Defining cutoffs to diagnose obesity using the relative fat mass (RFM): Association with mortality in NHANES 1999–2014

Woolcott, Orison O.; Bergman, Richard N.

doi:10.1038/s41366-019-0516-8

Cited by 97Body Composition Measurement TechniquesObesity, Physical Activity, DietDiabetes, Cardiovascular Risks, and Lipoproteins

Defining cutoffs to diagnose obesity using the relative fat mass (RFM): Association with mortality in NHANES 1999–2014

Woolcott, Orison O., Bergman, Richard N.·2020·International Journal of Obesity

DOI 10.1038/s41366-019-0516-8

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Abstract

Background/objectives We have recently proposed and validated a simple and accurate method to estimate whole-body fat percentage in adults, the relative fat mass (RFM), derived from the ratio of height to waist circumference. We aimed to identify RFM cutoffs to diagnose obesity based on the association between RFM and all-cause mortality.Subjects/methods We used data from adult participants (≥20 years of age, n = 43,793) of the National Health and Nutrition Examination Survey (NHANES) 1999-2014 linked with death certificate records from the National Death Index. Optimal RFM cutoffs were determined using receiver-operating characteristic analysis (the Youden's index and the Euclidean minimum distance to the coordinate (0,1)).Results Final dataset for analyses comprised 31,008 adults. During a median follow-up of 8.3 years (IQR, 7.6-13.7), 2,517 deaths occurred. Youden and Euclidean optimal cutoffs of baseline RFM for all-cause mortality were 40.8% and 41.6% for women, and 30.9% and 28.9% for men, respectively. Similar cutoffs were obtained using measured whole-body fat percentage by dual energy X-ray absorptiometry. Adjusting for age, BMI category, ethnicity, education level, and smoking status, the hazard ratio for mortality using Cox proportional hazard regression was 1.41 (95% CI, 1.02-1.95) among women who had an RFM of 40.0-44.9% compared with women who had an RFM <35% (P = 0.035). Among men, the hazard ratio was 1.57 (95% CI, 1.07-2.30) among those with an RFM of 30.0-34.9% compared with men who had an RFM <25% (P = 0.020). Similar adjusted hazard ratios for same RFM categories were obtained in our validation population (NHANES III, n = 12,650, median follow-up: 23.3 years): 1.42 (95% CI, 1.01-2.00) among women (P = 0.043) and 1.50 (95% CI, 1.07-2.10) among men (P = 0.021).Conclusions We suggest rounded RFM cutoffs of 40% for women and 30% for men to diagnose obesity and identify individuals at higher risk of death.

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The upper sex-specific cut-offs of 40% for female and 30% for male have been proposed as best predictors of mortality in the NHANES sample (American population) using DXA.
“Regarding body FM, the upper sex-speci fi c cut-offs of 40% for female and 30% for male have been proposed as best predictors of mortality in the NHANES sample (American population) using DXA [59].”
From Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO consensus statement (2022) · cites this paper as [59]

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“Although other promising anthropometric measures exist such as abdominal height [59], and alternative cut points for obesity have been proposed for measures such as %BF [60], this study compared widely accepted cut points for measures of adiposity used in clinical and research settings, including BMI, WC, WHR, and %BF…”
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Association of X-ray Absorptiometry Body Composition Measurements with Basic Anthropometrics and Mortality Hazard
Krakauer, N. Y., & Krakauer, J. C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18157927 · Cited by 20 · see citing works
“The ratio of height to WC has been used as a proxy for relative fat mass, with threshold values for elevated mortality risk determined using NHANES data [15].”
