 patients with T2D.²⁶ This review concluded that, as a class, SGLT-2 inhibitors reduce all-cause mortality, major adverse cardiac events, nonfatal MI, and HF and HHF, compared with placebo.

Continue to: CVD-REAL

CVD-REAL. A separate study, Comparative Effectiveness of Cardiovascular Outcomes in New Users of SGLT-2 Inhibitors (CVD-REAL), of 154,528 patients who were treated with canagliflozin, dapagliflozin, or empagliflozin, showed that initiation of SGLT-2 inhibitors, compared with other glucose- lowering therapies, was associated with a 39% reduction in HHF; a 51% reduction in death from any cause; and a 46% reduction in the composite of HHF or death (P < .001).²⁷

CVD-REAL was unique because it was the largest real-world study to assess the effectiveness of SGLT-2 inhibitors on HHF and mortality. The study utilized data from patients in the United States, Norway, Denmark, Sweden, Germany, and the United Kingdom, based on information obtained from medical claims, primary care and hospital records, and national registries that compared patients who were either newly started on an SGLT-2 inhibitor or another glucose-lowering drug. The drug used by most patients in the trial was canagliflozin (53%), followed by dapagliflozin (42%), and empagliflozin (5%).

In this meta-analysis, similar therapeutic effects were seen across countries, regardless of geographic differences, in the use of specific SGLT-2 inhibitors, suggesting a class effect. Of particular significance was that most (87%) patients enrolled in CVD-REAL did not have prior CV disease. Despite this, results for examined outcomes in CVD-REAL were similar to what was seen in other SGLT-2 inhibitor trials that were designed to study patients with established CV disease.

Risk of adverse effects of newer antidiabetic agents

DPP-4 inhibitors. Alogliptin and sitagliptin carry a black-box warning about potential risk of HF. In SAVOR-TIMI, a 27% increase was detected in the rate of HHF after approximately 2 years of saxagliptin therapy.⁶ Although HF should not be considered a class effect for DPP-4 inhibitors, patients who have risk factors for HF should be monitored for signs and symptoms of HF.

Continue to: Cases of acute pancreatitis...

Cases of acute pancreatitis have been reported in association with all DPP-4 inhibitors available in the United States. A combined analysis of DDP-4 inhibitor trials suggested an increased relative risk of 79% and an absolute risk of 0.13%, which translates to 1 or 2 additional cases of acute pancreatitis for every 1000 patients treated for 2 years.²⁸

There have been numerous postmarketing reports of severe joint pain in patients taking a DPP-4 inhibitor. Most recently, cases of bullous pemphigoid have been reported after initiation of DPP-4 inhibitor therapy.²⁹

GLP-1 receptor agonists carry a black box warning for medullary thyroid (C-cell) tumor risk. GLP-1 receptor agonists are contraindicated in patients with a personal or family history of this cancer, although this FDA warning is based solely on observations from animal models.

In addition, GLP-1 receptor agonists can increase the risk of cholecystitis and pancreatitis. Not uncommonly, they cause gastrointestinal symptoms when first started and when the dosage is titrated upward. Most GLP-1 receptor agonists can be used in patients with renal impairment, although data regarding their use in Stages 4 and 5 chronic kidney disease are limited.³⁰ Semaglutide was found, in the SUSTAIN-6 trial, to be associated with an increased rate of complications of retinopathy, including vitreous hemorrhage and blindness (P = .02)³¹

SGLT-2 inhibitors are associated with an increased incidence of genitourinary infection, bone fracture (canagliflozin), amputation (canagliflozin), and euglycemic diabetic ketoacidosis. Agents in this class should be avoided in patients with moderate or severe renal impairment, primarily due to a lack of efficacy. They are contraindicated in patients with an estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2. (Dapagliflozin is not recommended when eGFR is < 45 mL/min/ 1.73 m2.) These agents carry an FDA warning about the risk of acute kidney injury.³⁰

Continue to: Summing up

Summing up

All glucose-lowering medications used to treat T2D are not equally effective in reducing CV complications. Recent CVOTs have uncovered evidence that certain antidiabetic agents might confer CV and all-cause mortality benefits (TABLE 2^{6,7,9,11,14-17,19-24}).

Discussion of proposed mechanisms for CV outcome superiority of these agents is beyond the scope of this review. It is generally believed that benefits result from mechanisms other than a reduction in the serum glucose level, given the relatively short time frame of the studies and the magnitude of the CV benefit. It is almost certain that mechanisms of CV benefit in the 2 landmark studies—LEADER and EMPA-REG OUTCOME—are distinct from each other.³²

See “When planning T2D pharmacotherapy, include newer agents that offer CV benefit,” ^33-38 for a stepwise approach to treating T2D, including the role of agents that have efficacy in modifying the risk of CV disease.

ACKNOWLEDGMENTS
Linda Speer, MD, Kevin Phelps, DO, and Jay Shubrook, DO, provided support and editorial assistance.

CORRESPONDENCE
Robert Gotfried, DO, FAAFP, Department of Family Medicine, University of Toledo College of Medicine, 3333 Glendale Avenue, Toledo, OH 43614; [email protected].

The association between type 2 diabetes (T2D) and cardiovascular (CV) disease is well-established:

• Type 2 diabetes approximately doubles the risk of coronary artery disease, stroke, and peripheral arterial disease, independent of conventional risk factors¹
• CV disease is the leading cause of morbidity and mortality in patients with T2D
• CV disease is the largest contributor to direct and indirect costs of the health care of patients who have T2D.²

In recent years, new classes of agents for treating T2D have been introduced (TABLE 1). Prior to 2008, the US Food and Drug Administration (FDA) approved drugs in those new classes based simply on their effectiveness in reducing the blood glucose level. Concerns about the CV safety of specific drugs (eg, rosiglitazone, muraglitazar) emerged from a number of trials, suggesting that these agents might increase the risk of CV events.^3,4

All glucose-lowering medications used to treat type 2 diabetes are not equally effective in reducing CV complications.

Consequently, in 2008, the FDA issued guidance to the pharmaceutical industry: Preapproval and postapproval trials of all new antidiabetic drugs must now assess potential excess CV risk.⁵ CV outcomes trials (CVOTs), performed in accordance with FDA guidelines, have therefore become the focus of evaluating novel treatment options. In most CVOTs, combined primary CV endpoints have included CV mortality, nonfatal myocardial infarction (MI), and nonfatal stroke—taken together, what is known as the composite of these 3 major adverse CV events, or MACE-3.

To date, 15 CVOTs have been completed, assessing 3 novel classes of antihyperglycemic agents:

• dipeptidyl peptidase-4 (DPP-4) inhibitors
• glucagon-like peptide-1 (GLP-1) receptor agonists
• sodium–glucose cotransporter-2 (SGLT-2) inhibitors.

None of these trials identified any increased incidence of MACE; 7 found CV benefit. This review summarizes what the CVOTs revealed about these antihyperglycemic agents and their ability to yield a reduction in MACE and a decrease in all-cause mortality in patients with T2D and elevated CV disease risk. Armed with this information, you will have the tools you need to offer patients with T2D CV benefit while managing their primary disease.

Cardiovascular outcomes trials: DPP-4 inhibitors

Four trials. Trials of DPP-4 inhibitors that have been completed and reported are of saxagliptin (SAVOR-TIMI 53⁶), alogliptin (EXAMINE⁷), sitagliptin (TECOS⁸), and linagliptin (CARMELINA⁹); others are in progress. In general, researchers enrolled patients at high risk of CV events, although inclusion criteria varied substantially. Consistently, these studies demonstrated that DPP-4 inhibition neither increased nor decreased (ie, were noninferior) the 3-point MACE (SAVOR-TIMI 53 noninferiority, P < .001; EXAMINE, P < .001; TECOS, P < .001).

Continue to: Rather than improve...

Rather than improve CV outcomes, there was some evidence that DPP-4 inhibitors might be associated with an increased risk of hospitalization for heart failure (HHF). In the SAVOR-TIMI 53 trial, patients randomized to saxagliptin had a 0.7% absolute increase in risk of HHF (P = .98).⁶ In the EXAMINE trial, patients treated with alogliptin showed a nonsignificant trend for HHF.¹⁰ In both the TECOS and CARMELINA trials, no difference was recorded in the rate of HHF.^8,9,11 Subsequent meta-analysis that summarized the risk of HHF in CVOTs with DPP-4 inhibitors indicated a nonsignificant trend to increased risk.¹²

It’s likely that the CV benefits result from mechanisms other than a reduction in the serum glucose level, given the short time frame of the studies and the magnitude of the CV benefit.

From these trials alone, it appears that DPP-4 inhibitors are unlikely to provide CV benefit. Data from additional trials are needed to evaluate the possible association between these medications and heart failure (HF). However, largely as a result of the findings from SAVOR-TIMI 53 and EXAMINE, the FDA issued a Drug Safety Communication in April 2016, adding warnings about HF to the labeling of saxagliptin and alogliptin.¹³

CARMELINA was designed to also evaluate kidney outcomes in patients with T2D. As with other DPP-4 inhibitor trials, the primary aim was to establish noninferiority, compared with placebo, for time to MACE-3 (P < .001). Secondary outcomes were defined as time to first occurrence of end-stage renal disease, death due to renal failure, and sustained decrease from baseline of ≥ 40% in the estimated glomerular filtration rate. The incidence of the secondary kidney composite results was not significantly different between groups randomized to linagliptin or placebo.⁹

Cardiovascular outcomes trials: GLP-1 receptor agonists

ELIXA. The CV safety of GLP-1 receptor agonists has been evaluated in several randomized clinical trials. The Evaluation of Lixisenatide in Acute Coronary Syndrome (ELIXA) trial was the first¹⁴: Lixisenatide was studied in 6068 patients with recent hospitalization for acute coronary syndrome. Lixisenatide therapy was neutral with regard to CV outcomes, which met the primary endpoint: noninferiority to placebo (P < .001). There was no increase in either HF or HHF.

Continue to: LEADER

LEADER. The Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial (LEADER) evaluated long-term effects of liraglutide, compared to placebo, on CV events in patients with T2D.¹⁵ It was a multicenter, double-blind, placebocontrolled study that followed 9340 participants, most (81%) of whom had established CV disease, over 5 years. LEADER is considered a landmark study because it was the first large CVOT to show significant benefit for a GLP-1 receptor agonist.

Liraglutide demonstrated reductions in first occurrence of death from CV causes, nonfatal MI or nonfatal stroke, overall CV mortality, and all-cause mortality. The composite MACE-3 showed a relative risk reduction (RRR) of 13%, equivalent to an absolute risk reduction (ARR) of 1.9% (noninferiority, P < .001; superiority, P < .01). The RRR was 22% for death from CV causes, with an ARR of 1.3% (P = .007); the RRR for death from any cause was 15%, with an ARR of 1.4% (P = .02).

In addition, there was a lower rate of nephropathy (1.5 events for every 100 patient–years in the liraglutide group [P = .003], compared with 1.9 events every 100 patient–years in the placebo group).¹⁵

Results clearly demonstrated benefit. No significant difference was seen in the liraglutide rate of HHF, compared to the rate in the placebo group.

SUSTAIN-6. Evidence for the CV benefit of GLP-1 receptor agonists was also demonstrated in the phase 3 Trial to Evaluate Cardiovascular and Other Long-term Outcomes With Semaglutide in Subjects With Type 2 Diabetes (SUSTAIN-6).¹⁶ This was a study of 3297 patients with T2D at high risk of CV disease and with a mean hemoglobin A_1c (HbA_1c) value of 8.7%, 83% of whom had established CV disease. Patients were randomized to semaglutide or placebo. Note: SUSTAIN-6 was a noninferiority safety study; as such, it was not actually designed to assess or establish superiority.

Continue to: The incidence of MACE-3...

The incidence of MACE-3 was significantly reduced among patients treated with semaglutide (P = .02) after median followup of 2.1 years. The expanded composite outcome (death from CV causes, nonfatal MI, nonfatal stroke, coronary revascularization, or hospitalization for unstable angina or HF), also showed a significant reduction with semaglutide (P = .002), compared with placebo. There was no difference in the overall hospitalization rate or rate of death from any cause.

EXSCEL. The Exenatide Study of Cardiovascular Event Lowering trial (EXSCEL)^17,18 was a phase III/IV, double-blind, pragmatic placebo-controlled study of 14,752 patients at any level of CV risk, for a median 3.2 years. The study population was intentionally more diverse than in earlier GLP-1 receptor agonist studies. The researchers hypothesized that patients at increased risk of MACE would experience a comparatively greater relative treatment benefit with exenatide than those at lower risk. That did not prove to be the case.

EXSCEL did confirm noninferiority compared with placebo (P < .001), but once-weekly exenatide resulted in a nonsignificant reduction in major adverse CV events, and a trend for RRR in all-cause mortality (RRR = 14%; ARR = 1% [P = .06]).

HARMONY OUTCOMES. The Albiglutide and Cardiovascular Outcomes in Patients With Type 2 Diabetes and Cardiovascular Disease study (HARMONY OUTCOMES)¹⁹ was a double-blind, randomized, placebocontrolled trial conducted at 610 sites across 28 countries. The study investigated albiglutide, 30 to 50 mg once weekly, compared with placebo. It included 9463 patients ages ≥ 40 years with T2D who had an HbA_1c > 7% (median value, 8.7%) and established CV disease. Patients were evaluated for a median 1.6 years.

Albiglutide reduced the risk of CV causes of death, nonfatal MI, and nonfatal stroke by an RRR of 22%, (ARR, 2%) (noninferiority, P < .0001; superiority, P < .0006).

Continue to: REWIND

REWIND. The Researching Cardiovascular Events with a Weekly INcretin in Diabetes trial (REWIND),²⁰ the most recently completed GLP-1 receptor agonist CVOT (presented at the 2019 American Diabetes Association [ADA] Conference in June and published simultaneously in The Lancet), was a multicenter, randomized, double-blind placebo-controlled trial designed to assess the effect of weekly dulaglutide, 1.5 mg, compared with placebo, in 9901 participants enrolled at 371 sites in 24 countries. Mean patient age was 66.2 years, with women constituting 4589 (46.3%) of participants.

REWIND was distinct from other CVOTs in several ways:

• Other CVOTs were designed to show noninferiority compared with placebo regarding CV events; REWIND was designed to establish superiority
• In contrast to trials of other GLP-1 receptor agonists, in which most patients had established CV disease, only 31% of REWIND participants had a history of CV disease or a prior CV event (although 69% did have CV risk factors without underlying disease)
• REWIND was much longer (median follow-up, 5.4 years) than other GLP-1 receptor agonist trials (median follow-up, 1.5 to 3.8 years).

In REWIND, the primary composite outcome of MACE-3 occurred in 12% of participants assigned to dulaglutide, compared with 13.1% assigned to placebo (P = .026). This equated to 2.4 events for every 100 person– years on dulaglutide, compared with 2.7 events for every 100 person–years on placebo. There was a consistent effect on all MACE-3 components, although the greatest reductions were observed in nonfatal stroke (P = .017). Overall risk reduction was the same for primary and secondary prevention cohorts (P = .97), as well as in patients with either an HbA_1c value < 7.2% or ≥ 7.2% (P = .75). Risk reduction was consistent across age, sex, duration of T2D, and body mass index.

Dulaglutide did not significantly affect the incidence of all-cause mortality, heart failure, revascularization, or hospital admission. Forty-seven percent of patients taking dulaglutide reported gastrointestinal adverse effects (P = .0001).

Cases of bullous pemphigoid have been reported after initiation of DPP-4 inhibitor therapy.

In a separate analysis of secondary outcomes, ²¹ dulaglutide reduced the composite renal outcomes of new-onset macroalbuminuria (P = .0001); decline of ≥ 30% in the estimated glomerular filtration rate (P = .066); and chronic renal replacement therapy (P = .39). Investigators estimated that 1 composite renal outcome event would be prevented for every 31 patients treated with dulaglutide for a median 5.4 years.

Continue to: Cardiovascular outcomes trials...

Cardiovascular outcomes trials: SGLT-2 inhibitors

EMPA-REG OUTCOME. The Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes trial (EMPA-REG OUTCOME) was also a landmark study because it was the first dedicated CVOT to show that an antihyperglycemic agent 1) decreased CV mortality and all-cause mortality, and 2) reduced HHF in patients with T2D and established CV disease.22 In this trial, 7020 patients with T2D who were at high risk of CV events were randomized and treated with empagliflozin, 10 or 25 mg, or placebo, in addition to standard care, and were followed for a median 2.6 years.

In October, the FDA approved dapaglifozin to reduce the risk of hospitalization for heart failure in adults with T2D and established CV disease.

Compared with placebo, empagliflozin resulted in an RRR of 14% (ARR, 1.6%) in the primary endpoint of CV death, nonfatal MI, and stroke, confirming study drug superiority (P = .04). When compared with placebo, the empagliflozin group had an RRR of 38% in CV mortality, (ARR < 2.2%) (P < .001); an RRR of 35% in HHF (ARR, 1.4%) (P = .002); and an RRR of 32% (ARR, 2.6%) in death from any cause (P < .001).

CANVAS. The Canagliflozin Cardiovascular Assessment Study (CANVAS) integrated 2 multicenter, placebo-controlled, randomized trials with 10,142 participants and a mean follow-up of 3.6 years.²³ Patients were randomized to receive canagliflozin (100-300 mg/d) or placebo. Approximately two-thirds of patients had a history of CV disease (therefore representing secondary prevention); one-third had CV risk factors only (primary prevention).

In CANVAS, patients receiving canagliflozin had a risk reduction in MACE-3, establishing superiority compared with placebo (P < .001). There was also a significant reduction in progression of albuminuria (P < .05). Superiority was not shown for the secondary outcome of death from any cause. Canagliflozin had no effect on the primary endpoint (MACE-3) in the subgroup of participants who did not have a history of CV disease. Similar to what was found with empagliflozin in EMPA-REG OUTCOME, CANVAS participants had a reduced risk of HHF.

Continue to: Patients on canagliflozin...

Patients on canagliflozin unexpectedly had an increased incidence of amputations (6.3 participants, compared with 3.4 participants, for every 1000 patient–years). This finding led to a black box warning for canagliflozin about the risk of lower-limb amputation.

DECLARE-TIMI 58. The Dapagliflozin Effect of Cardiovascular Events-Thrombolysis in Myocardial Infarction 58 trial (DECLARETIMI 58) was the largest SGLT-2 inhibitor outcomes trial to date, enrolling 17,160 patients with T2D who also had established CV disease or multiple risk factors for atherosclerotic CV disease. The trial compared dapagliflozin, 10 mg/d, and placebo, following patients for a median 4.2 years.²⁴ Unlike CANVAS and EMPA-REG OUTCOME, DECLARE-TIMI 58 included CV death and HHF as primary outcomes, in addition to MACE-3.

Dapagliflozin was noninferior to placebo with regard to MACE-3. However, its use did result in a lower rate of CV death and HHF by an RRR of 17% (ARR, 1.9%). Risk reduction was greatest in patients with HF who had a reduced ejection fraction (ARR = 9.2%).²⁵

In October, the FDA approved dapagliflozin to reduce the risk of HHF in adults with T2D and established CV disease or multiple CV risk factors. Before initiating the drug, physicians should evaluate the patient's renal function and monitor periodically.

Meta-analyses of SGLT-2 inhibitors

Systematic review. Usman et al released a meta-analysis in 2018 that included 35 randomized, placebo-controlled trials (including EMPA-REG OUTCOME, CANVAS, and DECLARE-TIMI 58) that had assessed the use of SGLT-2 inhibitors in nearly 35,000 patients with T2D.²⁶ This review concluded that, as a class, SGLT-2 inhibitors reduce all-cause mortality, major adverse cardiac events, nonfatal MI, and HF and HHF, compared with placebo.

Continue to: CVD-REAL

CVD-REAL. A separate study, Comparative Effectiveness of Cardiovascular Outcomes in New Users of SGLT-2 Inhibitors (CVD-REAL), of 154,528 patients who were treated with canagliflozin, dapagliflozin, or empagliflozin, showed that initiation of SGLT-2 inhibitors, compared with other glucose- lowering therapies, was associated with a 39% reduction in HHF; a 51% reduction in death from any cause; and a 46% reduction in the composite of HHF or death (P < .001).²⁷

CVD-REAL was unique because it was the largest real-world study to assess the effectiveness of SGLT-2 inhibitors on HHF and mortality. The study utilized data from patients in the United States, Norway, Denmark, Sweden, Germany, and the United Kingdom, based on information obtained from medical claims, primary care and hospital records, and national registries that compared patients who were either newly started on an SGLT-2 inhibitor or another glucose-lowering drug. The drug used by most patients in the trial was canagliflozin (53%), followed by dapagliflozin (42%), and empagliflozin (5%).

In this meta-analysis, similar therapeutic effects were seen across countries, regardless of geographic differences, in the use of specific SGLT-2 inhibitors, suggesting a class effect. Of particular significance was that most (87%) patients enrolled in CVD-REAL did not have prior CV disease. Despite this, results for examined outcomes in CVD-REAL were similar to what was seen in other SGLT-2 inhibitor trials that were designed to study patients with established CV disease.

Risk of adverse effects of newer antidiabetic agents

DPP-4 inhibitors. Alogliptin and sitagliptin carry a black-box warning about potential risk of HF. In SAVOR-TIMI, a 27% increase was detected in the rate of HHF after approximately 2 years of saxagliptin therapy.⁶ Although HF should not be considered a class effect for DPP-4 inhibitors, patients who have risk factors for HF should be monitored for signs and symptoms of HF.

Continue to: Cases of acute pancreatitis...

Cases of acute pancreatitis have been reported in association with all DPP-4 inhibitors available in the United States. A combined analysis of DDP-4 inhibitor trials suggested an increased relative risk of 79% and an absolute risk of 0.13%, which translates to 1 or 2 additional cases of acute pancreatitis for every 1000 patients treated for 2 years.²⁸

There have been numerous postmarketing reports of severe joint pain in patients taking a DPP-4 inhibitor. Most recently, cases of bullous pemphigoid have been reported after initiation of DPP-4 inhibitor therapy.²⁹

GLP-1 receptor agonists carry a black box warning for medullary thyroid (C-cell) tumor risk. GLP-1 receptor agonists are contraindicated in patients with a personal or family history of this cancer, although this FDA warning is based solely on observations from animal models.

In addition, GLP-1 receptor agonists can increase the risk of cholecystitis and pancreatitis. Not uncommonly, they cause gastrointestinal symptoms when first started and when the dosage is titrated upward. Most GLP-1 receptor agonists can be used in patients with renal impairment, although data regarding their use in Stages 4 and 5 chronic kidney disease are limited.³⁰ Semaglutide was found, in the SUSTAIN-6 trial, to be associated with an increased rate of complications of retinopathy, including vitreous hemorrhage and blindness (P = .02)³¹

SGLT-2 inhibitors are associated with an increased incidence of genitourinary infection, bone fracture (canagliflozin), amputation (canagliflozin), and euglycemic diabetic ketoacidosis. Agents in this class should be avoided in patients with moderate or severe renal impairment, primarily due to a lack of efficacy. They are contraindicated in patients with an estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2. (Dapagliflozin is not recommended when eGFR is < 45 mL/min/ 1.73 m2.) These agents carry an FDA warning about the risk of acute kidney injury.³⁰

Continue to: Summing up

Summing up

All glucose-lowering medications used to treat T2D are not equally effective in reducing CV complications. Recent CVOTs have uncovered evidence that certain antidiabetic agents might confer CV and all-cause mortality benefits (TABLE 2^{6,7,9,11,14-17,19-24}).

Discussion of proposed mechanisms for CV outcome superiority of these agents is beyond the scope of this review. It is generally believed that benefits result from mechanisms other than a reduction in the serum glucose level, given the relatively short time frame of the studies and the magnitude of the CV benefit. It is almost certain that mechanisms of CV benefit in the 2 landmark studies—LEADER and EMPA-REG OUTCOME—are distinct from each other.³²

See “When planning T2D pharmacotherapy, include newer agents that offer CV benefit,” ^33-38 for a stepwise approach to treating T2D, including the role of agents that have efficacy in modifying the risk of CV disease.

