Multi-omics data unraveled 9 aging clocks for specific organs and systems

Biological age (BA) has been developed to calculate the aging status instead of chronological age. Chronological age is the number of years that an individual has been alive. On the other hand, biological age is based on many other factors, including how your telomeres have changed and the level of DNA methylation. As the aging rate varies for people of the same chronological age, biological age was developed to assess the actual aging rate. A study performed by Nie et al. shows evidence that there might be multiple aging "clocks" within the whole-body system, separate for specific organs and systems.

The multi-omics approach provides detailed evaluation of precise medical areas and can explore multiple systems. Researchers utilized multi-omics data such as clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness, and facial skin examinations to estimate the biological age of different organs and systems (immune and metabolic system) to compare aging rates of organs and systems.

The results showed that the aging rates of organs or systems are diverse. People's aging patterns are different. The study also demonstrates several applications of organs/systems biological age in two independent datasets. They compared mortality predictions and polygenic risk score of biological age. Mortality predictions were compared among organs' BAs in the United States National Health and Nutrition Examination Survey dataset. Polygenic risk score of biological age, which estimates an individual's genetic liability to a trait or disease, was calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. The score was constructed in the Chinese Longitudinal Healthy Longevity Survey cohort and can predict the possibility of becoming a centenarian.

For the study, 4,066 volunteers (48% males) aged between 20 and 45 years from the Shenzhen local area were recruited. Multi-omics level data were generated from blood samples, stool samples, physical fitness examination, and facial skin images. Altogether, 403 features were measured and classified into nine categories: cardiovascular-related, renal-related, liver-related, sex hormones, facial skin features, nutrition/metabolism features, immune-related, physical fitness-related, and the gut microbiome.

The study discovered that many features had sex-specific effects. Hence, the construction of BAs was conducted separately for male and female groups. Finally, nine biological ages of different organs and systems were generated, and distinct patterns of correlations with chronological ages were shown. The cardiovascular age has the least variance among subjects, while the variation in liver ages is quite significant, indicating potential differences of aging effects for organs and systems.

The study concluded that the biological ages of different organs/systems show diverse correlations, and Individuals can be clustered according to characteristics of multiple biological ages. What is more, specific biological age measures predict diseases or phenotypes of corresponding organs. Biological ages of different organs could be applied for clustering individuals and identifying the sources of age-related dysfunction. The application of the multi-omics-based organ-specific aging clocks could be the future of preventative medicine.

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