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Beyond BMI: How to Capture Influences from Body Composition in Health Surveys

  • Public Health and Translational Medicine (MEJ Lean, Section Editor)
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Abstract

Population monitoring of health risks is critical for resource allocation and planning of health services and preventive interventions. It also enables identification of population groups and regional areas where need may be greater. To support these functions, population monitoring needs to be accurate and reliable over time. With increasing prevalence of obesity over time, the need to monitor high risk adiposity is recognised internationally. Body composition is regularly monitored in population health surveys globally, primarily to identify high risk adiposity as an important contributor to future disease burden. Body mass index, a composite of height and weight, is the most commonly used population indicator of high risk adiposity, but its correlation with body fat is relatively poor. Many population surveys also collect waist and hip circumference, with a minority collecting further indicators such as skinfold thickness, bioelectrical impedance and dual-energy X-ray absorptiometry. Here, we review the advantages and disadvantages of the body composition indicators currently used in population health surveys and reflect on how the information from such indicators could be optimised. Our focus is on the use of indicators to identify those at increased metabolic health risk associated with excess body fat. We conclude that while most current indicators have reasonable correlation with body fat when tested, they have only been validated in small, specific samples that cannot be compared and are likely to have limited use over time as populations change demographically and in their body composition. Future population monitoring of high risk adiposity requires a more systematic analysis of which combined indicators from population health surveys will provide the best estimation of excess body fat and future cardio-metabolic risk across all adult ages, both sexes and a wide range of ethnicities.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. World Health Organisation. WHO STEPS instrument. Geneva: Department of Chronic Diseases and Health Promotion, World Health Organisation; 2002.

    Google Scholar 

  2. Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes. 2010;34(5):791–9. Most recent review and meta-analysis of the diagnostic value of BMI against reference methods.

    Article  CAS  Google Scholar 

  3. Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk—a review of the literature. Eur J Clin Nutr. 2010;64(1):16–22.

    Article  CAS  PubMed  Google Scholar 

  4. Albrecht SS, Gordon-Larsen P, Stern D, Popkin BM. Is waist circumference per body mass index rising differentially across the United States, England, China and Mexico? Eur J Clin Nutr. 2015;69(12):1306–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tanamas SK LM, Combet E, Vlassopoulos A, Zimmet PZ, Peeters A. Changing guards: time to move beyond body mass index for population monitoring of excess adiposity. QJM. 2015;accepted, 2015. Discussion of the range of reasons that we may need to move away from BMI as sole population monitoring indicator.

  6. Gearon E, Tanamas SK, Loh V, Stevenson C. A P. The implications of differential trends in weight and waist circumference on population level obesity monitoring. Obes Rev. 2016;17(S2):91–151.

    Google Scholar 

  7. Lean ME, Katsarou C, McLoone P, Morrison DS. Changes in BMI and waist circumference in Scottish adults: use of repeated cross-sectional surveys to explore multiple age groups and birth-cohorts. Int J Obes. 2013;37(6):800–8.

    Article  CAS  Google Scholar 

  8. Wannamethee SG, Atkins JL. Muscle loss and obesity: the health implications of sarcopenia and sarcopenic obesity. Proc Nutr Soc. 2015;74(4):405–12.

    Article  PubMed  Google Scholar 

  9. Tanamas SK, Ng WL, Backholer K, Hodge A, Zimmet PZ, Peeters A. Quantifying the proportion of deaths due to body mass index- and waist circumference-defined obesity. Obesity. 2016;24(3):735–42.

    Article  PubMed  Google Scholar 

  10. Fosbol MO, Zerahn B. Contemporary methods of body composition measurement. Clin Physiol Funct Imaging. 2015;35(2):81–97. Comprehensive review of the history and detail of body composition measurement.

    Article  PubMed  Google Scholar 

  11. Hong S, Oh HJ, Choi H, Kim JG, Lim SK, Kim EK, et al. Characteristics of body fat, body fat percentage and other body composition for Koreans from KNHANES IV. J Korean Med Sci. 2011;26(12):1599–605.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Zanovec M, Wang J, O’Neil CE. Development and comparison of two field-based body fat prediction equations: NHANES 1999–2004. Int J Exerc Sci. 2012;5(3):223–31.

    PubMed  PubMed Central  Google Scholar 

  13. Cui Z, Truesdale KP, Cai J, Stevens J. Evaluation of anthropometric equations to assess body fat in adults: NHANES 1999–2004. Med Sci Sports Exerc. 2014;46(6):1147–58.

    Article  PubMed  Google Scholar 

  14. Cui Z, Truesdale KP, Cai J, Koontz MB, Stevens J. Anthropometric indices as measures of body fat assessed by DXA in relation to cardiovascular risk factors in children and adolescents: NHANES 1999–2004. Int J Body Compos Res. 2013;11(3–4):85–96.

