External validation of the relative fat mass (RFM) index in adults from north-west Mexico using different reference methods

English version

Autoři: Alan E. Guzmán-León ^aff001; Ana G. Velarde ^aff001; Milca Vidal-Salas ^aff001; Lucía G. Urquijo-Ruiz ^aff001; Luz A. Caraveo-Gutiérrez ^aff001; Mauro E. Valencia ^aff001
Působiště autorů: Department of Chemical-Biological Sciences, University of Sonora, Sonora, México ^aff001
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0226767

Souhrn

Background

Analysis of body composition is becoming increasingly important for the assessment, understanding and monitoring of multiple health issues. The body mass index (BMI) has been questioned as a tool to estimate whole-body fat percentage (FM%). Recently, a simple equation described as relative fat mass (RFM) was proposed by Woolcott & Bergman. This equation estimates FM% using two anthropometric measurements: height and waist circumference (WC). The authors state that due to its simplicity and better performance than BMI, RFM could be used in daily clinical practice as a tool for the evaluation of body composition. The aim of this study was to externally validate the equation of Woolcott & Bergman to estimate FM% among adults from north-west Mexico compared with Dual-energy X-ray absorptiometry (DXA) as an alternative to BMI and secondly, to make the same comparison using air displacement plethysmography (ADP), Bioelectrical Impedance Analysis (BIA) and a 4-compartment model (4C model).

Methods

Weight, height and WC were measured following standard procedures. The RFM index was calculated for each of the 61 participating subjects (29 females and 32 males, ages 20–37 years). The RFM was then regressed against each of the four body composition methods for estimating FM%.

Results

Compared with BMI, RFM was a better predictor of FM% determined by each of the body composition methods. In terms of precision the best equation was RFM regressed against DXA (y = 1.12 + 0.99 x; R² = 0.84 p<0.001). Accuracy (represented by the closeness to the zero-intercept) was 1.12 (95% CI: -2.44, to 4.68) and thus, not significantly different from zero. For the rest of the methods, precision in the prediction of FM% was improved compared to BMI, with significant increases in the R² and reduction of the root mean squared error (RMSE). However, the intercepts of each regression did not show accuracy since they were different from zero, for ADP: -9.95 (95%CI: -15.7 to -4.14), for BIA: -12.6 (95%CI: -17.5 to -7.74) and for the 4C model: -13.6 (95%CI: -18.6 to -8.60). Irrespectively, FM% measured by each of the body composition methods was higher for DXA than the other three methods (p<0.001).

Conclusions

This external validation proved that the performance of the RFM equation used in this study to estimate FM% was more consistent than BMI in this Mexican population, showing a stronger correlation with DXA than with the other body composition methods.

Klíčová slova:

Body Mass Index – Body weight – Fats – Obesity – Mexican people – Anthropometry – Mexico – Bone and mineral metabolism

Zdroje

1. World Health Organization. Obesity and overweight. 2018. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight

2. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA. 2003;289: 76–79. doi: 10.1001/jama.289.1.76 12503980

3. Bray GA. Overweight is risking fate: definition, classification, prevalence, and risks. Ann N Y Acad Sci. 1987;499: 14–28. doi: 10.1111/j.1749-6632.1987.tb36194.x 3300479

4. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320: 1–6.

5. James WP, Ferro-Luzzi A, Waterlow JC. Definition of chronic energy deficiency in adults. Eur J Clin Nutr. 1988;42: 969–981. 3148462

6. Multicentre Growth Reference Study Group. WHO Child Growth Standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age. Geneva: WHO; 2006.

7. Kok P, Seidell JC, Meinders AE. The value and limitations of the body mass index (BMI) in the assessment of the health risks of overweight and obesity. Ned Tijdschr Geneeskd. 2004;148: 2379–2382. 15615272

8. Nuttall FQ. Body mass index: Obesity, BMI, and health: a critical review. Nutrition Today. Lippincott Williams and Wilkins; 2015. pp. 117–128. doi: 10.1097/NT.0000000000000092 27340299

9. 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. doi: 10.7326/M15-1181 26954388

10. 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. 2008;32: 959–966. doi: 10.1038/ijo.2008.11 18283284

11. 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. doi: 10.1038/s41598-018-29362-1 30030479