ACKNOWLEDGMENTS
Linda Speer, MD, Kevin Phelps, DO, and Jay Shubrook, DO, provided support and editorial assistance.

CORRESPONDENCE
Robert Gotfried, DO, FAAFP, Department of Family Medicine, University of Toledo College of Medicine, 3333 Glendale Avenue, Toledo, OH 43614; [email protected].

The association between type 2 diabetes (T2D) and cardiovascular (CV) disease is well-established:

• Type 2 diabetes approximately doubles the risk of coronary artery disease, stroke, and peripheral arterial disease, independent of conventional risk factors¹
• CV disease is the leading cause of morbidity and mortality in patients with T2D
• CV disease is the largest contributor to direct and indirect costs of the health care of patients who have T2D.²

In recent years, new classes of agents for treating T2D have been introduced (TABLE 1). Prior to 2008, the US Food and Drug Administration (FDA) approved drugs in those new classes based simply on their effectiveness in reducing the blood glucose level. Concerns about the CV safety of specific drugs (eg, rosiglitazone, muraglitazar) emerged from a number of trials, suggesting that these agents might increase the risk of CV events.^3,4

All glucose-lowering medications used to treat type 2 diabetes are not equally effective in reducing CV complications.

Consequently, in 2008, the FDA issued guidance to the pharmaceutical industry: Preapproval and postapproval trials of all new antidiabetic drugs must now assess potential excess CV risk.⁵ CV outcomes trials (CVOTs), performed in accordance with FDA guidelines, have therefore become the focus of evaluating novel treatment options. In most CVOTs, combined primary CV endpoints have included CV mortality, nonfatal myocardial infarction (MI), and nonfatal stroke—taken together, what is known as the composite of these 3 major adverse CV events, or MACE-3.

To date, 15 CVOTs have been completed, assessing 3 novel classes of antihyperglycemic agents:

• dipeptidyl peptidase-4 (DPP-4) inhibitors
• glucagon-like peptide-1 (GLP-1) receptor agonists
• sodium–glucose cotransporter-2 (SGLT-2) inhibitors.

None of these trials identified any increased incidence of MACE; 7 found CV benefit. This review summarizes what the CVOTs revealed about these antihyperglycemic agents and their ability to yield a reduction in MACE and a decrease in all-cause mortality in patients with T2D and elevated CV disease risk. Armed with this information, you will have the tools you need to offer patients with T2D CV benefit while managing their primary disease.

Cardiovascular outcomes trials: DPP-4 inhibitors

Four trials. Trials of DPP-4 inhibitors that have been completed and reported are of saxagliptin (SAVOR-TIMI 53⁶), alogliptin (EXAMINE⁷), sitagliptin (TECOS⁸), and linagliptin (CARMELINA⁹); others are in progress. In general, researchers enrolled patients at high risk of CV events, although inclusion criteria varied substantially. Consistently, these studies demonstrated that DPP-4 inhibition neither increased nor decreased (ie, were noninferior) the 3-point MACE (SAVOR-TIMI 53 noninferiority, P < .001; EXAMINE, P < .001; TECOS, P < .001).

Continue to: Rather than improve...

Rather than improve CV outcomes, there was some evidence that DPP-4 inhibitors might be associated with an increased risk of hospitalization for heart failure (HHF). In the SAVOR-TIMI 53 trial, patients randomized to saxagliptin had a 0.7% absolute increase in risk of HHF (P = .98).⁶ In the EXAMINE trial, patients treated with alogliptin showed a nonsignificant trend for HHF.¹⁰ In both the TECOS and CARMELINA trials, no difference was recorded in the rate of HHF.^8,9,11 Subsequent meta-analysis that summarized the risk of HHF in CVOTs with DPP-4 inhibitors indicated a nonsignificant trend to increased risk.¹²

It’s likely that the CV benefits result from mechanisms other than a reduction in the serum glucose level, given the short time frame of the studies and the magnitude of the CV benefit.

From these trials alone, it appears that DPP-4 inhibitors are unlikely to provide CV benefit. Data from additional trials are needed to evaluate the possible association between these medications and heart failure (HF). However, largely as a result of the findings from SAVOR-TIMI 53 and EXAMINE, the FDA issued a Drug Safety Communication in April 2016, adding warnings about HF to the labeling of saxagliptin and alogliptin.¹³

CARMELINA was designed to also evaluate kidney outcomes in patients with T2D. As with other DPP-4 inhibitor trials, the primary aim was to establish noninferiority, compared with placebo, for time to MACE-3 (P < .001). Secondary outcomes were defined as time to first occurrence of end-stage renal disease, death due to renal failure, and sustained decrease from baseline of ≥ 40% in the estimated glomerular filtration rate. The incidence of the secondary kidney composite results was not significantly different between groups randomized to linagliptin or placebo.⁹

Cardiovascular outcomes trials: GLP-1 receptor agonists

ELIXA. The CV safety of GLP-1 receptor agonists has been evaluated in several randomized clinical trials. The Evaluation of Lixisenatide in Acute Coronary Syndrome (ELIXA) trial was the first¹⁴: Lixisenatide was studied in 6068 patients with recent hospitalization for acute coronary syndrome. Lixisenatide therapy was neutral with regard to CV outcomes, which met the primary endpoint: noninferiority to placebo (P < .001). There was no increase in either HF or HHF.

Continue to: LEADER

LEADER. The Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial (LEADER) evaluated long-term effects of liraglutide, compared to placebo, on CV events in patients with T2D.¹⁵ It was a multicenter, double-blind, placebocontrolled study that followed 9340 participants, most (81%) of whom had established CV disease, over 5 years. LEADER is considered a landmark study because it was the first large CVOT to show significant benefit for a GLP-1 receptor agonist.

Liraglutide demonstrated reductions in first occurrence of death from CV causes, nonfatal MI or nonfatal stroke, overall CV mortality, and all-cause mortality. The composite MACE-3 showed a relative risk reduction (RRR) of 13%, equivalent to an absolute risk reduction (ARR) of 1.9% (noninferiority, P < .001; superiority, P < .01). The RRR was 22% for death from CV causes, with an ARR of 1.3% (P = .007); the RRR for death from any cause was 15%, with an ARR of 1.4% (P = .02).

In addition, there was a lower rate of nephropathy (1.5 events for every 100 patient–years in the liraglutide group [P = .003], compared with 1.9 events every 100 patient–years in the placebo group).¹⁵

Results clearly demonstrated benefit. No significant difference was seen in the liraglutide rate of HHF, compared to the rate in the placebo group.

SUSTAIN-6. Evidence for the CV benefit of GLP-1 receptor agonists was also demonstrated in the phase 3 Trial to Evaluate Cardiovascular and Other Long-term Outcomes With Semaglutide in Subjects With Type 2 Diabetes (SUSTAIN-6).¹⁶ This was a study of 3297 patients with T2D at high risk of CV disease and with a mean hemoglobin A_1c (HbA_1c) value of 8.7%, 83% of whom had established CV disease. Patients were randomized to semaglutide or placebo. Note: SUSTAIN-6 was a noninferiority safety study; as such, it was not actually designed to assess or establish superiority.

Continue to: The incidence of MACE-3...

The incidence of MACE-3 was significantly reduced among patients treated with semaglutide (P = .02) after median followup of 2.1 years. The expanded composite outcome (death from CV causes, nonfatal MI, nonfatal stroke, coronary revascularization, or hospitalization for unstable angina or HF), also showed a significant reduction with semaglutide (P = .002), compared with placebo. There was no difference in the overall hospitalization rate or rate of death from any cause.

EXSCEL. The Exenatide Study of Cardiovascular Event Lowering trial (EXSCEL)^17,18 was a phase III/IV, double-blind, pragmatic placebo-controlled study of 14,752 patients at any level of CV risk, for a median 3.2 years. The study population was intentionally more diverse than in earlier GLP-1 receptor agonist studies. The researchers hypothesized that patients at increased risk of MACE would experience a comparatively greater relative treatment benefit with exenatide than those at lower risk. That did not prove to be the case.

EXSCEL did confirm noninferiority compared with placebo (P < .001), but once-weekly exenatide resulted in a nonsignificant reduction in major adverse CV events, and a trend for RRR in all-cause mortality (RRR = 14%; ARR = 1% [P = .06]).

HARMONY OUTCOMES. The Albiglutide and Cardiovascular Outcomes in Patients With Type 2 Diabetes and Cardiovascular Disease study (HARMONY OUTCOMES)¹⁹ was a double-blind, randomized, placebocontrolled trial conducted at 610 sites across 28 countries. The study investigated albiglutide, 30 to 50 mg once weekly, compared with placebo. It included 9463 patients ages ≥ 40 years with T2D who had an HbA_1c > 7% (median value, 8.7%) and established CV disease. Patients were evaluated for a median 1.6 years.

Albiglutide reduced the risk of CV causes of death, nonfatal MI, and nonfatal stroke by an RRR of 22%, (ARR, 2%) (noninferiority, P < .0001; superiority, P < .0006).

Continue to: REWIND

REWIND. The Researching Cardiovascular Events with a Weekly INcretin in Diabetes trial (REWIND),²⁰ the most recently completed GLP-1 receptor agonist CVOT (presented at the 2019 American Diabetes Association [ADA] Conference in June and published simultaneously in The Lancet), was a multicenter, randomized, double-blind placebo-controlled trial designed to assess the effect of weekly dulaglutide, 1.5 mg, compared with placebo, in 9901 participants enrolled at 371 sites in 24 countries. Mean patient age was 66.2 years, with women constituting 4589 (46.3%) of participants.

REWIND was distinct from other CVOTs in several ways:

• Other CVOTs were designed to show noninferiority compared with placebo regarding CV events; REWIND was designed to establish superiority
• In contrast to trials of other GLP-1 receptor agonists, in which most patients had established CV disease, only 31% of REWIND participants had a history of CV disease or a prior CV event (although 69% did have CV risk factors without underlying disease)
• REWIND was much longer (median follow-up, 5.4 years) than other GLP-1 receptor agonist trials (median follow-up, 1.5 to 3.8 years).

In REWIND, the primary composite outcome of MACE-3 occurred in 12% of participants assigned to dulaglutide, compared with 13.1% assigned to placebo (P = .026). This equated to 2.4 events for every 100 person– years on dulaglutide, compared with 2.7 events for every 100 person–years on placebo. There was a consistent effect on all MACE-3 components, although the greatest reductions were observed in nonfatal stroke (P = .017). Overall risk reduction was the same for primary and secondary prevention cohorts (P = .97), as well as in patients with either an HbA_1c value < 7.2% or ≥ 7.2% (P = .75). Risk reduction was consistent across age, sex, duration of T2D, and body mass index.

Dulaglutide did not significantly affect the incidence of all-cause mortality, heart failure, revascularization, or hospital admission. Forty-seven percent of patients taking dulaglutide reported gastrointestinal adverse effects (P = .0001).

Cases of bullous pemphigoid have been reported after initiation of DPP-4 inhibitor therapy.

In a separate analysis of secondary outcomes, ²¹ dulaglutide reduced the composite renal outcomes of new-onset macroalbuminuria (P = .0001); decline of ≥ 30% in the estimated glomerular filtration rate (P = .066); and chronic renal replacement therapy (P = .39). Investigators estimated that 1 composite renal outcome event would be prevented for every 31 patients treated with dulaglutide for a median 5.4 years.

Continue to: Cardiovascular outcomes trials...

Cardiovascular outcomes trials: SGLT-2 inhibitors

EMPA-REG OUTCOME. The Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes trial (EMPA-REG OUTCOME) was also a landmark study because it was the first dedicated CVOT to show that an antihyperglycemic agent 1) decreased CV mortality and all-cause mortality, and 2) reduced HHF in patients with T2D and established CV disease.22 In this trial, 7020 patients with T2D who were at high risk of CV events were randomized and treated with empagliflozin, 10 or 25 mg, or placebo, in addition to standard care, and were followed for a median 2.6 years.

In October, the FDA approved dapaglifozin to reduce the risk of hospitalization for heart failure in adults with T2D and established CV disease.

Compared with placebo, empagliflozin resulted in an RRR of 14% (ARR, 1.6%) in the primary endpoint of CV death, nonfatal MI, and stroke, confirming study drug superiority (P = .04). When compared with placebo, the empagliflozin group had an RRR of 38% in CV mortality, (ARR < 2.2%) (P < .001); an RRR of 35% in HHF (ARR, 1.4%) (P = .002); and an RRR of 32% (ARR, 2.6%) in death from any cause (P < .001).

CANVAS. The Canagliflozin Cardiovascular Assessment Study (CANVAS) integrated 2 multicenter, placebo-controlled, randomized trials with 10,142 participants and a mean follow-up of 3.6 years.²³ Patients were randomized to receive canagliflozin (100-300 mg/d) or placebo. Approximately two-thirds of patients had a history of CV disease (therefore representing secondary prevention); one-third had CV risk factors only (primary prevention).

In CANVAS, patients receiving canagliflozin had a risk reduction in MACE-3, establishing superiority compared with placebo (P < .001). There was also a significant reduction in progression of albuminuria (P < .05). Superiority was not shown for the secondary outcome of death from any cause. Canagliflozin had no effect on the primary endpoint (MACE-3) in the subgroup of participants who did not have a history of CV disease. Similar to what was found with empagliflozin in EMPA-REG OUTCOME, CANVAS participants had a reduced risk of HHF.

Continue to: Patients on canagliflozin...

Patients on canagliflozin unexpectedly had an increased incidence of amputations (6.3 participants, compared with 3.4 participants, for every 1000 patient–years). This finding led to a black box warning for canagliflozin about the risk of lower-limb amputation.

DECLARE-TIMI 58. The Dapagliflozin Effect of Cardiovascular Events-Thrombolysis in Myocardial Infarction 58 trial (DECLARETIMI 58) was the largest SGLT-2 inhibitor outcomes trial to date, enrolling 17,160 patients with T2D who also had established CV disease or multiple risk factors for atherosclerotic CV disease. The trial compared dapagliflozin, 10 mg/d, and placebo, following patients for a median 4.2 years.²⁴ Unlike CANVAS and EMPA-REG OUTCOME, DECLARE-TIMI 58 included CV death and HHF as primary outcomes, in addition to MACE-3.

Dapagliflozin was noninferior to placebo with regard to MACE-3. However, its use did result in a lower rate of CV death and HHF by an RRR of 17% (ARR, 1.9%). Risk reduction was greatest in patients with HF who had a reduced ejection fraction (ARR = 9.2%).²⁵

In October, the FDA approved dapagliflozin to reduce the risk of HHF in adults with T2D and established CV disease or multiple CV risk factors. Before initiating the drug, physicians should evaluate the patient's renal function and monitor periodically.

Meta-analyses of SGLT-2 inhibitors

Systematic review. Usman et al released a meta-analysis in 2018 that included 35 randomized, placebo-controlled trials (including EMPA-REG OUTCOME, CANVAS, and DECLARE-TIMI 58) that had assessed the use of SGLT-2 inhibitors in nearly 35,000 patients with T2D.²⁶ This review concluded that, as a class, SGLT-2 inhibitors reduce all-cause mortality, major adverse cardiac events, nonfatal MI, and HF and HHF, compared with placebo.

Continue to: CVD-REAL

CVD-REAL. A separate study, Comparative Effectiveness of Cardiovascular Outcomes in New Users of SGLT-2 Inhibitors (CVD-REAL), of 154,528 patients who were treated with canagliflozin, dapagliflozin, or empagliflozin, showed that initiation of SGLT-2 inhibitors, compared with other glucose- lowering therapies, was associated with a 39% reduction in HHF; a 51% reduction in death from any cause; and a 46% reduction in the composite of HHF or death (P < .001).²⁷

CVD-REAL was unique because it was the largest real-world study to assess the effectiveness of SGLT-2 inhibitors on HHF and mortality. The study utilized data from patients in the United States, Norway, Denmark, Sweden, Germany, and the United Kingdom, based on information obtained from medical claims, primary care and hospital records, and national registries that compared patients who were either newly started on an SGLT-2 inhibitor or another glucose-lowering drug. The drug used by most patients in the trial was canagliflozin (53%), followed by dapagliflozin (42%), and empagliflozin (5%).

In this meta-analysis, similar therapeutic effects were seen across countries, regardless of geographic differences, in the use of specific SGLT-2 inhibitors, suggesting a class effect. Of particular significance was that most (87%) patients enrolled in CVD-REAL did not have prior CV disease. Despite this, results for examined outcomes in CVD-REAL were similar to what was seen in other SGLT-2 inhibitor trials that were designed to study patients with established CV disease.

Risk of adverse effects of newer antidiabetic agents

DPP-4 inhibitors. Alogliptin and sitagliptin carry a black-box warning about potential risk of HF. In SAVOR-TIMI, a 27% increase was detected in the rate of HHF after approximately 2 years of saxagliptin therapy.⁶ Although HF should not be considered a class effect for DPP-4 inhibitors, patients who have risk factors for HF should be monitored for signs and symptoms of HF.

Continue to: Cases of acute pancreatitis...

Cases of acute pancreatitis have been reported in association with all DPP-4 inhibitors available in the United States. A combined analysis of DDP-4 inhibitor trials suggested an increased relative risk of 79% and an absolute risk of 0.13%, which translates to 1 or 2 additional cases of acute pancreatitis for every 1000 patients treated for 2 years.²⁸

There have been numerous postmarketing reports of severe joint pain in patients taking a DPP-4 inhibitor. Most recently, cases of bullous pemphigoid have been reported after initiation of DPP-4 inhibitor therapy.²⁹

GLP-1 receptor agonists carry a black box warning for medullary thyroid (C-cell) tumor risk. GLP-1 receptor agonists are contraindicated in patients with a personal or family history of this cancer, although this FDA warning is based solely on observations from animal models.

In addition, GLP-1 receptor agonists can increase the risk of cholecystitis and pancreatitis. Not uncommonly, they cause gastrointestinal symptoms when first started and when the dosage is titrated upward. Most GLP-1 receptor agonists can be used in patients with renal impairment, although data regarding their use in Stages 4 and 5 chronic kidney disease are limited.³⁰ Semaglutide was found, in the SUSTAIN-6 trial, to be associated with an increased rate of complications of retinopathy, including vitreous hemorrhage and blindness (P = .02)³¹

SGLT-2 inhibitors are associated with an increased incidence of genitourinary infection, bone fracture (canagliflozin), amputation (canagliflozin), and euglycemic diabetic ketoacidosis. Agents in this class should be avoided in patients with moderate or severe renal impairment, primarily due to a lack of efficacy. They are contraindicated in patients with an estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2. (Dapagliflozin is not recommended when eGFR is < 45 mL/min/ 1.73 m2.) These agents carry an FDA warning about the risk of acute kidney injury.³⁰

Continue to: Summing up

Summing up

All glucose-lowering medications used to treat T2D are not equally effective in reducing CV complications. Recent CVOTs have uncovered evidence that certain antidiabetic agents might confer CV and all-cause mortality benefits (TABLE 2^{6,7,9,11,14-17,19-24}).

Discussion of proposed mechanisms for CV outcome superiority of these agents is beyond the scope of this review. It is generally believed that benefits result from mechanisms other than a reduction in the serum glucose level, given the relatively short time frame of the studies and the magnitude of the CV benefit. It is almost certain that mechanisms of CV benefit in the 2 landmark studies—LEADER and EMPA-REG OUTCOME—are distinct from each other.³²

See “When planning T2D pharmacotherapy, include newer agents that offer CV benefit,” ^33-38 for a stepwise approach to treating T2D, including the role of agents that have efficacy in modifying the risk of CV disease.

ACKNOWLEDGMENTS
Linda Speer, MD, Kevin Phelps, DO, and Jay Shubrook, DO, provided support and editorial assistance.

CORRESPONDENCE
Robert Gotfried, DO, FAAFP, Department of Family Medicine, University of Toledo College of Medicine, 3333 Glendale Avenue, Toledo, OH 43614; [email protected].

BARCELONA – As many as 40%-60% of children have diabetic ketoacidosis (DKA) at the time of being diagnosed with type 1 diabetes, according to data from two U.S. analyses – and the figures have been rising for the past 10 years.

Between 2010 and 2017, the prevalence of DKA at diagnosis in children who were followed up at the Barbara Davies Cancer Center in Denver (n = 2,429) went from 41% to 59%, with a 7% annual rise, Arleta Rewers, MD, PhD, of Children’s Hospital Colorado, Denver, reported at the annual meeting of the European Association for the Study of Diabetes.

Meanwhile, in another analysis that included multiple U.S. centers and about 7,600 cases of youth-onset type 1 diabetes, the overall prevalence of DKA at diagnosis was 38.5% between 2010 and 2016. However, the prevalence had increased from 35% in 2010 to 40.6% in 2016, according to Elizabeth T. Jensen, MPH, PhD, of Wake Forest University, Winston-Salem, N.C. The annual increase in prevalence of DKA at diagnosis of type 1 disease was 2%, adjusted for sociodemographic factors.
 

Rising prevalence

“DKA occurs most commonly at the time of type 1 diabetes diagnosis,” observed Dr. Jensen, who noted that “in the United States, among children, it’s younger children, uninsured or underinsured children, and children from minority racial or ethnic groups, who are at greatest risk.”

Sara Freeman/MDedge News

Dr. Jensen and colleagues had previously shown that the prevalence of DKA at diagnosis was around 30% between 2002 and 2010, with no significant change in its prevalence. However, more recent reports from referral-based, single-center studies had suggested there was an increase, and that led her and her colleagues to take a closer look at the data.

To characterize the risk factors for DKA and the prevalence of DKA over time, Dr. Jensen and her team used the SEARCH for Diabetes in Youth database, which, she said, was “uniquely suited” for this purpose. SEARCH is a population-based, multicenter study conducted in centers in five U.S. states: South Carolina, Ohio, Colorado, California, and Washington.

A diagnosis of DKA was based on blood bicarbonate levels of less than 15 mmol/L, a venous pH of less than 7.25 or arterial or capillary pH of less than 7.3, or if there was any documentation of a DKA diagnosis.

As expected, the prevalence of DKA was highest in the youngest age group (0-4 years), Dr. Jensen said, but the increase in prevalence in that group was no different from the increases seen over time in the other age groups (5-9 years, 10-14 years, and 15 years or older).

There were no differences in the prevalence of DKA between the sexes, although there was a general increase over time. Similar trends were seen in DKA prevalence by race or ethnicity and by season, or time of year.

Of note, higher rates of DKA were seen in children who were covered by public health insurance, than in those covered by private insurance, although there was no difference in the rate of increase in DKA prevalence between the two groups. Dr. Jensen noted that only 64% of this study population had private insurance.

She said that future research in this area would need to look at the economic drivers and the “changing landscape of health insurance coverage in the United States.”
 

Expansion in health coverage

In presenting the findings of a study showing an increase in the prevalence of DKA at diagnosis of type 1 diabetes in children in Colorado from 2010 to 2017, Dr. Rewers said that the increase “paradoxically occurred” at a time of increasing health insurance coverage, a reference to the expansion of Medicaid during 2008-2012 and implementation in 2013 of the Affordable Care Act.

“Our group in Colorado has followed the frequency of DKA for almost 2 decades,” Dr. Rewers said. It’s important to study DKA as it is linked to worse glycemic control – with children with DKA having an HbA_{1c level of} around 1% higher than those without DKA – and the potential for future, long-term complications.

Dr. Rewers noted that the increase in DKA at diagnosis of type 1 diabetes was more rapid in the children who had private rather than public health insurance. Of 1,187 patients with DKA, 57% had private health insurance, and 37% had public insurance, compared with 66% and 28%, respectively, in those without DKA. In 2010, the prevalence of DKA at diagnosis was 35.3% in those who were privately insured and 52.2% of those with public health insurance, but by 2017, a similar percentage of DKA was seen in the privately and publicly insured children (59.6% and 58.5%, respectively).

She said one possible explanation for that might be that “increased enrollment in high-deductible insurance plans could discourage families with private insurance from seeking timely care.”

Another explanation is that there is a low awareness of type 1 diabetes in the general population, she added. “Educational campaigns and autoimmunity screening have been shown to reduce DKA at diabetes diagnosis, but unfortunately they are not used widely at this point.”
 

