An accurate aging clock developed from large-scale gut microbiome and human gene expression data

• Published in: iScience 2024-01, Vol.27 (1), p.108538-108538, Article 108538
• Main Authors: Gopu, Vishakh, Camacho, Francine R., Toma, Ryan, Torres, Pedro J., Cai, Ying, Krishnan, Subha, Rajagopal, Sathyapriya, Tily, Hal, Vuyisich, Momchilo, Banavar, Guruduth
• Format: Article
• Language: English
• Subjects: Microbiome; Omics
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2023.108538

Accurate measurement of the biological markers of the aging process could provide an “aging clock” measuring predicted longevity and enable the quantification of the effects of specific lifestyle choices on healthy aging. Using machine learning techniques, we demonstrate that chronological age can be predicted accurately from (1) the expression level of human genes in capillary blood and (2) the expression level of microbial genes in stool samples. The latter uses a very large metatranscriptomic dataset, stool samples from 90,303 individuals, which arguably results in a higher quality microbiome-aging model than prior work. Our analysis suggests associations between biological age and lifestyle/health factors, e.g., people on a paleo diet or with IBS tend to have higher model-predicted ages and people on a vegetarian diet tend to have lower model-predicted ages. We delineate the key pathways of systems-level biological decline based on the age-specific features of our model. [Display omitted] •Biological aging clocks developed from human gut microbiome and blood transcriptome•Microbiome dataset from metatranscriptomic analysis of stool from 90,303 individuals•Aging models account for around 46% and 53% of the variance in age by R2 respectively•Associations found between biological age and diet, drinking, diabetes, IBS, etc Microbiome; Omics