Development,validation,and transportability of several machine-learned,non-exercise-based VO_(2max)prediction models for older adults  

作　　者：Benjamin T.Schumacher Michael J.LaMonte Andrea Z.LaCroix Eleanor M.Simonsick Steven P.Hooker Humberto Parada Jr. John Bellettiere Arun Kumar 

机构地区：[1]Herbert Wertheim School of Public Health and Human Longevity Science,University of California San Diego,La Jolla,CA 92093,USA [2]Department of Epidemiology and Environmental Health,School of Public Health and Health Professions,University at BuffaloState University of New York,Buffalo,NY 14214,USA [3]Translational Gerontology Branch,Intramural Research Program,National Institute on Aging,National Institutes of Health,Baltimore,MD 21225,USA [4]College of Health and Human Services,San Diego State University,San Diego,CA 92182,USA [5]Division of Epidemiology and Biostatistics,School of Public Health,San Diego State University,San Diego,CA 92182,USA [6]University of California San Diego Moores Cancer Center,La Jolla,CA 92093,USA [7]Computer Science and Engineering and Halicioglu Data Science Institute,University of California San Diego,La Jolla,CA 92093,USA

出　　处：《Journal of Sport and Health Science》2024年第5期611-620,共10页运动与健康科学（英文）

基　　金：supported in part by the Intramural Research Program of the National Institute on Aging;supported by the National Cancer Institute(K01 CA234317);the San Diego State University/UC San Diego Comprehensive Cancer Center Partnership(U54 CA132384 and U54 CA132379);the Alzheimer's Disease Resource Center for Minority Aging Research at the University of California San Diego(P30 AG059299)。

摘　　要：Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older adults.Since direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction algorithms.Methods:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)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 VO_(2max)and mortality.Results:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/min.Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors 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 VO_(2max)showed an inverse gradient in mortality risk.Predicted VO_(2max)variables yielded similar effect estimates but were not robust to adjustment.Conclusion:Measured VO_(2max)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 VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.

关 键 词：Cardiorespiratory fitness Prediction algorithms EPIDEMIOLOGY MORTALITY 

分 类 号：O17[理学—数学]