Identifying at-risk children

“Diabetic ketoacidosis is a serious complication of diabetes [and] is difficult to diagnose because of the variability of the symptoms, said Angela Ibald-Mulli, PhD, who presented the findings of a retrospective cohort study in which she and her colleagues used a “discovery algorithm” called Q-Finder to identify the predictive factors for DKA in youth with type 1 diabetes, based on data from the Diabetes Prospective Follow-up Registry (DPV).

Sara Freeman/MDedge News

“The better we know the risk factors, the better we can care for our patients,” she emphasized.

The investigators obtained data on 108,223 patients with a diagnosis of type 1 disease and with more than two visits related to diabetes. The prevalence of DKA – defined as a pH of less than 7.3 during hospitalization occurring at least 10 days after the onset of type 1 diabetes – was 5.2%, said Dr. Ibald-Mulli, head of Medical Evidence Generation Primary Care at Sanofi, Paris.

A total of 129 different features were considered for their association with DKA – including comorbidities, sociodemographic factors, laboratory values, and concomitant medications – and were then used to identify, test, and the validate likely risk profiles.

After comparing the characteristics of patients with and without DKA, eight significant factors, all of which have been reported previously in the DPV cohort, were seen: younger age, lower body weight, higher HbA_1c, younger age at onset of T1D; shorter disease duration; having a migration background; being less active; and having had more medical visits.

The investigators used the algorithm, and found 11 distinct profiles associated with DKA: an HbA_1c higher than 8.87%; being aged 6-10 years; being aged 11-15 years; a diagnosis of nephropathy; DKA being present at onset; a prevalence of hypoglycemia with coma; a diagnosis of thyroiditis; a standardized body mass index lower than 16.9; not using short-acting insulin; younger than age 15 years; and not using continuous glucose monitoring.

Almost two-thirds of patients (64.7%) belonged to at least one of these risk profiles, Dr. Ibald-Mulli observed, with 7.1% of them having DKA, compared with 1.6% who belonged to none of the profiles.

Dr. Ibald-Mulli said it was important to note that the DKA risk profiles could overlap. “The more profiles a patient belongs to, the higher is the risk of having DKA,” she emphasized, adding that most patients (88.8%) with DKA belonged to just one profile, and fewer than 5% belonged to three or more profiles.

“Overall, the results of the algorithm confirmed known risk-factor profiles that had been previously identified by conventional statistical methods,” she concluded. It also provided “additional insights that can be further explored.”

SEARCH is funded by the Centers for Disease and Prevention and the National Institute of Diabetes and Digestive and Kidney Diseases. The DPV Registry is funded by multiple sponsors, including the European Federation for the Study of Diabetes and other academic institutions with the support of several commercial partners. Sanofi sponsored the study presented by Dr. Ibald-Mulli. Dr. Rewers made no disclosures, and Dr. Jensen did not have any conflicts of interest to declare. Dr. Ibald-Mulli is an employee of Sanofi.

SOURCE: Rewers A et al. EASD 2019, Abstract 115; Jensen E et al. EASD 2019, Abstract 116; Ibald-Mulli A et al. EASD 2019, Abstract 117.

BARCELONA – As many as 40%-60% of children have diabetic ketoacidosis (DKA) at the time of being diagnosed with type 1 diabetes, according to data from two U.S. analyses – and the figures have been rising for the past 10 years.

Between 2010 and 2017, the prevalence of DKA at diagnosis in children who were followed up at the Barbara Davies Cancer Center in Denver (n = 2,429) went from 41% to 59%, with a 7% annual rise, Arleta Rewers, MD, PhD, of Children’s Hospital Colorado, Denver, reported at the annual meeting of the European Association for the Study of Diabetes.

Meanwhile, in another analysis that included multiple U.S. centers and about 7,600 cases of youth-onset type 1 diabetes, the overall prevalence of DKA at diagnosis was 38.5% between 2010 and 2016. However, the prevalence had increased from 35% in 2010 to 40.6% in 2016, according to Elizabeth T. Jensen, MPH, PhD, of Wake Forest University, Winston-Salem, N.C. The annual increase in prevalence of DKA at diagnosis of type 1 disease was 2%, adjusted for sociodemographic factors.
 

Rising prevalence

“DKA occurs most commonly at the time of type 1 diabetes diagnosis,” observed Dr. Jensen, who noted that “in the United States, among children, it’s younger children, uninsured or underinsured children, and children from minority racial or ethnic groups, who are at greatest risk.”

Sara Freeman/MDedge News

Dr. Jensen and colleagues had previously shown that the prevalence of DKA at diagnosis was around 30% between 2002 and 2010, with no significant change in its prevalence. However, more recent reports from referral-based, single-center studies had suggested there was an increase, and that led her and her colleagues to take a closer look at the data.

To characterize the risk factors for DKA and the prevalence of DKA over time, Dr. Jensen and her team used the SEARCH for Diabetes in Youth database, which, she said, was “uniquely suited” for this purpose. SEARCH is a population-based, multicenter study conducted in centers in five U.S. states: South Carolina, Ohio, Colorado, California, and Washington.

A diagnosis of DKA was based on blood bicarbonate levels of less than 15 mmol/L, a venous pH of less than 7.25 or arterial or capillary pH of less than 7.3, or if there was any documentation of a DKA diagnosis.

As expected, the prevalence of DKA was highest in the youngest age group (0-4 years), Dr. Jensen said, but the increase in prevalence in that group was no different from the increases seen over time in the other age groups (5-9 years, 10-14 years, and 15 years or older).

There were no differences in the prevalence of DKA between the sexes, although there was a general increase over time. Similar trends were seen in DKA prevalence by race or ethnicity and by season, or time of year.

Of note, higher rates of DKA were seen in children who were covered by public health insurance, than in those covered by private insurance, although there was no difference in the rate of increase in DKA prevalence between the two groups. Dr. Jensen noted that only 64% of this study population had private insurance.

She said that future research in this area would need to look at the economic drivers and the “changing landscape of health insurance coverage in the United States.”
 

Expansion in health coverage

In presenting the findings of a study showing an increase in the prevalence of DKA at diagnosis of type 1 diabetes in children in Colorado from 2010 to 2017, Dr. Rewers said that the increase “paradoxically occurred” at a time of increasing health insurance coverage, a reference to the expansion of Medicaid during 2008-2012 and implementation in 2013 of the Affordable Care Act.

“Our group in Colorado has followed the frequency of DKA for almost 2 decades,” Dr. Rewers said. It’s important to study DKA as it is linked to worse glycemic control – with children with DKA having an HbA_{1c level of} around 1% higher than those without DKA – and the potential for future, long-term complications.

Dr. Rewers noted that the increase in DKA at diagnosis of type 1 diabetes was more rapid in the children who had private rather than public health insurance. Of 1,187 patients with DKA, 57% had private health insurance, and 37% had public insurance, compared with 66% and 28%, respectively, in those without DKA. In 2010, the prevalence of DKA at diagnosis was 35.3% in those who were privately insured and 52.2% of those with public health insurance, but by 2017, a similar percentage of DKA was seen in the privately and publicly insured children (59.6% and 58.5%, respectively).

She said one possible explanation for that might be that “increased enrollment in high-deductible insurance plans could discourage families with private insurance from seeking timely care.”

Another explanation is that there is a low awareness of type 1 diabetes in the general population, she added. “Educational campaigns and autoimmunity screening have been shown to reduce DKA at diabetes diagnosis, but unfortunately they are not used widely at this point.”
 

Identifying at-risk children

“Diabetic ketoacidosis is a serious complication of diabetes [and] is difficult to diagnose because of the variability of the symptoms, said Angela Ibald-Mulli, PhD, who presented the findings of a retrospective cohort study in which she and her colleagues used a “discovery algorithm” called Q-Finder to identify the predictive factors for DKA in youth with type 1 diabetes, based on data from the Diabetes Prospective Follow-up Registry (DPV).

Sara Freeman/MDedge News

“The better we know the risk factors, the better we can care for our patients,” she emphasized.

The investigators obtained data on 108,223 patients with a diagnosis of type 1 disease and with more than two visits related to diabetes. The prevalence of DKA – defined as a pH of less than 7.3 during hospitalization occurring at least 10 days after the onset of type 1 diabetes – was 5.2%, said Dr. Ibald-Mulli, head of Medical Evidence Generation Primary Care at Sanofi, Paris.

A total of 129 different features were considered for their association with DKA – including comorbidities, sociodemographic factors, laboratory values, and concomitant medications – and were then used to identify, test, and the validate likely risk profiles.

After comparing the characteristics of patients with and without DKA, eight significant factors, all of which have been reported previously in the DPV cohort, were seen: younger age, lower body weight, higher HbA_1c, younger age at onset of T1D; shorter disease duration; having a migration background; being less active; and having had more medical visits.

The investigators used the algorithm, and found 11 distinct profiles associated with DKA: an HbA_1c higher than 8.87%; being aged 6-10 years; being aged 11-15 years; a diagnosis of nephropathy; DKA being present at onset; a prevalence of hypoglycemia with coma; a diagnosis of thyroiditis; a standardized body mass index lower than 16.9; not using short-acting insulin; younger than age 15 years; and not using continuous glucose monitoring.

Almost two-thirds of patients (64.7%) belonged to at least one of these risk profiles, Dr. Ibald-Mulli observed, with 7.1% of them having DKA, compared with 1.6% who belonged to none of the profiles.

Dr. Ibald-Mulli said it was important to note that the DKA risk profiles could overlap. “The more profiles a patient belongs to, the higher is the risk of having DKA,” she emphasized, adding that most patients (88.8%) with DKA belonged to just one profile, and fewer than 5% belonged to three or more profiles.

“Overall, the results of the algorithm confirmed known risk-factor profiles that had been previously identified by conventional statistical methods,” she concluded. It also provided “additional insights that can be further explored.”

SEARCH is funded by the Centers for Disease and Prevention and the National Institute of Diabetes and Digestive and Kidney Diseases. The DPV Registry is funded by multiple sponsors, including the European Federation for the Study of Diabetes and other academic institutions with the support of several commercial partners. Sanofi sponsored the study presented by Dr. Ibald-Mulli. Dr. Rewers made no disclosures, and Dr. Jensen did not have any conflicts of interest to declare. Dr. Ibald-Mulli is an employee of Sanofi.

SOURCE: Rewers A et al. EASD 2019, Abstract 115; Jensen E et al. EASD 2019, Abstract 116; Ibald-Mulli A et al. EASD 2019, Abstract 117.

BARCELONA – As many as 40%-60% of children have diabetic ketoacidosis (DKA) at the time of being diagnosed with type 1 diabetes, according to data from two U.S. analyses – and the figures have been rising for the past 10 years.

Between 2010 and 2017, the prevalence of DKA at diagnosis in children who were followed up at the Barbara Davies Cancer Center in Denver (n = 2,429) went from 41% to 59%, with a 7% annual rise, Arleta Rewers, MD, PhD, of Children’s Hospital Colorado, Denver, reported at the annual meeting of the European Association for the Study of Diabetes.

Meanwhile, in another analysis that included multiple U.S. centers and about 7,600 cases of youth-onset type 1 diabetes, the overall prevalence of DKA at diagnosis was 38.5% between 2010 and 2016. However, the prevalence had increased from 35% in 2010 to 40.6% in 2016, according to Elizabeth T. Jensen, MPH, PhD, of Wake Forest University, Winston-Salem, N.C. The annual increase in prevalence of DKA at diagnosis of type 1 disease was 2%, adjusted for sociodemographic factors.
 

Rising prevalence

“DKA occurs most commonly at the time of type 1 diabetes diagnosis,” observed Dr. Jensen, who noted that “in the United States, among children, it’s younger children, uninsured or underinsured children, and children from minority racial or ethnic groups, who are at greatest risk.”

Sara Freeman/MDedge News

Dr. Jensen and colleagues had previously shown that the prevalence of DKA at diagnosis was around 30% between 2002 and 2010, with no significant change in its prevalence. However, more recent reports from referral-based, single-center studies had suggested there was an increase, and that led her and her colleagues to take a closer look at the data.

To characterize the risk factors for DKA and the prevalence of DKA over time, Dr. Jensen and her team used the SEARCH for Diabetes in Youth database, which, she said, was “uniquely suited” for this purpose. SEARCH is a population-based, multicenter study conducted in centers in five U.S. states: South Carolina, Ohio, Colorado, California, and Washington.

A diagnosis of DKA was based on blood bicarbonate levels of less than 15 mmol/L, a venous pH of less than 7.25 or arterial or capillary pH of less than 7.3, or if there was any documentation of a DKA diagnosis.

As expected, the prevalence of DKA was highest in the youngest age group (0-4 years), Dr. Jensen said, but the increase in prevalence in that group was no different from the increases seen over time in the other age groups (5-9 years, 10-14 years, and 15 years or older).

There were no differences in the prevalence of DKA between the sexes, although there was a general increase over time. Similar trends were seen in DKA prevalence by race or ethnicity and by season, or time of year.

Of note, higher rates of DKA were seen in children who were covered by public health insurance, than in those covered by private insurance, although there was no difference in the rate of increase in DKA prevalence between the two groups. Dr. Jensen noted that only 64% of this study population had private insurance.

She said that future research in this area would need to look at the economic drivers and the “changing landscape of health insurance coverage in the United States.”
 

Expansion in health coverage

In presenting the findings of a study showing an increase in the prevalence of DKA at diagnosis of type 1 diabetes in children in Colorado from 2010 to 2017, Dr. Rewers said that the increase “paradoxically occurred” at a time of increasing health insurance coverage, a reference to the expansion of Medicaid during 2008-2012 and implementation in 2013 of the Affordable Care Act.

“Our group in Colorado has followed the frequency of DKA for almost 2 decades,” Dr. Rewers said. It’s important to study DKA as it is linked to worse glycemic control – with children with DKA having an HbA_{1c level of} around 1% higher than those without DKA – and the potential for future, long-term complications.

Dr. Rewers noted that the increase in DKA at diagnosis of type 1 diabetes was more rapid in the children who had private rather than public health insurance. Of 1,187 patients with DKA, 57% had private health insurance, and 37% had public insurance, compared with 66% and 28%, respectively, in those without DKA. In 2010, the prevalence of DKA at diagnosis was 35.3% in those who were privately insured and 52.2% of those with public health insurance, but by 2017, a similar percentage of DKA was seen in the privately and publicly insured children (59.6% and 58.5%, respectively).

She said one possible explanation for that might be that “increased enrollment in high-deductible insurance plans could discourage families with private insurance from seeking timely care.”

Another explanation is that there is a low awareness of type 1 diabetes in the general population, she added. “Educational campaigns and autoimmunity screening have been shown to reduce DKA at diabetes diagnosis, but unfortunately they are not used widely at this point.”
 

Identifying at-risk children

“Diabetic ketoacidosis is a serious complication of diabetes [and] is difficult to diagnose because of the variability of the symptoms, said Angela Ibald-Mulli, PhD, who presented the findings of a retrospective cohort study in which she and her colleagues used a “discovery algorithm” called Q-Finder to identify the predictive factors for DKA in youth with type 1 diabetes, based on data from the Diabetes Prospective Follow-up Registry (DPV).

Sara Freeman/MDedge News

“The better we know the risk factors, the better we can care for our patients,” she emphasized.

The investigators obtained data on 108,223 patients with a diagnosis of type 1 disease and with more than two visits related to diabetes. The prevalence of DKA – defined as a pH of less than 7.3 during hospitalization occurring at least 10 days after the onset of type 1 diabetes – was 5.2%, said Dr. Ibald-Mulli, head of Medical Evidence Generation Primary Care at Sanofi, Paris.

A total of 129 different features were considered for their association with DKA – including comorbidities, sociodemographic factors, laboratory values, and concomitant medications – and were then used to identify, test, and the validate likely risk profiles.

After comparing the characteristics of patients with and without DKA, eight significant factors, all of which have been reported previously in the DPV cohort, were seen: younger age, lower body weight, higher HbA_1c, younger age at onset of T1D; shorter disease duration; having a migration background; being less active; and having had more medical visits.

The investigators used the algorithm, and found 11 distinct profiles associated with DKA: an HbA_1c higher than 8.87%; being aged 6-10 years; being aged 11-15 years; a diagnosis of nephropathy; DKA being present at onset; a prevalence of hypoglycemia with coma; a diagnosis of thyroiditis; a standardized body mass index lower than 16.9; not using short-acting insulin; younger than age 15 years; and not using continuous glucose monitoring.

Almost two-thirds of patients (64.7%) belonged to at least one of these risk profiles, Dr. Ibald-Mulli observed, with 7.1% of them having DKA, compared with 1.6% who belonged to none of the profiles.

Dr. Ibald-Mulli said it was important to note that the DKA risk profiles could overlap. “The more profiles a patient belongs to, the higher is the risk of having DKA,” she emphasized, adding that most patients (88.8%) with DKA belonged to just one profile, and fewer than 5% belonged to three or more profiles.

“Overall, the results of the algorithm confirmed known risk-factor profiles that had been previously identified by conventional statistical methods,” she concluded. It also provided “additional insights that can be further explored.”

SEARCH is funded by the Centers for Disease and Prevention and the National Institute of Diabetes and Digestive and Kidney Diseases. The DPV Registry is funded by multiple sponsors, including the European Federation for the Study of Diabetes and other academic institutions with the support of several commercial partners. Sanofi sponsored the study presented by Dr. Ibald-Mulli. Dr. Rewers made no disclosures, and Dr. Jensen did not have any conflicts of interest to declare. Dr. Ibald-Mulli is an employee of Sanofi.

SOURCE: Rewers A et al. EASD 2019, Abstract 115; Jensen E et al. EASD 2019, Abstract 116; Ibald-Mulli A et al. EASD 2019, Abstract 117.

Providing access to clinical trials for patients with diabetes mellitus can be a challenge, but a significant number of trials are now recruiting patients. The clinical trials listed below are all open as of October 31, 2019; and are focused on diabetes mellitus-related treatments for American Indians. For additional information and full inclusion/exclusion criteria, please consult clinicaltrials.gov.

Cross-Sectional and Longitudinal Studies of “Pre-Diabetes” in the Pima Indians

The Pima Indians of Arizona have the highest prevalence and incidence of type 2 diabetes of any population in the world. Prospective analyses in this population have identified insulin resistance and a defect in early insulin secretion as risk factors for the development of the disease. To identify the genetic and environmental determinants of diabetes we plan to study Pima Indian families to determine: (1) if there are genes that segregate with metabolic risk factors for diabetes which might therefore be genetic markers for type 2 diabetes; and (2) the mechanisms mediating genetic and environmental determinants of insulin resistance and impaired insulin secretion.

ID: NCT00340132
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Clifton Bogardus, MD, [email protected]
Location: NIDDK, Phoenix, AZ

Empaglifozin in Early Diabetic Kidney Disease

Diabetes is common among American Indian people and diabetic kidney disease is a common complication. Kidney disease caused by diabetes can lead to the need for kidney replacement, by dialysis or kidney transplant, and is also associated with higher risk of early death. A new diabetes medicine called empagliflozin may slow kidney disease from type 2 diabetes. Researchers want to learn if it protects the kidneys when used in very early stages of diabetic kidney disease.

ID: NCT03173963
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Helen C Looker, [email protected]
Location: NIDDK, Phoenix, AZ

Family Investigation of Nephropathy and Diabetes

The Family Investigation of Nephropathy and Diabetes (FIND) is a multicenter study designed to identify genetic determinants of diabetic kidney disease. FIND will be conducted in 11 centers and in many ethnic groups throughout the United States. Two different strategies will be used to localize genes predisposing to kidney disease: a family-based genetic linkage study and a case-control study that utilizes admixture linkage disequilibrium. The center will conduct family-based linkage studies among American Indian populations in the southwestern United States.

ID: NCT00342927
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: William C Knowler, MD, [email protected]
Location: NIDDK, Phoenix, AZ

Look AHEAD: Action for Health in Diabetes

The Look AHEAD study is a multi-center, randomized clinical trial to examine the long-term effects of a lifestyle intervention designed to achieve and maintain weight loss. The study will investigate the effects of the intervention on heart attacks, stroke and cardiovascular-related death in individuals with type 2 diabetes who are also overweight or obese.

ID: NCT00017953
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Location: Southwestern American Indian Center, Phoenix, AZ

Vitamin D and Type 2 Diabetes Study

The goal of the Vitamin D and type 2 diabetes (D2d) study is to determine if vitamin D supplementation works to delay the onset of type 2 diabetes in people at risk for the disease and to gain a better understand how vitamin D affects glucose (sugar) metabolism.

ID: NCT01942694
Sponsor: Tufts Medical Center
Locations: Southwest American Indian Center; Phoenix, AZ; Orlando VA Medical Center, FL; Atlanta VA Medical Center, Decatur, GA; Omaha VA Medical Center, NE

Reducing Diabetes Risk Factors in American Indian Children: Tribal Turning Point (TTP)

This study will evaluate a behavioral intervention designed to reduce risk factors for type 2 diabetes in American Indian youth aged 7-10 years.

ID: NCT03573856
Sponsor: University of Colorado, Denver
Contact: Katherine Sauder, PhD, [email protected]; Dana Dabelea, MD, PhD, [email protected]
Location: Childrens Hospital Colorado, Aurora

Strong Men, Strong Communities Diabetes Risk Reduction in American Indian Men (SMSC)

SMSC will inform the design and implementation of culturally informed, community-based lifestyle interventions for diabetes prevention in AI men in our partner communities and elsewhere, as well as in men of other minority groups who experience a heavy burden of diabetes.

ID: NCT02953977
Sponsor: Washington State University
Contact: Kaimi Sinclair, PhD, MPH, [email protected] Location: IREACH, Seattle, WA

Growing Resilience in Wind River Indian Reservation (GR)

The Growing Resilience research leverages reservation-based assets of land, family, culture, and front-line tribal health organizations to develop and evaluate home food gardens as a family-based health promotion intervention to reduce disparities suffered by Native Americans in nearly every measure of health. Home gardening interventions show great promise for enabling families to improve their health, and this study aims to fulfill that promise with university and Wind River Indian Reservation partners. The investigators will develop an empowering, scalable, and sustainable family-based health promotion intervention with, by, and for Native American families and conduct the first randomized controlled trial to assess the health impacts of home gardens.

ID: NCT02672748
Sponsor: University of Wyoming
Location: University of Wyoming, Laramie

A Comparative Effectiveness Study of Major Glycemia-lowering Medications for Treatment of Type 2 Diabetes (GRADE)

The GRADE Study is a pragmatic, unmasked clinical trial that will compare commonly used diabetes medications, when combined with metformin, on glycemia-lowering effectiveness and patient-centered outcomes.

ID: NCT01794143
Sponsor: GRADE Study Group
Location: Southwestern American Indian Center, Phoenix, AZ

Home-Based Kidney Care in Native Americans of New Mexico (HBKC)

New Mexico American Indians are experiencing an epidemic of chronic kidney disease due primarily to the high rates of obesity and diabetes. The present study entitled Home-Based Kidney Care is designed to delay / reduce rates of end stage renal disease by early interventions in chronic kidney disease (CKD). Investigators propose to assess the safety and efficacy of conducting a full-scale study to determine if home based care delivered by a collaborative team composed of community health workers, the Albuquerque Area Indian Health Board and University of New Mexico faculty will decrease the risk for the development and the progression of CKD.

ID: NCT03179085
Sponsor: University of New Mexico
Contact: Vallabh Shah, PhD, [email protected]; Kevin English, PhD, [email protected]
Location: University of New Mexico, Albuquerque

Home-based Prediabetes Care in Acoma Pueblo - Study 1

Our major goal of implementing educational interventions to slow the current rate of increase in diabetes in Native communities is aligned with the National Institute of Health (NIGMS) and New Mexico INBRE’s vision in reducing health disparity using innovative interventions. The investigators propose following aims: (1) Recruit and Screen 300 community members in Acoma Pueblo, New Mexico to identify incident cases of pre-diabetes for the proposed study of Home Based Diabetes Care (HBDC); (2) Enroll 150 Acoma Natives aged 21-70 years, at risk for type 2 diabetes mellitus and conduct HBDC for a 16-week lifestyle intervention in a longitudinal cohort study.