12. Genton L, Hans D, Kyle UG, Pichard C. Dual-Energy X-ray absorptiometry and body composition: differences between devices and comparison with reference methods. Nutrition. 2002. pp. 66–70. doi: 10.1016/s0899-9007(01)00700-6 11827768

13. Silva-Zolezzi I, Hidalgo-Miranda A, Estrada-Gil J, Fernandez-López JC, Uribe-Figueroa L, Contreras A, et al. Analysis of genomic diversity in Mexican Mestizo populations to develop genomic medicine in Mexico. PNAS. 2009;106: 8611–8616. doi: 10.1073/pnas.0903045106 19433783

14. Martínez-Cortés G, Salazar-flores J, Fernández-Rodríguez LG, Rubi-castellanos R, Rodríguez-Loya C, Velarde-Félix JS, et al. Admixture and population structure in Mexican-Mestizos based on paternal lineages. J Hum Genet. 2012;57: 568–574. doi: 10.1038/jhg.2012.67 22832385

15. INEGI. II Conteo de población y vivienda 2005. Tabulados básicos: estados unidos mexicanos. Tomo I. México: INEGI; 2006. pp. 1–44.

16. Wang S, Ray N, Rojas W, Parra MV, Bedoya G, Gallo C, et al. Geographic patterns of genome admixture in latin american mestizos. PLoS Genet. 2008;4: 1–10. doi: 10.1371/journal.pgen.1000037 18369456

17. Jimenez-Sanchez G. Developing a platform for genomic medicine in Mexico. Science (80-). 2003;300: 295–297.

18. Martínez Rodríguez M. The colonizing project of Mexico in the late 19th century. Some comparative perspectives in Latin America. Secuencia. 2010; 101–132.

19. Reyes JM. Alemanes en el noroeste mexicano. Notas sobre su actividad comercial a inicios del siglo XX. Estud Hist Mod Contemp Mex. 2013;46: 55–86. doi: 10.1016/S0185-2620(13)71415-4

20. Cramaussel C. El perfil del migrante francés a México a mediados del siglo XIX. Cah des Amériques Lat. 2004; 139–156. doi: 10.4000/cal.7830

21. Zárate Valdez JL. Grupos étnicos de Sonora: territorios y condiciones actuales de vida y rezago. Región Y Soc. 2016;28: 5–44. doi: 10.22198/rys.2016.65.a356

22. CONAPO. Indice de marginación por entidad federativa y municipio 2010. 1st ed. México: CONAPO; 2011.

23. INEGI. Manual de cartografía geoestadística. México; 2010.

24. World Medical Association. Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310: 1–10. doi: 10.1001/jama.2013.281053 24141714

25. NORMA Oficial Mexicana NOM-008-SSA3-2010, Para el tratamiento integral del sobrepeso y la obesidad. Diario Oficial de la Federación; 2010.

26. Kushner RF, Schoeller DA. Estimation of total body water by bioelectrical impedance analysis. Am J Clin Nutr. 1986;44: 417–424. doi: 10.1093/ajcn/44.3.417 3529918

27. Pace N, Rathbun E. Studies on body composition III The body water and chemically combined nitrogen content in relation to fat content. J Biol Chem. 1945;158: 685–691.

28. Dempster P, Aitkens S. A new air displacement method for the determination of human body composition. Med Sci Sports Exerc. 1995;27: 1692–7. 8614327

29. Valencia ME, Villegas-Valle RC. Body fat measurement by air displacement plethysmography: theory, practice, procedures, and applications. Handbook of Anthropometry: Physical Measures of Human Form in Health and Disease. Springer New York; 2012. pp. 397–413.

30. Selinger A. Equations for estimating percent fat based on four component model of body composition. Human Body Composition. Human Kinetics; 1977. p. 18.

31. Heymsfield SB, Lohman TG, Wang Z, Going SB. Human Body Composition. 2nd ed. Human Kinetics; 2005.

32. Conover W. Practical Nonparametric Statistics. 3rd ed. John Wiley & Sons; 2006.

33. Goran MI, Toth MJ, Poehlman ET. Assessment of research-based body composition techniques in healthy elderly men and women using the 4-compartment model as a criterion method. Int J Obes. 1998;22: 135–42.

34. Passing H, Bablok W. A new procedure for testing the equality of measurements from two different analytical methods. J Clin Chem Clin Biochem. 1983.

