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Anthropometric surrogates are often substituted as anticipated correlates of absolute and relative body fat content in the clinical management of obesity and its associated medical risks. DXA and anthropometric data from a cohort of 9230 randomly selected American subjects, available through the ongoing National Health and Nutrition Examination Survey, was used to evaluate combinations of surrogates (age, height, total weight, waist circumference) as predictors of DXA\u2010determined absolute and relative body fat content. Multiple regression analysis yielded linear combinations of the 4 surrogates that were closely predictive of DXA\u2010determined<jats:italic>absolute<\/jats:italic>fat content (R<jats:sup>2<\/jats:sup>\u00a0=\u00a00.93 and 0.96 for male and female subjects). Accuracy of the new algorithm was improved over customary surrogate\u2010based predictors such as body mass index. However prediction of<jats:italic>relative<\/jats:italic>body fat was less robust (R<jats:sup>2<\/jats:sup>\u00a0less than\u00a00.75), probably due to the nonlinear relation between degree of obesity (based on body mass index) and relative body fat. The paradigm was validated using an independent cohort from the National Health and Nutrition Examination Survey, as well as two independent external subject groups. The described regression\u2010based algorithm is likely to be a sufficiently accurate predictor of absolute body fat (but not relative body fat) to substitute for DXA scanning in many clinical situations. Further work is needed to assess algorithm validity for subgroups of individuals with \u201catypical\u201d body construction.<\/jats:p>","DOI":"10.1002\/jcph.2306","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T12:34:41Z","timestamp":1699533281000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimation of Absolute and Relative Body Fat Content Using Noninvasive Surrogates: Can DXA Be Bypassed?"],"prefix":"10.1002","volume":"63","author":[{"given":"David J.","family":"Greenblatt","sequence":"first","affiliation":[{"name":"Program in Pharmacology and Drug Development Tufts University School of Medicine Boston MA USA"},{"name":"Clinical and Translational Science Institute Tufts Medical Center Boston MA USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher D.","family":"Bruno","sequence":"additional","affiliation":[{"name":"Program in Pharmacology 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