Background: Brain age deviation is a promising neuroimaging biomarker of brain health, but its relevance in young and mid-life adults and its biological underpinnings remain insufficiently characterised. We aimed to test whether a functional-connectivity-derived brain ageing index (BAI) captures reproducible variability in early brain ageing and whether it is associated with cognitive-affective function and gut-derived biological signatures. Methods: We analysed resting-state fMRI from a discovery cohort (n = 674) with validation in a replication cohort (n = 444) and an independent cohort (n = 344). Whole-brain functional connectivity was computed using a 100-region Schaefer parcellation, and Bayesian ridge regression was used to predict chronological age; BAI was defined as the age-bias-corrected residual (predicted brain age minus chronological age). We tested associations between BAI and cognitive and affective measures across cohorts. In the independent cohort, we applied multi-view sparse partial least squares to integrate stool metagenomic and metabolomic profiles with BAI, and performed KEGG pathway enrichment analyses on features with non-zero weights. Findings: Predicted brain age correlated with chronological age across cohorts (r = 0.50–0.59). Higher BAI was consistently associated with connectivity patterns involving posterior cingulate/praecuneus and medial frontal regions, poorer cognitive performance, particularly working memory and executive function, and greater depressive symptoms. Multi-omics integration identified microbial taxa and stool metabolites, including ceramides, 24-hydroxycholesterol, dicarboxylic acids, and inverse associations with estetrol, linked to BAI. Enrichment analyses suggested involvement of neuroimmune, vascular, synaptic, and mitochondrial pathways. Interpretation: A connectivity-derived BAI captures reproducible variability in early brain ageing and links large-scale brain network organisation to gut-derived biological signatures. These findings suggest that BAI captures individual variability associated with brain health-related phenotypes and support the potential association of peripheral brain-gut biological pathways in early brain ageing. Funding: National Institutes of Health, National Institute on Ageing.
Brain-gut crosstalk associated with brain ageing in young and mid-life adults: a multicohort cross-sectional study / Zhao, K., Vignolle, G.A., Labus, J.S., Mayer, E.A., Vaughan, A., Dy, M., Vora, P., Hung, M.W., Vossel, K., Gill, C., Del Rio, D., Stanton, C., Ross, R.P., Cryan, J.F., Kaddurah-Daouk, R., Zhang, Y., Church, A.. - In: EBIOMEDICINE. - ISSN 2352-3964. - 132:(2026). [10.1016/j.ebiom.2026.106468]
Brain-gut crosstalk associated with brain ageing in young and mid-life adults: a multicohort cross-sectional study
Del Rio D.;
2026-01-01
Abstract
Background: Brain age deviation is a promising neuroimaging biomarker of brain health, but its relevance in young and mid-life adults and its biological underpinnings remain insufficiently characterised. We aimed to test whether a functional-connectivity-derived brain ageing index (BAI) captures reproducible variability in early brain ageing and whether it is associated with cognitive-affective function and gut-derived biological signatures. Methods: We analysed resting-state fMRI from a discovery cohort (n = 674) with validation in a replication cohort (n = 444) and an independent cohort (n = 344). Whole-brain functional connectivity was computed using a 100-region Schaefer parcellation, and Bayesian ridge regression was used to predict chronological age; BAI was defined as the age-bias-corrected residual (predicted brain age minus chronological age). We tested associations between BAI and cognitive and affective measures across cohorts. In the independent cohort, we applied multi-view sparse partial least squares to integrate stool metagenomic and metabolomic profiles with BAI, and performed KEGG pathway enrichment analyses on features with non-zero weights. Findings: Predicted brain age correlated with chronological age across cohorts (r = 0.50–0.59). Higher BAI was consistently associated with connectivity patterns involving posterior cingulate/praecuneus and medial frontal regions, poorer cognitive performance, particularly working memory and executive function, and greater depressive symptoms. Multi-omics integration identified microbial taxa and stool metabolites, including ceramides, 24-hydroxycholesterol, dicarboxylic acids, and inverse associations with estetrol, linked to BAI. Enrichment analyses suggested involvement of neuroimmune, vascular, synaptic, and mitochondrial pathways. Interpretation: A connectivity-derived BAI captures reproducible variability in early brain ageing and links large-scale brain network organisation to gut-derived biological signatures. These findings suggest that BAI captures individual variability associated with brain health-related phenotypes and support the potential association of peripheral brain-gut biological pathways in early brain ageing. Funding: National Institutes of Health, National Institute on Ageing.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
