The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome

Pengju Liu, Fang Ma, Huiping Lou, Yanping Liu, Pengju Liu, Fang Ma, Huiping Lou, Yanping Liu

Abstract

Background: It has been well documented that obesity is closely associated with metabolic syndrome (MetS). Although body mass index (BMI) is the most frequently used method to assess overweightness and obesity, this method has been criticized because BMI does not always reflect true body fatness, which may be better evaluated by assessment of body fat and fat-free mass. The objective of this study was to investigate the best indicator to predict the presence of MetS among fat mass index, BMI and percentage of body fat (BF %) and determine its optimal cut-off value in the screening of MetS in practice.

Methods: A cross-sectional study of 1698 subjects (aged 20-79 years) who participated in the annual health check-ups was employed. Body composition was measured by bioelectrical impedance analysis (BIA). Fat mass index (FMI) was calculated. Sex-specific FMI quartiles were defined as follows: Q1: <4.39, Q2:4.39- < 5.65, Q3:5.65- < 7.03, Q4:≥7.03,in men; and Q1:<5.25, Q2:5.25- < 6.33, Q3:6.33- < 7.93,Q4:≥7.93, in women. MetS was defined by National Cholesterol Education Program/Adult Treatment Panel III criteria. The association between FMI quartiles and MetS was assessed using Binary logistic regression. Receiver operating curve (ROC) analysis was used to determine optimal cutoff points for BMI,BF% and FMI in relation to the area under the curve (AUC), sensitivity and specificity in men and women.

Results: The adjusted odds ratios (95% CI) for the presence of MetS in the highest FMI quartile versus lowest quartile were 79.143(21.243-294.852) for men (P < 0.01) and 52.039(4.144-653.436) for women (P < 0.01) after adjusting age, BMI, BF%, TC, LDL, CRP, smoking status and exercise status, and the odds ratios were 9.166(2.157-38.952) for men (P < 0.01) and 25.574(1.945-336.228) for women (P < 0.05) when WC was also added into the adjustment. It was determined that BMI values of 27.45 and 23.85 kg/m2, BF% of 23.95% and 31.35% and FMI of 7.00 and 7.90 kg/m2 were the optimal cutoff values to predict the presence of MetS among men and women according to the ROC curve analysis. Among the indicators used to predict MetS, FMI was the index that showed the greatest area under the ROC curve in both sexes.

Conclusions: Higher FMI levels appear to be independently and positively associated with the presence of MetS regardless of BMI and BF%. FMI seems to be a better screening tool in prediction of the presence of metabolic syndrome than BMI and percentage of body fat in men and women.

Figures

Figure 1
Figure 1
Prevalence of metabolic syndrome (MetS) according to the FMI quartiles.
Figure 2
Figure 2
Receiver-operating characteristic(ROC) analysis of BMI, BF%, and FMI as indicators to predict MetS in men.
Figure 3
Figure 3
Receiver-operating characteristic(ROC) analysis of BMI, BF%, and FMI as indicators to predict MetS in women.

