Can an Online Screening Tool Identify People With Early Parkinson’s Disease?

VANCOUVER—An algorithm that estimates a person’s risk of Parkinson’s disease based on responses to an online questionnaire may help identify people with the earliest stages of the disease, according to research described at the 68th Annual Meeting of the American Academy of Neurology. People with the highest risk scores have poorer smell, increased rates of REM sleep behavior disorder, and slower finger tapping—intermediate markers of Parkinson’s disease—compared with those with the lowest risk scores. In addition, researchers observed a significant relationship between baseline risk score and incident Parkinson’s disease at three years.

The researchers plan to evaluate the screening tool in a larger cohort, which will allow them to observe more incident cases of Parkinson’s disease and modify the algorithm to improve its strength, said Alastair Noyce, MRCP, PhD, a Parkinson’s UK Research Fellow at University College London Institute of Neurology.

Neuroprotective trials in Parkinson’s disease currently might not recruit patients early enough for the investigational treatments to have an effect. “If we could move that point at which diagnosis could reliably be made, when people have lost 10% or 20% of cells in the substantia nigra rather than 50% or 60%, then that would be potentially very powerful,” he said.

Calculating Risk

Dr. Noyce and his research colleagues developed a prediction algorithm based on a systematic review of Parkinson’s disease risk factors. Factors that increase risk include family history of the disease, constipation, anxiety, depression, pesticide exposure, and head injury. Factors that decrease risk include smoking, coffee and alcohol intake, use of calcium channel blockers, and hypertension.

To evaluate the algorithm, they initiated the longitudinal PREDICT-PD study. Approximately 1,500 people enrolled, and about 1,300 of them were eligible for the study, meaning they were between the ages of 60 and 80, lived in the United Kingdom, and did not have Parkinson’s disease, movement disorders, dementia, stroke, or motor neuron disease, and did not take drugs that can cause parkinsonism. Participants answered questionnaires about motor and nonmotor features and risk factors.

Three prominent features in the Parkinson’s disease prodrome—poor smell, REM sleep behavior disorder, and slow finger tapping—were not included in the prediction algorithm. Instead, the researchers considered those features intermediate markers of Parkinson’s disease and used them to assess at baseline whether the risk stratification process was working. The investigators hypothesized that, compared with the 100 lowest-risk participants, the 100 highest-risk participants would have deficits in smell, as measured by the University of Pennsylvania Smell Identification Test (UPSIT), higher rates of REM sleep behavior disorder (RBD), as measured by the RBD Screening Questionnaire, and slower finger tapping, as measured by the bradykinesia akinesia incoordination test. “That’s exactly what we saw,” Dr. Noyce said. The differences between groups were small but statistically significant.

Participants were asked to complete the questionnaire again each year, and their risk scores were recalculated. As fewer participants completed the survey in subsequent years, researchers compared the 15% of participants with the highest risk scores versus the 15% of participants with the lowest risk scores. When they evaluated intermediate markers in year three, they again observed small but statistically significant differences between the high- and low-risk groups.

In addition, Dr. Noyce recorded video of high-, low-, and intermediate-risk participants performing motor tests in their homes. Researchers blinded to participants’ risk estimates scored them on the Unified Parkinson’s Disease Rating Scale. Depending on the definition of mild parkinsonian signs used, 20% to 30% of the high-risk group had mild parkinsonian signs, compared with 5% of the low-risk group.

Conversion to Parkinson’s Disease

Investigators plan to see if high-risk participants are more likely to convert to Parkinson’s disease over time. Seven participants so far have received independent diagnoses of Parkinson’s disease. Prior to diagnosis, the participants had heterogeneous performance on the various intermediate markers, Dr. Noyce said. For example, two of the participants had normal UPSIT scores, one participant had a borderline UPSIT score, and the remaining four had abnormal UPSIT scores. The finger tapping score “seems to be particularly useful,” he said. Several of the participants who later were diagnosed with Parkinson’s disease had low finger tapping scores.

Using Cox regression analysis, the researchers found a significant relationship between baseline risk score and incident Parkinson’s disease at three years, with a hazard ratio of 4.4. The analysis was based on a small number of incident cases, however, and the hazard ratio had wide confidence intervals, Dr. Noyce noted.

