Author:
Qiang Yi-Xuan,You Jia,He Xiao-Yu,Guo Yu,Deng Yue-Ting,Gao Pei-Yang,Wu Xin-Rui,Feng Jian-Feng,Cheng Wei,Yu Jin-Tai
Abstract
Abstract
Background
Blood-based biomarkers for dementia are gaining attention due to their non-invasive nature and feasibility in regular healthcare settings. Here, we explored the associations between 249 metabolites with all-cause dementia (ACD), Alzheimer’s disease (AD), and vascular dementia (VaD) and assessed their predictive potential.
Methods
This study included 274,160 participants from the UK Biobank. Cox proportional hazard models were employed to investigate longitudinal associations between metabolites and dementia. The importance of these metabolites was quantified using machine learning algorithms, and a metabolic risk score (MetRS) was subsequently developed for each dementia type. We further investigated how MetRS stratified the risk of dementia onset and assessed its predictive performance, both alone and in combination with demographic and cognitive predictors.
Results
During a median follow-up of 14.01 years, 5274 participants developed dementia. Of the 249 metabolites examined, 143 were significantly associated with incident ACD, 130 with AD, and 140 with VaD. Among metabolites significantly associated with dementia, lipoprotein lipid concentrations, linoleic acid, sphingomyelin, glucose, and branched-chain amino acids ranked top in importance. Individuals within the top tertile of MetRS faced a significantly greater risk of developing dementia than those in the lowest tertile. When MetRS was combined with demographic and cognitive predictors, the model yielded the area under the receiver operating characteristic curve (AUC) values of 0.857 for ACD, 0.861 for AD, and 0.873 for VaD.
Conclusions
We conducted the largest metabolome investigation of dementia to date, for the first time revealed the metabolite importance ranking, and highlighted the contribution of plasma metabolites for dementia prediction.
Funder
Shanghai Municipal Science and Technology Major Project
ZHANGJIANG LAB
National Key R&D Program of China
111 Project
Shanghai Center for Brain Science and Brain-Inspired Technology
Shanghai Rising-Star Program
National Natural Sciences Foundation of China
Science and Technology Innovation 2030 Major Projects
Research Start-up Fund of Huashan Hospital
Excellence 2025 Talent Cultivation Program at Fudan University
Shanghai Talent Development Funding for The Project
Tianqiao and Chrissy Chen Institute
State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Cognitive Neuroscience,Neurology (clinical),Neurology