ID: NCT04029298
Sponsor: University of New Mexico
Contact: Matthew Bouchonville, MD, [email protected]; Vallabh Shah, PhD, [email protected]

Providing access to clinical trials for patients with diabetes mellitus can be a challenge, but a significant number of trials are now recruiting patients. The clinical trials listed below are all open as of October 31, 2019; and are focused on diabetes mellitus-related treatments for American Indians. For additional information and full inclusion/exclusion criteria, please consult clinicaltrials.gov.

Cross-Sectional and Longitudinal Studies of “Pre-Diabetes” in the Pima Indians

The Pima Indians of Arizona have the highest prevalence and incidence of type 2 diabetes of any population in the world. Prospective analyses in this population have identified insulin resistance and a defect in early insulin secretion as risk factors for the development of the disease. To identify the genetic and environmental determinants of diabetes we plan to study Pima Indian families to determine: (1) if there are genes that segregate with metabolic risk factors for diabetes which might therefore be genetic markers for type 2 diabetes; and (2) the mechanisms mediating genetic and environmental determinants of insulin resistance and impaired insulin secretion.

ID: NCT00340132
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Clifton Bogardus, MD, [email protected]
Location: NIDDK, Phoenix, AZ

Empaglifozin in Early Diabetic Kidney Disease

Diabetes is common among American Indian people and diabetic kidney disease is a common complication. Kidney disease caused by diabetes can lead to the need for kidney replacement, by dialysis or kidney transplant, and is also associated with higher risk of early death. A new diabetes medicine called empagliflozin may slow kidney disease from type 2 diabetes. Researchers want to learn if it protects the kidneys when used in very early stages of diabetic kidney disease.

ID: NCT03173963
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Helen C Looker, [email protected]
Location: NIDDK, Phoenix, AZ

Family Investigation of Nephropathy and Diabetes

The Family Investigation of Nephropathy and Diabetes (FIND) is a multicenter study designed to identify genetic determinants of diabetic kidney disease. FIND will be conducted in 11 centers and in many ethnic groups throughout the United States. Two different strategies will be used to localize genes predisposing to kidney disease: a family-based genetic linkage study and a case-control study that utilizes admixture linkage disequilibrium. The center will conduct family-based linkage studies among American Indian populations in the southwestern United States.

ID: NCT00342927
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: William C Knowler, MD, [email protected]
Location: NIDDK, Phoenix, AZ

Look AHEAD: Action for Health in Diabetes

The Look AHEAD study is a multi-center, randomized clinical trial to examine the long-term effects of a lifestyle intervention designed to achieve and maintain weight loss. The study will investigate the effects of the intervention on heart attacks, stroke and cardiovascular-related death in individuals with type 2 diabetes who are also overweight or obese.

ID: NCT00017953
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Location: Southwestern American Indian Center, Phoenix, AZ

Vitamin D and Type 2 Diabetes Study

The goal of the Vitamin D and type 2 diabetes (D2d) study is to determine if vitamin D supplementation works to delay the onset of type 2 diabetes in people at risk for the disease and to gain a better understand how vitamin D affects glucose (sugar) metabolism.

ID: NCT01942694
Sponsor: Tufts Medical Center
Locations: Southwest American Indian Center; Phoenix, AZ; Orlando VA Medical Center, FL; Atlanta VA Medical Center, Decatur, GA; Omaha VA Medical Center, NE

Reducing Diabetes Risk Factors in American Indian Children: Tribal Turning Point (TTP)

This study will evaluate a behavioral intervention designed to reduce risk factors for type 2 diabetes in American Indian youth aged 7-10 years.

ID: NCT03573856
Sponsor: University of Colorado, Denver
Contact: Katherine Sauder, PhD, [email protected]; Dana Dabelea, MD, PhD, [email protected]
Location: Childrens Hospital Colorado, Aurora

Strong Men, Strong Communities Diabetes Risk Reduction in American Indian Men (SMSC)

SMSC will inform the design and implementation of culturally informed, community-based lifestyle interventions for diabetes prevention in AI men in our partner communities and elsewhere, as well as in men of other minority groups who experience a heavy burden of diabetes.

ID: NCT02953977
Sponsor: Washington State University
Contact: Kaimi Sinclair, PhD, MPH, [email protected] Location: IREACH, Seattle, WA

Growing Resilience in Wind River Indian Reservation (GR)

The Growing Resilience research leverages reservation-based assets of land, family, culture, and front-line tribal health organizations to develop and evaluate home food gardens as a family-based health promotion intervention to reduce disparities suffered by Native Americans in nearly every measure of health. Home gardening interventions show great promise for enabling families to improve their health, and this study aims to fulfill that promise with university and Wind River Indian Reservation partners. The investigators will develop an empowering, scalable, and sustainable family-based health promotion intervention with, by, and for Native American families and conduct the first randomized controlled trial to assess the health impacts of home gardens.

ID: NCT02672748
Sponsor: University of Wyoming
Location: University of Wyoming, Laramie

A Comparative Effectiveness Study of Major Glycemia-lowering Medications for Treatment of Type 2 Diabetes (GRADE)

The GRADE Study is a pragmatic, unmasked clinical trial that will compare commonly used diabetes medications, when combined with metformin, on glycemia-lowering effectiveness and patient-centered outcomes.

ID: NCT01794143
Sponsor: GRADE Study Group
Location: Southwestern American Indian Center, Phoenix, AZ

Home-Based Kidney Care in Native Americans of New Mexico (HBKC)

New Mexico American Indians are experiencing an epidemic of chronic kidney disease due primarily to the high rates of obesity and diabetes. The present study entitled Home-Based Kidney Care is designed to delay / reduce rates of end stage renal disease by early interventions in chronic kidney disease (CKD). Investigators propose to assess the safety and efficacy of conducting a full-scale study to determine if home based care delivered by a collaborative team composed of community health workers, the Albuquerque Area Indian Health Board and University of New Mexico faculty will decrease the risk for the development and the progression of CKD.

ID: NCT03179085
Sponsor: University of New Mexico
Contact: Vallabh Shah, PhD, [email protected]; Kevin English, PhD, [email protected]
Location: University of New Mexico, Albuquerque

Home-based Prediabetes Care in Acoma Pueblo - Study 1

Our major goal of implementing educational interventions to slow the current rate of increase in diabetes in Native communities is aligned with the National Institute of Health (NIGMS) and New Mexico INBRE’s vision in reducing health disparity using innovative interventions. The investigators propose following aims: (1) Recruit and Screen 300 community members in Acoma Pueblo, New Mexico to identify incident cases of pre-diabetes for the proposed study of Home Based Diabetes Care (HBDC); (2) Enroll 150 Acoma Natives aged 21-70 years, at risk for type 2 diabetes mellitus and conduct HBDC for a 16-week lifestyle intervention in a longitudinal cohort study.

ID: NCT04029298
Sponsor: University of New Mexico
Contact: Matthew Bouchonville, MD, [email protected]; Vallabh Shah, PhD, [email protected]

Providing access to clinical trials for patients with diabetes mellitus can be a challenge, but a significant number of trials are now recruiting patients. The clinical trials listed below are all open as of October 31, 2019; and are focused on diabetes mellitus-related treatments for American Indians. For additional information and full inclusion/exclusion criteria, please consult clinicaltrials.gov.

Cross-Sectional and Longitudinal Studies of “Pre-Diabetes” in the Pima Indians

The Pima Indians of Arizona have the highest prevalence and incidence of type 2 diabetes of any population in the world. Prospective analyses in this population have identified insulin resistance and a defect in early insulin secretion as risk factors for the development of the disease. To identify the genetic and environmental determinants of diabetes we plan to study Pima Indian families to determine: (1) if there are genes that segregate with metabolic risk factors for diabetes which might therefore be genetic markers for type 2 diabetes; and (2) the mechanisms mediating genetic and environmental determinants of insulin resistance and impaired insulin secretion.

ID: NCT00340132
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Clifton Bogardus, MD, [email protected]
Location: NIDDK, Phoenix, AZ

Empaglifozin in Early Diabetic Kidney Disease

Diabetes is common among American Indian people and diabetic kidney disease is a common complication. Kidney disease caused by diabetes can lead to the need for kidney replacement, by dialysis or kidney transplant, and is also associated with higher risk of early death. A new diabetes medicine called empagliflozin may slow kidney disease from type 2 diabetes. Researchers want to learn if it protects the kidneys when used in very early stages of diabetic kidney disease.

ID: NCT03173963
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Helen C Looker, [email protected]
Location: NIDDK, Phoenix, AZ

Family Investigation of Nephropathy and Diabetes

The Family Investigation of Nephropathy and Diabetes (FIND) is a multicenter study designed to identify genetic determinants of diabetic kidney disease. FIND will be conducted in 11 centers and in many ethnic groups throughout the United States. Two different strategies will be used to localize genes predisposing to kidney disease: a family-based genetic linkage study and a case-control study that utilizes admixture linkage disequilibrium. The center will conduct family-based linkage studies among American Indian populations in the southwestern United States.

ID: NCT00342927
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: William C Knowler, MD, [email protected]
Location: NIDDK, Phoenix, AZ

Look AHEAD: Action for Health in Diabetes

The Look AHEAD study is a multi-center, randomized clinical trial to examine the long-term effects of a lifestyle intervention designed to achieve and maintain weight loss. The study will investigate the effects of the intervention on heart attacks, stroke and cardiovascular-related death in individuals with type 2 diabetes who are also overweight or obese.

ID: NCT00017953
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Location: Southwestern American Indian Center, Phoenix, AZ

Vitamin D and Type 2 Diabetes Study

The goal of the Vitamin D and type 2 diabetes (D2d) study is to determine if vitamin D supplementation works to delay the onset of type 2 diabetes in people at risk for the disease and to gain a better understand how vitamin D affects glucose (sugar) metabolism.

ID: NCT01942694
Sponsor: Tufts Medical Center
Locations: Southwest American Indian Center; Phoenix, AZ; Orlando VA Medical Center, FL; Atlanta VA Medical Center, Decatur, GA; Omaha VA Medical Center, NE

Reducing Diabetes Risk Factors in American Indian Children: Tribal Turning Point (TTP)

This study will evaluate a behavioral intervention designed to reduce risk factors for type 2 diabetes in American Indian youth aged 7-10 years.

ID: NCT03573856
Sponsor: University of Colorado, Denver
Contact: Katherine Sauder, PhD, [email protected]; Dana Dabelea, MD, PhD, [email protected]
Location: Childrens Hospital Colorado, Aurora

Strong Men, Strong Communities Diabetes Risk Reduction in American Indian Men (SMSC)

SMSC will inform the design and implementation of culturally informed, community-based lifestyle interventions for diabetes prevention in AI men in our partner communities and elsewhere, as well as in men of other minority groups who experience a heavy burden of diabetes.

ID: NCT02953977
Sponsor: Washington State University
Contact: Kaimi Sinclair, PhD, MPH, [email protected] Location: IREACH, Seattle, WA

Growing Resilience in Wind River Indian Reservation (GR)

The Growing Resilience research leverages reservation-based assets of land, family, culture, and front-line tribal health organizations to develop and evaluate home food gardens as a family-based health promotion intervention to reduce disparities suffered by Native Americans in nearly every measure of health. Home gardening interventions show great promise for enabling families to improve their health, and this study aims to fulfill that promise with university and Wind River Indian Reservation partners. The investigators will develop an empowering, scalable, and sustainable family-based health promotion intervention with, by, and for Native American families and conduct the first randomized controlled trial to assess the health impacts of home gardens.

ID: NCT02672748
Sponsor: University of Wyoming
Location: University of Wyoming, Laramie

A Comparative Effectiveness Study of Major Glycemia-lowering Medications for Treatment of Type 2 Diabetes (GRADE)

The GRADE Study is a pragmatic, unmasked clinical trial that will compare commonly used diabetes medications, when combined with metformin, on glycemia-lowering effectiveness and patient-centered outcomes.

ID: NCT01794143
Sponsor: GRADE Study Group
Location: Southwestern American Indian Center, Phoenix, AZ

Home-Based Kidney Care in Native Americans of New Mexico (HBKC)

New Mexico American Indians are experiencing an epidemic of chronic kidney disease due primarily to the high rates of obesity and diabetes. The present study entitled Home-Based Kidney Care is designed to delay / reduce rates of end stage renal disease by early interventions in chronic kidney disease (CKD). Investigators propose to assess the safety and efficacy of conducting a full-scale study to determine if home based care delivered by a collaborative team composed of community health workers, the Albuquerque Area Indian Health Board and University of New Mexico faculty will decrease the risk for the development and the progression of CKD.

ID: NCT03179085
Sponsor: University of New Mexico
Contact: Vallabh Shah, PhD, [email protected]; Kevin English, PhD, [email protected]
Location: University of New Mexico, Albuquerque

Home-based Prediabetes Care in Acoma Pueblo - Study 1

Our major goal of implementing educational interventions to slow the current rate of increase in diabetes in Native communities is aligned with the National Institute of Health (NIGMS) and New Mexico INBRE’s vision in reducing health disparity using innovative interventions. The investigators propose following aims: (1) Recruit and Screen 300 community members in Acoma Pueblo, New Mexico to identify incident cases of pre-diabetes for the proposed study of Home Based Diabetes Care (HBDC); (2) Enroll 150 Acoma Natives aged 21-70 years, at risk for type 2 diabetes mellitus and conduct HBDC for a 16-week lifestyle intervention in a longitudinal cohort study.

ID: NCT04029298
Sponsor: University of New Mexico
Contact: Matthew Bouchonville, MD, [email protected]; Vallabh Shah, PhD, [email protected]

Individuals with diabetes mellitus (DM), peripheral vascular disease, or end-stage renal disease are at risk for a nontraumatic lower limb amputation.¹ Veterans have a high number of risk factors and are especially vulnerable. More than 70% of veterans enrolled in US Department of Veterans Affairs (VA) healthcare are at increased risk for developing DM due to excess weight, poor eating habits, and physical inactivity.² One in 4 veterans has DM, compared with 1 in 6 in the general population.²

DM can lead to long-term complications including limb amputations. Annually in the US about 73,000 nontraumatic lower limb amputations are performed and > 60% occur among persons with DM.³ Complications from diabetic wounds are the cause of 90% of lower limb amputations, and foot ulcers are the most prevalent complication.⁴ Diabetic ulcers are slow to heal due to vascular impairments and nerve damage.⁵ Peripheral vascular disease, a common comorbid condition, contributes to restricted blood flow and can lead to tissue death or gangrene requiring amputation.⁶

Between 2010 and 2014, VA Portland Healthcare System (VAPORHCS) had one of the highest national amputation rates in VA.⁷ A clinical chart review found that annual foot examinations and amputation risk assessments (ARAs) were not completed with all at-risk veterans. In 2013, a VA Office of Inspector General (OIG) national report found that more than one-third of veterans enrolled in VA with DM had no documentation of required annual foot exams.⁸ In 2017, VA released Directive 1410, which outlined the scope of care required to prevent and treat lower limb complications and amputations for veterans at risk for primary or secondary limb loss.¹ This national initiative is a comprehensive approach that engages multiprofessional teams to perform routine foot examinations and amputation risk assessments; identify and promptly treat foot ulcers; track, monitor and educate at-risk veterans; and participate in clinical education to enhance staff skills.

To decrease the amputation rate, VAPORHCS redesigned its foot-care program to comply with the national initiative. As is typical in VA, VAPORHCS uses a team-based approach in primary care. The basic 4-member team patient-aligned care team (PACT) consists of a physician or nurse practitioner (NP) primary care provider (PCP), a registered nurse (RN) care manager, a licensed practical nurse (LPN), and a medical staff assistant (MSA) for administrative support. Each PACT cares for about 1,800 veterans. Formerly, LPNs completed the annual diabetic foot exams, and PCPs verified the exams and completed the ARA based on the LPNs’ findings. If patients were moderate risk or high risk, they were referred to podiatry. The VAPORHCS audit found that not all at-risk veterans had both the foot exam and ARA completed, or were referred to podiatry when indicated. There was a need for a process improvement project to develop a seamless program consisting of all recommended foot care components crucial for timely care.

This quality improvement project sought to evaluate the effectiveness of the process changes by examining PCPs’ adoption of, and consistency in completing annual diabetic foot exams and ARAs with veterans. The goals of the project were to evaluate changes in the: (1) Number of accurate diabetic foot exams and amputation risk assessments completed with veterans with DM; (2) Number and timeliness of appropriate referrals to podiatry for an in-depth assessment and treatment of veterans found to be at moderate-to-high risk for lower limb amputations; and (3) Number of administrative text orders entered by PCPs for nurse care managers to offer foot care education and the completion of the education with veterans found to be at normal-to-low risk for lower limb amputations. The institutional review boards of VAPORHCS and Gonzaga University approved the study.

Methods

Established by the American Diabetes Association and endorsed by the American Association of Clinical Endocrinologists, the comprehensive foot exam includes a visual exam, pedal pulse checks, and a sensory exam.^9,10 The templated Computerized Patient Record System (CPRS) electronic health record note specifies normal and abnormal parameters of each section. On the same template, the provider assigns an ARA score based on the results of the completed foot exam. Risk scores range from 0 to 3 (0, normal or no risk; 1, low risk, 2; moderate risk; 3, high risk) If the veteran has normal or low risk, the PCP can encourage the veteran to remain at low risk by entering an administrative CPRS text order for the nurse care manager to offer education about daily foot care at the same visit or at a scheduled follow-up visit. This process facilitates nurse care managers to include routine foot care as integral to their usual duties coaching veterans to engage in self-care to manage chronic conditions. If the risk is assessed as moderate or high risk, PCPs are prompted to send a referral to podiatry to repeat the foot exam, verify the ARA score, and provide appropriate foot care treatment and follow-up.

On October 31, 2017, following training on the updated foot exam and ARA template with staff at the 13 VAPORHCS outpatient clinic sites, 2 sites piloted all components of the Comprehensive Foot Care program. An in-person training was completed with PCPs to review the changes of the foot care template in CPRS and to answer their questions about it. PCPs were required to complete both the 3-part foot exam and ARA at least once annually with veterans with DM.

An electronic clinical reminder was built to alert PCPs and PACTs that a veteran was either due or overdue for an exam and risk assessment. VA podiatrists agreed to complete the reminder with veterans under their care. One of the 2 sites was randomly selected for this study. Data were collected from August 1, 2017 to July 31, 2018. Patients were identified from the Diabetes Registry, a database established at VAPORHCS in 2008 to track veterans with DM to ensure quality care.¹¹ Veterans’ personal health identifiers from the registry were used to access their health records to complete chart reviews and assess the completion, accuracy and timeliness of all foot care components.

The Diabetes Registry lists a veterans’ upcoming appointments and tracks their most recent clinic visits; laboratory tests; physical exams; and screening exams for foot, eye, and renal care. Newly diagnosed veterans are uploaded automatically into this registry by tracking all DM-related International Classification of Diseases (ICD-10) codes, hemoglobin A_1c (HbA_1c) levels ≥ 6.5%, or outpatient prescriptions for insulin or oral hypoglycemic agents.¹¹

Study Design

This quality improvement project evaluated PCPs’ actions in a program process change intended to improve foot care provided with veterans at-risk for nontraumatic lower limb amputations. Audits of CPRS records and the Diabetes Registry determined the results of the practice change. Data on the total number of foot exams, amputation risk scores, appropriate podiatry referrals, administrative orders for nurse coaching, and completed foot care education were collected during the study period. Data collected for the 3-month period preceding the process change established preimplementation comparison vs the postimplementation data. Data were collected at 3, 6, and 9 months after implementation. The foot exams and ARAs were reviewed to determine whether exams and assessments were completed accurately during the pre- and post-implementation timeframes. Incomplete or clearly incorrectly completed documentation were considered inaccurate. For example, it was considered inaccurate if only the foot exam portion was completed in the assessment and the ARA was not.

Data Analysis

Data on the total number of accurately completed foot examinations and ARAs, total number of podiatry referrals, and total number of administrative text orders placed by PCPs, and education completed by nurse care managers were assessed. Statistical significance was evaluated using χ² and Fisher exact test as appropriate. A Pearson correlation coefficient was used to determine whether there was a statistically significant increase in accurate foot examinations and ARAs as well as total number of podiatry referrals during the study period. Statistical analyses were performed using Stata 14.1 statistical software (College Station, TX).

Results

A total of 1,242 completed diabetic foot examinations were identified from August 1, 2017 to July 31, 2018 using the Diabetes Registry (Table). For the 3 months prior to the change, there were 191 appropriately completed foot examinations and ARAs. This number increased progressively over three 3-month periods (Figure 1). Within the 1-year study period, there was a statistically significant increase in the number of appropriate foot examinations (r = 0.495). PCPs placed 34 podiatry referrals during the prechange period. After the change, the number of appropriate referrals increased statistically significantly in the 3 following 3-month-periods (r = 0.222) (Figure 2).

To determine the accuracy of documentation and ratio of appropriate referrals, the 3-month prechange data was compared with the 9-month postchange period. There was a statistically significant increase from pre- to postchange accuracy of documentation for examinations and ARAs (53.1% vs. 97.7%). The percentage of appropriate podiatry referrals increased significantly from 41.5% to 76.8%. Overall, there was poor adherence to protocol for the text order and education that was implemented during the change. Only 4.6% of patients had an administrative text order entered into CPRS and 3.9% were provided with foot care coaching. There was no statistical difference in the use of text orders between the first 3-month period and the last 3-month period (5.2% vs. 2.1%). Similarly, there was no statistical difference in the rate of patient education between the first 3-month period and the final 3-month period (2.6% vs. 2.1%).

Notably, at the end of the first year of this evaluation, 119 veterans at the clinic did not show a recorded comprehensive foot examination since receiving a DM diagnosis and 299 veterans were due for an annual examination—a 25.2% gap of veterans without the recommended progression of foot care services. Of those that previously had a recorded foot examination, 51 (17.0%) veterans were found to be ≥ 2 years overdue.

Discussion

DM management requires a comprehensive team-based approach to help monitor for associated complications. At the VA, PACTs are veterans’ initial and primary point of contact for chronic condition management. PACTs have regular opportunities to engage veterans in initial and follow-up care and appropriate self-care. PCPs are critical in placing referrals for specialized care promptly to prevent and minimize complications such as foot ulcers, and ultimately, lower limb amputations.^9,10,12

When PCPs assume responsibility for the entire foot examination, they are able to identify problems early.¹ Left untreated, foot wounds and ulcers have the potential to grow into serious infections.⁹ Early risk identification and management can lead to increased patient satisfaction, improved life expectancy, quality of life, and ultimately, lower healthcare costs.¹²

Multiple studies have shown the clinical importance of foot examinations in preventative care. In one study, researchers found that completing foot examinations, among other early interventions, increased life expectancy and reduced foot complications.¹³ Diabetic foot management programs involving screening and categorizing patients into low- and high-risk groups had a 47.4% decrease in the incidence of amputations and 37.8% decrease in hospital admissions.¹⁴ Amputations were found to be inversely correlated with multidisciplinary foot care programs with reduction of lower limb amputations at 2 years.¹⁵ The Centers for Disease Control and Prevention found that comprehensive foot care programs that include a foot examination, ARA, appropriate referrals to specialists, and foot-care education and preventative services can reduce lower limb amputation rates by 45% to 85%.¹⁶

With one of the highest amputation rates in VA, VAPORHCS needed an integrated approach to ensure that appropriate foot care occurred regularly with veterans with DM. Prior to the process change, LPNs completed foot examinations and PCPs completed the ARA. Separating these clinical services resulted in few veterans receiving an amputation risk score. Of those with scores, the lack of a standardized program protocol resulted in discrepancies between ARAs from patient to patient as health care providers did not have clear or enough information to select the correct score and make the appropriate referrals. Thus, veterans previously identified as at moderate or high risk also lacked podiatric follow-up care.

