Inferring and predicting Fried physical frailty phenotype deficits

TL;DR

This study compares two frailty measures, FI and NFPFP5, in predicting Fried physical frailty deficits across multiple aging datasets, finding FI to be a more effective predictor and raising questions about the causal structure of frailty components.

Contribution

It provides a comparative analysis of frailty measures for predicting physical deficits, highlighting the superior predictive power of the frailty index over the deficit count.

Findings

Frailty index outperforms deficit count in prediction accuracy.

FI, age, and current deficits are key predictors of future frailty deficits.

Results suggest reevaluating causal assumptions in frailty models.

Abstract

We predict the Fried physical frailty phenotype health deficits (FPFP5: slow gait, weakness, weight loss, low activity, and exhaustion) using two measures of frailty: frailty index (FI) or frailty phenotype (FP). The FP theorizes that the FPFP5 are mutually dependent through shared etiology and positive feedbacks, so that the total number of FPFP5 deficits (NFPFP5) should be highly predictive of existing deficits. Alternatively, the FI theorizes that strong mutual dependencies exist between \emph{all} age-related health deficits, so that the FI would be more predictive. We investigated predictive models of FPFP5 using FI or NFPFP5 in the Health and Retirement Study (HRS), the English Longitudinal Study of Aging (ELSA), and the National Health and Nutrition Examination Survey (NHANES). We find that the FI, chronological age, and current deficit state are all important predictors of future FPFP5 deficits. Notably, the FI consistently out-performed NFPFP5, raising questions regarding FPFP5 causal connections and how best to measure the physical component of frailty. We discuss implications for both FPFP5 forecasting, and inference when data are missing or incomplete.