A Random Forest-Induced Distance-Based Measure of Physiologic al Dysregulation

Aging involves gradual, multisystemic Physiological Dysregulation (PD) which increases risk of age-related comorbidities. Ability to quantify age-related PD could provide insights into biological mechanisms underlying the aging process. One approach to measuring PD exploits the fact that increasing PD manifests as a gradual deviation of physiological parameters away from normal levels. A recent geometric approach for quantifying PD uses Mahalanobis distance to measure the extent to which an individual's physiological parameters (measured via biomarkers from clinical blood biochemistry panels) deviate from normal levels. While useful, this approach has shortcomings that may impact its accuracy, primarily the incorrect assumption of multivariate normality among biomarkers, and identical weighting of biomarkers. Herein, we develop a more robust multivariate distance-based measure of PD.

Proximity matrices induced by survival tree ensembles (Random Survival Forests) were used to compute a robust distance metric for quantifying how abnormal an individual's biomarker profile is. This approach requires no distributional assumptions and allows differential weighting of biomarkers based on association with mortality. Using receiver operating characteristic analysis and model fit statistics we compared performance of our measure to the standard approach based on Mahalanobis distance.

Our new metric showed statistically significant improvements in predicting mortality, health status and biological age, compared to the standard approach. Additional advantages offered by our method are the ability to handle missing values in biomarkers and to accommodate categorical risk factors. These results suggest our approach could provide greater precision in the evaluation of PD, which could enable better characterization of the extent and impact of degenerative processes resulting from aging.