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.

BACKGROUND: 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. METHODS: We utilize data from the Health and Retirement Study, a nationally representative sample of about 4000 US adults over age 55. We used eXtreme Gradient Boosting (xgboost) in a training subsample to create system-specific mortality risk scores based on sets of biomarkers representing biological systems (e.g., brain and nervous system, adaptive immune system, cardiovascular system, renal system) as well as general multisystem aging. RESULTS: Results suggest that the effects of most biological systems may be well captured by one or a small number of biomarkers and that female sex appears to be a protective or risk factor depending on specific biological system. CONCLUSIONS: The importance of studying both general and system-specific aging is discussed.