The new quality-driven process change corrected the documentation process to nationally accepted standards. The goal was to create a consistent template in the electronic health record for all health care providers. The new template simplifies the documentation process and clarifies the amputation risk score assignment, which allows for proper foot care management. The PCP completes the process from assessment through referral, removing gaps in care and improving efficiency. Although this change was initially met with resistance from PCPs, it led to a significant increase in the number of patients with accurately documented examinations. Similarly, the number of appropriate referrals significantly rose during the study period. The standardized documentation process resulted in improved accurate examinations and ARAs over the past year. The new program also resulted in an increased number of appropriate podiatry referrals for those identified to be at moderate or high risk. This elevation of care is crucial for veterans to receive frequent follow-up visits for preventative care and/or treatment, including surgical modalities to promote limb salvage.

Barriers

This project identified several barriers to the Comprehensive Foot Care program. One major barrier was health care provider resistance to using the new process. For example, VAPORHCS podiatrists are not using the new template with established patients, which requires PCPs to complete the clinical reminder template for quality performance, an additional burden unrelated to clinical care. PCPs that do complete the foot examination/ARA templated note use the podiatrist’s visit note without personally assessing the patient.

PCPs also have been resistant to entering administrative text orders for preventative foot care in normal- or low-risk veterans (4.6% overall), which has resulted in decreased patient education (3.9% overall). Education for normal-risk and low-risk patients is designed to engage veterans in self-care and prevent risk progression, critical to prevention.

It was found that PCPs often did not ask nurses to coach normal- or low-risk veterans on preventative foot care, as suggested by the low rates at which patients were offered education. This is an area we will target with future quality improvement efforts. All patients with DM should have general education about risk factors and appropriate management of them to decrease their risk for complications.⁹ Preventative foot care education is a critical resource to share with patients during health coaching opportunities to clarify misunderstandings and support change talk when patients are ambivalent or resistant to change. Individual or group-based nurse visits can facilitate better coaching for patients.

At the VA, coaching begins with a conversation about what matters most to the veteran, facilitating the development of a personalized plan based on patients’ values, needs, preferences and goals.^9,10,12,17 Coaching allows nurses to assess veterans’ knowledge and willingness to engage in healthy habits; and identify additional resources to help them achieve their goals.

Limitations

There are many limitations to this short quality improvement analysis. For example, only 1 of 2 clinics that piloted the program change was evaluated. In addition, there are 11 other clinics that need additional in-depth education on the program change. Although this analysis was overwhelmingly positive, it may not be as successful at other clinic sites and may be subject to the Hawthorne effect—since the 2 piloted locations knew they were being observed for the quality improvement program and may have made an extra effort to be compliant.¹⁸ Additionally, we were unable to track the records of veterans receiving care through the VA Choice Program for this analysis resulting in a lack of documentation of completed diabetic foot examinations and a lack of internal referrals to VA podiatry.

Another major limitation of this project involved calculating the number of referrals placed to podiatry. On January 1, 2018, about halfway through the program evaluation, a national VA decision enabled veterans to self-refer to podiatry, which may have limited the number of podiatry referrals placed by PCPs. Finally, patients could refuse podiatry referrals. In the 9-month postimplementation period, 57 (64.8%) veterans declined podiatry referrals, according to their CPRS records.

Although, there was an improvement in the accuracy of diabetic foot examinations, ARAs, and appropriate podiatry referrals, the ultimate goal of reducing diabetic foot ulcers and lower limb amputations was not tracked due to the limited timeframe of this analysis. Tracking these endpoints with continuous plan-do-study-act cycles are needed for this ongoing quality improvement project.

Conclusion

The goal of the VAPORHCS Comprehensive Foot Care program is to provide veterans with a program that is predictable, easy and consistent to prevent and treat foot ulcers to reduce the rate of lower limb amputations. It requires multidisciplinary team collaboration for success. Implementation of this new comprehensive program has increased the number of accurate annual foot exams, ARAs and podiatry referrals. Despite these improvements, areas of future improvement include emphasizing patient education and ongoing provider compliance with annual assessments.

Author contributions
MHG proposed the program evaluation project idea. TVQ collected and analyzed the data and wrote the manuscript. MHG oversaw the project and edited the manuscript. TVQ is the guarantor of this project and takes responsibility for the contents of this journal article.

Acknowledgments
The authors thank Tyra Haebe, VAPORHCS Prevention of Amputation in Veterans Everywhere (PAVE) Manager, and the entire VAPORHCS PAVE committee for their support in this program evaluation project.

Individuals with diabetes mellitus (DM), peripheral vascular disease, or end-stage renal disease are at risk for a nontraumatic lower limb amputation.¹ Veterans have a high number of risk factors and are especially vulnerable. More than 70% of veterans enrolled in US Department of Veterans Affairs (VA) healthcare are at increased risk for developing DM due to excess weight, poor eating habits, and physical inactivity.² One in 4 veterans has DM, compared with 1 in 6 in the general population.²

DM can lead to long-term complications including limb amputations. Annually in the US about 73,000 nontraumatic lower limb amputations are performed and > 60% occur among persons with DM.³ Complications from diabetic wounds are the cause of 90% of lower limb amputations, and foot ulcers are the most prevalent complication.⁴ Diabetic ulcers are slow to heal due to vascular impairments and nerve damage.⁵ Peripheral vascular disease, a common comorbid condition, contributes to restricted blood flow and can lead to tissue death or gangrene requiring amputation.⁶

Between 2010 and 2014, VA Portland Healthcare System (VAPORHCS) had one of the highest national amputation rates in VA.⁷ A clinical chart review found that annual foot examinations and amputation risk assessments (ARAs) were not completed with all at-risk veterans. In 2013, a VA Office of Inspector General (OIG) national report found that more than one-third of veterans enrolled in VA with DM had no documentation of required annual foot exams.⁸ In 2017, VA released Directive 1410, which outlined the scope of care required to prevent and treat lower limb complications and amputations for veterans at risk for primary or secondary limb loss.¹ This national initiative is a comprehensive approach that engages multiprofessional teams to perform routine foot examinations and amputation risk assessments; identify and promptly treat foot ulcers; track, monitor and educate at-risk veterans; and participate in clinical education to enhance staff skills.

To decrease the amputation rate, VAPORHCS redesigned its foot-care program to comply with the national initiative. As is typical in VA, VAPORHCS uses a team-based approach in primary care. The basic 4-member team patient-aligned care team (PACT) consists of a physician or nurse practitioner (NP) primary care provider (PCP), a registered nurse (RN) care manager, a licensed practical nurse (LPN), and a medical staff assistant (MSA) for administrative support. Each PACT cares for about 1,800 veterans. Formerly, LPNs completed the annual diabetic foot exams, and PCPs verified the exams and completed the ARA based on the LPNs’ findings. If patients were moderate risk or high risk, they were referred to podiatry. The VAPORHCS audit found that not all at-risk veterans had both the foot exam and ARA completed, or were referred to podiatry when indicated. There was a need for a process improvement project to develop a seamless program consisting of all recommended foot care components crucial for timely care.

This quality improvement project sought to evaluate the effectiveness of the process changes by examining PCPs’ adoption of, and consistency in completing annual diabetic foot exams and ARAs with veterans. The goals of the project were to evaluate changes in the: (1) Number of accurate diabetic foot exams and amputation risk assessments completed with veterans with DM; (2) Number and timeliness of appropriate referrals to podiatry for an in-depth assessment and treatment of veterans found to be at moderate-to-high risk for lower limb amputations; and (3) Number of administrative text orders entered by PCPs for nurse care managers to offer foot care education and the completion of the education with veterans found to be at normal-to-low risk for lower limb amputations. The institutional review boards of VAPORHCS and Gonzaga University approved the study.

Methods

Established by the American Diabetes Association and endorsed by the American Association of Clinical Endocrinologists, the comprehensive foot exam includes a visual exam, pedal pulse checks, and a sensory exam.^9,10 The templated Computerized Patient Record System (CPRS) electronic health record note specifies normal and abnormal parameters of each section. On the same template, the provider assigns an ARA score based on the results of the completed foot exam. Risk scores range from 0 to 3 (0, normal or no risk; 1, low risk, 2; moderate risk; 3, high risk) If the veteran has normal or low risk, the PCP can encourage the veteran to remain at low risk by entering an administrative CPRS text order for the nurse care manager to offer education about daily foot care at the same visit or at a scheduled follow-up visit. This process facilitates nurse care managers to include routine foot care as integral to their usual duties coaching veterans to engage in self-care to manage chronic conditions. If the risk is assessed as moderate or high risk, PCPs are prompted to send a referral to podiatry to repeat the foot exam, verify the ARA score, and provide appropriate foot care treatment and follow-up.

On October 31, 2017, following training on the updated foot exam and ARA template with staff at the 13 VAPORHCS outpatient clinic sites, 2 sites piloted all components of the Comprehensive Foot Care program. An in-person training was completed with PCPs to review the changes of the foot care template in CPRS and to answer their questions about it. PCPs were required to complete both the 3-part foot exam and ARA at least once annually with veterans with DM.

An electronic clinical reminder was built to alert PCPs and PACTs that a veteran was either due or overdue for an exam and risk assessment. VA podiatrists agreed to complete the reminder with veterans under their care. One of the 2 sites was randomly selected for this study. Data were collected from August 1, 2017 to July 31, 2018. Patients were identified from the Diabetes Registry, a database established at VAPORHCS in 2008 to track veterans with DM to ensure quality care.¹¹ Veterans’ personal health identifiers from the registry were used to access their health records to complete chart reviews and assess the completion, accuracy and timeliness of all foot care components.

The Diabetes Registry lists a veterans’ upcoming appointments and tracks their most recent clinic visits; laboratory tests; physical exams; and screening exams for foot, eye, and renal care. Newly diagnosed veterans are uploaded automatically into this registry by tracking all DM-related International Classification of Diseases (ICD-10) codes, hemoglobin A_1c (HbA_1c) levels ≥ 6.5%, or outpatient prescriptions for insulin or oral hypoglycemic agents.¹¹

Study Design

This quality improvement project evaluated PCPs’ actions in a program process change intended to improve foot care provided with veterans at-risk for nontraumatic lower limb amputations. Audits of CPRS records and the Diabetes Registry determined the results of the practice change. Data on the total number of foot exams, amputation risk scores, appropriate podiatry referrals, administrative orders for nurse coaching, and completed foot care education were collected during the study period. Data collected for the 3-month period preceding the process change established preimplementation comparison vs the postimplementation data. Data were collected at 3, 6, and 9 months after implementation. The foot exams and ARAs were reviewed to determine whether exams and assessments were completed accurately during the pre- and post-implementation timeframes. Incomplete or clearly incorrectly completed documentation were considered inaccurate. For example, it was considered inaccurate if only the foot exam portion was completed in the assessment and the ARA was not.

Data Analysis

Data on the total number of accurately completed foot examinations and ARAs, total number of podiatry referrals, and total number of administrative text orders placed by PCPs, and education completed by nurse care managers were assessed. Statistical significance was evaluated using χ² and Fisher exact test as appropriate. A Pearson correlation coefficient was used to determine whether there was a statistically significant increase in accurate foot examinations and ARAs as well as total number of podiatry referrals during the study period. Statistical analyses were performed using Stata 14.1 statistical software (College Station, TX).

Results

A total of 1,242 completed diabetic foot examinations were identified from August 1, 2017 to July 31, 2018 using the Diabetes Registry (Table). For the 3 months prior to the change, there were 191 appropriately completed foot examinations and ARAs. This number increased progressively over three 3-month periods (Figure 1). Within the 1-year study period, there was a statistically significant increase in the number of appropriate foot examinations (r = 0.495). PCPs placed 34 podiatry referrals during the prechange period. After the change, the number of appropriate referrals increased statistically significantly in the 3 following 3-month-periods (r = 0.222) (Figure 2).

To determine the accuracy of documentation and ratio of appropriate referrals, the 3-month prechange data was compared with the 9-month postchange period. There was a statistically significant increase from pre- to postchange accuracy of documentation for examinations and ARAs (53.1% vs. 97.7%). The percentage of appropriate podiatry referrals increased significantly from 41.5% to 76.8%. Overall, there was poor adherence to protocol for the text order and education that was implemented during the change. Only 4.6% of patients had an administrative text order entered into CPRS and 3.9% were provided with foot care coaching. There was no statistical difference in the use of text orders between the first 3-month period and the last 3-month period (5.2% vs. 2.1%). Similarly, there was no statistical difference in the rate of patient education between the first 3-month period and the final 3-month period (2.6% vs. 2.1%).

Notably, at the end of the first year of this evaluation, 119 veterans at the clinic did not show a recorded comprehensive foot examination since receiving a DM diagnosis and 299 veterans were due for an annual examination—a 25.2% gap of veterans without the recommended progression of foot care services. Of those that previously had a recorded foot examination, 51 (17.0%) veterans were found to be ≥ 2 years overdue.

Discussion

DM management requires a comprehensive team-based approach to help monitor for associated complications. At the VA, PACTs are veterans’ initial and primary point of contact for chronic condition management. PACTs have regular opportunities to engage veterans in initial and follow-up care and appropriate self-care. PCPs are critical in placing referrals for specialized care promptly to prevent and minimize complications such as foot ulcers, and ultimately, lower limb amputations.^9,10,12

When PCPs assume responsibility for the entire foot examination, they are able to identify problems early.¹ Left untreated, foot wounds and ulcers have the potential to grow into serious infections.⁹ Early risk identification and management can lead to increased patient satisfaction, improved life expectancy, quality of life, and ultimately, lower healthcare costs.¹²

Multiple studies have shown the clinical importance of foot examinations in preventative care. In one study, researchers found that completing foot examinations, among other early interventions, increased life expectancy and reduced foot complications.¹³ Diabetic foot management programs involving screening and categorizing patients into low- and high-risk groups had a 47.4% decrease in the incidence of amputations and 37.8% decrease in hospital admissions.¹⁴ Amputations were found to be inversely correlated with multidisciplinary foot care programs with reduction of lower limb amputations at 2 years.¹⁵ The Centers for Disease Control and Prevention found that comprehensive foot care programs that include a foot examination, ARA, appropriate referrals to specialists, and foot-care education and preventative services can reduce lower limb amputation rates by 45% to 85%.¹⁶

With one of the highest amputation rates in VA, VAPORHCS needed an integrated approach to ensure that appropriate foot care occurred regularly with veterans with DM. Prior to the process change, LPNs completed foot examinations and PCPs completed the ARA. Separating these clinical services resulted in few veterans receiving an amputation risk score. Of those with scores, the lack of a standardized program protocol resulted in discrepancies between ARAs from patient to patient as health care providers did not have clear or enough information to select the correct score and make the appropriate referrals. Thus, veterans previously identified as at moderate or high risk also lacked podiatric follow-up care.

The new quality-driven process change corrected the documentation process to nationally accepted standards. The goal was to create a consistent template in the electronic health record for all health care providers. The new template simplifies the documentation process and clarifies the amputation risk score assignment, which allows for proper foot care management. The PCP completes the process from assessment through referral, removing gaps in care and improving efficiency. Although this change was initially met with resistance from PCPs, it led to a significant increase in the number of patients with accurately documented examinations. Similarly, the number of appropriate referrals significantly rose during the study period. The standardized documentation process resulted in improved accurate examinations and ARAs over the past year. The new program also resulted in an increased number of appropriate podiatry referrals for those identified to be at moderate or high risk. This elevation of care is crucial for veterans to receive frequent follow-up visits for preventative care and/or treatment, including surgical modalities to promote limb salvage.

Barriers

This project identified several barriers to the Comprehensive Foot Care program. One major barrier was health care provider resistance to using the new process. For example, VAPORHCS podiatrists are not using the new template with established patients, which requires PCPs to complete the clinical reminder template for quality performance, an additional burden unrelated to clinical care. PCPs that do complete the foot examination/ARA templated note use the podiatrist’s visit note without personally assessing the patient.

PCPs also have been resistant to entering administrative text orders for preventative foot care in normal- or low-risk veterans (4.6% overall), which has resulted in decreased patient education (3.9% overall). Education for normal-risk and low-risk patients is designed to engage veterans in self-care and prevent risk progression, critical to prevention.

It was found that PCPs often did not ask nurses to coach normal- or low-risk veterans on preventative foot care, as suggested by the low rates at which patients were offered education. This is an area we will target with future quality improvement efforts. All patients with DM should have general education about risk factors and appropriate management of them to decrease their risk for complications.⁹ Preventative foot care education is a critical resource to share with patients during health coaching opportunities to clarify misunderstandings and support change talk when patients are ambivalent or resistant to change. Individual or group-based nurse visits can facilitate better coaching for patients.

At the VA, coaching begins with a conversation about what matters most to the veteran, facilitating the development of a personalized plan based on patients’ values, needs, preferences and goals.^9,10,12,17 Coaching allows nurses to assess veterans’ knowledge and willingness to engage in healthy habits; and identify additional resources to help them achieve their goals.

Limitations

There are many limitations to this short quality improvement analysis. For example, only 1 of 2 clinics that piloted the program change was evaluated. In addition, there are 11 other clinics that need additional in-depth education on the program change. Although this analysis was overwhelmingly positive, it may not be as successful at other clinic sites and may be subject to the Hawthorne effect—since the 2 piloted locations knew they were being observed for the quality improvement program and may have made an extra effort to be compliant.¹⁸ Additionally, we were unable to track the records of veterans receiving care through the VA Choice Program for this analysis resulting in a lack of documentation of completed diabetic foot examinations and a lack of internal referrals to VA podiatry.

Another major limitation of this project involved calculating the number of referrals placed to podiatry. On January 1, 2018, about halfway through the program evaluation, a national VA decision enabled veterans to self-refer to podiatry, which may have limited the number of podiatry referrals placed by PCPs. Finally, patients could refuse podiatry referrals. In the 9-month postimplementation period, 57 (64.8%) veterans declined podiatry referrals, according to their CPRS records.

Although, there was an improvement in the accuracy of diabetic foot examinations, ARAs, and appropriate podiatry referrals, the ultimate goal of reducing diabetic foot ulcers and lower limb amputations was not tracked due to the limited timeframe of this analysis. Tracking these endpoints with continuous plan-do-study-act cycles are needed for this ongoing quality improvement project.

Conclusion

The goal of the VAPORHCS Comprehensive Foot Care program is to provide veterans with a program that is predictable, easy and consistent to prevent and treat foot ulcers to reduce the rate of lower limb amputations. It requires multidisciplinary team collaboration for success. Implementation of this new comprehensive program has increased the number of accurate annual foot exams, ARAs and podiatry referrals. Despite these improvements, areas of future improvement include emphasizing patient education and ongoing provider compliance with annual assessments.

Author contributions
MHG proposed the program evaluation project idea. TVQ collected and analyzed the data and wrote the manuscript. MHG oversaw the project and edited the manuscript. TVQ is the guarantor of this project and takes responsibility for the contents of this journal article.

Acknowledgments
The authors thank Tyra Haebe, VAPORHCS Prevention of Amputation in Veterans Everywhere (PAVE) Manager, and the entire VAPORHCS PAVE committee for their support in this program evaluation project.

Individuals with diabetes mellitus (DM), peripheral vascular disease, or end-stage renal disease are at risk for a nontraumatic lower limb amputation.¹ Veterans have a high number of risk factors and are especially vulnerable. More than 70% of veterans enrolled in US Department of Veterans Affairs (VA) healthcare are at increased risk for developing DM due to excess weight, poor eating habits, and physical inactivity.² One in 4 veterans has DM, compared with 1 in 6 in the general population.²

DM can lead to long-term complications including limb amputations. Annually in the US about 73,000 nontraumatic lower limb amputations are performed and > 60% occur among persons with DM.³ Complications from diabetic wounds are the cause of 90% of lower limb amputations, and foot ulcers are the most prevalent complication.⁴ Diabetic ulcers are slow to heal due to vascular impairments and nerve damage.⁵ Peripheral vascular disease, a common comorbid condition, contributes to restricted blood flow and can lead to tissue death or gangrene requiring amputation.⁶

Between 2010 and 2014, VA Portland Healthcare System (VAPORHCS) had one of the highest national amputation rates in VA.⁷ A clinical chart review found that annual foot examinations and amputation risk assessments (ARAs) were not completed with all at-risk veterans. In 2013, a VA Office of Inspector General (OIG) national report found that more than one-third of veterans enrolled in VA with DM had no documentation of required annual foot exams.⁸ In 2017, VA released Directive 1410, which outlined the scope of care required to prevent and treat lower limb complications and amputations for veterans at risk for primary or secondary limb loss.¹ This national initiative is a comprehensive approach that engages multiprofessional teams to perform routine foot examinations and amputation risk assessments; identify and promptly treat foot ulcers; track, monitor and educate at-risk veterans; and participate in clinical education to enhance staff skills.

To decrease the amputation rate, VAPORHCS redesigned its foot-care program to comply with the national initiative. As is typical in VA, VAPORHCS uses a team-based approach in primary care. The basic 4-member team patient-aligned care team (PACT) consists of a physician or nurse practitioner (NP) primary care provider (PCP), a registered nurse (RN) care manager, a licensed practical nurse (LPN), and a medical staff assistant (MSA) for administrative support. Each PACT cares for about 1,800 veterans. Formerly, LPNs completed the annual diabetic foot exams, and PCPs verified the exams and completed the ARA based on the LPNs’ findings. If patients were moderate risk or high risk, they were referred to podiatry. The VAPORHCS audit found that not all at-risk veterans had both the foot exam and ARA completed, or were referred to podiatry when indicated. There was a need for a process improvement project to develop a seamless program consisting of all recommended foot care components crucial for timely care.

This quality improvement project sought to evaluate the effectiveness of the process changes by examining PCPs’ adoption of, and consistency in completing annual diabetic foot exams and ARAs with veterans. The goals of the project were to evaluate changes in the: (1) Number of accurate diabetic foot exams and amputation risk assessments completed with veterans with DM; (2) Number and timeliness of appropriate referrals to podiatry for an in-depth assessment and treatment of veterans found to be at moderate-to-high risk for lower limb amputations; and (3) Number of administrative text orders entered by PCPs for nurse care managers to offer foot care education and the completion of the education with veterans found to be at normal-to-low risk for lower limb amputations. The institutional review boards of VAPORHCS and Gonzaga University approved the study.

Methods

Established by the American Diabetes Association and endorsed by the American Association of Clinical Endocrinologists, the comprehensive foot exam includes a visual exam, pedal pulse checks, and a sensory exam.^9,10 The templated Computerized Patient Record System (CPRS) electronic health record note specifies normal and abnormal parameters of each section. On the same template, the provider assigns an ARA score based on the results of the completed foot exam. Risk scores range from 0 to 3 (0, normal or no risk; 1, low risk, 2; moderate risk; 3, high risk) If the veteran has normal or low risk, the PCP can encourage the veteran to remain at low risk by entering an administrative CPRS text order for the nurse care manager to offer education about daily foot care at the same visit or at a scheduled follow-up visit. This process facilitates nurse care managers to include routine foot care as integral to their usual duties coaching veterans to engage in self-care to manage chronic conditions. If the risk is assessed as moderate or high risk, PCPs are prompted to send a referral to podiatry to repeat the foot exam, verify the ARA score, and provide appropriate foot care treatment and follow-up.

On October 31, 2017, following training on the updated foot exam and ARA template with staff at the 13 VAPORHCS outpatient clinic sites, 2 sites piloted all components of the Comprehensive Foot Care program. An in-person training was completed with PCPs to review the changes of the foot care template in CPRS and to answer their questions about it. PCPs were required to complete both the 3-part foot exam and ARA at least once annually with veterans with DM.

An electronic clinical reminder was built to alert PCPs and PACTs that a veteran was either due or overdue for an exam and risk assessment. VA podiatrists agreed to complete the reminder with veterans under their care. One of the 2 sites was randomly selected for this study. Data were collected from August 1, 2017 to July 31, 2018. Patients were identified from the Diabetes Registry, a database established at VAPORHCS in 2008 to track veterans with DM to ensure quality care.¹¹ Veterans’ personal health identifiers from the registry were used to access their health records to complete chart reviews and assess the completion, accuracy and timeliness of all foot care components.