References

    1. Reaven GM. Insulin resistance: the link between obesity and cardiovascular disease. Endocrinol Metab Clin North Am. 2008;37:581–601. doi: 10.1016/j.ecl.2008.06.005.
    1. Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP. The continuing epidemics of obesity and diabetes in the United States. JAMA. 2001;286:1195–1200. doi: 10.1001/jama.286.10.1195.
    1. Lavie CJ, Milani RV, Ventura HO. Obesity and cardiovascular disease: risk factor, paradox, and impact of weight loss. J Am Coll Cardiol. 2009;53:1925–1932. doi: 10.1016/j.jacc.2008.12.068.
    1. Lavie CJ, De Schutter A, Patel D, Artham SM, Milani RV. Body composition and coronary heart disease mortality: an obesity or a lean paradox? Mayo Clinic Proc. 2011;86:857–864. doi: 10.4065/mcp.2011.0092.
    1. Lavie CJ, Milani RV, Ventura HO, Romero-Corral A. Body composition and heart failure prevalence and prognosis: getting to the fat of the matter in the “obesity paradox”. Mayo Clin Proc. 2010;85:605–608. doi: 10.4065/mcp.2010.0333.
    1. Romero-Corral A, Montori VM, Somers VK, Korinek J, Thomas RJ, Allison TG, Mookadam F, Lopez-Jimenez F. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet. 2006;368:666–678. doi: 10.1016/S0140-6736(06)69251-9.
    1. Thibault R, Pichard C. The evaluation of body composition: a useful tool for clinical practice. Ann Nutr Metab. 2012;60:6–16. doi: 10.1159/000334879.
    1. Madeira FB, Silva AA, Veloso HF, Goldani MZ, Kac G, Cardoso VC, Bettiol H, Barbieri MA. Normal Weight Obesity Is Associated with Metabolic Syndrome and Insulin Resistance in Young Adults from a Middle-Income Country. PLoS One. 2013;8:e60673. doi: 10.1371/journal.pone.0060673.
    1. Lavie CJ, De Schutter A, Patel DA, Romero-Corral A, Artham SM, Milani RV. Body composition and survival in stable coronary heart disease-Impact of lean mass index and body fat in the “obesity paradox”. J Am Coll Cardiol. 2012;60:1374–1380. doi: 10.1016/j.jacc.2012.05.037.
    1. Zeng Q, Dong SY, Sun XN, Xie J, Cui Y. Percent body fat is a better predictor of cardiovascular risk factors than body mass index. Braz J Med Biol Res. 2012;45:591–600. doi: 10.1590/S0100-879X2012007500059.
    1. Deurenberg-Yap M, Chew SK, Deurenberg P. Elevated body fat percentage and cardiovascular risks at low body mass index levels among Singaporean Chinese, Malays and Indians. Obes Rev. 2002;3:209–215. doi: 10.1046/j.1467-789X.2002.00069.x.
    1. Cruz P, Johnson BD, Karpinski SC, Limoges KA, Warren BA, Olsen KD, Somers VK, Jensen MD, Clark MM, Lopez-Jimenez F. Validity of weight loss to estimate improvement in body composition in individuals attending a wellness center. Obesity (Silver Spring) 2011;19:2274–2279. doi: 10.1038/oby.2011.102.
    1. Wright CM, Sherriff A, Ward SC, McColl JH, Reilly JJ, Ness AR. Development of bioelectrical impedance-derived indices of fat and fat-free mass for assessment of nutritional status in childhood. Eur J Clin Nutr. 2008;62:210–217. doi: 10.1038/sj.ejcn.1602714.
    1. Bintvibok W, Cbaikittisilpa S, Panyakamlerd K, Jaisamrarn U, Taecbakraicbana N. Cut-off value of body fat in association with metabolic syndrome in Thai peri- and postmenopausal women. Climacteric. 2013;16:1–5. doi: 10.3109/13697137.2013.756623.
    1. Jeong DLS, Min H, Kim Y, Choi S, Kim Y. Measuring performance evaluation of body fat measuring instrument applying body measuring value method. Korean J Health Promot Dis Prev. 2006;6:79–87.
    1. Bolanowski M, Nilsson BE. Assessment of human body composition using dual-energy x-ray absorptiometry and bioelectrical impedance analysis. Med Sci Monit. 2001;7:1029–1033.
    1. Xu L, Cheng X, Wang J, Cao Q, Sato T, Wang M, Zhao X, Liang W. Comparisons of body-composition prediction accuracy: a study of 2 bioelectric impedance consumer devices in healthy Chinese persons using DXA and MRI as criteria methods. J Clin Densitom. 2011;14:458–464. doi: 10.1016/j.jocd.2011.04.001.
