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Association Between Plasma Metabolomic Profile and Machine Learning-Based Brain Age.

Yang Li^1,2, Jiao Wang^3,4, Yuyang Miao¹

¹Aging Research Center, Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China.

|September 1, 2025

Summary

Metabolomics, the study of metabolites, is linked to brain aging. Specific lipid profiles in plasma are associated with brain age gap, offering potential early indicators of accelerated brain aging, irrespective of APOE ε4 status.

Keywords:

UK biobank brain age machine learning magnetic resonance imaging metabolites

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Area of Science:

Neuroscience
Metabolomics
Aging Research

Background:

Metabolomics is implicated in cognitive decline and dementia.
The precise link between specific metabolites and brain aging is not fully understood.
Investigating these associations is crucial for understanding brain health trajectories.

Purpose of the Study:

To examine the relationship between plasma metabolites and brain age.
To determine if these associations differ based on apolipoprotein E (APOE) ε4 genotype.
To identify potential metabolic biomarkers for accelerated brain aging.

Main Methods:

Utilized data from 17,770 UK Biobank participants (aged 40-69).
Measured 249 plasma metabolites via nuclear magnetic resonance spectroscopy.
Estimated brain age and brain age gap (BAG) using neuroimaging and LASSO regression.

Main Results:

Identified 64 metabolites associated with brain age and 77 with BAG.
Specific lipid profiles in lipoproteins (HDL, VLDL, LDL) correlated with BAG.
Associations between certain metabolites and brain age were consistent across APOE ε4 carriers and non-carriers.

Conclusions:

Plasma metabolite profiles demonstrate broad associations with brain aging.
Metabolic profiles may serve as early indicators of accelerated brain aging.
These findings hold true irrespective of APOE ε4 genetic risk.