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Machine learning integration of serial blood biomarkers enhances cognitive decline prediction in early Parkinson’s disease
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  • Published: 25 February 2026

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

  • Raziyeh Mohammadi1,
  • Samuel Y. E. Ng2,
  • Jayne Y. Tan3,
  • Adeline S. L. Ng3,4,
  • Xiao Deng3,
  • Xinyi Choi2,
  • Dede L. Heng2,
  • Shermyn Neo3,
  • Zheyu Xu3,
  • Kay-Yaw Tay3,4,
  • Wing-Lok Au3,4,
  • Eng-King Tan2,3,
  • Louis C. S. Tan2,3,4,
  • William Greene5,
  • Maria Liakata6 &
  • …
  • Seyed Ehsan Saffari1,2,3,7,8 

npj Parkinson's Disease , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biomarkers
  • Neurology
  • Neuroscience

Abstract

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.

Data availability

The study data will be made available upon reasonable request to the corresponding author. The data are not publicly available due to privacy and ethical concerns.

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Acknowledgements

This research was funded by the Singapore Ministry of Health’s National Medical Research Council (MOH-OFLCG18May-0002, MOH-CSAINV21-0005, CNIG22jul-0004).

Author information

Authors and Affiliations

  1. Centre for Biomedical Data Science, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore

    Raziyeh Mohammadi & Seyed Ehsan Saffari

  2. Department of Research, National Neuroscience Institute, Singapore, Singapore

    Samuel Y. E. Ng, Xinyi Choi, Dede L. Heng, Eng-King Tan, Louis C. S. Tan & Seyed Ehsan Saffari

  3. Department of Neurology, National Neuroscience Institute, Singapore, Singapore

    Jayne Y. Tan, Adeline S. L. Ng, Xiao Deng, Shermyn Neo, Zheyu Xu, Kay-Yaw Tay, Wing-Lok Au, Eng-King Tan, Louis C. S. Tan & Seyed Ehsan Saffari

  4. Duke-NUS Medical School, National University of Singapore, Singapore, Singapore

    Adeline S. L. Ng, Kay-Yaw Tay, Wing-Lok Au & Louis C. S. Tan

  5. Department of Economics, University of South Florida, Tampa, FL, USA

    William Greene

  6. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK

    Maria Liakata

  7. Duke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School, Singapore, Singapore

    Seyed Ehsan Saffari

  8. NUS Artificial Intelligence Institute, National University of Singapore, Singapore, Singapore

    Seyed Ehsan Saffari

Authors
  1. Raziyeh Mohammadi
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  2. Samuel Y. E. Ng
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  7. Dede L. Heng
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  16. Seyed Ehsan Saffari
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Contributions

Conceptualization, R.M., A.S.L.N., E.-K.T., L.C.S.T., and S.E.S.; Methodology, R.M., W.G., and S.E.S.; Software, R.M. and S.E.S.; Validation, R.M. and S.E.S.; Formal Analysis, R.M. and S.E.S.; Investigation, R.M. and S.E.S.; Resources, S.Y.E.N., J.Y.T., A.S.L.N., X.D., X.C., D.L.H., S.N., Z.X., K.-Y.T., W.-L.A., E.-K.T., L.C.S.T., and S.E.S.; Data Curation, S.Y.E.N., J.Y.T., A.S.L.N., X.D., X.C., and S.E.S.; Writing—Original Draft Preparation, R.M. and S.E.S.; Writing—Review and Editing, R.M., S.E.S., J.Y.T., A.S.L.N., X.D., X.C., D.L.H., S.N., Z.X., K.-Y.T., W.-L.A., E.-K.T., L.C.S.T., W.G., M.L., and S.E.S.; Visualization, R.M. and S.E.S.; Supervision, A.S.L.N., E.-K.T., L.C.S.T., W.G., M.L., and S.E.S.; Project Administration, R.M., S.Y.E.N., and J.Y.T.; Funding Acquisition, E.-K.T., L.C.S.T., and S.E.S. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Seyed Ehsan Saffari.

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Mohammadi, R., Ng, S.Y.E., Tan, J.Y. et al. Machine learning integration of serial blood biomarkers enhances cognitive decline prediction in early Parkinson’s disease. npj Parkinsons Dis. (2026). https://doi.org/10.1038/s41531-026-01298-8

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  • Received: 12 July 2025

  • Accepted: 12 February 2026

  • Published: 25 February 2026

  • DOI: https://doi.org/10.1038/s41531-026-01298-8

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