Machine Learning Predicts Lifespan And Suggests Underlying Causes Of Death In Aging C. Elegans
Aging is a complex process that ultimately leads to various health problems and, eventually, death. Scientists are working to understand the intricate connections between our genes, the diseases that develop as we age, and how long we live. A recent study utilized a powerful computational approach, known as machine learning, to shed light on these connections in a tiny worm called C. elegans, a common model organism in aging research.
The researchers collected a vast amount of data on how different factors, such as diet, genetic makeup, and sex, influence the patterns of age-related diseases in these worms. They found that various treatments designed to extend life actually suppressed age-related pathologies in unique ways.
By analyzing these patterns, the machine learning models were remarkably effective, predicting a significant portion (up to 79%) of the variation in the worms’ lifespans. The most crucial indicators for predicting how long a worm would live were the health of its pharynx (a part of its digestive system) and intestine. This suggests that for these worms, a major cause of death in old age is disease affecting these specific organs.
Interestingly, the study also uncovered notable differences in age-related pathologies between male and hermaphrodite worms. Pathologies linked to reproduction, which were prominent in hermaphrodites, were absent in males, hinting that reproductive decline might be a specific cause of death for hermaphrodite worms. This research helps to fill a critical gap in our understanding of how genetic factors lead to aging and ultimately influence lifespan by highlighting the role of specific age-related diseases.
Source: link to paper