| Title: | Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants |
| Journal: | Frontiers in Psychology |
| Published: | 9 Jun 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37359862/ |
| DOI: | https://doi.org/10.3389/fpsyg.2023.1117732 |
| URL: | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1117732/pdf |
| Citations: | 6 (6 in last 2 years) as of 8 Aug 2024 |
Publication 11544
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Abstract
Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.</p>
10 Authors
- Max Korbmacher
- Tiril P. Gurholt
- Ann-Marie G. de Lange
- Dennis van der Meer
- Dani Beck
- Eli Eikefjord
- Arvid Lundervold
- Ole A. Andreassen
- Lars T. Westlye
- Ivan I. Maximov
1 Application
| Application ID | Title |
|---|---|
| 27412 | Boosting the power of GWAS using novel statistical tools |
Enabling scientific discoveries that improve human health