35. COESPO. Indicadores Demográficos Y Socioeconómicos 2010. 2010; 2010.

36. Durnin J. Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr. 1978;40: 497–504.

37. Jensen MD. Role of body fat distribution and the metabolic complications of obesity. Journal of Clinical Endocrinology and Metabolism. 2008. doi: 10.1210/jc.2008-1585 18987271

38. Baumgartner RN, Heymsfield SB, Roche AF. Human body composition and the epidemiology of chronic disease. Obes Res. 1995;3: 73–95.

39. Ortiz-Hernández L, Vega López AV, Ramos-Ibáñez N, Cázares Lara LJ, Medina Gómez RJ, Pérez-Salgado D. Equations based on anthropometry to predict body fat measured by absorptiometry in schoolchildren and adolescents. J Pediatr. 2017;93: 365–373. doi: 10.1016/j.jped.2016.08.008 28132762

40. Visscher TL, Seidell JC, Molarius A, van der Kuip D, Hofman A, Witteman JC. A comparison of body mass index, waist-hip ratio and waist circumference as predictors of all-cause mortality among the elderly: the Rotterdam study. Int J Obes Relat Metab Disord. 2001;25: 1730–5. doi: 10.1038/sj.ijo.0801787 11753597

41. Bigaard J, Frederiksen K, Tjønneland A, Thomsen BL, Overvad K, Heitmann BL, et al. Waist circumference and body composition in relation to all-cause mortality in middle-aged men and women. Int J Obes. 2005;29: 778–784. doi: 10.1038/sj.ijo.0802976 15917857

42. Bigaard J, Tjønneland A, Thomsen BL, Overvad K, Heitmann BL, Sørensen TIA. Waist circumference, BMI, smoking, and mortality in middle-aged men and women. Obes Res. 2003;11: 895–903. doi: 10.1038/oby.2003.123 12855760

43. Klein S, Allison DB, Heymsfield SB, Kelley DE, Leibel RL, Nonas C, et al. Waist circumference and cardiometabolic risk: a consensus statement from Shaping America’s Health: Association for Weight Management and Obesity Prevention; NAASO, The Obesity Society; the American Society for Nutrition; and the American Diabetes Associat. Am J Clin Nutr. 2007;85: 1197–1202. doi: 10.1093/ajcn/85.5.1197 17490953

44. Pinho CPS, Diniz A da S, de Arruda IKG, Leite APDL, de Petribu MMV, Rodrigues IG. Waist circumference measurement sites and their association with visceral and subcutaneous fat and cardiometabolic abnormalities. Arch Endocrinol Metab. 2018;62: 416–423. doi: 10.20945/2359-3997000000055 30304105

45. Guerra RS, Amaral TF, Marques EA, Mota J, Restivo MT. Anatomical location for waist circumference measurement in older adults: a preliminary study. Nutr Hosp. 27: 1554–61. doi: 10.3305/nh.2012.27.5.5922 23478705

46. Ross R, Berentzen T, Bradshaw AJ, Janssen I, Kahn HS, Katzmarzyk PT, et al. Does the relationship between waist circumference, morbidity and mortality depend on measurement protocol for waist circumference? Obes Rev. 2008;9: 312–25. doi: 10.1111/j.1467-789X.2007.00411.x 17956544

47. Kawamoto R, Kikuchi A, Akase T, Ninomiya D, Kumagi T. Usefulness of waist-to-height ratio in screening incident metabolic syndrome among Japanese community-dwelling elderly individuals. PLoS One. 2019;14: e0216069. doi: 10.1371/journal.pone.0216069 31034487

48. Yang H, Xin Z, Feng JP, Yang JK. Waist-to-height ratio is better than body mass index and waist circumference as a screening criterion for metabolic syndrome in Han Chinese adults. Med (United States). 2017;96: e8192. doi: 10.1097/MD.0000000000008192 28953680

49. Ware LJ, Rennie KL, Kruger HS, Kruger IM, Greeff M, Fourie CMT, et al. Evaluation of waist-to-height ratio to predict 5 year cardiometabolic risk in sub-Saharan African adults. Nutr Metab Cardiovasc Dis. 2014;24: 900–7. doi: 10.1016/j.numecd.2014.02.005 24675009

