Machine learning predicts lifespan and suggests underlying causes of death in aging C. elegans

Journal article

Aging leads to age-related pathology that causes death, and genes affect lifespan by determining such pathology. Here we investigate how age-related pathology mediates the effect of genetic and environmental interventions on lifespan in C. elegans by means of a data-driven approach employing machine learning (ML). To this end, extensive data on how diverse determinants of lifespan (sex, nutrition, genotype, mean lifespan range: 7.5 to 40 days) affect patterns of age-related pathology was gathered. This revealed that different life-extending treatments result in distinct patterns of suppression of senescent pathology. By analysing the differential effects on mid-life pathology levels and lifespan, the ML models developed were able to predict lifespan variation, explaining 79% of the variance. Levels of pathology in the pharynx and intestine proved to be the strongest predictors of lifespan. This suggests that elderly C. elegans die predominantly from late-life disease affecting these organs. In addition, we noted profound sex differences in age-related pathology: the striking age-related pathologies in hermaphrodites affecting organs linked to reproduction are absent from males, suggesting that reproductive death may be hermaphrodite limited

Authors: Kern, CC; Manescu, P; Cuffaro, M; Au, C; Zhang, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Communications Biology
• Volume: 8
• Issue: 1
• Pages: 1630-1630
• Publication date: 2025-11-21
• DOI: 10.1038/s42003-025-09012-9
• EISSN: 2399-3642
• ISSN: 2399-3642
• Language: English
• Keywords: Disease; Affect (linguistics); Caenorhabditis elegans; Model organism; Hermaphrodite; Ageing; Senescence