The Diabetes Registry lists a veterans’ upcoming appointments and tracks their most recent clinic visits; laboratory tests; physical exams; and screening exams for foot, eye, and renal care. Newly diagnosed veterans are uploaded automatically into this registry by tracking all DM-related International Classification of Diseases (ICD-10) codes, hemoglobin A_1c (HbA_1c) levels ≥ 6.5%, or outpatient prescriptions for insulin or oral hypoglycemic agents.¹¹

Study Design

This quality improvement project evaluated PCPs’ actions in a program process change intended to improve foot care provided with veterans at-risk for nontraumatic lower limb amputations. Audits of CPRS records and the Diabetes Registry determined the results of the practice change. Data on the total number of foot exams, amputation risk scores, appropriate podiatry referrals, administrative orders for nurse coaching, and completed foot care education were collected during the study period. Data collected for the 3-month period preceding the process change established preimplementation comparison vs the postimplementation data. Data were collected at 3, 6, and 9 months after implementation. The foot exams and ARAs were reviewed to determine whether exams and assessments were completed accurately during the pre- and post-implementation timeframes. Incomplete or clearly incorrectly completed documentation were considered inaccurate. For example, it was considered inaccurate if only the foot exam portion was completed in the assessment and the ARA was not.

Data Analysis

Data on the total number of accurately completed foot examinations and ARAs, total number of podiatry referrals, and total number of administrative text orders placed by PCPs, and education completed by nurse care managers were assessed. Statistical significance was evaluated using χ² and Fisher exact test as appropriate. A Pearson correlation coefficient was used to determine whether there was a statistically significant increase in accurate foot examinations and ARAs as well as total number of podiatry referrals during the study period. Statistical analyses were performed using Stata 14.1 statistical software (College Station, TX).

Results

A total of 1,242 completed diabetic foot examinations were identified from August 1, 2017 to July 31, 2018 using the Diabetes Registry (Table). For the 3 months prior to the change, there were 191 appropriately completed foot examinations and ARAs. This number increased progressively over three 3-month periods (Figure 1). Within the 1-year study period, there was a statistically significant increase in the number of appropriate foot examinations (r = 0.495). PCPs placed 34 podiatry referrals during the prechange period. After the change, the number of appropriate referrals increased statistically significantly in the 3 following 3-month-periods (r = 0.222) (Figure 2).

To determine the accuracy of documentation and ratio of appropriate referrals, the 3-month prechange data was compared with the 9-month postchange period. There was a statistically significant increase from pre- to postchange accuracy of documentation for examinations and ARAs (53.1% vs. 97.7%). The percentage of appropriate podiatry referrals increased significantly from 41.5% to 76.8%. Overall, there was poor adherence to protocol for the text order and education that was implemented during the change. Only 4.6% of patients had an administrative text order entered into CPRS and 3.9% were provided with foot care coaching. There was no statistical difference in the use of text orders between the first 3-month period and the last 3-month period (5.2% vs. 2.1%). Similarly, there was no statistical difference in the rate of patient education between the first 3-month period and the final 3-month period (2.6% vs. 2.1%).

Notably, at the end of the first year of this evaluation, 119 veterans at the clinic did not show a recorded comprehensive foot examination since receiving a DM diagnosis and 299 veterans were due for an annual examination—a 25.2% gap of veterans without the recommended progression of foot care services. Of those that previously had a recorded foot examination, 51 (17.0%) veterans were found to be ≥ 2 years overdue.

Discussion

DM management requires a comprehensive team-based approach to help monitor for associated complications. At the VA, PACTs are veterans’ initial and primary point of contact for chronic condition management. PACTs have regular opportunities to engage veterans in initial and follow-up care and appropriate self-care. PCPs are critical in placing referrals for specialized care promptly to prevent and minimize complications such as foot ulcers, and ultimately, lower limb amputations.^9,10,12

When PCPs assume responsibility for the entire foot examination, they are able to identify problems early.¹ Left untreated, foot wounds and ulcers have the potential to grow into serious infections.⁹ Early risk identification and management can lead to increased patient satisfaction, improved life expectancy, quality of life, and ultimately, lower healthcare costs.¹²

Multiple studies have shown the clinical importance of foot examinations in preventative care. In one study, researchers found that completing foot examinations, among other early interventions, increased life expectancy and reduced foot complications.¹³ Diabetic foot management programs involving screening and categorizing patients into low- and high-risk groups had a 47.4% decrease in the incidence of amputations and 37.8% decrease in hospital admissions.¹⁴ Amputations were found to be inversely correlated with multidisciplinary foot care programs with reduction of lower limb amputations at 2 years.¹⁵ The Centers for Disease Control and Prevention found that comprehensive foot care programs that include a foot examination, ARA, appropriate referrals to specialists, and foot-care education and preventative services can reduce lower limb amputation rates by 45% to 85%.¹⁶

With one of the highest amputation rates in VA, VAPORHCS needed an integrated approach to ensure that appropriate foot care occurred regularly with veterans with DM. Prior to the process change, LPNs completed foot examinations and PCPs completed the ARA. Separating these clinical services resulted in few veterans receiving an amputation risk score. Of those with scores, the lack of a standardized program protocol resulted in discrepancies between ARAs from patient to patient as health care providers did not have clear or enough information to select the correct score and make the appropriate referrals. Thus, veterans previously identified as at moderate or high risk also lacked podiatric follow-up care.

The new quality-driven process change corrected the documentation process to nationally accepted standards. The goal was to create a consistent template in the electronic health record for all health care providers. The new template simplifies the documentation process and clarifies the amputation risk score assignment, which allows for proper foot care management. The PCP completes the process from assessment through referral, removing gaps in care and improving efficiency. Although this change was initially met with resistance from PCPs, it led to a significant increase in the number of patients with accurately documented examinations. Similarly, the number of appropriate referrals significantly rose during the study period. The standardized documentation process resulted in improved accurate examinations and ARAs over the past year. The new program also resulted in an increased number of appropriate podiatry referrals for those identified to be at moderate or high risk. This elevation of care is crucial for veterans to receive frequent follow-up visits for preventative care and/or treatment, including surgical modalities to promote limb salvage.

Barriers

This project identified several barriers to the Comprehensive Foot Care program. One major barrier was health care provider resistance to using the new process. For example, VAPORHCS podiatrists are not using the new template with established patients, which requires PCPs to complete the clinical reminder template for quality performance, an additional burden unrelated to clinical care. PCPs that do complete the foot examination/ARA templated note use the podiatrist’s visit note without personally assessing the patient.

PCPs also have been resistant to entering administrative text orders for preventative foot care in normal- or low-risk veterans (4.6% overall), which has resulted in decreased patient education (3.9% overall). Education for normal-risk and low-risk patients is designed to engage veterans in self-care and prevent risk progression, critical to prevention.

It was found that PCPs often did not ask nurses to coach normal- or low-risk veterans on preventative foot care, as suggested by the low rates at which patients were offered education. This is an area we will target with future quality improvement efforts. All patients with DM should have general education about risk factors and appropriate management of them to decrease their risk for complications.⁹ Preventative foot care education is a critical resource to share with patients during health coaching opportunities to clarify misunderstandings and support change talk when patients are ambivalent or resistant to change. Individual or group-based nurse visits can facilitate better coaching for patients.

At the VA, coaching begins with a conversation about what matters most to the veteran, facilitating the development of a personalized plan based on patients’ values, needs, preferences and goals.^9,10,12,17 Coaching allows nurses to assess veterans’ knowledge and willingness to engage in healthy habits; and identify additional resources to help them achieve their goals.

Limitations

There are many limitations to this short quality improvement analysis. For example, only 1 of 2 clinics that piloted the program change was evaluated. In addition, there are 11 other clinics that need additional in-depth education on the program change. Although this analysis was overwhelmingly positive, it may not be as successful at other clinic sites and may be subject to the Hawthorne effect—since the 2 piloted locations knew they were being observed for the quality improvement program and may have made an extra effort to be compliant.¹⁸ Additionally, we were unable to track the records of veterans receiving care through the VA Choice Program for this analysis resulting in a lack of documentation of completed diabetic foot examinations and a lack of internal referrals to VA podiatry.

Another major limitation of this project involved calculating the number of referrals placed to podiatry. On January 1, 2018, about halfway through the program evaluation, a national VA decision enabled veterans to self-refer to podiatry, which may have limited the number of podiatry referrals placed by PCPs. Finally, patients could refuse podiatry referrals. In the 9-month postimplementation period, 57 (64.8%) veterans declined podiatry referrals, according to their CPRS records.

Although, there was an improvement in the accuracy of diabetic foot examinations, ARAs, and appropriate podiatry referrals, the ultimate goal of reducing diabetic foot ulcers and lower limb amputations was not tracked due to the limited timeframe of this analysis. Tracking these endpoints with continuous plan-do-study-act cycles are needed for this ongoing quality improvement project.

Conclusion

The goal of the VAPORHCS Comprehensive Foot Care program is to provide veterans with a program that is predictable, easy and consistent to prevent and treat foot ulcers to reduce the rate of lower limb amputations. It requires multidisciplinary team collaboration for success. Implementation of this new comprehensive program has increased the number of accurate annual foot exams, ARAs and podiatry referrals. Despite these improvements, areas of future improvement include emphasizing patient education and ongoing provider compliance with annual assessments.

Author contributions
MHG proposed the program evaluation project idea. TVQ collected and analyzed the data and wrote the manuscript. MHG oversaw the project and edited the manuscript. TVQ is the guarantor of this project and takes responsibility for the contents of this journal article.

Acknowledgments
The authors thank Tyra Haebe, VAPORHCS Prevention of Amputation in Veterans Everywhere (PAVE) Manager, and the entire VAPORHCS PAVE committee for their support in this program evaluation project.

Obesity is associated with a significant increase in mortality. It increases the risk of type 2 diabetes mellitus (T2DM), hypertension, hyperlipidemia, and coronary artery disease.¹ T2DM is strongly associated with obesity in all ethnic groups.

Medical nutrition therapy and weight loss are very important for DM management.² This includes providing education about diet modification, increased physical activity, daily calorie intake evaluation, and consistent carbohydrate intake. For patients with T2DM, health care providers (HCPs) should emphasize lowering caloric intake and inducing weight loss for those who are overweight (body mass index [BMI] between 25 and 29.9) and obese (BMI ≥ 30). This can improve glycemic control by decreasing insulin resistance. Initial recommendations for weight loss and physical activity are to lose between 5% and 10% of initial body weight and to accumulate at least 30 minutes of moderate physical activity over the course of most days of the week.^3,4

Several formulas are available to estimate baseline caloric intake for weight maintenance. For weight loss of 1 to 2 pounds per week, lowering 500 to 1,000 calories from daily weight maintenance calories serves the goal. The American Diabetes Association (ADA) also suggests that HCPs recommend diet, physical activity, and behavioral therapy designed to achieve > 5% weight loss to overweight and obese patients with T2DM.⁵

Recognizing the clinical benefits of achieving weight loss in overweight or obese patients with T2DM, we aimed to increase the number of visits in the Endocrine Clinic at Central Arkansas Veterans Healthcare System (CAVHS) in Little Rock that addressed obesity, documented calorie goal for patients who are overweight or obese, and performed an intervention with further education for the patient.

Methods

The study population included veterans with either type 1 DM (T1DM) or T2DM with BMI > 25 on any DM control regimen. We performed a health record review of the eligible patients seen in the CAVHS Endocrine Clinic from June 1, 2016 to July 31, 2016 to determine the baseline percentage of visits that addressed obesity and provided weight loss advice to patients. We obtained a list of patients seen in the clinic during the study period from Strategic Management Service Services at CAVHS. We also obtained information that age, gender, medications, BMI, and last Endocrine clinic HCP assessment from the electronic health record. We reviewed the HCPs notes, including fellows and faculty who were involved in the patients’ treatment, to determine whether their notes documented a BMI > 25 and whether they discussed an intervention for overweight or obesity with the patient. The CAVHS Institutional Review Board reviewed and approved the initiative as a quality improvement study.

Intervention

Our clinic has a defined group of HCPs that we targeted for the intervention. After getting baseline information, during August 2017 we educated these HCPs on the tools available to calculate calorie goal for the patients. We advised the HCPs to use the Mifflin St Jyor equation for estimating energy expenditure and set a goal of initial weight loss between 5% and 7% of body weight. We gave specific instructions and advice to the providers (Table 1). HCPs also received educational material to distribute to patients that provided information on the healthy plate method, discussed how to count calories, and advised them on ADA goals with carbohydrate limitation. We encouraged HCPs to recommend that patients cut between 500 and 1,000 calories daily from their current diet. HCPs also received advice to seek help from clinical dieticians and the VA MOVE! Weight Management Program when appropriate.

Study of Effect of the Intervention

To study the effect of this intervention, we reviewed documentation by HCPs and assessed patient satisfaction. We obtained a list of patients and reviewed HCP notes on patients with BMI > 25 to assess whether providers addressed obesity in November and December 2017. We also evaluated whether HCPs offered a specific intervention to address the problem, such as providing education material to the patient or an estimate of daily calorie goal, or referring them to clinical dietician and/or the MOVE program. Patients received a 5-question survey that assessed their understanding and satisfaction at the end of the visit (Table 2).

Results

Of the 100 charts reviewed prior to intervention, HCPs discussed obesity management with only 6% of patients. After the intervention, we collected data again through chart review of the patients who were overweight or obese and seen for DM in the same clinic during a 2-month period. Of the 100 charts reviewed, we noticed that recognition and management of obesity improved to 60%.

To evaluate the impact of this intervention, patients received a questionnaire at the end of the visit. Nearly all (97%) patients mentioned that the provider discussed weight management during that visit. Most (83%) patients mentioned that weight management was discussed with them during prior visits, while 70% of patients felt their knowledge on working on weight loss had improved. Almost half (46%) were interested in further referral to a dietician or the MOVE program if they did not achieve desired results, but 78% were confident that they could implement the discussed weight management measures.

Discussion

Increased body weight is associated with worsening of DM and can result in poor glycemic control. Achieving weight loss in overweight or obese patients with DM can lead to clinical benefits; however, this is a challenge. In one study, a DM prevention program with lifestyle intervention leading to weight loss significantly reduced the rate of progression from impaired glucose tolerance to DM over a 3-year period and improved cardiovascular risk factors like elevated blood pressure and dyslipidemia.⁶ A randomized trial of an intensive lifestyle intervention to increase physical activity and decrease caloric intake vs standard DM education in people with T2DM showed a modest weight loss of 8.6% of initial weight at 1 year.⁷ This weight loss was associated with significant improvement in blood pressure, glycemic control, fasting blood glucose, high-density lipoprotein (HDL) cholesterol, and triglyceride levels and significant reductions in the use of DM, hypertension, and lipid-lowering medications.⁷ Obesity attributes to dyslipidemia with increased levels of cholesterol, low-density lipoprotein, very low-density lipoprotein, triglycerides, and decreased levels of HDL by about 5%.⁸ Obesity also is associated with hypertension, coronary heart disease, heart failure, and cardiovascular and all-cause mortality.⁹

Limitations

Limitations of this study include the small sample size and that multiple HCPs were involved. The nature of intervention might have differed with different HCPs or in a different setting than a VA clinic. In addition, we did not evaluate the effect on weight loss in specific patients as we only reviewed charts to check whether HCPs addressed weight loss. Nevertheless, our intervention was effective because it improved patient and provider awareness. It also gave us the opportunity to create framework for further collaborations and community building. The Endocrinology department at CAVHS is currently collaborating with the MOVE program, which is a part of the nutrition and food services. We hope to have an endocrinologist involved to provide guidance on medication management for obesity.

Conclusion

At CAVHS a simple intervention was instituted to evaluate whether HCPs were discussing weight loss in patients with DM, providing them with information to assess patients’ daily calorie goal, and prompting them for intervention to achieve weight loss. The intervention led to better management of patients with DM and obesity and greater engagement in weight loss from patients.

This project was a team effort. The clinic nurse documented patient’s BMI on the check in slip. HCPs discussed the problem and specific intervention. The clinical dieticians provided focused education for patients. The clerks collected the patient responses to questionnaire. This project also improved communication within the Endocrine Clinic team. Documentation of HCPs pertaining to addressing obesity improved by 54%. Improved patient satisfaction and insight was evident on patient responses to the questionnaire.

We believe that HCP apathy is a major contributor to the problem of obesity. Small steps like these go a long way for further management of obesity. Most VA hospitals have MOVE programs that provide dietary advice and encourage behavioral changes. However, getting patients to commit to these programs is a challenge. Primary care and endocrine clinics are important services that may help with patient awareness.

This project helped us better recognize patients with obesity and provide them with initial counseling and dietary advice. We received help from clinical dieticians and gave patients the option to join MOVE in situations where initial advice did not yield results and for more consistent follow up.

We tried to improve the care for patients with DM who were overweight or obese at CAVHS by prompting HCPs to focus on obesity as a problem and perform interventions to address this problem. The activities carried out and the data collected were used for internal quality improvement and for encouraging further interventions in the care of these patients.

Obesity is associated with a significant increase in mortality. It increases the risk of type 2 diabetes mellitus (T2DM), hypertension, hyperlipidemia, and coronary artery disease.¹ T2DM is strongly associated with obesity in all ethnic groups.

Medical nutrition therapy and weight loss are very important for DM management.² This includes providing education about diet modification, increased physical activity, daily calorie intake evaluation, and consistent carbohydrate intake. For patients with T2DM, health care providers (HCPs) should emphasize lowering caloric intake and inducing weight loss for those who are overweight (body mass index [BMI] between 25 and 29.9) and obese (BMI ≥ 30). This can improve glycemic control by decreasing insulin resistance. Initial recommendations for weight loss and physical activity are to lose between 5% and 10% of initial body weight and to accumulate at least 30 minutes of moderate physical activity over the course of most days of the week.^3,4

Several formulas are available to estimate baseline caloric intake for weight maintenance. For weight loss of 1 to 2 pounds per week, lowering 500 to 1,000 calories from daily weight maintenance calories serves the goal. The American Diabetes Association (ADA) also suggests that HCPs recommend diet, physical activity, and behavioral therapy designed to achieve > 5% weight loss to overweight and obese patients with T2DM.⁵

Recognizing the clinical benefits of achieving weight loss in overweight or obese patients with T2DM, we aimed to increase the number of visits in the Endocrine Clinic at Central Arkansas Veterans Healthcare System (CAVHS) in Little Rock that addressed obesity, documented calorie goal for patients who are overweight or obese, and performed an intervention with further education for the patient.

Methods

The study population included veterans with either type 1 DM (T1DM) or T2DM with BMI > 25 on any DM control regimen. We performed a health record review of the eligible patients seen in the CAVHS Endocrine Clinic from June 1, 2016 to July 31, 2016 to determine the baseline percentage of visits that addressed obesity and provided weight loss advice to patients. We obtained a list of patients seen in the clinic during the study period from Strategic Management Service Services at CAVHS. We also obtained information that age, gender, medications, BMI, and last Endocrine clinic HCP assessment from the electronic health record. We reviewed the HCPs notes, including fellows and faculty who were involved in the patients’ treatment, to determine whether their notes documented a BMI > 25 and whether they discussed an intervention for overweight or obesity with the patient. The CAVHS Institutional Review Board reviewed and approved the initiative as a quality improvement study.

Intervention

Our clinic has a defined group of HCPs that we targeted for the intervention. After getting baseline information, during August 2017 we educated these HCPs on the tools available to calculate calorie goal for the patients. We advised the HCPs to use the Mifflin St Jyor equation for estimating energy expenditure and set a goal of initial weight loss between 5% and 7% of body weight. We gave specific instructions and advice to the providers (Table 1). HCPs also received educational material to distribute to patients that provided information on the healthy plate method, discussed how to count calories, and advised them on ADA goals with carbohydrate limitation. We encouraged HCPs to recommend that patients cut between 500 and 1,000 calories daily from their current diet. HCPs also received advice to seek help from clinical dieticians and the VA MOVE! Weight Management Program when appropriate.

Study of Effect of the Intervention

To study the effect of this intervention, we reviewed documentation by HCPs and assessed patient satisfaction. We obtained a list of patients and reviewed HCP notes on patients with BMI > 25 to assess whether providers addressed obesity in November and December 2017. We also evaluated whether HCPs offered a specific intervention to address the problem, such as providing education material to the patient or an estimate of daily calorie goal, or referring them to clinical dietician and/or the MOVE program. Patients received a 5-question survey that assessed their understanding and satisfaction at the end of the visit (Table 2).

Results

Of the 100 charts reviewed prior to intervention, HCPs discussed obesity management with only 6% of patients. After the intervention, we collected data again through chart review of the patients who were overweight or obese and seen for DM in the same clinic during a 2-month period. Of the 100 charts reviewed, we noticed that recognition and management of obesity improved to 60%.

To evaluate the impact of this intervention, patients received a questionnaire at the end of the visit. Nearly all (97%) patients mentioned that the provider discussed weight management during that visit. Most (83%) patients mentioned that weight management was discussed with them during prior visits, while 70% of patients felt their knowledge on working on weight loss had improved. Almost half (46%) were interested in further referral to a dietician or the MOVE program if they did not achieve desired results, but 78% were confident that they could implement the discussed weight management measures.

Discussion

Increased body weight is associated with worsening of DM and can result in poor glycemic control. Achieving weight loss in overweight or obese patients with DM can lead to clinical benefits; however, this is a challenge. In one study, a DM prevention program with lifestyle intervention leading to weight loss significantly reduced the rate of progression from impaired glucose tolerance to DM over a 3-year period and improved cardiovascular risk factors like elevated blood pressure and dyslipidemia.⁶ A randomized trial of an intensive lifestyle intervention to increase physical activity and decrease caloric intake vs standard DM education in people with T2DM showed a modest weight loss of 8.6% of initial weight at 1 year.⁷ This weight loss was associated with significant improvement in blood pressure, glycemic control, fasting blood glucose, high-density lipoprotein (HDL) cholesterol, and triglyceride levels and significant reductions in the use of DM, hypertension, and lipid-lowering medications.⁷ Obesity attributes to dyslipidemia with increased levels of cholesterol, low-density lipoprotein, very low-density lipoprotein, triglycerides, and decreased levels of HDL by about 5%.⁸ Obesity also is associated with hypertension, coronary heart disease, heart failure, and cardiovascular and all-cause mortality.⁹

Limitations

Limitations of this study include the small sample size and that multiple HCPs were involved. The nature of intervention might have differed with different HCPs or in a different setting than a VA clinic. In addition, we did not evaluate the effect on weight loss in specific patients as we only reviewed charts to check whether HCPs addressed weight loss. Nevertheless, our intervention was effective because it improved patient and provider awareness. It also gave us the opportunity to create framework for further collaborations and community building. The Endocrinology department at CAVHS is currently collaborating with the MOVE program, which is a part of the nutrition and food services. We hope to have an endocrinologist involved to provide guidance on medication management for obesity.

Conclusion

At CAVHS a simple intervention was instituted to evaluate whether HCPs were discussing weight loss in patients with DM, providing them with information to assess patients’ daily calorie goal, and prompting them for intervention to achieve weight loss. The intervention led to better management of patients with DM and obesity and greater engagement in weight loss from patients.

This project was a team effort. The clinic nurse documented patient’s BMI on the check in slip. HCPs discussed the problem and specific intervention. The clinical dieticians provided focused education for patients. The clerks collected the patient responses to questionnaire. This project also improved communication within the Endocrine Clinic team. Documentation of HCPs pertaining to addressing obesity improved by 54%. Improved patient satisfaction and insight was evident on patient responses to the questionnaire.

We believe that HCP apathy is a major contributor to the problem of obesity. Small steps like these go a long way for further management of obesity. Most VA hospitals have MOVE programs that provide dietary advice and encourage behavioral changes. However, getting patients to commit to these programs is a challenge. Primary care and endocrine clinics are important services that may help with patient awareness.

This project helped us better recognize patients with obesity and provide them with initial counseling and dietary advice. We received help from clinical dieticians and gave patients the option to join MOVE in situations where initial advice did not yield results and for more consistent follow up.

We tried to improve the care for patients with DM who were overweight or obese at CAVHS by prompting HCPs to focus on obesity as a problem and perform interventions to address this problem. The activities carried out and the data collected were used for internal quality improvement and for encouraging further interventions in the care of these patients.