Method
Research Progress on the Effect and Mechanism of Exercise Intervention on Sarcopenia Obesity
Chen, J., Jia, S., Guo, C., Fan, Z., Yan, W., & Dong, K. (2024) · Clinical Interventions in Aging · doi:10.2147/cia.s473083 · Cited by 21 · see citing works
“21”
Low muscle mass is associated with a higher risk of all–cause and cardiovascular disease–specific mortality in cancer survivors
Zhang, D., Spiropoulos, K. A., Wijayabahu, A. T., Christou, D. D., Karanth, S. D., Anton, S. D., Leeuwenburgh, C., Liang, M., Wheeler, M., Yang, D., et al. (2022) · Nutrition · doi:10.1016/j.nut.2022.111934 · Cited by 16 · see citing works
Association between relative fat mass and kidney stones in American adults
Liu, H., Jin, M., Hao, H., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-78061-7 · Cited by 16 · see citing works
“Obesity, as defined by RFM, was diagnosed using validated cutoff points: RFM ≥ 40% for females and RFM ≥ 30% for males 7,18 .”
Method
Relationship between Atherogenic Dyslipidaemia and Lipid Triad with Different Scales of Overweight and Obesity in 418,343 Spanish Workers
Busquets-Cortés, C., López, C., Paublini, H., Bote, S. A., López‐González, Á. A., & Ramírez‐Manent, J. I. (2022) · Journal of Nutrition and Metabolism · doi:10.1155/2022/9946255 · Cited by 15 · see citing works
“Suggested cutoff points are 40% in women and 30% in men [19].”
American Society for Metabolic and Bariatric Surgery review of the body mass index
Carter, J. T., Husain, F. A., Papasavas, P. K., Docimo, S., Albaugh, V. L., Aylward, L., Blalock, C. A., & Benson‐Davies, S. (2024) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2024.10.038 · Cited by 14 · see citing works
Relative Fat Mass: Refining Adiposity Measurement in the Era Beyond Body Mass Index
Suthahar, N., Lau, E. S., & Savarese, G. (2025) · Current Heart Failure Reports · doi:10.1007/s11897-025-00709-w · Cited by 16 · see citing works
“In the general population, average RFM values are approximately 25% in males and 35% in females [4], with obesity defined as RFM > 30% in males and > 40% in females [5].”
Ratio of red blood cell distribution width to albumin level and risk of mortality in sarcopenic obesity
Ma, X.-B., Lv, Y.-L., Qian, L., Huang, S.-T., Pu, X.-X., & Liu, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-79055-1 · Cited by 13 · see citing works
“RFM ≥ 22.8% for males and RFM ≥ 33.9% for females were used to define obesity 24 .”
Influential
Validity of non‐traditional measures of obesity compared to total body fat across the life course: A systematic review and meta‐analysis
Palumbo, A. M., Jacob, C. M., Khademioore, S., Sakib, M. N., Yoshida‐Montezuma, Y., Christodoulakis, N., Yassa, P., Vanama, M. S., Gamra, S., Ho, P., et al. (2025) · Obesity Reviews · doi:10.1111/obr.13894 · Cited by 14 · see citing works
“For the correlation meta-analysis, 3 studies were excluded since they did not provide correlation coefficients, 110,141,142 and one study was excluded from the mean difference meta-analysis as the mean and SD were not available.”
Phase angle and rectus femoris cross-sectional area as predictors of severe malnutrition and their relationship with complications in outpatients with post-critical SARS-CoV2 disease
Simón‐Frapolli, V. J., Vegas-Aguilar, I. M., Fernández‐Jiménez, R., et al. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1218266 · Cited by 13 · see citing works
Non-linear relationship between relative fat mass and diabetes risk in Japanese adults: a retrospective cohort study
Xiao, B., Cao, C., Han, Y. Y., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-74635-7 · Cited by 12 · see citing works
Applicability of Martin-Hopkins formula and comparison with Friedewald formula for estimated low-density lipoprotein cholesterol in e_COR study population
Ferrinho, C., Alves, A. C., Bourbon, M., & Duarte, S. (2021) · Revista Portuguesa de Cardiologia (English Edition) · doi:10.1016/j.repce.2021.07.001 · Cited by 11 · see citing works
Anthropometric indicators of obesity. Are the new indicators a better predictor of body fat content than BMI?
