The metabolome as a biomarker of aging in Drosophila melanogaster. Issue 2 (12th January 2022)

• Record Type: Journal Article
• Authors: Zhao, Xiaqing; Golic, Forrest T.; Harrison, Benjamin R.; Manoj, Meghna; Hoffman, Elise V.; Simon, Neta; Johnson, Richard; MacCoss, Michael J.; McIntyre, Lauren M.; Promislow, Daniel E. L.
• Is Part Of: Aging cell. Volume 21:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-12
• Subjects: aging -- biomarker -- drosophila -- metabolomics -- mortality; Cells -- Aging -- Periodicals; 571.8783605
• DOI: 10.1111/acel.13548
• Languages: English
• ISSNs: 1474-9718

Abstract

Abstract: Many biomarkers have been shown to be associated not only with chronological age but also with functional measures of biological age. In human populations, it is difficult to show whether variation in biological age is truly predictive of life expectancy, as such research would require longitudinal studies over many years, or even decades. We followed adult cohorts of 20 Drosophila Genetic Reference Panel (DGRP) strains chosen to represent the breadth of lifespan variation, obtain estimates of lifespan, baseline mortality, and rate of aging, and associate these parameters with age‐specific functional traits including fecundity and climbing activity and with age‐specific targeted metabolomic profiles. We show that activity levels and metabolome‐wide profiles are strongly associated with age, that numerous individual metabolites show a strong association with lifespan, and that the metabolome provides a biological clock that predicts not only sample age but also future mortality rates and lifespan. This study with 20 genotypes and 87 metabolites, while relatively small in scope, establishes strong proof of principle for the fly as a powerful experimental model to test hypotheses about biomarkers and aging and provides further evidence for the potential value of metabolomic profiles as biomarkers of aging. Abstract : Age‐specific metabolome‐wide profiles of Drosophila melanogaster are highly correlated with sample age. In this study, we build a metabolome clock to measure age acceleration, the difference between predicted sample age and chronological age, and show that this can predict mortality risk and lifespan outcome.