Global external validation of fat mass prediction models for adults

Several models using weight, height and demographic variables to estimate adult fat mass (FM) have shown promising performance but require external validation within new independent settings. Here we evaluated the performance of these models globally, using data from the International Atomic Energy Agency Doubly Labelled Water Database, which includes reference-standard FM information on individuals aged 16+ years spanning 30 countries across all World Health Organization regions. Seven published models predicting adult FM, fat-free mass or FM% using combinations of height, weight (or body mass index), age, sex and/or ethnicity were identified and evaluated. FM from each model was compared with reference-standard FM using R2, calibration and root mean square error within World Health Organization regions and then summarized across regions using meta-analysis. The validation dataset contained 8,228 individuals aged 16–101 years (38% male). Median reference-standard FM was 24.6 kg. Across regions, while models exhibited moderate-to-good R2 values (range 0.69–0.84), most demonstrated miscalibration, with pooled calibration slopes between 0.78 and 1.03, and calibration-in-the-large between 0.59 kg and 3.37 kg. Root mean square error ranged between 4.3 kg and 6.6 kg. Although most models performed well overall, systematic subgroup miscalibration and heterogeneity in predictive performance was observed, suggesting they are not suitable for routine implementation. Future models should ensure consistent high model performance within key subgroups to allow implementation. Using data from 8,228 individuals from 30 countries, this benchmark analysis investigates the performance of fat mass prediction models that use only basic anthropometric features when exposed to previously unseen data.

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Publication Details

Journal
Nature Health
Published
2026-09-11
DOI
https://doi.org/10.1038/s44360-026-00196-w
Primary Topic
Body Composition Measurement Techniques
Type
article
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article

Global external validation of fat mass prediction models for adults

Annelies Goris, Christel Larsson, Samuel S. Urlacher, Hiroyuki Sagayama et al.
Nature Health
Body Composition Measurement Techniques
article

Global external validation of fat mass prediction models for adults

Annelies Goris, Christel Larsson, Samuel S. Urlacher, Hiroyuki Sagayama, Albert Koulman, L M Redman, Susan B. Roberts, Lenore Arab, Michele Novaes Ravelli, Surabhi Bhutani, Michele Lynn Polfuss, Srishti Sinha, David A. Raichlen, Kay Nguo, Christopher G. Owen, Barry W. Fudge, Sarah Whitehead, Tsukasa Yoshida, Cornelia Loechl, John R. Speakman, Wantanee Kriengsinyos, Regina C. Casper, William W. Wong, Jennifer Rood, Peter H. Whincup, Peter T. Katzmarzyk, Mohammed T Hudda, Éric Ravussin, Sai Krupa Das, Yosuke Yamada, Sumei Hu, Amy H. Luke, Klaas R. Westerterp, Alexia J. Murphy‐Alford, Lucinda Archer, Susan B. Racette, Herman Pontzer, Eric Stice, J. Porter, Aisha Alsaqabi, K. H. Pietiläinen, Marian L. Neuhouser, Ross L. Prentice, Yannis P. Pitsiladis, Sanjoy Deb, Richard D. Riley, Melanie Gillingham, William R. Leonard, Bruce Wolfe, Lara R. Dugas, Dina Ibrahim, Soren Brage, Pascal Bovet, Michelle C. Venables, Noor Al-Zahraa Safar, J. Josh Snodgrass, Misaka Kimura, Robert F. Kushner, Robert M. Ojiambo, Estelle V. Lambert, Rebecca Kuriyan, Marie Löf, Jamie A. Cooper, on behalf of the IAEA DLW Database Group, Corby K. Martin, William E. Kraus, Brian Wood
article en

Abstract

Several models using weight, height and demographic variables to estimate adult fat mass (FM) have shown promising performance but require external validation within new independent settings. Here we evaluated the performance of these models globally, using data from the International Atomic Energy Agency Doubly Labelled Water Database, which includes reference-standard FM information on individuals aged 16+ years spanning 30 countries across all World Health Organization regions. Seven published models predicting adult FM, fat-free mass or FM% using combinations of height, weight (or body mass index), age, sex and/or ethnicity were identified and evaluated. FM from each model was compared with reference-standard FM using R2, calibration and root mean square error within World Health Organization regions and then summarized across regions using meta-analysis. The validation dataset contained 8,228 individuals aged 16–101 years (38% male). Median reference-standard FM was 24.6 kg. Across regions, while models exhibited moderate-to-good R2 values (range 0.69–0.84), most demonstrated miscalibration, with pooled calibration slopes between 0.78 and 1.03, and calibration-in-the-large between 0.59 kg and 3.37 kg. Root mean square error ranged between 4.3 kg and 6.6 kg. Although most models performed well overall, systematic subgroup miscalibration and heterogeneity in predictive performance was observed, suggesting they are not suitable for routine implementation. Future models should ensure consistent high model performance within key subgroups to allow implementation. Using data from 8,228 individuals from 30 countries, this benchmark analysis investigates the performance of fat mass prediction models that use only basic anthropometric features when exposed to previously unseen data.

Nature Health
Pennington Biomedical Research Center (US), Dartmouth College (US), Northwestern University (US), University of Southern California (US), Tufts University (US), Children's Hospital of Wisconsin (US), International Atomic Energy Agency (AT), University of Wisconsin–Madison (US), Hong Kong Baptist University (HK), University of Tsukuba (JP), Deakin University (AU), Baylor University (US), University of Cape Town (ZA), University of California, Los Angeles (US), Oregon Health & Science University (US), St George's, University of London (GB), St. John's College of Nursing (US), Duke University (US), Shenzhen University (CN), University of Oregon (US), Loyola University Chicago (US), University of Aberdeen (GB), Chinese Academy of Sciences (CN), University of Washington (US), Tohoku University (JP), University of Cambridge (GB), Mahidol University (TH), San Diego State University (US), Maastricht University Medical Centre (NL), Helsinki University Hospital (FI), Karolinska Institutet (SE), National Institute for Health and Care Research (GB), Maritime Research Institute Netherlands (NL), Kuwait University (KW), National Institute of Biomedical Innovation, Health and Nutrition (JP), Fred Hutch Cancer Center (US), Jean Mayer Human Nutrition Research Center on Aging (US), Duke Institute for Health Innovation (US), Institute of Genetics and Developmental Biology (CN), St. John's National Academy of Health Sciences (IN), Children's Nutrition Research Center at Baylor College of Medicine (US), St.John's Medical College Hospital (IN), Shenzhen Institutes of Advanced Technology (CN), Centre universitaire de médecine générale et santé publique, Lausanne (CH), Max Planck Institute for Evolutionary Biology (DE), Shenzhen Technology University (CN), Weill Cornell Medical College in Qatar (QA), Loyola Medicine (US), NIHR Birmingham Biomedical Research Centre (GB), Dasman Diabetes Institute (KW), Anglia Ruskin University (GB), Arizona State University (US), Monash University (AU), University of Glasgow (GB), Kyoto University of Advanced Science (JP), University of Birmingham (GB), Leeds Beckett University (GB), University of Gothenburg (SE), Medical Research Council (GB), Stanford University (US)
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