A life course perspective on gut microbiome aging and health in a non-human primate model

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PROJECT SUMMARY The gut microbiome has repeatedly been linked to major diseases of aging, including frailty, osteoporosis, and diabetes. However, after more than a decade of searching, there is still no consensus on which microbial species or taxonomic features provide reliable hallmarks of aging in adults or the elderly. Different people harbor different collections of microbes with densities and dynamics that vary considerably from one person to the next. This personalization arises, in part, because a given microbe may perform different functions in different people, and even in the same person at different times. This variability constrains the utility of microbiome taxa (e.g. species, phyla, biodiversity) to measure health and healthy aging. Overcoming this hurdle requires a shift in strategy, away from taxonomic data and towards data types that reflect the gut microbiome’s functional capacities, including the microbial genes and metabolic pathways found in the gut microbiome’s metagenome. Developing gut microbiome markers of healthy aging will also require prospective, longitudinal population-based research. However, we lack prospective data sets that track longitudinal changes in individual gut microbiome function and health outcomes across adulthood and old age. Our objectives in this proposal are to use a prospective, full life course, nonhuman primate model to: (i) identify changes in the microbiome’s functional capacities across the life course; (ii) test how social and environmental factors affect the nature and pace of microbiome aging; (iii) test how taxa-function relationships change at different life stages; and (iv) learn which microbiome features predict physical/behavioral aging and all-cause mortality. Our system, the well-studied Amboseli baboon population in Kenya, captures the complexity of human behavioral and social conditions better than other animal models. We have already profiled gut microbial taxonomic composition in 17,277 fecal samples collected over 14 years from 501 baboons. These data reveal personalized microbiome dynamics and aging trajectories that are shaped by individual social and environmental conditions. We propose to expand this data set for 10 more years to include 800 total individuals and analyze microbiome functional capacity in 12,000 samples. By identifying drivers and patterns of microbiome functional aging, we will identify targets for interventions aimed at building and sustaining healthy aging. Our results will help harness the promise of the gut microbiome to predict and improve human health.