—Jake Remaly

VANCOUVER—An algorithm that estimates a person’s risk of Parkinson’s disease based on responses to an online questionnaire may help identify people with the earliest stages of the disease, according to research described at the 68th Annual Meeting of the American Academy of Neurology. People with the highest risk scores have poorer smell, increased rates of REM sleep behavior disorder, and slower finger tapping—intermediate markers of Parkinson’s disease—compared with those with the lowest risk scores. In addition, researchers observed a significant relationship between baseline risk score and incident Parkinson’s disease at three years.

The researchers plan to evaluate the screening tool in a larger cohort, which will allow them to observe more incident cases of Parkinson’s disease and modify the algorithm to improve its strength, said Alastair Noyce, MRCP, PhD, a Parkinson’s UK Research Fellow at University College London Institute of Neurology.

Neuroprotective trials in Parkinson’s disease currently might not recruit patients early enough for the investigational treatments to have an effect. “If we could move that point at which diagnosis could reliably be made, when people have lost 10% or 20% of cells in the substantia nigra rather than 50% or 60%, then that would be potentially very powerful,” he said.

Calculating Risk

Dr. Noyce and his research colleagues developed a prediction algorithm based on a systematic review of Parkinson’s disease risk factors. Factors that increase risk include family history of the disease, constipation, anxiety, depression, pesticide exposure, and head injury. Factors that decrease risk include smoking, coffee and alcohol intake, use of calcium channel blockers, and hypertension.

To evaluate the algorithm, they initiated the longitudinal PREDICT-PD study. Approximately 1,500 people enrolled, and about 1,300 of them were eligible for the study, meaning they were between the ages of 60 and 80, lived in the United Kingdom, and did not have Parkinson’s disease, movement disorders, dementia, stroke, or motor neuron disease, and did not take drugs that can cause parkinsonism. Participants answered questionnaires about motor and nonmotor features and risk factors.

Three prominent features in the Parkinson’s disease prodrome—poor smell, REM sleep behavior disorder, and slow finger tapping—were not included in the prediction algorithm. Instead, the researchers considered those features intermediate markers of Parkinson’s disease and used them to assess at baseline whether the risk stratification process was working. The investigators hypothesized that, compared with the 100 lowest-risk participants, the 100 highest-risk participants would have deficits in smell, as measured by the University of Pennsylvania Smell Identification Test (UPSIT), higher rates of REM sleep behavior disorder (RBD), as measured by the RBD Screening Questionnaire, and slower finger tapping, as measured by the bradykinesia akinesia incoordination test. “That’s exactly what we saw,” Dr. Noyce said. The differences between groups were small but statistically significant.

Participants were asked to complete the questionnaire again each year, and their risk scores were recalculated. As fewer participants completed the survey in subsequent years, researchers compared the 15% of participants with the highest risk scores versus the 15% of participants with the lowest risk scores. When they evaluated intermediate markers in year three, they again observed small but statistically significant differences between the high- and low-risk groups.

In addition, Dr. Noyce recorded video of high-, low-, and intermediate-risk participants performing motor tests in their homes. Researchers blinded to participants’ risk estimates scored them on the Unified Parkinson’s Disease Rating Scale. Depending on the definition of mild parkinsonian signs used, 20% to 30% of the high-risk group had mild parkinsonian signs, compared with 5% of the low-risk group.

Conversion to Parkinson’s Disease

Investigators plan to see if high-risk participants are more likely to convert to Parkinson’s disease over time. Seven participants so far have received independent diagnoses of Parkinson’s disease. Prior to diagnosis, the participants had heterogeneous performance on the various intermediate markers, Dr. Noyce said. For example, two of the participants had normal UPSIT scores, one participant had a borderline UPSIT score, and the remaining four had abnormal UPSIT scores. The finger tapping score “seems to be particularly useful,” he said. Several of the participants who later were diagnosed with Parkinson’s disease had low finger tapping scores.

Using Cox regression analysis, the researchers found a significant relationship between baseline risk score and incident Parkinson’s disease at three years, with a hazard ratio of 4.4. The analysis was based on a small number of incident cases, however, and the hazard ratio had wide confidence intervals, Dr. Noyce noted.

