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Large-scale proteomic analyses of incident Parkinson’s disease reveal new pathophysiological insights and potential biomarkers

Nature Aging volume 5, pages 642–657 (2025)Cite this article

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Abstract

The early pathophysiology of Parkinson’s disease (PD) is poorly understood. We analyzed 2,920 Olink-measured plasma proteins in 51,804 UK Biobank participants, identifying 859 incident PD cases after 14.45 years. We found 38 PD-related proteins, with six of the top ten validated in the Parkinson’s Progression Markers Initiative (PPMI) cohort. ITGAV, HNMT and ITGAM showed consistent significant association (hazard ratio: 0.11–0.57, P = 6.90 × 10−24 to 2.10 × 10−11). Lipid metabolism dysfunction was evident 15 years before PD onset, and levels of BAG3, HPGDS, ITGAV and PEPD continuously decreased before diagnosis. These proteins were linked to prodromal symptoms and brain measures. Mendelian randomization suggested ITGAM and EGFR as potential causes of PD. A predictive model using machine learning combined the top 16 proteins and demographics, achieving high accuracy for 5-year (area under the curve (AUC) = 0.887) and over-5-year PD prediction (AUC = 0.816), outperforming demographic-only models. It was externally validated in PPMI (AUC = 0.802). Our findings reveal early peripheral pathophysiological changes in PD crucial for developing early biomarkers and precision therapies.

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Fig. 1: Study overview.
Fig. 2: Proteins associated with incident PD and their functional highlights.
Fig. 3: Temporal evolutions of plasma proteins before diagnosis of PD and their trajectory clustering.
Fig. 4: Associations between PD-related proteins and PD prodromal symptoms and brain structures.
Fig. 5: Predictor selection and importance, and ROC curves for the prediction of future PD.

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Data availability

The data used in the present study are available from the UKB, with restrictions applied. Data were used under a license and are thus not publicly available. Access to the UKB data can be requested through the standard protocol (https://www.ukbiobank.ac.uk/register-apply/). The PPMI (Parkinson’s Progression Markers Initiative) database is publicly available, and researchers can apply for access. To request data, please refer to the PPMI Data User Guide at: https://www.ppmi-info.org/sites/default/files/docs/PPMI%20Data%20User%20Guide.pdf. This guide provides detailed instructions on how to apply for access and outlines the associated usage policies. We obtained protein quantitative trait loci (pQTLs) from the GWAS summary statistics in the UKB-PPP, which includes 34,557 participants of European ancestry (https://www.nature.com/articles/s41586-023-06592-6). GWAS summary statistics on PD were obtained from a meta-analysis conducted by the International Parkinson’s Disease Genomics Consortium with a total of 33,674 cases and 449,056 controls of European descent (https://gwas.mrcieu.ac.uk/datasets/ieu-b-7/).

Code availability

Analyses were performed using R software (v4.3.1) and Python (v3.9). We considered a two-tailed P value < 0.05 to be significant. Code is available at https://github.com/YiHanGan/PD-Proteomic-Project.

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Acknowledgements

We thank all the participants and researchers from the UKB and PPMI. This study was supported by grants from the Science and Technology Innovation 2030 Major Projects (2022ZD0211600, J.-T.Y.), National Natural Science Foundation of China (92249305, J.-T.Y., 82472055 and 82071997, W.C., 82402381 and 82471940, J.Y.), National Key R&D Program of China (2023YFC3605400, W.C.), Research Start-up Fund of Huashan Hospital (2022QD002, J.-T.Y.), Excellence 2025 Talent Cultivation Program at Fudan University (3030277001, J.-T.Y.), Shanghai Pujiang Talent Program (23PJD006, J.Y.), the National Postdoctoral Program for Innovative Talents (BX20240073, Y.G.) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University. PPMI is funded by the Michael J. Fox Foundation for Parkinson’s Research funding partners 4D Pharma, AbbVie, Acurex Therapeutics, Allergan, Amathus Therapeutics, ASAP, Avid Radiopharmaceuticals, Bial Biotech, Biogen, BioLegend, Bristol-Myers Squibb, Calico, Celgene, Dacapo Brain Science, Denali, The Edmond J. Safra Foundaiton, GE Healthcare, Genentech, GlaxoSmithKline, Golub Capital, Handl Therapeutics, Insitro, Janssen Neuroscience, Lilly, Lundbeck, Merck, Meso Scale Discovery, Neurocrine Biosciences, Pfizer, Piramal, Prevail, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, Verily and Voyager Therapeutics. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Yi-Han Gan, Ling-Zhi Ma, Yi Zhang, Jia You, Yu Guo.

Authors and Affiliations

  1. Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science,Shanghai Medical College, Fudan University, Shanghai, China

    Yi-Han Gan, Ling-Zhi Ma, Yi Zhang, Jia You, Yu Guo, Yu He, Lin-Bo Wang, Xiao-Yu He, Yu-Zhu Li, Qiang Dong, Wei Cheng & Jin-Tai Yu

  2. Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China

    Ling-Zhi Ma

  3. Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China

    Jia You, Lin-Bo Wang, Yu-Zhu Li, Jian-Feng Feng & Wei Cheng

  4. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China

    Jian-Feng Feng & Wei Cheng

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  1. Yi-Han Gan
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Contributions

J.-T.Y. conceptualized and designed the study and interpreted data. Y.-H.G., L.Z.M., Y.Z., J.Y. and Y.H. collected, analyzed and interpreted the data and drafted the paper. All authors revised the paper.

Corresponding authors

Correspondence to Wei Cheng or Jin-Tai Yu.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The study was conducted following the Declaration of Helsinki. The UKB has research tissue bank approval from the North West Multi-Center Research Ethics Committee (11/NW/0382). Written informed consent was obtained from all participants. The present study was approved by the UKB under application number 19542.

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Nature Aging thanks Lucilla Parnetti, Yue Qi, Marcel Verbeek, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Baseline NPX levels of the 38 PD-associated proteins.

Violin plots display the baseline NPX levels (y axis) 38 PD-associated proteins compared between PD patients observed during follow-up and controls. The width of the violin reflects the density of the corresponding data. The box plots show the median (center line), the interquartile range (top and bottom edge), and the maximum and minimum (whiskers) of the NPX levels. The comparisons were conducted using two-sided wilcoxon test.

Extended Data Fig. 2 The expression of the PD-related proteins in the central nervous system (CNS) cell types.

Expression levels of the PD-associated-protein coding genes in different cell types of normal human brain. Brain snRNA-seq was retrieved from Garcia et al.1. 1. Garcia, F. J. et al. Single-cell dissection of the human brain vasculature. Nature 603, 893–899 (2022).

Extended Data Fig. 3 Functional enrichment analysis of cluster 1 and 2 proteins identified by protein trajectory clustering.

Top enriched GO biological pathways, GO molecular function pathways, and KEGG pathways were presented. The analyses were conducted using the clusterProfiler package (Hypergeometric test) and were adjusted for multiple testing using the Benjamini–Hochberg method. The shade of color corresponds to the magnitude of statistical significance (−log10 of P values).

Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Figure 1.

Reporting Summary

Supplementary Tables 1–38.

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Gan, YH., Ma, LZ., Zhang, Y. et al. Large-scale proteomic analyses of incident Parkinson’s disease reveal new pathophysiological insights and potential biomarkers. Nat Aging 5, 642–657 (2025). https://doi.org/10.1038/s43587-025-00818-0

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