| Title: | Association and biological pathways between metabolic syndrome and incident Parkinson's disease: A prospective cohort study of 289,150 participants |
| Journal: | Psychoneuroendocrinology |
| Published: | 26 Mar 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40179596/ |
| DOI: | https://doi.org/10.1016/j.psyneuen.2025.107444 |
Publication 15838
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Abstract
The relationship between metabolic syndrome (MetS) and Parkinson's disease (PD) remains uncertain due to inconsistent findings in previous studies. This prospective cohort study investigated the association between MetS and PD risk, along with potential biological mechanisms, using data from 289,150 PD-free participants in the UK Biobank. MetS was defined by the presence of at least three of the following components, while preMetS included one or two: increased waist circumference, elevated triglycerides (TG), high blood pressure (BP), elevated HbA1c, or reduced high-density lipoprotein cholesterol (HDL-C). Cox proportional hazards models were utilized to assess the risk of PD, and mediation analyses explored the role of blood biomarkers. Over a median follow-up of 13.1 years, 1682 participants developed PD. PreMetS (HR: 1.24, 95 % CI: 1.02-1.51, P = 0.028) and MetS (HR: 1.32, 95 % CI: 1.08-1.61, P = 0.008) were associated with an increased PD risk, with Kaplan-Meier analysis showing risk escalation with more MetS components. Among individual MetS components, increased waist circumference, elevated HbA1c, and reduced HDL-C were significantly associated with higher PD risk, while elevated TG and BP showed no significant association. Mediation analysis indicated that biomarkers of liver function (alkaline phosphatase) and kidney function (cystatin C) partially mediated the MetS-PD relationship. These findings highlight a significant link between MetS and higher PD risk, with possible mediation through specific blood biomarkers, though temporal ambiguity warrants cautious interpretation.</p>
9 Authors
- HuiMin Liu
- Tao Peng
- YuDi Xu
- QingSheng Li
- LingFei Yang
- Zhe Gong
- JunFang Teng
- Qiang Zhang
- YanJie Jia
1 Application
| Application ID | Title |
|---|---|
| 71051 | Integration of traditional risk factors and biomarkers from UK Biobank and other omics data to diagnose, predict, understand, and inform treatment of complex traits or diseases. |
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