Biomarkers help us understand how cellular and systemic aging contribute to mortality: A study utilizing a machine learning approach in the Health and Retirement Study.
Abstract
Research suggests aging is a coordinated physiological decline occurring in multiple systems and at multiple biological levels. However, it is largely unknown how general biological aging and specific systemic aging co-occur and influence one another to affect health outcomes. There is also emerging interest in understanding how social exposures may differentially accelerate decline in individual physiological systems.
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Research summary
The paper investigates how cellular and systemic aging interact and influence health outcomes, utilizing machine learning to analyze these relationships. This research is relevant as it addresses the underlying mechanisms of aging and their impact on mortality, contributing to the understanding of biological aging processes.
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