    PubMed  PubMed Central  Google Scholar 

  15. Beechy L, Galpern J, Petrone A, Das SK. Assessment tools in obesity—psychological measures, diet, activity, and body composition. Physiol Behav. 2012;107(1):154–71.

    Article  CAS  PubMed  Google Scholar 

  16. Al-Gindan YY, Hankey CR, Leslie W, Govan L, Lean ME. Predicting muscle mass from anthropometry using magnetic resonance imaging as reference: a systematic review. Nutr Rev. 2014;72(2):113–26.

    Article  PubMed  Google Scholar 

  17. Neamat-Allah J, Wald D, Husing A, Teucher B, Wendt A, Delorme S, et al. Validation of anthropometric indices of adiposity against whole-body magnetic resonance imaging—a study within the German European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts. PLoS ONE. 2014;9(3), e91586. One of the few indicator validation studies with a large number of participants.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ross R, Leger L, Morris D, de Guise J, Guardo R. Quantification of adipose tissue by MRI: relationship with anthropometric variables. J Appl Physiol (1985). 1992;72(2):787–95.

    CAS  Google Scholar 

  19. Kamel EG, McNeill G, Han TS, Smith FW, Avenell A, Davidson L, et al. Measurement of abdominal fat by magnetic resonance imaging, dual-energy X-ray absorptiometry and anthropometry in non-obese men and women. Int J Obes Relat Metab Disord: J Int Assoc Study Obes. 1999;23(7):686–92.

    Article  CAS  Google Scholar 

  20. Taylor AE, Kuper H, Varma RD, Wells JC, Bell JD, V Radhakrishna K, et al. Validation of dual energy X-ray absorptiometry measures of abdominal fat by comparison with magnetic resonance imaging in an Indian population. PLoS ONE. 2012;7(12), e51042.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Cheung AS, de Rooy C, Hoermann R, Gianatti EJ, Hamilton EJ, Roff G, et al. Correlation of visceral adipose tissue measured by Lunar Prodigy dual X-ray absorptiometry with MRI and CT in older men. Int J Obes. 2016;40(8):1325–8.

    Article  CAS  Google Scholar 

  22. Deurenberg P, Deurenberg-Yap M. Validity of body composition methods across ethnic population groups. Acta Diabetol. 2003;40 Suppl 1:S246–9.

    Article  PubMed  Google Scholar 

  23. Hu FB. Measurements of adiposity and body composition. Obesity Epidemiology: Oxford University Press; 2009.

  24. Freedman DS, Thornton JC, Pi-Sunyer FX, Heymsfield SB, Wang J, Pierson Jr RN, et al. The body adiposity index (hip circumference / height(1.5)) is not a more accurate measure of adiposity than is BMI, waist circumference, or hip circumference. Obesity. 2012;20(12):2438–44.

    Article  PubMed  PubMed Central  Google Scholar 

  25. National Institutes of Health. Bioelectrical Impedance Analysis in Body Composition Measurement 1994 [Available from: https://consensus.nih.gov/1994/1994bioelectricimpedancebodyta015html.htm.

  26. Otto M, Farber J, Haneder S, Michaely H, Kienle P, Hasenberg T. Postoperative changes in body composition—comparison of bioelectrical impedance analysis and magnetic resonance imaging in bariatric patients. Obes Surg. 2015;25(2):302–9.

    Article  PubMed  Google Scholar 

  27. Lang PO, Trivalle C, Vogel T, Proust J, Papazian JP. Markers of metabolic and cardiovascular health in adults: Comparative analysis of DEXA-based body composition components and BMI categories. J Cardiol. 2015;65(1):42–9.

    Article  PubMed  Google Scholar 

  28. Sharp DS, Andrew ME, Burchfiel CM, Violanti JM, Wactawski-Wende J. Body mass index versus dual energy x-ray absorptiometry-derived indexes: predictors of cardiovascular and diabetic disease risk factors. Am J Hum Biol. 2012;24(4):400–5.

    Article  PubMed  Google Scholar 

  29. Ulijaszek SJ, Kerr DA. Anthropometric measurement error and the assessment of nutritional status. Br J Nutr. 1999;82(3):165–77.

    Article  CAS  PubMed  Google Scholar 

  30. Seidell JC. Waist circumference and waist/hip ratio in relation to all-cause mortality, cancer and sleep apnea. Eur J Clin Nutr. 2010;64(1):35–41.

    Article  CAS  PubMed  Google Scholar 

  31. Clasey JL, Bouchard C, Teates CD, Riblett JE, Thorner MO, Hartman ML, et al. The use of anthropometric and dual-energy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obes Res. 1999;7(3):256–64.

    Article  CAS  PubMed  Google Scholar 

  32. 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: Off J Int Assoc Study Obes. 2012;13(3):275–86.

