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Epidemiology and Population Health

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

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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Fig. 1
Fig. 2: Discriminative accuracy of RFM for obesity.
Fig. 3: Adjusted hazard ratio (HR) for mortality by RFM category among women and men.

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References

  1. Heitmann BL, Erikson H, Ellsinger BM, Mikkelsen KL, Larsson B. Mortality associated with body fat, fat-free mass and body mass index among 60-year-old swedish men-a 22-year follow-up. The study of men born in 1913. Int J Obes Relat Metab Disord. 2000;24:33–37.

    Article  CAS  PubMed  Google Scholar 

  2. Ortega FB, Sui X, Lavie CJ, Blair SN. Body mass index, the most widely used but also widely criticized index: would a criterion standard measure of total body fat be a better predictor of cardiovascular disease mortality? Mayo Clin Proc. 2016;91:443–455.

    Article  PubMed  Google Scholar 

  3. Padwal R, Leslie WD, Lix LM, Majumdar SR. Relationship among body fat percentage, body mass index, and all-cause mortality: a cohort study. Ann Intern Med. 2016;164:532–541.

    Article  PubMed  Google Scholar 

  4. Zong G, Zhang Z, Yang Q, Wu H, Hu FB, Sun Q. Total and regional adiposity measured by dual-energy X-ray absorptiometry and mortality in NHANES 1999-2006. Obesity (Silver Spring). 2016;24:2414–21.

    Article  CAS  PubMed Central  Google Scholar 

  5. Dong B, Peng Y, Wang Z, Adegbija O, Hu J, Ma J, et al. Joint association between body fat and its distribution with all-cause mortality: a data linkage cohort study based on NHANES (1988–2011). PLoS ONE. 2018;13:e0193368.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Lahmann PH, Lissner L, Gullberg B, Berglund G. A prospective study of adiposity and all-cause mortality: the Malmo Diet and Cancer Study. Obes Res. 2002;10:361–9.

    Article  PubMed  Google Scholar 

  7. Jenkins DA, Bowden J, Robinson HA, Sattar N, Loos RJF, Rutter MK, et al. Adiposity-mortality relationships in type 2 diabetes, coronary heart disease, and cancer subgroups in the UK Biobank, and their modification by smoking. Diabetes Care. 2018;41:1878–86.

    Article  PubMed  Google Scholar 

  8. Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage—a cross-sectional study in American adult individuals. Sci Rep. 2018;8:10980.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Sui X, LaMonte MJ, Laditka JN, Hardin JW, Chase N, Hooker SP, et al. Cardiorespiratory fitness and adiposity as mortality predictors in older adults. J Am Med Assoc. 2007;298:2507–16.

    Article  CAS  Google Scholar 

  10. Lee CD, Blair SN, Jackson AS. Cardiorespiratory fitness, body composition, and all-cause and cardiovascular disease mortality in men. Am J Clin Nutr. 1999;69:373–80.

    Article  CAS  PubMed  Google Scholar 

  11. Bray GA. Fat distribution and body weight. Obes Res. 1993;1:203–5.

    Article  CAS  PubMed  Google Scholar 

  12. Lobman TG, Houtkooper L, Going SB. Body fat measurement goes high-tech: not all are created equal. ACSM's Health Fit J. 1997;1:30–35.

    Google Scholar 

  13. Deurenberg P, Yap M, van Staveren WA. Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord. 1998;22:1164–71.

    Article  CAS  PubMed  Google Scholar 

  14. Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr. 2000;72:694–701.

    Article  CAS  PubMed  Google Scholar 

  15. Ho-Pham LT, Lai TQ, Nguyen MT, Nguyen TV. Relationship between body mass index and percent body fat in Vietnamese: implications for the diagnosis of obesity. PLoS ONE. 2015;10:e0127198.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Bahat G, Kilic C, Topcu Y, Aydin K, Karan MA. Fat percentage cutoff values to define obesity and prevalence of sarcopenic obesity in community-dwelling older adults in Turkey. Aging Male 2018. https://doi.org/10.1080/13685538.2018.1530208. [e-pub ahead of print].

  17. Johnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM, et al. National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital Health Stat 2. 2013:1–24.

