Classification of nutritional status by fat mass index: does the measurement tool matter?

Authors

DOI:

https://doi.org/10.1590/1980-0037.2022v24e84048

Keywords:

Adiposity , Anthropometry, Body Composition, Body mass index, Electric impedance

Abstract

Assessment of the Nutritional Status (NS) allows screening for malnutrition and obesity, conditions associated with chronic non-communicable diseases. The fat mass index (FMI) stands out in relation to traditional NS indicators. However, proposals that define thresholds for FMI are not sensitive to discriminate extreme cases (degrees of obesity or thinness). Only one proposal (NHANES), determined by total body densitometry (DXA), establishing eight categories of NS classification (FMI). However, DXA is expensive and not always clinically available. Our study aim to test the validity of the NHANES method using electrical bioimpedance (BIA) and skinfold thickness (ST) to classify NS. The FMI of 135 (69 women) university students aged 18 to 30 years old was determined using DXA, BIA and ST. The agreement between the instruments (Bland-Altman) and the agreement coefficient in the NS classifications (Chi square and Kappa index) were tested. The agreement test against DXA indicated that ST underestimated the FMI (-1.9 kg/m2) for both sexes and for BIA in women (-2.0 kg/m2). However, BIA overestimated FMI (1.4 kg/m2) in men, although with less bias. There was no agreement between the NS classifications (NHANES) by FMI between DXA and BIA, or DXA and ST. The exception occurred between DXA and BIA in men who showed a slightly better consensus, considered "fair" (k = 0.214; p = 0.001). In conclusion, ST and BIA did not show enough agreement to replace DXA for NS classification, within NHANES thresholds. The FMI measurement tools for the NHANES classification of the categories of NS matters.

References

Dias PC, Henriques P, Anjos LAD, Burlandy L. Obesity and public policies: the Brazilian government's definitions and strategies. Cad Saude Publica. 2017;33(7):e00006016. doi: 10.1590/0102-311x00006016

VanItallie TB, Yang MU, Heymsfield SB, Funk RC, Boileau RA. Height-normalized indices of the body's fat-free mass and fat mass: potentially useful indicators of nutritional status. Am J Clin Nutr. 1990;52(6):953-9. doi: 10.1093/ajcn/52.6.953

Peltz G, Aguirre MT, Sanderson M, Fadden MK. The role of fat mass index in determining obesity. American journal of human biology : the official journal of the Human Biology Council. 2010;22(5):639-47. doi: 10.1002/ajhb.21056

Ramírez-Vélez R, Correa-Bautista JE, Sanders-Tordecilla A, Ojeda-Pardo ML, Cobo-Mejía EA, Castellanos-Vega RDP, et al. Percentage of Body Fat and Fat Mass Index as a Screening Tool for Metabolic Syndrome Prediction in Colombian University Students. Nutrients. 2017;9(9):1009.

Rao KM, Arlappa N, Radhika MS, Balakrishna N, Laxmaiah A, Brahmam GN. Correlation of Fat Mass Index and Fat-Free Mass Index with percentage body fat and their association with hypertension among urban South Indian adult men and women. Annals of human biology. 2012;39(1):54-8. doi: 10.3109/03014460.2011.637513

Pourshahidi LK, Wallace JM, Mulhern MS, Horigan G, Strain JJ, McSorley EM, et al. Indices of adiposity as predictors of cardiometabolic risk and inflammation in young adults. Journal of human nutrition and dietetics : the official journal of the British Dietetic Association. 2016;29(1):26-37. doi: 10.1111/jhn.12295

Kelly TL, Wilson KE, Heymsfield SB. Dual energy X-Ray absorptiometry body composition reference values from NHANES. PloS one. 2009;4(9):e7038. doi: 10.1371/journal.pone.0007038

Hinton BJ, Fan B, Ng BK, Shepherd JA. Dual energy X-ray absorptiometry body composition reference values of limbs and trunk from NHANES 1999-2004 with additional visualization methods. PloS one. 2017;12(3):e0174180. doi: 10.1371/journal.pone.0174180

Schutz Y, Kyle UU, Pichard C. Fat-free mass index and fat mass index percentiles in Caucasians aged 18-98 y. International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity. 2002;26(7):953-60. doi: 10.1038/sj.ijo.0802037

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. doi: 10.3346/jkms.2011.26.12.1599

Guedes DP. Procedimentos clínicos utilizados para análise da composição corporal. Revista Brasileira de Cineantropometria & Desempenho Humano. 2013;15:113-29.

