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As a predictor of coronary heart disease (CHD) events, the coronary artery calcium (CAC) score on computed tomography had better risk discrimination than the polygenic risk score, a binational study found. And when added to classic cardiovascular risk factors, the CAC score significantly improved risk classification while the polygenic risk factor score did not.
These findings emerged from two large cohorts of middle-aged and older White adults from the United States and the Netherlands in the first head-to-head comparison of these two approaches. Led by Sadiya S. Kahn, MD, MSc, an assistant professor of medicine (cardiology) and preventive medicine (epidemiology) at Northwestern University, Chicago, the study was published online in JAMA.
There has been much interest in using both genetic factors and CT imaging to better identify individuals at risk for heart disease. “Each approach has advantages and disadvantages, and we wanted to better understand the comparative predictive utility to provide support for what the preferred approach should be,” Dr. Kahn said in an interview. “We focused on middle-aged to older adults for whom current risk prediction equations are relevant in estimating risk with the Pooled Cohort Equation, or PCE.”
The superiority of the CT-imaged coronary artery risk score may be because of its direct visualization of calcification in the arteries and the subclinical disease burden rather than a focus on common genetic variants, Dr. Kahn explained. “In addition, prior studies have demonstrated that genetics, or inherited risk, is not destiny, so this score may not perform as well for risk discrimination as the traditional risk factors themselves along with CT.”
The study
Study participants came from the U.S. Multi-Ethnic Study of Atherosclerosis (MESA, n = 1,991) and the Dutch Rotterdam Study (RS, n = 1,217). Ages ranged from 45 to 79, with the medians in the two cohorts 61 and 68 years, respectively. Slightly more than half of participants in both groups were female.
Traditional risk factors were used to calculate CHD risk with pooled cohort equations, while computed tomography was used to determine the CAC score and genotyped samples for a validated polygenic risk score.
Both scores were significantly associated with 10-year risk of incident CHD.
The median predicted atherosclerotic disease risk based on traditional risk factors was 6.99% in MESA and 5.93% in RS. During the total available follow-up in MESA (median, 16.0 years) and RS (median, 14.2 years), incident CHD occurred in 187 participants (9.4%) and 98 participants (8.1%), respectively.
C (concordance) statistics for the two scores showed the superiority of the CAC. This statistic measures a model’s ability to rank patients from high to low risk, with a value of 1 being perfect risk fit or concordance and 0.70 or more indicating good concordance and risk discrimination. The CAC score had a C statistic of 0.76 (95% confidence interval, 0.71-0.79) vs. 0.69 for the polygenic risk score (95% CI, 0.63-0.71).
When each score was added to PCEs, the C statistics changed as follows: CAC score, 0.09 (95% CI, 0.06-0.13); polygenic risk score, 0.02 (95% CI, 0.00-0.04); and 0.10 (95% CI, 0.07-0.14) for both.
Net reclassification significantly improved with the CAC plus PCEs by the following values: 0.19 (95% CI, 0.06-0.28). The change was not significant, however, with the polygenic risk score plus PCEs: 0.04 (95% CI, –0.05-0.10).
In the clinical setting, Dr. Kahn said, “The use of CT in patients who are at intermediate risk for heart disease can be helpful in refining risk estimation and guiding recommendations for lipid-lowering therapy. Polygenic risk scores are not helpful in middle-aged to older adults above and beyond traditional risk factors for predicting risk of heart disease.”
This study was supported by the National Heart, Lung, and Blood Institute. MESA is supported by the NHLBI. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research; the Netherlands Organization for Health Research and Development; the Research Institute for Diseases in the Elderly; the Netherlands Genomics Initiative; the Ministry of Education, Culture and Science, the Ministry of Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. Dr. Khan reported grants from the NHLBI and the NIH during the study and outside of the submitted work. Several coauthors reported grant support from, variously, the NIH, the NHLBI, and the American Heart Association.