  18. Mohadjer L, Montaquila J, Waksberg J et al. National Health and Nutrition Examination Survey III: Weighting and estimation methodology: executive summary. Rockville, MD, 1996.

  19. National Center for Health Statistics, Office of Analysis and Epidemiology. Public-use linked mortality file, 2015. Hyattsville, Maryland: NCHS. 2015. https://www.cdcgov/nchs/data-linkage/mortality-publichtm. Accessed 07 Sept 2018.

  20. Berrington de Gonzalez A, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ, et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med. 2010;363:2211–9.

    Article  CAS  PubMed  Google Scholar 

  21. Singh PN, Wang X. Simulation study of the effect of the early mortality exclusion on confounding of the exposure-mortality relation by preexisting disease. Am J Epidemiol. 2001;154:963–71.

    Article  CAS  PubMed  Google Scholar 

  22. Allison DB, Heo M, Flanders DW, Faith MS, Williamson DF. Examination of "early mortality exclusion" as an approach to control for confounding by occult disease in epidemiologic studies of mortality risk factors. Am J Epidemiol. 1997;146:672–80.

    Article  CAS  PubMed  Google Scholar 

  23. Webster AC, Nagler EV, Morton RL, Masson P. Chronic kidney disease. Lancet. 2017;389:1238–52.

    Article  PubMed  Google Scholar 

  24. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145:247–54.

    Article  CAS  PubMed  Google Scholar 

  25. National Health and Nutrition Examination Survey 1999–2000 data documentation, codebook, and frequencies standard biochemistry profile & hormones (LAB18). https://wwwn.cdc.gov/Nchs/Nhanes/1999-2000/LAB18.htm. Accessed 18 February 2019.

  26. National Health and Nutrition Examination Survey 2005–2006 data documentation, codebook, and frequencies standard biochemistry profile (BIOPRO_D). https://wwwn.cdc.gov/Nchs/Nhanes/2005-2006/BIOPRO_D.htm. Accessed 18 February 2019.

  27. Ingram DD, Lochner KA, Cox CS. Mortality experience of the 1986–2000 National Health Interview Survey Linked Mortality Files participants. Vital Health Stat 2. 2008:1–37.

  28. Jacobs E, Hoyer A, Brinks R, Kuss O, Rathmann W. Burden of mortality attributable to diagnosed diabetes: a nationwide analysis based on claims data from 65 million people in Germany. Diabetes Care. 2017;40:1703–9.

    Article  PubMed  Google Scholar 

  29. National Health and Nutrition Examination Survey (NHANES). Anthropometry procedures manual. 2007. https://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf. Accessed 16 May 2016.

  30. National Health and Nutrition Examination Survey III: Body measurements (anthropometry). Rockville, MD, 1988.

  31. Kelly TL, Wilson KE, Heymsfield SB. Dual energy X-Ray absorptiometry body composition reference values from NHANES. PLoS ONE. 2009;4:e7038.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. National Health and Nutrition Examination Survey: Technical documentation for the 1999–2004. Dual Energy X-Ray Absorptiometry (DXA) multiple imputation data files; 2008. https://wwwn.cdc.gov/nchs/data/nhanes/dxa/dxa_techdoc.pdf. Accessed 17 May 2016.

  33. Perkins NJ, Schisterman EF. The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol. 2006;163:670–675.

    Article  PubMed  Google Scholar 

  34. Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32–35.

    Article  CAS  PubMed  Google Scholar 

  35. Wolfowitz J. The minimum distance method. Ann Math Stat. 1957;28:75–88.

    Article  Google Scholar 

  36. Bray GA, Heisel WE, Afshin A, Jensen MD, Dietz WH, Long M, et al. The science of obesity management: an endocrine society scientific statement. Endocr Rev. 2018;39:79–132.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Haukoos JS, Lewis RJ. Advanced statistics: bootstrapping confidence intervals for statistics with "difficult" distributions. Acad Emerg Med. 2005;12:360–5.