Walczyk, T. (2021) · Journal of Education Health and Sport · doi:10.12775/jehs.2021.11.08.001 · Cited by 10 · see citing works
The Moli-sani risk score, a new algorithm for measuring the global impact of modifiable cardiovascular risk factors
Castelnuovo, A. F. D., Bonaccio, M., Costanzo, S., Curtis, A. D., Persichillo, M., Panzera, T., Bracone, F., Baldassarre, D., Roncaglioni, M. C., Baviera, M., et al. (2023) · International Journal of Cardiology · doi:10.1016/j.ijcard.2023.131228 · Cited by 10 · see citing works
The nonlinear connection between relative fat mass and non-alcoholic fatty liver disease in the Japanese population: an analysis based on data from a cross-sectional study
Cao, C., Huang, M., Han, Y. Y., Zhang, X., Hu, H., & Wang, Y. (2024) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-024-01472-z · Cited by 10 · see citing works
Association between novel anthropometric indices and overactive bladder: a population-based study
Liu, H., Dong, H., Jin, M., et al. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1493792 · Cited by 10 · see citing works
“Among them, RFM is strongly associated with mortality, depression, dyslipidemia, and metabolic syndrome, while ABSI and BRI are effective in predicting percent body fat and visceral fat (17–19).”
Beyond BMI: central obesity measures and cardiovascular risk in late life
Erdoğan, O., Erdoğan, T., Önür, N. H., Özkök, S., Karan, M. A., & Bahat, G. (2025) · Aging Clinical and Experimental Research · doi:10.1007/s40520-025-03197-z · Cited by 10 · see citing works
“RFM: ≥30 for men and ≥ 40 for women [18 ].”
Easy Diet Screener: A quick and easy tool for determining dietary patterns associated with lipid profile and body adiposity
Malinowska, A. M. (2021) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.12973 · Cited by 9 · see citing works
Aplicabilidade da fórmula Martin‐Hopkins e comparação com a fórmula Friedewald na estimativa do colesterol LDL na população do estudo e_COR
Ferrinho, C., Alves, A. C., Bourbon, M., & Duarte, S. (2021) · Revista Portuguesa de Cardiologia · doi:10.1016/j.repc.2020.11.011 · Cited by 8 · see citing works
Association of body image perception and (dis)satisfaction with adiposity in adults: The Pró-Saúde study
Cabral, M. C., Coelho, G. M. O., Oliveira, N., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304987 · Cited by 7 · see citing works
“Body fat percentage (percentage of total body fat relative to total body mass) was estimated, and men and women with a body fat percentage above 30% and 40%, respectively, were classified with obesity [24–26].”
Method
The modified role including mediating and synergistic interactive effects of glucose tolerance status in the associations between relative fat mass and the risks of cardiovascular disease and all-cause mortality from the 4C cohort study
Luo, P., Li, D., Guo, Y., Meng, X., Kan, R., & Yu, X. (2024) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-024-01558-8 · Cited by 6 · see citing works
Lower Prevalence of Body Fat-Defined Obesity at Higher Altitudes in Peruvian Adults
Woolcott, O. O., Seuring, T., & Castillo, O. A. (2023) · High Altitude Medicine & Biology · doi:10.1089/ham.2022.0097 · Cited by 6 · see citing works
“Overall, RFM improves obesity misclassiﬁcation over BMI to diagnose body fat-deﬁned obesity when using DXA as the reference method (Paek et al., 2019; Woolcott and Bergman, 2020).”
Influential
Relative fat mass as an estimator of body fat percentage in Chilean adults
Aguirre, C. A., Tumani, M. F., Fernando Carrasco, N., Inostroza, J., Obregón, A. M., Reyes, A. P., & Pettinelli, P. (2024) · European Journal of Clinical Nutrition · doi:10.1038/s41430-024-01464-2 · Cited by 6 · see citing works
Gender Differences in the Predictive Value of Obesity Indices for Insulin Resistance in Adult Mexican Individuals
Elizalde-Barrera, C. I., Estrada‐García, T., López-Saucedo, C., Rubio-Guerra, A. F., & Elena, R.-V. D. (2024) · Endocrine Metabolic & Immune Disorders - Drug Targets · doi:10.2174/0118715303284893240215070923 · Cited by 4 · see citing works
Sheer drop ahead: reviewing sarcopenia outcomes in elderly patients undergoing bariatric surgery
Stumpf, M. A. M., Cercato, C., de Melo, M. E., & Mancini, M. C. (2025) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-025-09946-9 · Cited by 4 · see citing works
“For sarcopenic obesity, besides a low muscle mass, the presence of a percentage of body fat > 30% in men or > 40% in women is required [40, 53].”