50. Weiler Miralles CS, Wollinger LM, Marin D, Genro JP, Contini V, Morelo Dal Bosco S. Waist-to-height ratio (WHtR) and triglyceride to HDL-C ratio (TG/HDL-c) as predictors of cardiometabolic risk. Nutr Hosp. 2015;31: 2115–21. doi: 10.3305/nh.2015.31.5.7773 25929382

51. Kobo O, Leiba R, Avizohar O, Karban A. Relative fat mass is a better predictor of dyslipidemia and metabolic syndrome than body mass index. Cardiovasc Endocrinol Metab. 2019;8: 77–81. doi: 10.1097/XCE.0000000000000176 31646301

52. McCarthy HD, Ashwell M. A study of central fatness using waist-to-height ratios in UK children and adolescents over two decades supports the simple message—’keep your waist circumference to less than half your height’. Int J Obes. 2006;30: 988–92. doi: 10.1038/sj.ijo.0803226 16432546

53. 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: 247–69. doi: 10.1017/S0954422410000144 20819243

54. Aristizabal JC, Estrada-Restrepo A, Barona J. Waist-to-height ratio may be an alternative tool to the body mass index for identifying Colombian adolescents with cardiometabolic risk factors. Nutr Hosp. 2019;36: 96–102. doi: 10.20960/nh.1909 30834755

55. Aguilar-Morales I, Colin-Ramirez E, Rivera-Mancía S, Vallejo M, Vázquez-Antona C. Performance of waist-to-height ratio, waist circumference, and body mass index in discriminating cardio-metabolic risk factors in a sample of school-aged Mexican children. Nutrients. 2018;10. doi: 10.3390/nu10121850 30513720

56. Camhi SM, Bray GA, Bouchard C, Greenway FL, Johnson WD, Newton RL, et al. The relationship of waist circumference and BMI to visceral, subcutaneous, and total body fat: Sex and race differences. Obesity. 2011;19: 402–408. doi: 10.1038/oby.2010.248 20948514

57. Savva SC, Tornaritis M, Savva ME, Kourides Y, Panagi A, Silikiotou N, et al. Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. Int J Obes Relat Metab Disord. 2000;24: 1453–8. doi: 10.1038/sj.ijo.0801401 11126342

58. Grundy SM, Neeland IJ, Turer AT, Vega GL. Waist circumference as measure of abdominal fat compartments. J Obes. 2013;2013: 454285. doi: 10.1155/2013/454285 23762536

59. Lee JE. Simply the best: Anthropometric indices for predicting cardiovascular disease. Diabetes and Metabolism Journal. Korean Diabetes Association; 2019. pp. 156–157. doi: 10.4093/dmj.2019.0057 30993939

60. Visser M, Fuerst T, Lang T, Salamone L, Harris TB. Validity of fan-beam dual-energy X-ray absorptiometry for measuring fat- free mass and leg muscle mass. J Appl Physiol. 1999;87: 1513–1520. doi: 10.1152/jappl.1999.87.4.1513 10517786

61. Van Der Ploeg GE, Withers RT, Laforgia J. Percent body fat via DEXA: comparison with a four-compartment model. J Appl Physiol. 2003;94: 499–506. doi: 10.1152/japplphysiol.00436.2002 12531910

62. Levitt DG, Beckman LM, Mager JR, Valentine B, Sibley SD, Beckman TR, et al. Comparison of DXA and water measurements of body fat following gastric bypass surgery and a physiological model of body water, fat, and muscle composition. J Appl Physiol. 2010;109: 786–95. doi: 10.1152/japplphysiol.00278.2010 20558754

63. Sopher AB, Thornton JC, Wang J, Pierson RN, Heymsfield SB, Horlick M. Measurement of percentage of body fat in 411 children and adolescents: a comparison of dual-energy X-ray absorptiometry with a four-compartment model. Pediatrics. 2004;113: 1285–90. doi: 10.1542/peds.113.5.1285 15121943

64. Pietrobelli A, Formica C, Wang Z, Heymsfield SB. Dual-energy X-ray absorptiometry body composition model: review of physical concepts. Am J Physiol Metab. 1996;271: 941–951. doi: 10.1152/ajpendo.1996.271.6.E941 8997211

65. McLester CN, Nickerson BS, Kliszczewicz BM, Hicks CS, Williamson CM, Bechke EE, et al. Validity of DXA body volume equations in a four-compartment model for adults with varying body mass index and waist circumference classifications. PLoS One. 2018;13. doi: 10.1371/journal.pone.0206866 30395588