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<p>Cognitive decline is a major non-motor complication in early Parkinson’s disease (PD), but predicting its progression remains challenging. Using data from 193 participants in the Early Parkinson’s Disease Longitudinal Singapore (PALS) cohort, we evaluated whether repeated blood biomarker measurements (baseline, year 3, year 5)—neurofilament light chain (NfL) and total tau (t-tau)—could improve prediction of cognitive decline, defined as a one-point annual or sustained two-year drop in Montreal Cognitive Assessment scores. We applied three variable selection methods and five machine learning models across seven feature sets. Overall, 23% of participants experienced cognitive decline over five years. The XGBoost model trained on Random Forest–selected variables achieved the highest performance (AUC = 0.806), a substantial improvement over the baseline-only model (AUC = 0.560). Key predictors included diastolic blood pressure and summaries of t-tau and NfL. Time-varying biomarkers improved predictions over baseline data alone, supporting their integration with machine learning for early cognitive risk assessment in PD.</p>

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Machine learning integration of serial blood biomarkers enhances cognitive decline prediction in early Parkinson’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月25日

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  • Raziyeh Mohammadi,
  • Samuel Y. E. Ng,
  • Jayne Y. Tan,
  • Adeline S. L. Ng,
  • Xiao Deng,
  • Xinyi Choi,
  • Dede L. Heng,
  • Shermyn Neo,
  • Zheyu Xu,
  • Kay-Yaw Tay,
  • Wing-Lok Au,
  • Eng-King Tan,
  • Louis C. S. Tan,
  • William Greene,
  • Maria Liakata,
  • Seyed Ehsan Saffari

摘要

Cognitive decline is a major non-motor complication in early Parkinson’s disease (PD), but predicting its progression remains challenging. Using data from 193 participants in the Early Parkinson’s Disease Longitudinal Singapore (PALS) cohort, we evaluated whether repeated blood biomarker measurements (baseline, year 3, year 5)—neurofilament light chain (NfL) and total tau (t-tau)—could improve prediction of cognitive decline, defined as a one-point annual or sustained two-year drop in Montreal Cognitive Assessment scores. We applied three variable selection methods and five machine learning models across seven feature sets. Overall, 23% of participants experienced cognitive decline over five years. The XGBoost model trained on Random Forest–selected variables achieved the highest performance (AUC = 0.806), a substantial improvement over the baseline-only model (AUC = 0.560). Key predictors included diastolic blood pressure and summaries of t-tau and NfL. Time-varying biomarkers improved predictions over baseline data alone, supporting their integration with machine learning for early cognitive risk assessment in PD.