—Jake Remaly

VANCOUVER—An algorithm that estimates a person’s risk of Parkinson’s disease based on responses to an online questionnaire may help identify people with the earliest stages of the disease, according to research described at the 68th Annual Meeting of the American Academy of Neurology. People with the highest risk scores have poorer smell, increased rates of REM sleep behavior disorder, and slower finger tapping—intermediate markers of Parkinson’s disease—compared with those with the lowest risk scores. In addition, researchers observed a significant relationship between baseline risk score and incident Parkinson’s disease at three years.

The researchers plan to evaluate the screening tool in a larger cohort, which will allow them to observe more incident cases of Parkinson’s disease and modify the algorithm to improve its strength, said Alastair Noyce, MRCP, PhD, a Parkinson’s UK Research Fellow at University College London Institute of Neurology.

Neuroprotective trials in Parkinson’s disease currently might not recruit patients early enough for the investigational treatments to have an effect. “If we could move that point at which diagnosis could reliably be made, when people have lost 10% or 20% of cells in the substantia nigra rather than 50% or 60%, then that would be potentially very powerful,” he said.

Calculating Risk

Dr. Noyce and his research colleagues developed a prediction algorithm based on a systematic review of Parkinson’s disease risk factors. Factors that increase risk include family history of the disease, constipation, anxiety, depression, pesticide exposure, and head injury. Factors that decrease risk include smoking, coffee and alcohol intake, use of calcium channel blockers, and hypertension.

To evaluate the algorithm, they initiated the longitudinal PREDICT-PD study. Approximately 1,500 people enrolled, and about 1,300 of them were eligible for the study, meaning they were between the ages of 60 and 80, lived in the United Kingdom, and did not have Parkinson’s disease, movement disorders, dementia, stroke, or motor neuron disease, and did not take drugs that can cause parkinsonism. Participants answered questionnaires about motor and nonmotor features and risk factors.

Three prominent features in the Parkinson’s disease prodrome—poor smell, REM sleep behavior disorder, and slow finger tapping—were not included in the prediction algorithm. Instead, the researchers considered those features intermediate markers of Parkinson’s disease and used them to assess at baseline whether the risk stratification process was working. The investigators hypothesized that, compared with the 100 lowest-risk participants, the 100 highest-risk participants would have deficits in smell, as measured by the University of Pennsylvania Smell Identification Test (UPSIT), higher rates of REM sleep behavior disorder (RBD), as measured by the RBD Screening Questionnaire, and slower finger tapping, as measured by the bradykinesia akinesia incoordination test. “That’s exactly what we saw,” Dr. Noyce said. The differences between groups were small but statistically significant.

Participants were asked to complete the questionnaire again each year, and their risk scores were recalculated. As fewer participants completed the survey in subsequent years, researchers compared the 15% of participants with the highest risk scores versus the 15% of participants with the lowest risk scores. When they evaluated intermediate markers in year three, they again observed small but statistically significant differences between the high- and low-risk groups.

In addition, Dr. Noyce recorded video of high-, low-, and intermediate-risk participants performing motor tests in their homes. Researchers blinded to participants’ risk estimates scored them on the Unified Parkinson’s Disease Rating Scale. Depending on the definition of mild parkinsonian signs used, 20% to 30% of the high-risk group had mild parkinsonian signs, compared with 5% of the low-risk group.

Conversion to Parkinson’s Disease

Investigators plan to see if high-risk participants are more likely to convert to Parkinson’s disease over time. Seven participants so far have received independent diagnoses of Parkinson’s disease. Prior to diagnosis, the participants had heterogeneous performance on the various intermediate markers, Dr. Noyce said. For example, two of the participants had normal UPSIT scores, one participant had a borderline UPSIT score, and the remaining four had abnormal UPSIT scores. The finger tapping score “seems to be particularly useful,” he said. Several of the participants who later were diagnosed with Parkinson’s disease had low finger tapping scores.

Using Cox regression analysis, the researchers found a significant relationship between baseline risk score and incident Parkinson’s disease at three years, with a hazard ratio of 4.4. The analysis was based on a small number of incident cases, however, and the hazard ratio had wide confidence intervals, Dr. Noyce noted.

—Jake Remaly