Exploring Vegetarian and Omnivorous Approaches to Cardiovascular Risk and Body Composition
Fontes, T., Lopes, S., Menezes, R., Esgalhado, M., Rodrigues, L. M., & Ferreira‐Pêgo, C. (2024) · Nutrients · doi:10.3390/nu16132013 · Cited by 4 · see citing works
“To identify excess fat mass, the NHAMES 1999–2014 cut-off for obesity diagnosis was used, considering excess fat mass values > 30% for men and values > 40% for women [25].”
Method
The Nutritional Phenotyping of Idiopathic Pulmonary Fibrosis Through Morphofunctional Assessment: A Bicentric Cross-Sectional Case–Control Study
Sánchez, A. S., Fernández‐Jiménez, R., Cabrera-César, E., Espíldora-Hernández, F., Vegas-Aguilar, I. M., Amaya-Campos, M. D. M., Palmas-Candia, F. X., Alcolea, J. O., Simón‐Frapolli, V. J., Cornejo-Pareja, I. M., et al. (2025) · Life · doi:10.3390/life15040516 · Cited by 5 · see citing works
“Nevertheless, BMI alone is increasingly being deprioritized in the diagnosis of obesity in favor of body composition metrics such as FM percentage standardized by height in BIVA [29] and/or waist circumference [55,56].”
Estimating trunk fat in children according to sex using basic somatic readings: an opportunity for improving evaluation among girls
Benavent, M. M., & Pérez‐Fernández, V. (2021) · BMC Pediatrics · doi:10.1186/s12887-021-02918-3 · Cited by 4 · see citing works
“Note that for both equations, sex implies male 0 and female 1, and the results are given [26] as percentages.”
Obesity among Medical Students of a Medical College: A Descriptive Cross-sectional Study
Aryal, V., Ghimire, D., Kandel, S., et al. (2022) · Journal of Nepal Medical Association · doi:10.31729/jnma.7519 · Cited by 4 · see citing works
Obesity strengthens the associations between sarcopenia and both frailty and hospitalization, whereas reduces the risk of mortality
Álvarez‐Bustos, A., Coelho-Junior, H. J., Carnicero, J. A. C., Sepúlveda‐Loyola, W., García‐García, F. J., & Rodriguez-Mañas, L. (2026) · The journal of nutrition health & aging · doi:10.1016/j.jnha.2026.100803 · Cited by 3 · see citing works
Relative fat mass is a valuable predictor of lower urinary tract symptoms suggestive of benign prostatic hyperplasia in aging males: clinical implications
Luo, X., Ma, Q., Xiong, Y., et al. (2024) · Translational Andrology and Urology · doi:10.21037/tau-24-446 · Cited by 3 · see citing works
“R F M w a s c o m p u t e d a s (21,25,29,30) .”
Association of adiposity evaluated by anthropometric and bioelectrical impedance analysis measures with cognitive performance in the ELSA-Brasil study
Coelho, P. H. L., Gonçalves, N. G., Santos, I. S., Goulart, A. C., Barreto, S. M., Giatti, L., Lotufo, P. A., Benseñor, I. M., & Suemoto, C. K. (2025) · International Journal of Obesity · doi:10.1038/s41366-025-01781-x · Cited by 2 · see citing works
“Adiposity categories were de ﬁ ned as follows: obesity by WHR was determined using cutoffs of 0.90 for men and 0.85 for women [41]; obesity by WHtR using cutoffs of 0.50 [42]; obesity by BFP was determined using cutoffs of 30% for men and 40% for women [43].”
