In our study, we linked machine-learning-derived biological age gaps (BAGs) to common genetic variants in nine human organ systems, which revealed how these BAGs are causally associated with organ health and chronic diseases such as Alzheimer’s disease and diabetes. The findings provide insights into therapeutic and lifestyle interventions that might enhance organ health.
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References
Tian, Y. E. et al. Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nat. Med. 29, 1221–1231 (2023). An article that presents the original research on the nine BAGs and that explores the phenotypic heterogeneity of these BAGs.
Zhao, B. et al. Heart-brain connections: phenotypic and genetic insights from magnetic resonance images. Science 380, abn6598 (2023). A paper that reports brain–heart connections using imaging-derived phenotypes and genetics.
Wen, J. et al. The genetic architecture of multimodal human brain age. Nat. Commun. 15, 2604 (2024). An article that shows the genetic architecture of three brain BAGs derived from multimodal MRI data.
Cheverud, J. M. A comparison of genetic and phenotypic correlations. Evolution 42, 958–968 (1988). An article that presents the original findings of Cheverud’s conjecture.
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This is a summary of: Wen, J. et al. The genetic architecture of biological age in nine human organ systems. Nat. Aging https://doi.org/10.1038/s43587-024-00662-8 (2024).
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Multiorgan biological age shows that no organ system is an island. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00690-4
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DOI: https://doi.org/10.1038/s43587-024-00690-4
