Multidimensional integrative analysis unveils genetic architecture, risk factors, and health consequences of brain...

Accelerated brain aging, reflected by greater brain-age-gap (BAG), has been linked to increased susceptibility to neurodegenerative and psychiatric disorders, yet its genetic underpinnings, modifiable factors, and broader health consequences remain undetermined.

To precisely estimate brain age and systematically investigate the genetic architecture, lifestyle and environmental determinants, and systemic disease risks associated with accelerated brain aging.

Using routinely collected magnetic resonance imaging data, we applied a DenseNet model to estimate brain age and calculated BAG as a biomarker of aging pace. We analyzed 500 K whole genome sequencing data from UK Biobank to elucidate the genomic architecture of BAG, capturing both common and rare variants, and used AlphaFold3 to assess protein structural alterations induced by missense mutations. We implemented multiple linear regression to examine associations of BAG with demographics, lifestyle, environment, and telomere length. We conducted a Phenome-wide association study (PheWAS) to investigate multisystem disease risk linked to BAG.

Among a total of 42,385 participants (mean [SD] age, 65 [7.7] years; 52.8% female), the brain age model estimated BAG with a mean absolute error of 2.49 years. Whole genome sequencing identified two novel BAG-related noncoding variants, rare missense variants in KIAA0513 with a suggestive association, and significant enhancer DNase Hypersensitivity sites (DHS) of PAX6. Structural modeling revealed protein alterations from missense mutations, suggesting potential mechanisms of accelerated brain aging. In addition, unhealthy lifestyles, adverse environmental exposures, and shorter telomere length were linked to BAG. PheWAS analysis showed that 69 disease were significantly associated with BAG (HRs: 1.03-1.39 per BAG year), with mental and neurological disorders ranking highest, followe by cardiovascular and metabolic diseases.

This study integrates neuroimaging, genomics, questionnaire, and medical records to provide a comprehensive framework for understanding the multifactorial mechanisms of brain aging and guiding precision prevention strategies.