Influential
Exploring Sarcopenic Obesity in the Cancer Setting: Insights from the National Health and Nutrition Examination Survey on Prognosis and Predictors Using Machine Learning
Jiang, Y., Jiang, W., Wang, Q., Wei, T., & Chan, L. (2025) · Bioengineering · doi:10.3390/bioengineering12090921 · Cited by 2 · see citing works
“RFM cutoffs are 40% for women and 30% for men to diagnose obesity [18].”
Association of Relative Fat Mass with Incident Depressive Symptoms among Older Adults: A 14-Year Analysis of the ELSA and HRS Cohorts
Tu, J., Yi, T.-P., & Tu, M.-Q. (2025) · The International Journal of Psychiatry in Medicine · doi:10.1177/00912174251410075 · Cited by 2 · see citing works
“…validity, RFM has been linked to multiple adverse outcomes: all-cause and obesity-related mortality and proposed diagnostic cutoffs for obesity 15 ; incident cardiovascular disease including atrial ﬁ bril-lation, heart failure, and coronary artery disease 22 ; dyslipidemia, hypertension,…”
Update on Obesity and Its Relationship to Atherosclerotic Cardiovascular Disease and Associated Risk Factors
Ahmad, Y., Wasan, R., Perchik, J. D., & Nanda, N. C. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15124430 · Cited by 2 · see citing works
“Obesity is defined as an RFM cutoff of 30% in males and 40% in females [16].”
Changes in Novel Anthropometric Indices of Abdominal Obesity during Weight Loss with Selected Obesity-Associated Single-Nucleotide Polymorphisms: A Small One-Year Pilot Study
Iłowiecka, K., Glibowski, P., Libera, J., & Koch, W. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191811837 · Cited by 2 · see citing works
Sarcopenic obesity: epidemiology and cut-off values in the Ukrainian population
Grygorieva, N. V., Bystrytska, M. А., & Musiіenko, А. S. (2023) · Fìzìologìčnij žurnal · doi:10.15407/fz69.04.065 · Cited by 3 · see citing works
The association between relative fat mass (RFM) and lumbar bone density in US adults: Insight from 2011–2018 NHANES
Zhang, H., & Mei, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0323243 · Cited by 2 · see citing works
“RFM assesses the percentage of total body fat, is simple to calculate, and is gender-specific [44].”
Development and Validation of Waist Girth-Based Equations to Evaluate Body Composition in Colombian Adults: Rationale and STROBE–Nut-Based Protocol of the F20 Project
Bonilla, D. A., Duque-Zuluaga, L. T., Muñoz-Urrego, L. P., Moreno, Y., Vélez-Gutiérrez, J. M., Hoyos, K. F., Agudelo-Martínez, A., Humeres, G., Kreider, R. B., & Petro, J. L. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191710690 · Cited by 2 · see citing works
“It is worth noting that the RFM defines obesity according to the cutoff points of the DXA measurements, to the point that following the rounded thresholds of the RFM baseline, the current recommendation for diagnosing obesity and predicting the risk of death with this parameter is 40% for women and 30% for men [34].”
Background
Ontology-Based Recommender System for Personalized Physical Exercise in Obesity Management
Muhammad, W. S. F., Baizal, Z. K. A., & Dharayani, R. (2023) · SinkrOn · doi:10.33395/sinkron.v8i3.12689 · Cited by 2 · see citing works
Obesity-related Cancers: The Coming Epidemic
Venniyoor, A. (2020) · Indian Journal of Medical and Paediatric Oncology · doi:10.4103/ijmpo.ijmpo_117_20 · Cited by 2 · see citing works
“[13] WHR is one way to measure the ectopic fat stored in VAT and correlates better with metabolic syndrome than BMI; [14] other methods such as relative fat mass (RFM) have been proposed to overcome limitations of BMI.”