    1. Ellis KJ, Abrams SA, Wong WW. Monitoring childhood obesity: assessment of the weight ⁄ height index. Am J Epidemiol. 1999;150:939–946. doi: 10.1093/oxfordjournals.aje.a010102.
    1. Schooling CM, Thomas GN, Leung GM, Ho SY, Janus ED, Lam TH. Is height associated with cardiovascular risk in Chinese adults? Epidemiology. 2007;18:274–278. doi: 10.1097/01.ede.0000254656.02400.27.
    1. Wells JC. A critique of the expression of paediatric body composition data. Arch Dis Child. 2001;85:67–72. doi: 10.1136/adc.85.1.67.
    1. 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;52:953–959.
    1. Kyle UG, Schutz Y, Dupertuis YM, Pichard C. Body composition interpretation. Contributions of the fat-free mass index and the body fat mass index. Nutrition. 2003;19:597–604. doi: 10.1016/S0899-9007(03)00061-3.
    1. Kyle UG, Morabia A, Schutz Y, Pichard C. Sedentarism affects body fat mass index and fat-free mass index in adults aged 18 to 98 years. Nutrition. 2004;20:255–260. doi: 10.1016/j.nut.2003.11.019.
    1. Schutz Y, Kyle UU, Pichard C. Fat-free mass index and fat mass index percentiles in Caucasians aged 18–98 y. Int J Obes Relat Metab Disord. 2002;26:953–960.
    1. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SC Jr, Spertus JA, Costa F. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112:2735–2752. doi: 10.1161/CIRCULATIONAHA.105.169404.
    1. Bei-Fan Z. Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults: study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci. 2002;15:83–96.
    1. Lopez AD. Assessing the burden of mortality from cardiovascular diseases. World Health Stat Q. 1993;46:91–96.
    1. Dongfeng G, Kristi R, Xigui W, Jing C, Xiufang D, Reynolds RF, Whelton PK, Jiang H. InterASIA Collaborative Group. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet. 2005;365:1398–1405. doi: 10.1016/S0140-6736(05)66375-1.
    1. Li CI, Kardia SL, Liu CS, Lin WY, Lin CH, Lee YD, Sung FC, Li TC, Lin CC. Metabolic syndrome is associated with change in subclinical arterial stiffness: a community-based Taichung community health study. BMC Publ Health. 2011;11:808. doi: 10.1186/1471-2458-11-808.
    1. Liu Y, Tong G, Tong W, Lu L, Qin X. Can body mass index, waist circumference, waist-hip ratio and waist-height ratio predict the presence of multiple metabolic risk factors in Chinese subjects? BMC Publ Health. 2011;11:35. doi: 10.1186/1471-2458-11-35.
    1. Cho YG, Song HJ, Kim JM, Park KH, Paek YJ, Cho JJ, Caterson I, Kang JG. The estimation of cardiovascular risk factors by body mass index and body fat percentage in Korean male adults. Metabolism. 2009;58:765–771. doi: 10.1016/j.metabol.2009.01.004.
    1. Gómez-Ambrosi J, Silva C, Galofré JC, Escalada J, Santos S, Millán D, Vila N, Ibañez P, Gil MJ, Valentí V, Rotellar F, Ramírez B, Salvador J, Frühbeck G. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes (Lond) 2012;36:286–294. doi: 10.1038/ijo.2011.100.
    1. Wang J, Rennie KL, Gu W, Li H, Yu Z, Lin X. Independent associations of body-size adjusted fat mass and fat-free mass with the metabolic syndrome in Chinese. Ann Hum Biol. 2009;36:110–121. doi: 10.1080/03014460802585079.
    1. Kim JY, Oh S, Chang MR, Cho YG, Park KH, Paek YJ, Yoo SH, Cho JJ, Caterson ID, Song HJ. Comparability and utility of body composition measurement vs. anthropometric measurement for assessing obesity related health risks in Korean men. Int J Clin Pract. 2013;67:73–80. doi: 10.1111/ijcp.12038.
    1. 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. Obesity Comorbidity Diagnostic. 2012;13:275–286. doi: 10.1111/j.1467-789X.2011.00952.x.
    1. Yang T, Chu CH, Hsieh PC, Hsu CH, Chou YC, Yang SH, Bai CH, You SL, Hwang LC, Chung TC, Sun CA. C-reactive protein concentration as a significant correlate for metabolic syndrome:a Chinese population-based study. Endocrine. 2013;43:351–359. doi: 10.1007/s12020-012-9743-7.

Source: PubMed

スポンサーと協力者

医学的状態

薬物療法

3
購読する