Obesity is associated with a significant increase in mortality. It increases the risk of type 2 diabetes mellitus (T2DM), hypertension, hyperlipidemia, and coronary artery disease.¹ T2DM is strongly associated with obesity in all ethnic groups.

Medical nutrition therapy and weight loss are very important for DM management.² This includes providing education about diet modification, increased physical activity, daily calorie intake evaluation, and consistent carbohydrate intake. For patients with T2DM, health care providers (HCPs) should emphasize lowering caloric intake and inducing weight loss for those who are overweight (body mass index [BMI] between 25 and 29.9) and obese (BMI ≥ 30). This can improve glycemic control by decreasing insulin resistance. Initial recommendations for weight loss and physical activity are to lose between 5% and 10% of initial body weight and to accumulate at least 30 minutes of moderate physical activity over the course of most days of the week.^3,4

Several formulas are available to estimate baseline caloric intake for weight maintenance. For weight loss of 1 to 2 pounds per week, lowering 500 to 1,000 calories from daily weight maintenance calories serves the goal. The American Diabetes Association (ADA) also suggests that HCPs recommend diet, physical activity, and behavioral therapy designed to achieve > 5% weight loss to overweight and obese patients with T2DM.⁵

Recognizing the clinical benefits of achieving weight loss in overweight or obese patients with T2DM, we aimed to increase the number of visits in the Endocrine Clinic at Central Arkansas Veterans Healthcare System (CAVHS) in Little Rock that addressed obesity, documented calorie goal for patients who are overweight or obese, and performed an intervention with further education for the patient.

Methods

The study population included veterans with either type 1 DM (T1DM) or T2DM with BMI > 25 on any DM control regimen. We performed a health record review of the eligible patients seen in the CAVHS Endocrine Clinic from June 1, 2016 to July 31, 2016 to determine the baseline percentage of visits that addressed obesity and provided weight loss advice to patients. We obtained a list of patients seen in the clinic during the study period from Strategic Management Service Services at CAVHS. We also obtained information that age, gender, medications, BMI, and last Endocrine clinic HCP assessment from the electronic health record. We reviewed the HCPs notes, including fellows and faculty who were involved in the patients’ treatment, to determine whether their notes documented a BMI > 25 and whether they discussed an intervention for overweight or obesity with the patient. The CAVHS Institutional Review Board reviewed and approved the initiative as a quality improvement study.

Intervention

Our clinic has a defined group of HCPs that we targeted for the intervention. After getting baseline information, during August 2017 we educated these HCPs on the tools available to calculate calorie goal for the patients. We advised the HCPs to use the Mifflin St Jyor equation for estimating energy expenditure and set a goal of initial weight loss between 5% and 7% of body weight. We gave specific instructions and advice to the providers (Table 1). HCPs also received educational material to distribute to patients that provided information on the healthy plate method, discussed how to count calories, and advised them on ADA goals with carbohydrate limitation. We encouraged HCPs to recommend that patients cut between 500 and 1,000 calories daily from their current diet. HCPs also received advice to seek help from clinical dieticians and the VA MOVE! Weight Management Program when appropriate.

Study of Effect of the Intervention

To study the effect of this intervention, we reviewed documentation by HCPs and assessed patient satisfaction. We obtained a list of patients and reviewed HCP notes on patients with BMI > 25 to assess whether providers addressed obesity in November and December 2017. We also evaluated whether HCPs offered a specific intervention to address the problem, such as providing education material to the patient or an estimate of daily calorie goal, or referring them to clinical dietician and/or the MOVE program. Patients received a 5-question survey that assessed their understanding and satisfaction at the end of the visit (Table 2).

Results

Of the 100 charts reviewed prior to intervention, HCPs discussed obesity management with only 6% of patients. After the intervention, we collected data again through chart review of the patients who were overweight or obese and seen for DM in the same clinic during a 2-month period. Of the 100 charts reviewed, we noticed that recognition and management of obesity improved to 60%.

To evaluate the impact of this intervention, patients received a questionnaire at the end of the visit. Nearly all (97%) patients mentioned that the provider discussed weight management during that visit. Most (83%) patients mentioned that weight management was discussed with them during prior visits, while 70% of patients felt their knowledge on working on weight loss had improved. Almost half (46%) were interested in further referral to a dietician or the MOVE program if they did not achieve desired results, but 78% were confident that they could implement the discussed weight management measures.

Discussion

Increased body weight is associated with worsening of DM and can result in poor glycemic control. Achieving weight loss in overweight or obese patients with DM can lead to clinical benefits; however, this is a challenge. In one study, a DM prevention program with lifestyle intervention leading to weight loss significantly reduced the rate of progression from impaired glucose tolerance to DM over a 3-year period and improved cardiovascular risk factors like elevated blood pressure and dyslipidemia.⁶ A randomized trial of an intensive lifestyle intervention to increase physical activity and decrease caloric intake vs standard DM education in people with T2DM showed a modest weight loss of 8.6% of initial weight at 1 year.⁷ This weight loss was associated with significant improvement in blood pressure, glycemic control, fasting blood glucose, high-density lipoprotein (HDL) cholesterol, and triglyceride levels and significant reductions in the use of DM, hypertension, and lipid-lowering medications.⁷ Obesity attributes to dyslipidemia with increased levels of cholesterol, low-density lipoprotein, very low-density lipoprotein, triglycerides, and decreased levels of HDL by about 5%.⁸ Obesity also is associated with hypertension, coronary heart disease, heart failure, and cardiovascular and all-cause mortality.⁹

Limitations

Limitations of this study include the small sample size and that multiple HCPs were involved. The nature of intervention might have differed with different HCPs or in a different setting than a VA clinic. In addition, we did not evaluate the effect on weight loss in specific patients as we only reviewed charts to check whether HCPs addressed weight loss. Nevertheless, our intervention was effective because it improved patient and provider awareness. It also gave us the opportunity to create framework for further collaborations and community building. The Endocrinology department at CAVHS is currently collaborating with the MOVE program, which is a part of the nutrition and food services. We hope to have an endocrinologist involved to provide guidance on medication management for obesity.

Conclusion

At CAVHS a simple intervention was instituted to evaluate whether HCPs were discussing weight loss in patients with DM, providing them with information to assess patients’ daily calorie goal, and prompting them for intervention to achieve weight loss. The intervention led to better management of patients with DM and obesity and greater engagement in weight loss from patients.

This project was a team effort. The clinic nurse documented patient’s BMI on the check in slip. HCPs discussed the problem and specific intervention. The clinical dieticians provided focused education for patients. The clerks collected the patient responses to questionnaire. This project also improved communication within the Endocrine Clinic team. Documentation of HCPs pertaining to addressing obesity improved by 54%. Improved patient satisfaction and insight was evident on patient responses to the questionnaire.

We believe that HCP apathy is a major contributor to the problem of obesity. Small steps like these go a long way for further management of obesity. Most VA hospitals have MOVE programs that provide dietary advice and encourage behavioral changes. However, getting patients to commit to these programs is a challenge. Primary care and endocrine clinics are important services that may help with patient awareness.

This project helped us better recognize patients with obesity and provide them with initial counseling and dietary advice. We received help from clinical dieticians and gave patients the option to join MOVE in situations where initial advice did not yield results and for more consistent follow up.

We tried to improve the care for patients with DM who were overweight or obese at CAVHS by prompting HCPs to focus on obesity as a problem and perform interventions to address this problem. The activities carried out and the data collected were used for internal quality improvement and for encouraging further interventions in the care of these patients.

BARCELONA – Disturbed HDL cholesterol metabolism is one of the earliest features that may predispose individuals to the development of type 2 diabetes, according to data from a genetics and metabolomics study conducted in the United Kingdom.

Sara Freeman/MDedge News

Changes in HDL cholesterol metabolism were seen in children as young as 8 years, decades before the clinical onset of disease, Joshua Bell, PhD, a research fellow at the University of Bristol (England), reported at the annual meeting of the European Association for the Study of Diabetes.

“We know that type 2 diabetes certainly doesn’t develop overnight,” Dr. Bell said. Indeed, data exist showing that there are changes in glucose metabolism several years before a formal diagnosis may be made in adults. “What we don’t know is what the very earliest features of diabetes look like,” he added.

“The main assumption is that type 2 diabetes is a metabolic disease, and so disease features are visible in systemic metabolism,” explained Dr. Bell. What was not clear, however, was that if any metabolic features – seen mainly in observational studies and in adults – were caused by the disease itself or perhaps were independent causes of type 2 diabetes.To investigate, Dr. Bell and associates performed a study linking genetic liability with metabolomic data collected at four time points from 4,761 offspring from participants in the Avon Longitudinal Study of Parents and Children cohort, which is also known as the Children of the 90s cohort. More than 200 metabolic traits were considered, and a genetic risk score comprising more than 162 single nucleotide polymorphisms previously linked to adult type 2 diabetes was used.

The metabolomic traits considered included lipoprotein subclass-specific cholesterol and triglyceride content, amino and fatty acids, and inflammatory glycoprotein acetyls, which had been measured in childhood at the age of 8 years, in adolescence at 16 years, in young adulthood at 18 years, and in adulthood at 25 years.

Early metabolic features of type 2 diabetes liability were grouped together and one feature that stood out was the sizes of lipid particles. In particular, it was the size of HDL cholesterol particle subtypes in children at the age of 8 years. Before other types of changes in lipid particles were being seen, there were reductions in the lipid content of HDL cholesterol particle subtypes, notably those that were very large.

By age 16 years, strong associations remained with lower lipids in HDL cholesterol particle subtypes and type 2 diabetes liability, which became stronger with preglycemic traits, such as citrate, and with glycoprotein acetyls. By age 18 years, elevations were seen in branched amino acids, and by age 25, association had strengthened for the lipid content of very low–density lipoprotein cholesterol.

“Linking genetic liability to adult disease with traits measured much earlier in life can tell you something about how the disease activity unfolds over a lifetime,” Dr. Bell said, adding that the feature that was “most consistently tracked” could be evaluated and could help reveal whether or not an individual might go on to develop type 2 diabetes.

In a press release issued by the EASD, Dr. Bell observed: “It’s remarkable that we can see signs of adult diabetes in the blood from such a young age. Knowing what early features of type 2 diabetes look like, could help us to intervene much earlier to halt progression to full-blown diabetes and its complications.”

The study was funded by Diabetes U.K., Cancer Research U.K., the Elizabeth Blackwell Institute for Health Research, the Wellcome Trust, the Medical Research Council, and the University of Bristol. Dr. Bell said he had no conflicts of interest to declare.

SOURCE: Bell J et al. bioRxiv. 2019 Sep 17. doi: 10.1101/767756.
 

BARCELONA – Disturbed HDL cholesterol metabolism is one of the earliest features that may predispose individuals to the development of type 2 diabetes, according to data from a genetics and metabolomics study conducted in the United Kingdom.

Sara Freeman/MDedge News

Changes in HDL cholesterol metabolism were seen in children as young as 8 years, decades before the clinical onset of disease, Joshua Bell, PhD, a research fellow at the University of Bristol (England), reported at the annual meeting of the European Association for the Study of Diabetes.

“We know that type 2 diabetes certainly doesn’t develop overnight,” Dr. Bell said. Indeed, data exist showing that there are changes in glucose metabolism several years before a formal diagnosis may be made in adults. “What we don’t know is what the very earliest features of diabetes look like,” he added.

“The main assumption is that type 2 diabetes is a metabolic disease, and so disease features are visible in systemic metabolism,” explained Dr. Bell. What was not clear, however, was that if any metabolic features – seen mainly in observational studies and in adults – were caused by the disease itself or perhaps were independent causes of type 2 diabetes.To investigate, Dr. Bell and associates performed a study linking genetic liability with metabolomic data collected at four time points from 4,761 offspring from participants in the Avon Longitudinal Study of Parents and Children cohort, which is also known as the Children of the 90s cohort. More than 200 metabolic traits were considered, and a genetic risk score comprising more than 162 single nucleotide polymorphisms previously linked to adult type 2 diabetes was used.

The metabolomic traits considered included lipoprotein subclass-specific cholesterol and triglyceride content, amino and fatty acids, and inflammatory glycoprotein acetyls, which had been measured in childhood at the age of 8 years, in adolescence at 16 years, in young adulthood at 18 years, and in adulthood at 25 years.

Early metabolic features of type 2 diabetes liability were grouped together and one feature that stood out was the sizes of lipid particles. In particular, it was the size of HDL cholesterol particle subtypes in children at the age of 8 years. Before other types of changes in lipid particles were being seen, there were reductions in the lipid content of HDL cholesterol particle subtypes, notably those that were very large.

By age 16 years, strong associations remained with lower lipids in HDL cholesterol particle subtypes and type 2 diabetes liability, which became stronger with preglycemic traits, such as citrate, and with glycoprotein acetyls. By age 18 years, elevations were seen in branched amino acids, and by age 25, association had strengthened for the lipid content of very low–density lipoprotein cholesterol.

“Linking genetic liability to adult disease with traits measured much earlier in life can tell you something about how the disease activity unfolds over a lifetime,” Dr. Bell said, adding that the feature that was “most consistently tracked” could be evaluated and could help reveal whether or not an individual might go on to develop type 2 diabetes.

In a press release issued by the EASD, Dr. Bell observed: “It’s remarkable that we can see signs of adult diabetes in the blood from such a young age. Knowing what early features of type 2 diabetes look like, could help us to intervene much earlier to halt progression to full-blown diabetes and its complications.”

The study was funded by Diabetes U.K., Cancer Research U.K., the Elizabeth Blackwell Institute for Health Research, the Wellcome Trust, the Medical Research Council, and the University of Bristol. Dr. Bell said he had no conflicts of interest to declare.

SOURCE: Bell J et al. bioRxiv. 2019 Sep 17. doi: 10.1101/767756.
 

BARCELONA – Disturbed HDL cholesterol metabolism is one of the earliest features that may predispose individuals to the development of type 2 diabetes, according to data from a genetics and metabolomics study conducted in the United Kingdom.

Sara Freeman/MDedge News

Changes in HDL cholesterol metabolism were seen in children as young as 8 years, decades before the clinical onset of disease, Joshua Bell, PhD, a research fellow at the University of Bristol (England), reported at the annual meeting of the European Association for the Study of Diabetes.

“We know that type 2 diabetes certainly doesn’t develop overnight,” Dr. Bell said. Indeed, data exist showing that there are changes in glucose metabolism several years before a formal diagnosis may be made in adults. “What we don’t know is what the very earliest features of diabetes look like,” he added.

“The main assumption is that type 2 diabetes is a metabolic disease, and so disease features are visible in systemic metabolism,” explained Dr. Bell. What was not clear, however, was that if any metabolic features – seen mainly in observational studies and in adults – were caused by the disease itself or perhaps were independent causes of type 2 diabetes.To investigate, Dr. Bell and associates performed a study linking genetic liability with metabolomic data collected at four time points from 4,761 offspring from participants in the Avon Longitudinal Study of Parents and Children cohort, which is also known as the Children of the 90s cohort. More than 200 metabolic traits were considered, and a genetic risk score comprising more than 162 single nucleotide polymorphisms previously linked to adult type 2 diabetes was used.

The metabolomic traits considered included lipoprotein subclass-specific cholesterol and triglyceride content, amino and fatty acids, and inflammatory glycoprotein acetyls, which had been measured in childhood at the age of 8 years, in adolescence at 16 years, in young adulthood at 18 years, and in adulthood at 25 years.

Early metabolic features of type 2 diabetes liability were grouped together and one feature that stood out was the sizes of lipid particles. In particular, it was the size of HDL cholesterol particle subtypes in children at the age of 8 years. Before other types of changes in lipid particles were being seen, there were reductions in the lipid content of HDL cholesterol particle subtypes, notably those that were very large.

By age 16 years, strong associations remained with lower lipids in HDL cholesterol particle subtypes and type 2 diabetes liability, which became stronger with preglycemic traits, such as citrate, and with glycoprotein acetyls. By age 18 years, elevations were seen in branched amino acids, and by age 25, association had strengthened for the lipid content of very low–density lipoprotein cholesterol.

“Linking genetic liability to adult disease with traits measured much earlier in life can tell you something about how the disease activity unfolds over a lifetime,” Dr. Bell said, adding that the feature that was “most consistently tracked” could be evaluated and could help reveal whether or not an individual might go on to develop type 2 diabetes.

In a press release issued by the EASD, Dr. Bell observed: “It’s remarkable that we can see signs of adult diabetes in the blood from such a young age. Knowing what early features of type 2 diabetes look like, could help us to intervene much earlier to halt progression to full-blown diabetes and its complications.”

The study was funded by Diabetes U.K., Cancer Research U.K., the Elizabeth Blackwell Institute for Health Research, the Wellcome Trust, the Medical Research Council, and the University of Bristol. Dr. Bell said he had no conflicts of interest to declare.

SOURCE: Bell J et al. bioRxiv. 2019 Sep 17. doi: 10.1101/767756.
 

BARCELONA – Individuals with type 1 diabetes and a high body mass index gain the most benefit with the least risk when sodium-glucose cotransporter 2 (SGLT2) inhibitors are added to insulin therapy, according to data presented at the annual meeting of the European Association for the Study of Diabetes.

Sara Freeman/MDedge News

Results from new analyses of the inTandem 1 and inTandem 2 trials with sotagliflozin (Zynquista), and the DEPICT-1 and DEPICT-2 trials with dapagliflozin (Farxiga), support the recent decision of the European Medicines Agency to license the use of the drugs only in patients with a BMI of 27 kg/m² or higher.
 

inTandem with sotagliflozin

Weight gain is a challenge in patients with type 1 diabetes, said Thomas Danne, MD, who presented post hoc data from the two inTandem studies. “It’s a little bit counterintuitive,” he acknowledged, “but you have to realize, particularly in patients who have hypoglycemia, that they have to take in extra carbohydrates,” which may tip them to becoming overweight or obese.

SGLT2-inhibitor therapy with sotagliflozin or dapagliflozin added to insulin therapy has been shown to reduce body weight in individuals with type 1 diabetes, but there is an increased risk for diabetic ketoacidosis (DKA). That risk, however, seems to be lower in the higher body-weight categories.

Dr. Danne, director of the department of general pediatrics, endocrinology, and diabetology, and clinical research at the Auf der Bult Hospital for Children and Adolescents, at the Hannover (Germany) Medical School, presented data looking at the outcomes of patients treated with sotagliflozin or placebo based on their BMI.

In all, 1,575 patients were included in the analysis, of whom 659 were of normal weight (BMI of less than 27 kg/m²; average mean, 24 kg/m² at baseline), and 916 had a higher weight (BMI of 27 kg/m² or higher; average mean, 32 kg/m² at baseline). The mean age of patients at study entry was 42 years for those with the lower BMI, and 45 years for those with the higher BMI.

Patients in the two inTandem trials had been treated with insulin plus placebo (n = 228, BMI less than 27 kg/m²; n = 298, BMI 27 kg/m² or higher), or insulin plus sotagliflozin at a dose of 200 mg (n = 219, BMI less than 27 kg/m²; n = 305, BMI 27 kg/m² or higher) or 400 mg (n = 212; BMI less than 27 kg/m²; n = 313, BMI 27 kg/m² or higher).
 

Glycemic control and body weight

Greater reductions in glycated hemoglobin (HbA_1c) were seen with sotagliflozin versus placebo, and even more so, if the BMI was 27 kg/m² or higher. At week 24, the least-squares mean difference in HbA_1c comparing sotagliflozin 200 mg and placebo was –0.32 in patients with the lower BMI, compared with –0.39 in those with the higher BMI. Corresponding values for the 400-mg sotagliflozin group in the higher-BMI group were –0.27 and –0.45, respectively (P less than .001 for all comparisons).

In the lower-BMI group, week 24 least-squares mean differences in body weight comparing sotagliflozin and placebo were –2.06 kg for the 200-mg group and –2.55 kg for the 400-mg group, and –2.27 kg and 3.32 kg in the higher-BMI group (P less than .001 for all comparisons).

“This is why this class of drugs holds so much of a promise, [because] it’s not only one good effect regarding improvement of glycemia judged by A_1c,” Dr. Danne said.

He also reported that treatment with sotagliflozin was associated with an increased time in range, compared with placebo, again, with greater effects seen in the higher- versus lower-BMI groups. In those with a BMI of 27 kg/m² or more, there was an additional 1 hour 58 minutes time in range for the 200-mg dose, and 3 hours 37 minutes for the 200-mg dose, compared with an extra 24 minutes and 1 hour 31 minutes, respectively, in the lower-BMI category.

“We also see a trend to improved reduction in systolic blood pressure in those with the higher BMI,” Dr. Danne said.
 

Risk for DKA

“The big charm of these drugs is that not only do you improve A_1c and all the other good things, but also you do this without increasing the risk of hypoglycemia,” said Dr. Danne. “Again, you can see a trend of a lower risk of severe hypoglycemia for both sotagliflozin doses [compared with placebo] in the group with the body mass index of greater than 27 kg/m² [versus BMI of less than 27 kg/m²].”

The risk of DKA was higher than placebo in both BMI groups, but the number of DKA events was very small when comparing the low and high body weight categories (0 and 1 events, respectively, in the placebo groups; 7 and 9, in the sotagliflozin 200-mg group; and 9 and 11, in the 400-mg group. The absolute risk difference in the exposure adjusted incidence rate was slightly lower in the lower-BMI group, he said, but the numbers were so small that it is difficult to draw conclusions from that finding.

“There is no doubt that we have an increase for the risk of DKA with this class of drugs in general ... but it is futile to discuss whether or not, just on the basis of a body mass index or something else, we will be able to reduce it in a big fashion,” Dr. Danne suggested.
 

Body weight and composition

Other data on the long-term effect of sotagliflozin on body weight and composition were presented by Sangeeta Sawhney, MD, vice-president of clinical development at Lexicon Pharmaceuticals, Chapel Hill, North Carolina.

She presented data from the DEXA substudy of the inTandem phase 3 studies in which 243 patients underwent fat mass and bone density scanning.

SGLT2 inhibitors are associated with weight loss through glycuresis and net caloric loss, Dr. Sawhney reminded the audience. As sotagliflozin is a dual inhibitor of SGLT1 and SGLT2, however, it is important to estimate the contribution of changes in fat mass and lean mass to the weight loss that could be achieved with the drug.

Pooled data from the inTandem 1 and inTandem 2 studies showed that at week 24, there were reductions in body weight of –1.7 kg and –2.6 kg with sotagliflozin 200 mg and 400 mg, respectively, and at 52 weeks, reductions of –1.9 kg and –2.9 kg. However, there was an increase in body weight with placebo (+0.5 and +0.8 kg, respectively).

For the substudy, patients underwent dual-energy x-ray absorptiometry at baseline and weeks 24 and 52. Fat mass was measured at all three time points, and bone density was evaluated at the start and end of the study.

The least-square mean change in total fat mass from baseline to week 24 and week 52 were +0.6 and +0.1 kg, respectively, for placebo, –1.6 and –1.6 kg for the sotagliflozin 200-mg dose; and –1.9 and –2.1 kg for the 400-mg dose, “which really parallels the reduction in total body weight,” Dr. Sawhney observed.

The changes in total lean mass were much smaller for sotagliflozin, she added, at –0.6 kg at week 24 and 0.3 kg at week 52 for the 200-mg dose, and –0.7 kg and –0.4 kg, respectively, for the 400-mg dose, and rises in lean mass of 0.2 kg and 0.4 kg, respectively, in placebo.

Taken together, these data show that “about 80% of the body weight reduction is really from the fat mass, and a much smaller proportion of the total body weight reduction is really coming from the lean fat mass,” said Dr. Sawhney.
 

DEPICT with dapagliflozin

In a poster, Paresh Dandona, MD, PhD, of the State University of New York at Buffalo, and associates presented data from a pooled analysis of the DEPICT-1 and DEPICT-2 studies looking at safety and efficacy outcomes with dapagliflozin according to five BMI categories: less than or equal to 23 kg/m²; greater than 23 kg/m² to less than or equal to 25 kg/m²; greater than 25 kg/m² to less than or equal to 27 kg/m²; greater than 27 kg/m² to less than or equal to 30 kg/m²; and greater than 30 kg/m².