As a predictor of coronary heart disease (CHD) events, the coronary artery calcium (CAC) score on computed tomography had better risk discrimination than the polygenic risk score, a binational study found. And when added to classic cardiovascular risk factors, the CAC score significantly improved risk classification while the polygenic risk factor score did not.
These findings emerged from two large cohorts of middle-aged and older White adults from the United States and the Netherlands in the first head-to-head comparison of these two approaches. Led by Sadiya S. Kahn, MD, MSc, an assistant professor of medicine (cardiology) and preventive medicine (epidemiology) at Northwestern University, Chicago, the study was published online in JAMA.
There has been much interest in using both genetic factors and CT imaging to better identify individuals at risk for heart disease. “Each approach has advantages and disadvantages, and we wanted to better understand the comparative predictive utility to provide support for what the preferred approach should be,” Dr. Kahn said in an interview. “We focused on middle-aged to older adults for whom current risk prediction equations are relevant in estimating risk with the Pooled Cohort Equation, or PCE.”
The superiority of the CT-imaged coronary artery risk score may be because of its direct visualization of calcification in the arteries and the subclinical disease burden rather than a focus on common genetic variants, Dr. Kahn explained. “In addition, prior studies have demonstrated that genetics, or inherited risk, is not destiny, so this score may not perform as well for risk discrimination as the traditional risk factors themselves along with CT.”
The study
Study participants came from the U.S. Multi-Ethnic Study of Atherosclerosis (MESA, n = 1,991) and the Dutch Rotterdam Study (RS, n = 1,217). Ages ranged from 45 to 79, with the medians in the two cohorts 61 and 68 years, respectively. Slightly more than half of participants in both groups were female.
Traditional risk factors were used to calculate CHD risk with pooled cohort equations, while computed tomography was used to determine the CAC score and genotyped samples for a validated polygenic risk score.
Both scores were significantly associated with 10-year risk of incident CHD.
The median predicted atherosclerotic disease risk based on traditional risk factors was 6.99% in MESA and 5.93% in RS. During the total available follow-up in MESA (median, 16.0 years) and RS (median, 14.2 years), incident CHD occurred in 187 participants (9.4%) and 98 participants (8.1%), respectively.
C (concordance) statistics for the two scores showed the superiority of the CAC. This statistic measures a model’s ability to rank patients from high to low risk, with a value of 1 being perfect risk fit or concordance and 0.70 or more indicating good concordance and risk discrimination. The CAC score had a C statistic of 0.76 (95% confidence interval, 0.71-0.79) vs. 0.69 for the polygenic risk score (95% CI, 0.63-0.71).
When each score was added to PCEs, the C statistics changed as follows: CAC score, 0.09 (95% CI, 0.06-0.13); polygenic risk score, 0.02 (95% CI, 0.00-0.04); and 0.10 (95% CI, 0.07-0.14) for both.
Net reclassification significantly improved with the CAC plus PCEs by the following values: 0.19 (95% CI, 0.06-0.28). The change was not significant, however, with the polygenic risk score plus PCEs: 0.04 (95% CI, –0.05-0.10).
In the clinical setting, Dr. Kahn said, “The use of CT in patients who are at intermediate risk for heart disease can be helpful in refining risk estimation and guiding recommendations for lipid-lowering therapy. Polygenic risk scores are not helpful in middle-aged to older adults above and beyond traditional risk factors for predicting risk of heart disease.”
This study was supported by the National Heart, Lung, and Blood Institute. MESA is supported by the NHLBI. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research; the Netherlands Organization for Health Research and Development; the Research Institute for Diseases in the Elderly; the Netherlands Genomics Initiative; the Ministry of Education, Culture and Science, the Ministry of Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. Dr. Khan reported grants from the NHLBI and the NIH during the study and outside of the submitted work. Several coauthors reported grant support from, variously, the NIH, the NHLBI, and the American Heart Association.
