Plasma proteomic profiling reveals Parkinson's disease-associated proteins: A UK Biobank study

Abstract

Introduction

The rapid advancement of proteomics has provided new insights into early detection and prediction of Parkinson's disease (PD), particularly in identifying risk factors for PD. This study aims to develop a proteomics-based model to predict the risk of PD in patients.

Methods

We analyzed data from the UK Biobank cohort, including 52,851 PD-free participants at baseline, with a median follow-up of 15.3 years and 811 newly diagnosed PD cases. A prospective proteomic analysis was conducted to assess the predictive value of 2,923 plasma proteins, and LightGBM models were used to calculate protein importance, followed by an evaluation of the proteins' predictive performance.

Results

The study found that higher levels of NEFL and MERTK were significantly associated with future PD events, while lower levels of ITGAV, BAG3, CLEC10A, ITGAM, HNMT, and TPK1 were identified as potential risk factors for PD. Notably, the axonal injury marker NEFL and the thiamine metabolism-related protein TPK1 ranked higher than other proteins in terms of importance. The combination of NEFL and TPK1 significantly enhanced the predictive accuracy of conventional clinical models, increasing the Area Under the Curve (AUC) of the full-cohort prediction model from 0.784 to 0.842 and the 5-year prediction model from 0.780 to 0.908.

Conclusions

This study provides a novel insight for screening high-risk PD populations and underscores the significant role of nutritional metabolism in PD development, offering valuable insights for precision prevention strategies.