Background
Relative fat mass is associated with vitamin D deficiency in individuals with diabetes: evidence from NHANES and a Chinese cohort
Yang, Q., Xu, M., Lu, Q., et al. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1659361 · Cited by 1 · see citing works
Body roundness index vs. Relative fat mass: associations with cardiovascular-kidney-metabolic syndrome in a cross-sectional study
Qiu, J., Jiang, S., Gou, X., & Zhou, Y. (2025) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-025-01999-9 · Cited by 1 · see citing works
Sarcopenic obesity prevalence and clinical implications in patients with advanced knee osteoarthritis and class II-III obesity
Godziuk, K., Vieira, F. T., Forhan, M. A., Batsis, J. A., Donini, L. M., González, M. C., Siervo, M., & Prado, C. M. M. (2025) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2025.11.143 · Cited by 1 · see citing works
Association between relative fat mass and the risk of hypertension in Peruvian adults: A longitudinal analytical study
Valencia, J. G., Hernández‐Vásquez, A., & Barengo, N. C. (2025) · Medical Education & Clinical Practice · doi:10.21142/mecp.2026.1.1.e002 · Cited by 1 · see citing works
“Continuous RFM values were subsequently categorized into a dichotomous variable for obesity (0 = No, 1 = Yes), using cut-off points of ≥ 40% for women and ≥ 30% for men, consistent with thresholds proposed by Woolcott and subsequently used in analyses of the Peruvian population [16,17] .”
Relative Fat Mass, a new tool for measuring obesity, could predict the complications of type 2 diabetes
Jambart, I. (2021) · Endocrinology and Disorders · doi:10.31579/2640-1045/074 · Cited by 1 · see citing works
Relative Fat Mass Associated with Hyperuricemia in Adults: A Cross-Sectional Study
Gu, T., Wang, Z., Yang, Q., et al. (2025) · Endocrine Metabolic & Immune Disorders - Drug Targets · doi:10.2174/0118715303344427241218114648 · Cited by 1 · see citing works
Triglyceride-glucose index, a silent predictor for osteosarcopenic adiposity occurrence and risk of cardiovascular and all-cause mortality
Shao, X., Liang, L., Shen, X., Lang, X., Wang, Y., Lin, W., Wang, R., & Weng, C. (2025) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-025-01985-1 · Cited by 1 · see citing works
“The RFM was calculated as follows: 64 − (20 × height/waist circumference) + (12 × sex), with sex equaling 0 for men and 1 for women [31].”
Diferentes abordagens de intervenções para a redução do comportamento sedentário em mulheres durante COVID-19 / Different approaches to interventions to reduce sedentary behavior in women during COVID-19
Martins, A. P. P., de Oliveira, J. A. F., Faria, G. B. M., Santos, J. P. S., Gontijo, H. C., Diniz, L. N., de Paula, O. R., & Brandão, C. (2021) · Brazilian Journal of Health Review · doi:10.34119/bjhrv4n6-423
October 2020 New in Review
(2020) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2020.08.011
Massa Gorda Relativa (RFM) como nova ferramenta de estimativa da gordura corporal: uma revisão sistemática
Malta, I. S., & Coelho, S. B. (2023) · Revista da Associação Brasileira de Nutrição - RASBRAN · doi:10.47320/rasbran.2023.2285
A cross-sectional study from the National Health and Nutrition Examination Survey reveals the Prognosis and predictors of sarcopenic obesity among cancer patientsPreprint
Jiang, Y., Yang, C., Zhang, J., Luo, P., Jiang, W., & Wei, T. (2023) · Research Square · doi:10.21203/rs.3.rs-3099213/v1
6. Composição Corporal na Avaliação do Estado Nutricional
Wahrlich, V., Costa, A. C., & dos Anjos, L. A. (2024) · Editora FIOCRUZ eBooks · doi:10.7476/9786557081891.0009
Body Composition and Systemic Inflammation as Predictors of Survival in Locally Advanced Cervical Cancer Patients Undergoing Primary Radio-Chemotherapy: A Subgroup Analysis of a Prospective Randomized TrialPreprint
Trommer, M., Marnitz, S., Koehler, C., Tsunoda, A. T., Wegen, S., Morgenthaler, J., Lapuz, C., Khor, R., TACEY, M. A., & Theurich, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5054588
Diagnostic accuracy and reliability of equations for the calculation of body fat mass percentage in the Colombian military population: a diagnostic test study
Guarin-Rivera, S. K., Munive, J. E. U., & Estrada-Orozco, K. (2025) · BMJ Military Health · doi:10.1136/military-2024-002944
“25 Gómez-Ambrosi et al identified similar problems in athletes and populations with high physical activity, where BMI-based equations do not adequately reflect the unique physiological demands of these groups.”