The pooled analysis included 548 patients treated with dapagliflozin 5 mg and 532 who received placebo. The investigators found that patients with higher BMIs who were treated with dapagliflozin had greater weight loss, showed a trend toward achieving an HbA_1c reduction of 5.5 mmol/mol (greater than or equal to 0.5%) or more without the risk of severe hypoglycemia, and had fewer episodes of definite DKA, compared with those with those with lower BMIs.

The adjusted mean percentage change from baseline in body weight in the lowest BMI (less than or equal to 23 kg/m²) group at week 24 was +0.06 kg for placebo and –2.71 kg for dapagliflozin, and at week 52, +0.33 kg and –2.91 kg, respectively. Corresponding values comparing placebo and dapagliflozin at 24 and 52 weeks in the highest BMI group (greater than 30 kg/m²) were –0.30 kg and –3.03 kg, and +0.56 and –3.58 kg.

Odds ratios for achieving an HbA_1c reduction of 5.5 mmol/mol (greater than or equal to 0.5%) without severe hypoglycemia at week 24 with dapagliflozin, compared with placebo, were, in increasing order of BMI groups: 1.85, 1.93, 3.87, 2.91, and 4.20.

“Generally, more events of definite DKA were observed in patients treated with dapagliflozin than in those treated with placebo,” but there were fewer events as BMI increased, Dr. Dandona and associates reported. “These data should be interpreted with caution due to the low number of events in each subgroup,” they added.

The number of adjudicated DKA events comparing dapagliflozin and placebo across the BMI groups were: 4 versus 1 (BMI less than or equal to 23 kg/m²); 6 versus 1 (BMI greater than 23 kg/m² to less than or equal to 25 kg/m²); 7 versus 1 (BMI greater than 25 kg/m² to less than or equal to 27 kg/m²); 3 versus 1 (BMI greater than 27 kg/m² to less than or equal to 30 kg/m²); and 2 versus 1 (BMI greater than 30 kg/m²).

In regard to limitations, “this was a post hoc analysis,” the investigators noted, adding that the studies were not originally powered for comparison between BMI subgroups, so the results should be considered exploratory. Moreover, DKA and hypoglycemia were strictly monitored in the trials, which “may differ from real-world situations,” they said.

The inTandem studies were sponsored by Lexicon and Sanofi. Dr. Danne disclosed receiving research funding and serving as a consultant, advisory board or steering committee member, or speaker for various companies, including Sanofi. Dr. Sawhney is an employee of and holds stoke in Lexicon. The DEPICT studies were sponsored by AstraZeneca. The lead author, Dr. Dandona, disclosed employment or consultancy services for multiple companies, including AstraZeneca.

SOURCE: Danne T et al. EASD 2018, Oral Presentation 2; Dandona P et al. EASD 2019, ePoster 720; Sawhney S et al. EASD 2019, Oral Presentation 3.

BARCELONA – Individuals with type 1 diabetes and a high body mass index gain the most benefit with the least risk when sodium-glucose cotransporter 2 (SGLT2) inhibitors are added to insulin therapy, according to data presented at the annual meeting of the European Association for the Study of Diabetes.

Sara Freeman/MDedge News

Results from new analyses of the inTandem 1 and inTandem 2 trials with sotagliflozin (Zynquista), and the DEPICT-1 and DEPICT-2 trials with dapagliflozin (Farxiga), support the recent decision of the European Medicines Agency to license the use of the drugs only in patients with a BMI of 27 kg/m² or higher.
 

inTandem with sotagliflozin

Weight gain is a challenge in patients with type 1 diabetes, said Thomas Danne, MD, who presented post hoc data from the two inTandem studies. “It’s a little bit counterintuitive,” he acknowledged, “but you have to realize, particularly in patients who have hypoglycemia, that they have to take in extra carbohydrates,” which may tip them to becoming overweight or obese.

SGLT2-inhibitor therapy with sotagliflozin or dapagliflozin added to insulin therapy has been shown to reduce body weight in individuals with type 1 diabetes, but there is an increased risk for diabetic ketoacidosis (DKA). That risk, however, seems to be lower in the higher body-weight categories.

Dr. Danne, director of the department of general pediatrics, endocrinology, and diabetology, and clinical research at the Auf der Bult Hospital for Children and Adolescents, at the Hannover (Germany) Medical School, presented data looking at the outcomes of patients treated with sotagliflozin or placebo based on their BMI.

In all, 1,575 patients were included in the analysis, of whom 659 were of normal weight (BMI of less than 27 kg/m²; average mean, 24 kg/m² at baseline), and 916 had a higher weight (BMI of 27 kg/m² or higher; average mean, 32 kg/m² at baseline). The mean age of patients at study entry was 42 years for those with the lower BMI, and 45 years for those with the higher BMI.

Patients in the two inTandem trials had been treated with insulin plus placebo (n = 228, BMI less than 27 kg/m²; n = 298, BMI 27 kg/m² or higher), or insulin plus sotagliflozin at a dose of 200 mg (n = 219, BMI less than 27 kg/m²; n = 305, BMI 27 kg/m² or higher) or 400 mg (n = 212; BMI less than 27 kg/m²; n = 313, BMI 27 kg/m² or higher).
 

Glycemic control and body weight

Greater reductions in glycated hemoglobin (HbA_1c) were seen with sotagliflozin versus placebo, and even more so, if the BMI was 27 kg/m² or higher. At week 24, the least-squares mean difference in HbA_1c comparing sotagliflozin 200 mg and placebo was –0.32 in patients with the lower BMI, compared with –0.39 in those with the higher BMI. Corresponding values for the 400-mg sotagliflozin group in the higher-BMI group were –0.27 and –0.45, respectively (P less than .001 for all comparisons).

In the lower-BMI group, week 24 least-squares mean differences in body weight comparing sotagliflozin and placebo were –2.06 kg for the 200-mg group and –2.55 kg for the 400-mg group, and –2.27 kg and 3.32 kg in the higher-BMI group (P less than .001 for all comparisons).

“This is why this class of drugs holds so much of a promise, [because] it’s not only one good effect regarding improvement of glycemia judged by A_1c,” Dr. Danne said.

He also reported that treatment with sotagliflozin was associated with an increased time in range, compared with placebo, again, with greater effects seen in the higher- versus lower-BMI groups. In those with a BMI of 27 kg/m² or more, there was an additional 1 hour 58 minutes time in range for the 200-mg dose, and 3 hours 37 minutes for the 200-mg dose, compared with an extra 24 minutes and 1 hour 31 minutes, respectively, in the lower-BMI category.

“We also see a trend to improved reduction in systolic blood pressure in those with the higher BMI,” Dr. Danne said.
 

Risk for DKA

“The big charm of these drugs is that not only do you improve A_1c and all the other good things, but also you do this without increasing the risk of hypoglycemia,” said Dr. Danne. “Again, you can see a trend of a lower risk of severe hypoglycemia for both sotagliflozin doses [compared with placebo] in the group with the body mass index of greater than 27 kg/m² [versus BMI of less than 27 kg/m²].”

The risk of DKA was higher than placebo in both BMI groups, but the number of DKA events was very small when comparing the low and high body weight categories (0 and 1 events, respectively, in the placebo groups; 7 and 9, in the sotagliflozin 200-mg group; and 9 and 11, in the 400-mg group. The absolute risk difference in the exposure adjusted incidence rate was slightly lower in the lower-BMI group, he said, but the numbers were so small that it is difficult to draw conclusions from that finding.

“There is no doubt that we have an increase for the risk of DKA with this class of drugs in general ... but it is futile to discuss whether or not, just on the basis of a body mass index or something else, we will be able to reduce it in a big fashion,” Dr. Danne suggested.
 

Body weight and composition

Other data on the long-term effect of sotagliflozin on body weight and composition were presented by Sangeeta Sawhney, MD, vice-president of clinical development at Lexicon Pharmaceuticals, Chapel Hill, North Carolina.

She presented data from the DEXA substudy of the inTandem phase 3 studies in which 243 patients underwent fat mass and bone density scanning.

SGLT2 inhibitors are associated with weight loss through glycuresis and net caloric loss, Dr. Sawhney reminded the audience. As sotagliflozin is a dual inhibitor of SGLT1 and SGLT2, however, it is important to estimate the contribution of changes in fat mass and lean mass to the weight loss that could be achieved with the drug.

Pooled data from the inTandem 1 and inTandem 2 studies showed that at week 24, there were reductions in body weight of –1.7 kg and –2.6 kg with sotagliflozin 200 mg and 400 mg, respectively, and at 52 weeks, reductions of –1.9 kg and –2.9 kg. However, there was an increase in body weight with placebo (+0.5 and +0.8 kg, respectively).

For the substudy, patients underwent dual-energy x-ray absorptiometry at baseline and weeks 24 and 52. Fat mass was measured at all three time points, and bone density was evaluated at the start and end of the study.

The least-square mean change in total fat mass from baseline to week 24 and week 52 were +0.6 and +0.1 kg, respectively, for placebo, –1.6 and –1.6 kg for the sotagliflozin 200-mg dose; and –1.9 and –2.1 kg for the 400-mg dose, “which really parallels the reduction in total body weight,” Dr. Sawhney observed.

The changes in total lean mass were much smaller for sotagliflozin, she added, at –0.6 kg at week 24 and 0.3 kg at week 52 for the 200-mg dose, and –0.7 kg and –0.4 kg, respectively, for the 400-mg dose, and rises in lean mass of 0.2 kg and 0.4 kg, respectively, in placebo.

Taken together, these data show that “about 80% of the body weight reduction is really from the fat mass, and a much smaller proportion of the total body weight reduction is really coming from the lean fat mass,” said Dr. Sawhney.
 

DEPICT with dapagliflozin

In a poster, Paresh Dandona, MD, PhD, of the State University of New York at Buffalo, and associates presented data from a pooled analysis of the DEPICT-1 and DEPICT-2 studies looking at safety and efficacy outcomes with dapagliflozin according to five BMI categories: less than or equal to 23 kg/m²; greater than 23 kg/m² to less than or equal to 25 kg/m²; greater than 25 kg/m² to less than or equal to 27 kg/m²; greater than 27 kg/m² to less than or equal to 30 kg/m²; and greater than 30 kg/m².

The pooled analysis included 548 patients treated with dapagliflozin 5 mg and 532 who received placebo. The investigators found that patients with higher BMIs who were treated with dapagliflozin had greater weight loss, showed a trend toward achieving an HbA_1c reduction of 5.5 mmol/mol (greater than or equal to 0.5%) or more without the risk of severe hypoglycemia, and had fewer episodes of definite DKA, compared with those with those with lower BMIs.

The adjusted mean percentage change from baseline in body weight in the lowest BMI (less than or equal to 23 kg/m²) group at week 24 was +0.06 kg for placebo and –2.71 kg for dapagliflozin, and at week 52, +0.33 kg and –2.91 kg, respectively. Corresponding values comparing placebo and dapagliflozin at 24 and 52 weeks in the highest BMI group (greater than 30 kg/m²) were –0.30 kg and –3.03 kg, and +0.56 and –3.58 kg.

Odds ratios for achieving an HbA_1c reduction of 5.5 mmol/mol (greater than or equal to 0.5%) without severe hypoglycemia at week 24 with dapagliflozin, compared with placebo, were, in increasing order of BMI groups: 1.85, 1.93, 3.87, 2.91, and 4.20.

“Generally, more events of definite DKA were observed in patients treated with dapagliflozin than in those treated with placebo,” but there were fewer events as BMI increased, Dr. Dandona and associates reported. “These data should be interpreted with caution due to the low number of events in each subgroup,” they added.

The number of adjudicated DKA events comparing dapagliflozin and placebo across the BMI groups were: 4 versus 1 (BMI less than or equal to 23 kg/m²); 6 versus 1 (BMI greater than 23 kg/m² to less than or equal to 25 kg/m²); 7 versus 1 (BMI greater than 25 kg/m² to less than or equal to 27 kg/m²); 3 versus 1 (BMI greater than 27 kg/m² to less than or equal to 30 kg/m²); and 2 versus 1 (BMI greater than 30 kg/m²).

In regard to limitations, “this was a post hoc analysis,” the investigators noted, adding that the studies were not originally powered for comparison between BMI subgroups, so the results should be considered exploratory. Moreover, DKA and hypoglycemia were strictly monitored in the trials, which “may differ from real-world situations,” they said.

The inTandem studies were sponsored by Lexicon and Sanofi. Dr. Danne disclosed receiving research funding and serving as a consultant, advisory board or steering committee member, or speaker for various companies, including Sanofi. Dr. Sawhney is an employee of and holds stoke in Lexicon. The DEPICT studies were sponsored by AstraZeneca. The lead author, Dr. Dandona, disclosed employment or consultancy services for multiple companies, including AstraZeneca.

SOURCE: Danne T et al. EASD 2018, Oral Presentation 2; Dandona P et al. EASD 2019, ePoster 720; Sawhney S et al. EASD 2019, Oral Presentation 3.

BARCELONA – Individuals with type 1 diabetes and a high body mass index gain the most benefit with the least risk when sodium-glucose cotransporter 2 (SGLT2) inhibitors are added to insulin therapy, according to data presented at the annual meeting of the European Association for the Study of Diabetes.

Sara Freeman/MDedge News

Results from new analyses of the inTandem 1 and inTandem 2 trials with sotagliflozin (Zynquista), and the DEPICT-1 and DEPICT-2 trials with dapagliflozin (Farxiga), support the recent decision of the European Medicines Agency to license the use of the drugs only in patients with a BMI of 27 kg/m² or higher.
 

inTandem with sotagliflozin

Weight gain is a challenge in patients with type 1 diabetes, said Thomas Danne, MD, who presented post hoc data from the two inTandem studies. “It’s a little bit counterintuitive,” he acknowledged, “but you have to realize, particularly in patients who have hypoglycemia, that they have to take in extra carbohydrates,” which may tip them to becoming overweight or obese.

SGLT2-inhibitor therapy with sotagliflozin or dapagliflozin added to insulin therapy has been shown to reduce body weight in individuals with type 1 diabetes, but there is an increased risk for diabetic ketoacidosis (DKA). That risk, however, seems to be lower in the higher body-weight categories.

Dr. Danne, director of the department of general pediatrics, endocrinology, and diabetology, and clinical research at the Auf der Bult Hospital for Children and Adolescents, at the Hannover (Germany) Medical School, presented data looking at the outcomes of patients treated with sotagliflozin or placebo based on their BMI.

In all, 1,575 patients were included in the analysis, of whom 659 were of normal weight (BMI of less than 27 kg/m²; average mean, 24 kg/m² at baseline), and 916 had a higher weight (BMI of 27 kg/m² or higher; average mean, 32 kg/m² at baseline). The mean age of patients at study entry was 42 years for those with the lower BMI, and 45 years for those with the higher BMI.

Patients in the two inTandem trials had been treated with insulin plus placebo (n = 228, BMI less than 27 kg/m²; n = 298, BMI 27 kg/m² or higher), or insulin plus sotagliflozin at a dose of 200 mg (n = 219, BMI less than 27 kg/m²; n = 305, BMI 27 kg/m² or higher) or 400 mg (n = 212; BMI less than 27 kg/m²; n = 313, BMI 27 kg/m² or higher).
 

Glycemic control and body weight

Greater reductions in glycated hemoglobin (HbA_1c) were seen with sotagliflozin versus placebo, and even more so, if the BMI was 27 kg/m² or higher. At week 24, the least-squares mean difference in HbA_1c comparing sotagliflozin 200 mg and placebo was –0.32 in patients with the lower BMI, compared with –0.39 in those with the higher BMI. Corresponding values for the 400-mg sotagliflozin group in the higher-BMI group were –0.27 and –0.45, respectively (P less than .001 for all comparisons).

In the lower-BMI group, week 24 least-squares mean differences in body weight comparing sotagliflozin and placebo were –2.06 kg for the 200-mg group and –2.55 kg for the 400-mg group, and –2.27 kg and 3.32 kg in the higher-BMI group (P less than .001 for all comparisons).

“This is why this class of drugs holds so much of a promise, [because] it’s not only one good effect regarding improvement of glycemia judged by A_1c,” Dr. Danne said.

He also reported that treatment with sotagliflozin was associated with an increased time in range, compared with placebo, again, with greater effects seen in the higher- versus lower-BMI groups. In those with a BMI of 27 kg/m² or more, there was an additional 1 hour 58 minutes time in range for the 200-mg dose, and 3 hours 37 minutes for the 200-mg dose, compared with an extra 24 minutes and 1 hour 31 minutes, respectively, in the lower-BMI category.

“We also see a trend to improved reduction in systolic blood pressure in those with the higher BMI,” Dr. Danne said.
 

Risk for DKA

“The big charm of these drugs is that not only do you improve A_1c and all the other good things, but also you do this without increasing the risk of hypoglycemia,” said Dr. Danne. “Again, you can see a trend of a lower risk of severe hypoglycemia for both sotagliflozin doses [compared with placebo] in the group with the body mass index of greater than 27 kg/m² [versus BMI of less than 27 kg/m²].”

The risk of DKA was higher than placebo in both BMI groups, but the number of DKA events was very small when comparing the low and high body weight categories (0 and 1 events, respectively, in the placebo groups; 7 and 9, in the sotagliflozin 200-mg group; and 9 and 11, in the 400-mg group. The absolute risk difference in the exposure adjusted incidence rate was slightly lower in the lower-BMI group, he said, but the numbers were so small that it is difficult to draw conclusions from that finding.

“There is no doubt that we have an increase for the risk of DKA with this class of drugs in general ... but it is futile to discuss whether or not, just on the basis of a body mass index or something else, we will be able to reduce it in a big fashion,” Dr. Danne suggested.
 

Body weight and composition

Other data on the long-term effect of sotagliflozin on body weight and composition were presented by Sangeeta Sawhney, MD, vice-president of clinical development at Lexicon Pharmaceuticals, Chapel Hill, North Carolina.

She presented data from the DEXA substudy of the inTandem phase 3 studies in which 243 patients underwent fat mass and bone density scanning.

SGLT2 inhibitors are associated with weight loss through glycuresis and net caloric loss, Dr. Sawhney reminded the audience. As sotagliflozin is a dual inhibitor of SGLT1 and SGLT2, however, it is important to estimate the contribution of changes in fat mass and lean mass to the weight loss that could be achieved with the drug.

Pooled data from the inTandem 1 and inTandem 2 studies showed that at week 24, there were reductions in body weight of –1.7 kg and –2.6 kg with sotagliflozin 200 mg and 400 mg, respectively, and at 52 weeks, reductions of –1.9 kg and –2.9 kg. However, there was an increase in body weight with placebo (+0.5 and +0.8 kg, respectively).

For the substudy, patients underwent dual-energy x-ray absorptiometry at baseline and weeks 24 and 52. Fat mass was measured at all three time points, and bone density was evaluated at the start and end of the study.

The least-square mean change in total fat mass from baseline to week 24 and week 52 were +0.6 and +0.1 kg, respectively, for placebo, –1.6 and –1.6 kg for the sotagliflozin 200-mg dose; and –1.9 and –2.1 kg for the 400-mg dose, “which really parallels the reduction in total body weight,” Dr. Sawhney observed.

The changes in total lean mass were much smaller for sotagliflozin, she added, at –0.6 kg at week 24 and 0.3 kg at week 52 for the 200-mg dose, and –0.7 kg and –0.4 kg, respectively, for the 400-mg dose, and rises in lean mass of 0.2 kg and 0.4 kg, respectively, in placebo.

Taken together, these data show that “about 80% of the body weight reduction is really from the fat mass, and a much smaller proportion of the total body weight reduction is really coming from the lean fat mass,” said Dr. Sawhney.
 

DEPICT with dapagliflozin

In a poster, Paresh Dandona, MD, PhD, of the State University of New York at Buffalo, and associates presented data from a pooled analysis of the DEPICT-1 and DEPICT-2 studies looking at safety and efficacy outcomes with dapagliflozin according to five BMI categories: less than or equal to 23 kg/m²; greater than 23 kg/m² to less than or equal to 25 kg/m²; greater than 25 kg/m² to less than or equal to 27 kg/m²; greater than 27 kg/m² to less than or equal to 30 kg/m²; and greater than 30 kg/m².

The pooled analysis included 548 patients treated with dapagliflozin 5 mg and 532 who received placebo. The investigators found that patients with higher BMIs who were treated with dapagliflozin had greater weight loss, showed a trend toward achieving an HbA_1c reduction of 5.5 mmol/mol (greater than or equal to 0.5%) or more without the risk of severe hypoglycemia, and had fewer episodes of definite DKA, compared with those with those with lower BMIs.

The adjusted mean percentage change from baseline in body weight in the lowest BMI (less than or equal to 23 kg/m²) group at week 24 was +0.06 kg for placebo and –2.71 kg for dapagliflozin, and at week 52, +0.33 kg and –2.91 kg, respectively. Corresponding values comparing placebo and dapagliflozin at 24 and 52 weeks in the highest BMI group (greater than 30 kg/m²) were –0.30 kg and –3.03 kg, and +0.56 and –3.58 kg.

Odds ratios for achieving an HbA_1c reduction of 5.5 mmol/mol (greater than or equal to 0.5%) without severe hypoglycemia at week 24 with dapagliflozin, compared with placebo, were, in increasing order of BMI groups: 1.85, 1.93, 3.87, 2.91, and 4.20.

“Generally, more events of definite DKA were observed in patients treated with dapagliflozin than in those treated with placebo,” but there were fewer events as BMI increased, Dr. Dandona and associates reported. “These data should be interpreted with caution due to the low number of events in each subgroup,” they added.

The number of adjudicated DKA events comparing dapagliflozin and placebo across the BMI groups were: 4 versus 1 (BMI less than or equal to 23 kg/m²); 6 versus 1 (BMI greater than 23 kg/m² to less than or equal to 25 kg/m²); 7 versus 1 (BMI greater than 25 kg/m² to less than or equal to 27 kg/m²); 3 versus 1 (BMI greater than 27 kg/m² to less than or equal to 30 kg/m²); and 2 versus 1 (BMI greater than 30 kg/m²).

In regard to limitations, “this was a post hoc analysis,” the investigators noted, adding that the studies were not originally powered for comparison between BMI subgroups, so the results should be considered exploratory. Moreover, DKA and hypoglycemia were strictly monitored in the trials, which “may differ from real-world situations,” they said.

The inTandem studies were sponsored by Lexicon and Sanofi. Dr. Danne disclosed receiving research funding and serving as a consultant, advisory board or steering committee member, or speaker for various companies, including Sanofi. Dr. Sawhney is an employee of and holds stoke in Lexicon. The DEPICT studies were sponsored by AstraZeneca. The lead author, Dr. Dandona, disclosed employment or consultancy services for multiple companies, including AstraZeneca.

SOURCE: Danne T et al. EASD 2018, Oral Presentation 2; Dandona P et al. EASD 2019, ePoster 720; Sawhney S et al. EASD 2019, Oral Presentation 3.

Information was omitted from Table 1 on page 596 of the article, Makin V, Lansang MC. Diabetes management: beyond hemoglobin A_1c (Cleve Clin J Med 2019; 86[9]:595–600, doi:10.3949/ccjm.86a.18031).

The sodium-glucose cotransporter 2 (SGLT2) inhibitors pose a low risk of hypoglyemia, and that should have been noted in the table. The corrected table appears below and online.

Information was omitted from Table 1 on page 596 of the article, Makin V, Lansang MC. Diabetes management: beyond hemoglobin A_1c (Cleve Clin J Med 2019; 86[9]:595–600, doi:10.3949/ccjm.86a.18031).

The sodium-glucose cotransporter 2 (SGLT2) inhibitors pose a low risk of hypoglyemia, and that should have been noted in the table. The corrected table appears below and online.

Information was omitted from Table 1 on page 596 of the article, Makin V, Lansang MC. Diabetes management: beyond hemoglobin A_1c (Cleve Clin J Med 2019; 86[9]:595–600, doi:10.3949/ccjm.86a.18031).

The sodium-glucose cotransporter 2 (SGLT2) inhibitors pose a low risk of hypoglyemia, and that should have been noted in the table. The corrected table appears below and online.