Lohman T, Roche A, Martorell R. Anthropometric standardization reference manual. Champaign: Human Kinetics; 1988.

Sun SS, Chumlea WC, Heymsfield SB, Lukaski HC, Schoeller D, Friedl K, et al. Development of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic surveys. Am J Clin Nutr. 2003;77(2):331-40. doi: 10.1093/ajcn/77.2.331

Jackson AS, Pollock ML. Generalized equations for predicting body density of men. The British journal of nutrition. 1978;40(3):497-504. doi: 10.1079/bjn19780152

Jackson AS, Pollock ML, Ward A. Generalized equations for predicting body density of women. Med Sci Sports Exerc. 1980;12(3):175-81.

Siri WE. Body composition from fluid spaces and density: analysis of methods. In: Brozek JH, A, editor. Techniques for measuring body composition. 1st ed. Massachusetts: Headquaters Quartermaster Research and Engineering Command; 1961. p. 223-44.

Perini TA, Oliveira GLd, Ornellas JdS, Oliveira FPd. Technical error of measurement in anthropometry. Rev Bras Med Esporte. 2005;11:81-5.

Heo M, Faith MS, Pietrobelli A, Heymsfield SB. Percentage of body fat cutoffs by sex, age, and race-ethnicity in the US adult population from NHANES 1999-2004. Am J Clin Nutr. 2012;95(3):594-602. doi: 10.3945/ajcn.111.025171

Kwak SG, Kim JH. Central limit theorem: the cornerstone of modern statistics. Korean journal of anesthesiology. 2017;70(2):144-56. doi: 10.4097/kjae.2017.70.2.144

Hattori K, Tatsumi N, Tanaka S. Assessment of body composition by using a new chart method. American journal of human biology : the official journal of the Human Biology Council. 1997;9(5):573-8. doi: 10.1002/(sici)1520-6300(1997)9:5<573::aid-ajhb5>3.0.co;2-v

Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159-74.

Forte Freitas I, Rupp de Paiva SA, Godoy I, Smaili Santos SM, Campana ÁO. Análise comparativa de métodos de avaliação da composição corporal em homens sadios e em pacientes com doença pulmonar obstrutiva crônica: antropometria, impedância bioelétrica e absortiometria de raios-X de dupla energia. Archivos Latinoamericanos de Nutrición. 2005;55:124-31.

Loenneke JP, Wilson JM, Wray ME, Barnes JT, Kearney ML, Pujol TJ. The estimation of the fat free mass index in athletes. Asian J Sports Med. 2012;3(3):200-3. doi: 10.5812/asjsm.34691

Wang ZM, Pierson RN, Jr., Heymsfield SB. The five-level model: a new approach to organizing body-composition research. Am J Clin Nutr. 1992;56(1):19-28. doi: 10.1093/ajcn/56.1.19

Heymsfield SB, Wang Z, Baumgartner RN, Ross R. Human body composition: advances in models and methods. Annual review of nutrition. 1997;17:527-58. doi: 10.1146/annurev.nutr.17.1.527

Guofeng Q, Wei W, Wei D, Fan Z, Sinclair AJ, Chatwin CR. Bioimpedance analysis for the characterization of breast cancer cells in suspension. IEEE transactions on bio-medical engineering. 2012;59(8):2321-9. doi: 10.1109/tbme.2012.2202904

Silva AM. Structural and functional body components in athletic health and performance phenotypes. Eur J Clin Nutr. 2019;73(2):215-24. doi: 10.1038/s41430-018-0321-9

Marins JC, Fernandes AA, Cano SP, Moreira DG, da Silva FS, Costa CM, et al. Thermal body patterns for healthy Brazilian adults (male and female). Journal of thermal biology. 2014;42:1-8. doi: 10.1016/j.jtherbio.2014.02.020

Bahadori B, Uitz E, Tonninger-Bahadori K, Pestemer-Lach I, Trummer M, Thonhofer R, et al. Body composition: the fat-free mass index (FFMI) and the body fat mass index (BFMI) distribution among the adult Austrian population-results of a cross-sectional pilot study. International journal of body composition research. 2006;4(3):123.

Pereira-da-Silva L, Dias MP-G, Dionísio E, Virella D, Alves M, Diamantino C, et al. Fat mass index performs best in monitoring management of obesity in prepubertal children☆,☆☆. Jornal de pediatria. 2016;92:421-6.

Downloads

Published

2023-02-23