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Improving estimation of Parkinson's disease risk-the enhanced PREDICT-PD algorithm.

Jonathan P Bestwick1, Stephen D Auger1, Cristina Simonet1

  • 1Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.

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Summary

An enhanced algorithm improves Parkinson's disease (PD) risk prediction by incorporating prodromal markers like hyposmia and REM sleep behavior disorder (RBD). This advanced model offers more accurate risk stratification for early PD detection.

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Area of Science:

  • Neurology
  • Biostatistics

Background:

  • Previous research established a basic algorithm for Parkinson's disease (PD) risk identification using known risk factors and prodromal features.
  • The PREDICT-PD study utilized this algorithm to identify at-risk individuals, employing tapping speed, hyposmia, and REM sleep behavior disorder (RBD) as intermediate prodromal markers.

Purpose of the Study:

  • To develop and validate an enhanced algorithm for Parkinson's disease (PD) risk prediction by integrating intermediate prodromal markers into the existing risk model.
  • To compare the risk estimation accuracy of the enhanced algorithm against the basic algorithm using data from the PREDICT-PD pilot cohort.

Main Methods:

  • An enhanced algorithm was developed by incorporating intermediate prodromal markers (tapping speed, hyposmia, RBD) into a previously established PD risk model.
  • Risk estimates from the enhanced and basic algorithms were compared in the PREDICT-PD pilot cohort.
  • Correlation analysis was performed between algorithm-derived risk estimates and subclinical striatal dopamine transporter (DaT) depletion measured by SPECT imaging.

Main Results:

  • The enhanced PREDICT-PD algorithm generated a significantly wider range of risk estimates compared to the basic algorithm (93-609-fold vs 10-13-fold difference).
  • Increasing risk scores in the enhanced algorithm showed a greater association with PD risk (hazard ratio 2.75) than in the basic algorithm (hazard ratio 1.47).
  • Enhanced algorithm estimates demonstrated a stronger correlation with subclinical striatal dopamine depletion (R²=0.164) than the basic algorithm (R²=0.043).

Conclusions:

  • Incorporating intermediate prodromal markers and utilizing likelihood ratios significantly improved the accuracy of the PREDICT-PD risk prediction algorithm.
  • The enhanced algorithm provides more precise risk stratification for individuals at risk of developing Parkinson's disease.
  • This refined prediction tool holds potential for earlier identification and intervention strategies in prodromal Parkinson's disease.