    Article  PubMed  Google Scholar 

  38. Simpson JA, MacInnis RJ, Peeters A, Hopper JL, Giles GG, English DR. A comparison of adiposity measures as predictors of all-cause mortality: the Melbourne Collaborative Cohort Study. Obesity (Silver Spring). 2007;15:994–1003.

    Article  Google Scholar 

  39. Myint PK, Kwok CS, Luben RN, Wareham NJ, Khaw KT. Body fat percentage, body mass index and waist-to-hip ratio as predictors of mortality and cardiovascular disease. Heart. 2014;100:1613–9.

    Article  PubMed  Google Scholar 

  40. Kim CH, Park HS, Park M, Kim H, Kim C. Optimal cutoffs of percentage body fat for predicting obesity-related cardiovascular disease risk factors in Korean adults. Am J Clin Nutr. 2011;94:34–39.

    Article  CAS  PubMed  Google Scholar 

  41. Pasco JA, Holloway KL, Dobbins AG, Kotowicz MA, Williams LJ, Brennan SL. Body mass index and measures of body fat for defining obesity and underweight: a cross-sectional, population-based study. BMC Obes. 2014;1:9.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Oreopoulos A, Lavie CJ, Snitker S, Romero-Corral A. More on body fat cutoff points–Reply–I. Mayo Clin Proc. 2011;86:584–5.

    Article  PubMed Central  Google Scholar 

  43. AACE/ACE Obesity Task Force. AACE/ACE position statement on the prevention, diagnosis, and treatment of obesity. Endocr Pract. 1998;4:297–350.

    Google Scholar 

  44. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63.

    Article  Google Scholar 

  45. Qiao Q, Nyamdorj R. Is the association of type II diabetes with waist circumference or waist-to-hip ratio stronger than that with body mass index? Eur J Clin Nutr. 2010;64:30–34.

    Article  CAS  PubMed  Google Scholar 

  46. Lee CMY, Woodward M, Pandeya N, Adams R, Barrett-Connor E, Boyko EJ, et al. Comparison of relationships between four common anthropometric measures and incident diabetes. Diabetes Res Clin Pract. 2017;132:36–44.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13:275–86.

    Article  CAS  PubMed  Google Scholar 

  48. Savva SC, Lamnisos D, Kafatos AG. Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis. Diabetes Metab Syndr Obes. 2013;6:403–19.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Van Der Ploeg GE, Withers RT, Laforgia J. Percent body fat via DEXA: comparison with a four-compartment model. J Appl Physiol (1985). 2003;94:499–506.

    Article  Google Scholar 

  50. Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond). 2008;32:959–66.

    Article  CAS  Google Scholar 

  51. Dhurandhar NV, Schoeller D, Brown AW, Heymsfield SB, Thomas D, Sorensen TI, et al. Energy balance measurement: when something is not better than nothing. Int J Obes (Lond). 2015;39:1109–13.

    Article  CAS  Google Scholar 

  52. Black AE, Goldberg GR, Jebb SA, Livingstone MB, Cole TJ, Prentice AM. Critical evaluation of energy intake data using fundamental principles of energy physiology: 2. Evaluating the results of published surveys. Eur J Clin Nutr. 1991;45:583–99.

    CAS  PubMed  Google Scholar 

  53. Archer E, Hand GA, Blair SN. Validity of U.S. nutritional surveillance: National Health and Nutrition Examination Survey caloric energy intake data, 1971–2010. PLoS ONE. 2013;8:e76632.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Walsh MC, Hunter GR, Sirikul B, Gower BA. Comparison of self-reported with objectively assessed energy expenditure in black and white women before and after weight loss. Am J Clin Nutr. 2004;79:1013–9.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS) for providing access to the NHANES datasets. This study was self-funded. The author’s responsibilities were as follows: OOW designed the research, conducted the research, performed the statistical analysis, and wrote the paper. RNB contributed with the design of the study and revised the final draft. OOW takes full responsibility for the work as a whole.

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Correspondence to Orison O. Woolcott.

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Woolcott, O.O., Bergman, R.N. Defining cutoffs to diagnose obesity using the relative fat mass (RFM): Association with mortality in NHANES 1999–2014. Int J Obes 44, 1301–1310 (2020). https://doi.org/10.1038/s41366-019-0516-8

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