There exist few maximal oxygen uptake (VO2max) non-exercise-based prediction equations, fewer using machine-learning (ML), and none specifically for older adults. Since direct measurement of VO2max is infeasible in large epidemiologic cohort studies, we sought to develop, validate, compare, and assess the transportability of several ML VO2max prediction algorithms.

Baltimore Longitudinal Study of Aging (BLSA) participants with valid VO2max tests were included (n = 1080). Least Absolute Shrinkage and Selection Operator (LASSO), linear- and tree-boosted xgboost, random forest, and Support Vector Machine (SVM) algorithms were trained to predict VO2max values. We developed these algorithms for: (a) the overall BLSA, (b) by sex, (c) using all BLSA variables, and (d) variables common in aging cohorts. Finally, we quantified the associations between measured and predicted VO2max and mortality.

The age was 69.0 ± 10.4 years (mean ± SD) and the measured VO2max was 21.6 ± 5.9 mL/kg/min. LASSO, linear- and tree-boosted xgboost, random forest, and SVM yielded root mean squared errors (RMSEs) of 3.4 mL/kg/min, 3.6 mL/kg/min, 3.4 mL/kg/min, 3.6 mL/kg/min, and 3.5 mL/kg/min, respectively. Incremental quartiles of measured VO2max showed an inverse gradient in mortality risk. Predicted VO2max variables yielded similar effect estimates but were not robust to adjustment.

Measured VO2max is a strong predictor of mortality. Using ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to adjustment. Future studies should seek to reproduce these results so that VO2max, an important vital sign, can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.