    Article  CAS  Google Scholar 

  33. 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 

  34. Al-Gindan YY, Hankey CR, Govan L, Gallagher D, Heymsfield SB, Lean ME. Derivation and validation of simple anthropometric equations to predict adipose tissue mass and total fat mass with MRI as the reference method. Br J Nutr. 2015;114(11):1852–67. The only indicator validation study against reference methods comparing a range of indicators.

    Article  CAS  PubMed  Google Scholar 

  35. Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath Jr CW. Body-mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med. 1999;341(15):1097–105.

    Article  CAS  PubMed  Google Scholar 

  36. Adams KF, Schatzkin A, Harris TB, Kipnis V, Mouw T, Ballard-Barbash R, et al. Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old. N Engl J Med. 2006;355(8):763–78.

    Article  CAS  PubMed  Google Scholar 

  37. Chen Y, Copeland WK, Vedanthan R, Grant E, Lee JE, Gu D, et al. Association between body mass index and cardiovascular disease mortality in east Asians and south Asians: pooled analysis of prospective data from the Asia Cohort Consortium. BMJ. 2013;347:f5446.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Pan WH, Yeh WT. How to define obesity? Evidence-based multiple action points for public awareness, screening, and treatment: an extension of Asian-Pacific recommendations. Asia Pac J Clin Nutr. 2008;17(3):370–4.

    PubMed  Google Scholar 

  39. Deurenberg-Yap M, Deurenberg P. Is a re-evaluation of WHO body mass index cut-off values needed? The case of Asians in Singapore. Nutr Rev. 2003;61(5 Pt 2):S80–7.

    Article  PubMed  Google Scholar 

  40. Deurenberg P, Andreoli A, Borg P, Kukkonen-Harjula K, de Lorenzo A, van Marken Lichtenbelt WD, et al. The validity of predicted body fat percentage from body mass index and from impedance in samples of five European populations. Eur J Clin Nutr. 2001;55(11):973–9.

    Article  CAS  PubMed  Google Scholar 

  41. Gallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol. 1996;143(3):228–39.

    Article  CAS  PubMed  Google Scholar 

  42. Soto Gonzalez A, Bellido D, Buno MM, Pertega S, De Luis D, Martinez-Olmos M, et al. Predictors of the metabolic syndrome and correlation with computed axial tomography. Nutrition. 2007;23(1):36–45.

    Article  PubMed  Google Scholar 

  43. National Institutes of Health NH, Lung and Blood Institute;, Obesity; NAAftSo. The practical guide: identification, evaluation, and treatment of overweight and obesity in adults. Bethesda, MD: National Institutes of Health, National Heart, Lung and Blood Institute, 2000.

  44. Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev. 2010;23(2):247–69.

    Article  PubMed  Google Scholar 

  45. Ashwell M, Cole TJ, Dixon AK. Ratio of waist circumference to height is strong predictor of intra-abdominal fat. BMJ. 1996;313(7056):559–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lean ME, Han TS, Deurenberg P. Predicting body composition by densitometry from simple anthropometric measurements. Am J Clin Nutr. 1996;63(1):4–14.

    CAS  PubMed  Google Scholar 

  47. Tanamas SK, Permatahati V, Ng WL, Backholer K, Wolfe R, Shaw JE, et al. Estimating the proportion of metabolic health outcomes attributable to obesity: a cross-sectional exploration of body mass index and waist circumference combinations. BMC Obes. 2015;3:4.

    Article  PubMed  Google Scholar 

  48. Suchanek P, Kralova Lesna I, Mengerova O, Mrazkova J, Lanska V, Stavek P. Which index best correlates with body fat mass: BAI, BMI, waist or WHR? Neuro Endocrinol Lett. 2012;33 Suppl 2:78–82.

    PubMed  Google Scholar 

  49. Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral adiposity index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33(4):920–2.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Dhana K, Kavousi M, Ikram MA, Tiemeier HW, Hofman A, Franco OH. Body shape index in comparison with other anthropometric measures in prediction of total and cause-specific mortality. J Epidemiol Community Health. 2016;70(1):90–6.

    Article  PubMed  Google Scholar 

  51. Cameron AJ, Magliano DJ, Soderberg S. A systematic review of the impact of including both waist and hip circumference in risk models for cardiovascular diseases, diabetes and mortality. Obes Rev: Off J Int Assoc Study Obes. 2013;14(1):86–94.

    Article  CAS  Google Scholar 

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Acknowledgments

Anna Peeters is the recipient of a Career Development Award from the National Health and Medical Research Council (Australia).

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Correspondence to Anna Peeters.

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Anna Peeters, Stephanie Tanamas, Emma Gearon, Yasmin Al-Gindan and Mike Lean declare that they have no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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Peeters, A., Tanamas, S., Gearon, E. et al. Beyond BMI: How to Capture Influences from Body Composition in Health Surveys. Curr Nutr Rep 5, 286–294 (2016). https://doi.org/10.1007/s13668-016-0183-5

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  • DOI: https://doi.org/10.1007/s13668-016-0183-5

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