A recent study published in the journal Nature Aging investigated the artificial intelligence (AI)-derived genetic makeup of biological age differences (BAGs) across multiple organ systems and their associations with lifestyle, chronic diseases, and aging.
Aging is a multifaceted biological process shaped by lifestyle, genetic, and environmental factors that affect organ systems and lead to chronic diseases. Uncovering the heterogeneity of aging phenotypes across organs can lead to advances in precision medicine. One study used AI to estimate BAGs to explore this heterogeneity.
BAG represents the difference between an individual’s AI-predicted age and their chronological age. Despite progress in multi-organ studies, two questions remain: which genetic variants influence the phenotypic heterogeneity of BAG, and how are they causally linked to each other and to lifestyle factors and chronic diseases?
Study: Multi-organ biological age shows that no organ system exists in isolation. Image credit: Ws Studio1985 / Shutterstock
Research and Results
In this study, researchers used computational genomics and AI to investigate the genetic makeup of BAGs in nine organ systems and their causal and associations with lifestyle factors, organ aging, and chronic diseases. They used multi-omics data from over 370,000 participants from the United Kingdom (UK) Biobank.
First, support vector regression was used to derive BAGs for metabolic, musculoskeletal, cardiovascular, eye, brain, immune, kidney, liver, and lung systems using clinical and organ-specific imaging data. The BAGs were then matched as phenotypes in genome-wide association studies (GWAS) to identify independent genetic signals, i.e., loci.
Several downstream analyses were then performed to validate the genetic signals, including single-nucleotide polymorphism-based heritability estimation, tissue-specific gene expression analysis, gene set enrichment analysis, gene-drug-disease network analysis, causal relationship analysis, and genetic correlation.
Overall, 393 BAG genomic locus pairs linked to 9 BAGs were identified. The researchers noted organ specificity and crosstalk between organs. The phenotypic and genetic correlations between BAGs were similar, supporting Cheverud’s conjecture that phenotypic correlations are likely to be reasonable estimates of genetic correlations.
Tissue-specific gene expression analysis validated gene signals showing organ-specific enrichment, i.e., genes associated with cardiovascular BAG were overexpressed or enriched in arterial and cardiac tissues. Furthermore, the research team identified potential causal relationships between BAG, lifestyle factors (e.g., weight and sleep), and chronic diseases (e.g., diabetes and Alzheimer’s disease).
Conclusion
Overall, this study reinforces that organ systems do not function in isolation and highlights organ-specific correlations within organs and interrelationships between organ systems. Interrelationships between organs suggest that drugs for diseases of different organ systems may be reusable, which may improve the success of drug development. Limitations of this study include that findings cannot be generalized to diverse ethnic groups.
“We are very excited about this study and future research directions. We predict that there will be a paradigm shift from single-organ to multi-organ approaches, enabling more comprehensive modeling of human aging and disease,” – lead author Junhao Wen.
Although strong correlations in GWAS beta values were found between European and other ancestral populations, studies should focus on less-representative populations. Furthermore, sex differences were prominent in several organ systems, especially cardiovascular BAG. Overall, the results suggest that this study should explore disease and aging together more comprehensively, as sex differences often exist in chronic diseases such as autism and Alzheimer’s disease.