Prevalence and factors associated with Relative Fat Mass in Peru: a cross-sectional analysis of the 2017–2018 national survey.
Miranda-Torvisco, F., Chaquila, J. A., Yepez, H. C., & Ramirez-Jeri, G. (2025) · Nutrición clínica y dietética hospitalaria/Nutrición clínica, dietética hospitalaria · doi:10.12873/452chaquila
“Obesity was classified as RFM ≥ 40% in women and RFM ≥ 30% in men 15 .”
Progress in research on the effects and scientific validity of injectable carboxytherapy in regenerative antiaging programs
Kulikova, N. G., & Nekrasova, A. N. (2025) · Fizioterapevt (Physiotherapist) · doi:10.33920/med-14-2503-17
Interactive effects of sleep duration and dietary patterns on obesity moderated by age
Lin, L.-Y., Hsu, C.-Y., Chao, J. C.-J., Chien, Y., & Chiou, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-17886-2 · Cited by 1 · see citing works
The influence of vegetarian and omnivorous diet quality on metabolic profiles and cardiovascular risk
Fontes, T., Lopes, S., Cebola, M., Carolino, E., Menezes, R., Rodrigues, L. M., & Ferreira‐Pêgo, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-09421-0
“To identify individuals with excess body fat, cut-off points for diagnosing obesity developed in NHANES 1999–2014 were used, considering excess body fat when values were greater than 30% in men and over 40% in women 31 .”
A comparison of anthropometric indicators in predicting the incidence of diabetes and intermediate hyperglycaemia in Finland: a 22-year observational study
Lybeck, J., Tuomilehto, J., Saramies, J., Koiranen, M., Uusitalo, H. M. T., Keinänen-Kiukaanniemi, S., & Suija, K. (2025) · BMC Public Health · doi:10.1186/s12889-025-25071-3
“RFM is a newly developed sex-specific anthropometric equation (based on height and waist measurements) for estimating whole-body fat percentage [17].”
Association between relative fat mass and sleep disorder among US adults: Results from NHANES 2007 to 2014
Shen, J., Que, X., Li, J., Fu, P., Lü, Y., & Zhu, Q. (2025) · Journal of clinical lipidology · doi:10.1016/j.jacl.2025.10.055 · Cited by 1 · see citing works
Association between low-density lipoprotein and structural changes in the knee joint of patients with symptomatic osteoarthritis is mediated by relative fat mass
Li, Q., Bo, K., & Shen, C. (2026) · European journal of medical research · doi:10.1186/s40001-025-03826-3
Identifying sarcopenia and sarcopenic obesity in a lower extremity arthroplasty clinical setting: a pragmatic pilot study
Godziuk, K., Hollyer, I., Loughran, G. J., & Giori, N. J. (2026) · The Journal of Frailty & Aging · doi:10.1016/j.tjfa.2025.100125
“Individuals who had low strength + low muscle mass + obesity (BMI ≥ 30 kg/m 2 and measured percent fat mass > 40 % F, > 30 % M[21]) were confirmed to have sarcopenic obesity.”
Sex differences in the association between relative Fat Mass and cognitive impairment in hospitalized middle-aged and older patients with type 2 diabetes mellitus in China: a single-center cross-sectional study
Liu, Y., Liu, Y., Qiu, H., et al. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-07856-x
Association Between Relative Fat Mass and New-Onset Arthritis Among Middle-Aged and Older Chinese Females
Zhang, M., Jiang, L., Wang, Q., & Meng, J. (2026) · Population Health Management · doi:10.1177/19427891261416128
Relative fat mass and cardiovascular risk in Peruvian adults: Findings from a national survey
Chaquila, J. A., Hernández-Vásquez, A., Valencia, J. G., Miranda-Torvisco, F., & Ramirez-Jeri, G. (2026) · PLoS ONE · doi:10.1371/journal.pone.0343062
“The selection of these cut-off points was based on prior validation studies for obesity diagnosis and mortality prediction [23], as well as their use in a Peruvian population-based survey [24].”
