{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T17:27:08Z","timestamp":1790184428536,"version":"4.1.0"},"reference-count":14,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T00:00:00Z","timestamp":1752883200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T00:00:00Z","timestamp":1752883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003246","name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["101095426 (EnerLIGHT project, funded through the ERA4Health CARDINNOV 2023 call, under the EU Horizon Europe Research and Innovation Programme)"],"award-info":[{"award-number":["101095426 (EnerLIGHT project, funded through the ERA4Health CARDINNOV 2023 call, under the EU Horizon Europe Research and Innovation Programme)"]}],"id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Curr Heart Fail Rep"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose of Review<\/jats:title>\n                    <jats:p>To position relative fat mass (RFM) as a more accurate, physiologically grounded, and clinically useful alternative to body mass index (BMI) for assessing adiposity and predicting cardiometabolic risk, including heart failure.\u00a0<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Recent Findings<\/jats:title>\n                    <jats:p>RFM estimates body fat percentage using a sex-specific formula based on waist circumference and height. RFM not only correlates more strongly with fat mass than BMI, but also shows a weaker correlation with muscle mass. This distinction helps reduce lean mass-related confounding in the assessment of adiposity. In clinical studies, RFM has emerged as a robust predictor of incident heart failure, cardiometabolic disease, and all-cause mortality.\u00a0<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Summary\u00a0<\/jats:title>\n                    <jats:p>RFM avoids misclassification of adiposity in individuals with high muscle mass and better reflects abdominal adiposity than BMI. As the prevalence of heart failure and other obesity-related diseases continues to rise, RFM offers a practical and intuitive tool for assessment of adiposity and heart failure risk \u2013 challenging the long-standing dominance of BMI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11897-025-00709-w","type":"journal-article","created":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T07:45:50Z","timestamp":1752911150000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Relative Fat Mass: Refining Adiposity Measurement in the Era Beyond Body Mass Index"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7781-2897","authenticated-orcid":false,"given":"Navin","family":"Suthahar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9361-6397","authenticated-orcid":false,"given":"Emily S.","family":"Lau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7732-0887","authenticated-orcid":false,"given":"Gianluigi","family":"Savarese","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,19]]},"reference":[{"key":"709_CR1","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/S2213-8587(24)00316-4","volume":"13","author":"F Rubino","year":"2025","unstructured":"Rubino F, Cummings DE, Eckel RH, Cohen RV, Wilding JPH, Brown WA, et al. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol. 2025;13:221\u201362.","journal-title":"Lancet Diabetes Endocrinol"},{"key":"709_CR2","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1093\/bja\/aev541","volume":"116","author":"U Gurunathan","year":"2016","unstructured":"Gurunathan U, Myles PS. Limitations of body mass index as an obesity measure of perioperative risk. Br J Anaesth. 2016;116:319\u201321.","journal-title":"Br J Anaesth"},{"key":"709_CR3","doi-asserted-by":"publisher","first-page":"10980","DOI":"10.1038\/s41598-018-29362-1","volume":"8","author":"OO Woolcott","year":"2018","unstructured":"Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage \u2500 A cross-sectional study in American adult individuals. 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Int J Obes. 2020;44:1301\u201310.","journal-title":"Int J Obes"},{"key":"709_CR6","doi-asserted-by":"crossref","unstructured":"Suthahar N, Zwartkruis V, Geelhoed B, Withaar C, Meems LMG, Bakker SJL, et al. Associations of relative fat mass and BMI with all-cause mortality: confounding effect of muscle mass. Obesity (Silver Spring). 2024;32:603\u201311.","DOI":"10.1002\/oby.23953"},{"key":"709_CR7","doi-asserted-by":"publisher","first-page":"108011","DOI":"10.1016\/j.maturitas.2024.108011","volume":"185","author":"S Cacciatore","year":"2024","unstructured":"Cacciatore S, Calvani R, Marzetti E, Coelho-J\u00fanior HJ, Picca A, Fratta AE, et al. Predictive values of relative fat mass and body mass index on cardiovascular health in community-dwelling older adults: Results from the Longevity Check-up (Lookup) 7. 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E.S.L. has served as a consultant for SystoleHealth, Amissa Health, and Roon, and is supported by grants from the National Institutes of Health (K23-HL159243), the Massachusetts Life Sciences Center, and the Tiangiao and Chrissy Chen Institute. G.S. reports grants and personal fees from CSL Vifor, Boehringer Ingelheim, AstraZeneca, Servier, Novartis, Cytokinetics, Pharmacosmos, Medtronic, and Bayer; personal fees from Roche, Abbott, Edwards Lifesciences, TEVA, Menarini, INTAS, GETZ, and Laboratori Guidotti; and grants from Boston Scientific and Merck, all outside the submitted work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"22"}}