As a predictor of coronary heart disease (CHD) events, the coronary artery calcium (CAC) score on computed tomography had better risk discrimination than the polygenic risk score, a binational study found. And when added to classic cardiovascular risk factors, the CAC score significantly improved risk classification while the polygenic risk factor score did not.
These findings emerged from two large cohorts of middle-aged and older White adults from the United States and the Netherlands in the first head-to-head comparison of these two approaches. Led by Sadiya S. Kahn, MD, MSc, an assistant professor of medicine (cardiology) and preventive medicine (epidemiology) at Northwestern University, Chicago, the study was published online in JAMA.
There has been much interest in using both genetic factors and CT imaging to better identify individuals at risk for heart disease. “Each approach has advantages and disadvantages, and we wanted to better understand the comparative predictive utility to provide support for what the preferred approach should be,” Dr. Kahn said in an interview. “We focused on middle-aged to older adults for whom current risk prediction equations are relevant in estimating risk with the Pooled Cohort Equation, or PCE.”
The superiority of the CT-imaged coronary artery risk score may be because of its direct visualization of calcification in the arteries and the subclinical disease burden rather than a focus on common genetic variants, Dr. Kahn explained. “In addition, prior studies have demonstrated that genetics, or inherited risk, is not destiny, so this score may not perform as well for risk discrimination as the traditional risk factors themselves along with CT.”
The study
Study participants came from the U.S. Multi-Ethnic Study of Atherosclerosis (MESA, n = 1,991) and the Dutch Rotterdam Study (RS, n = 1,217). Ages ranged from 45 to 79, with the medians in the two cohorts 61 and 68 years, respectively. Slightly more than half of participants in both groups were female.
Traditional risk factors were used to calculate CHD risk with pooled cohort equations, while computed tomography was used to determine the CAC score and genotyped samples for a validated polygenic risk score.
Both scores were significantly associated with 10-year risk of incident CHD.
The median predicted atherosclerotic disease risk based on traditional risk factors was 6.99% in MESA and 5.93% in RS. During the total available follow-up in MESA (median, 16.0 years) and RS (median, 14.2 years), incident CHD occurred in 187 participants (9.4%) and 98 participants (8.1%), respectively.
C (concordance) statistics for the two scores showed the superiority of the CAC. This statistic measures a model’s ability to rank patients from high to low risk, with a value of 1 being perfect risk fit or concordance and 0.70 or more indicating good concordance and risk discrimination. The CAC score had a C statistic of 0.76 (95% confidence interval, 0.71-0.79) vs. 0.69 for the polygenic risk score (95% CI, 0.63-0.71).
When each score was added to PCEs, the C statistics changed as follows: CAC score, 0.09 (95% CI, 0.06-0.13); polygenic risk score, 0.02 (95% CI, 0.00-0.04); and 0.10 (95% CI, 0.07-0.14) for both.
Net reclassification significantly improved with the CAC plus PCEs by the following values: 0.19 (95% CI, 0.06-0.28). The change was not significant, however, with the polygenic risk score plus PCEs: 0.04 (95% CI, –0.05-0.10).
In the clinical setting, Dr. Kahn said, “The use of CT in patients who are at intermediate risk for heart disease can be helpful in refining risk estimation and guiding recommendations for lipid-lowering therapy. Polygenic risk scores are not helpful in middle-aged to older adults above and beyond traditional risk factors for predicting risk of heart disease.”
This study was supported by the National Heart, Lung, and Blood Institute. MESA is supported by the NHLBI. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research; the Netherlands Organization for Health Research and Development; the Research Institute for Diseases in the Elderly; the Netherlands Genomics Initiative; the Ministry of Education, Culture and Science, the Ministry of Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. Dr. Khan reported grants from the NHLBI and the NIH during the study and outside of the submitted work. Several coauthors reported grant support from, variously, the NIH, the NHLBI, and the American Heart Association.
FROM JAMA