Obesity Subtypes and Longitudinal Trajectories of Function Over Seven Years of Follow‐Up: Data From the Multicenter Osteoarthritis Study
Godziuk, K., Tilley, S., LaValley, M. P., Nevitt, M. C., Lewis, C. E., Torner, J. C., & Neogi, T. (2026) · Arthritis Care & Research · doi:10.1002/acr.80066
Advanced anthropometric indices and metabolic syndrome (AIMS): A comparative analysis
Meenakshi, S., Mainde, R., Sharma, R., Vali, S., Devi, N. P., Pritham Kapil, D., & Ishika Jain, R. (2026) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-026-01665-4
Cultural evolution of beauty standards
Boucherie, L., Kumar, S., Ledebur, K., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2602380123
“It has been found to be a more accurate predictor of mortality and to reduce the risk of underdiagnosing obesity (26) (34, 48).”
Do single or clustered body composition parameters predict mortality? A multicenter Italian study
Carnevale, V., Nieddu, L., Merlotti, D., Scillitani, A., Gazzotti, S., Cavati, G., Battista, G., Caffarelli, C., DIPASQUALE, G., Guglielmi, G., et al. (2026) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2026.104863
An Interpretable FP-Growth-Based Association Rule Mining Framework for Multi-Source Obesity Risk Analysis Using NHANES Data
Naseef, R. S., Mukhtar, A. M., Mahammed, R. A., Attallah, S. B., Abdulmohsin, A. S., Alsabah, M., Abduljabbar, Z. A., Ali, A. H., & Nyangaresi, V. O. (2026) · International Conference on Electronics, Computers and Artificial Intelligence (ECAI) · doi:10.1109/ecai69016.2026.11613566
“Though multiple studies have employed FP-Growth with health data [33, 34] and previous analyses of obesity using NHANES did exist [19]–[22], there has not been any attempt to integrate multi-source NHANES in combination with FP-Growth rule mining constrained by clinically interpretable thresholds,…”
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Authors: Woolcott, O. O., & Bergman, R. N.
Affiliations: Cedars-Sinai Medical Center (US)
Corresponding author: Woolcott, Orison O.
ORCID: Woolcott, Orison O.
Bergman, Richard N.
Venue: International Journal of Obesity
Publisher: Springer Science and Business Media LLC
Published: 2020-01-07
Volume: 44
Issue: 6
Pages: 1301-1310
ISSN: 0307-0565 (ISSN-L)
1476-5497
DOI: 10.1038/s41366-019-0516-8
OpenAlex: W2999749765
Semantic Scholar: 1c08e1de8e2ac205fe78e4ddeee45b3da97ba516
Cited by: 97
References: 47
Citations by year: 2026: 17 · 2025: 24 · 2024: 17 · 2023: 12 · 2022: 13 · 2021: 11 · 2020: 3
FWCI: 5.87
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: International Journal of Obesity (publishedVersion)
PubMed (publishedVersion)
Funders: National Institute of Diabetes and Digestive and Kidney Diseases (R37DK027619, R01DK027619)
Topics: Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
Keywords: relative fat mass, obesity diagnosis, cut-off values, all-cause mortality, dual-energy X-ray absorptiometry, sex differences, NHANES, body composition, mortality prediction, body mass index limitations, National Death Index, NHANES III
MeSH terms: Adult, Anthropometry, Body Height, Female, Humans, Male, Middle Aged, Mortality, Nutrition Surveys, Obesity, ROC Curve, United States, Absorptiometry, Photon, Adiposity, Waist Circumference

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Defining cutoffs to diagnose obesity using the relative fat mass (RFM): Association with mortality in NHANES 1999–2014

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