Metabolomic signatures of brain aging: A multimodal and genetic study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, No.1163 Xinmin Street, Changchun, Jilin, 130021, China.
- Department of Social Medicine and Health Management, School of Public Health, Jilin University, Changchun, Jilin, 130021, China.
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention, Ministry of Education, China Medical University; Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, No.77 Puhe Road, Shenyang, Liaoning Province, 110122, China.
- Department of Social Medicine and Health Management, School of Public Health, Jilin University, Changchun, Jilin, 130021, China. [email protected].
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention, Ministry of Education, China Medical University; Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, No.77 Puhe Road, Shenyang, Liaoning Province, 110122, China. [email protected].
- Health Sciences Institute, China Medical University; Liaoning Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, No.77 Puhe Road, Shenyang, Liaoning Province, 110122, China. [email protected].
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, No.1163 Xinmin Street, Changchun, Jilin, 130021, China. [email protected].
Abstract
Accelerated brain aging is increasingly recognized as a transdiagnostic risk factor for neuropsychiatric and neurodegenerative disorders, yet its metabolic underpinnings remain poorly understood. Here we integrated multimodal neuroimaging (MRI), plasma metabolomics, and genomic data from the UK Biobank to identify metabolic markers of brain aging and evaluate their causal relevance. Using 1079 imaging-derived phenotypes (IDPs) from 4333 healthy participants, we trained and validated machine learning models for brain age prediction, with a least absolute shrinkage and selection operator (LASSO) regression model achieving the best performance (mean absolute error = 3.26 years, R² = 0.68). Brain age gap (BAG) was then estimated in 37,458 participants. Association analyses in 21,780 individuals identified nine plasma metabolites significantly linked to BAG after Bonferroni correction, with glucose showing the strongest effect (β = 0.32, P = 9.90 × 10⁻¹²). Genome-wide association studies (GWAS) identified 392 BAG-associated single-nucleotide polymorphisms (SNPs) (P < 5 × 10⁻⁸), and two-sample Mendelian randomization (MR) provided evidence supporting a potential causal role of glucose in accelerating brain aging. Clinically, elevated plasma glucose was positively associated with seven brain disorders, including all-cause dementia, Alzheimer's disease, vascular dementia, Parkinson's disease, stroke, depression, and anxiety, and negatively associated with cognitive performance, movement function, and mental health outcomes. Higher glucose concentrations were also associated with reduced regional brain volumes across 80 cortical, subcortical, and cerebellar regions. These findings implicate glucose metabolism as a modifiable pathway in brain aging, with implications for early intervention strategies aimed at preserving brain health across the lifespan.