Body Composition Assessment by Bioelectrical Impedance Analysis in Prediction of Cardio-Metabolic Risk Factors: Tehran Lipid and Glucose Study (TLGS)
Abstract
Background: We aimed at evaluating the best body mass index (BMI) and percent body fat (PBF) cutoffs related to cardio-metabolic risk factors and comparing the discriminative power of PBF and BMI for predicting these risk factors.
Methods: In this cross-sectional study in phase V (2012-2015), 1271 participants (age ≥ 20 yr; 54.3% women) were enrolled. Bioelectrical impedance analysis (BIA) was used to estimate PBF. Joint Interim Statement criteria were used for defining metabolic syndrome (MetS). We compared PBF with BMI through logistic regression and area under the curve of the receiver operating characteristic (ROC) curve. Percent body fat cutoff points were > 25 in men and >35 in women.
Results: Percent body fat and BMI cutoff points for predicting MetS were 25.6% and 27.2 kg/m2 in men and 36.2% and 27.5 kg/m2 in women, respectively. There were no significant differences between BMI and PBF area under the ROC curves for predicting MetS and its components, except for abdominal obesity in men and low high-density lipoprotein (HDL) in women in favor of BMI. Logistic regression analysis indicated that BMI in women was better for predicting MetS and its components, except for abdominal obesity. Moreover, BMI was equal or superior to PBF in men, except for low HDL and high triglyceride levels.
Conclusion: Comparison of PBF with BMI showed that the use of PBF is not significantly better than BMI in predicting cardio-metabolic risks in the general population.
2. De Lorenzo A, Bianchi A, Maroni P, et al (2013). Adiposity rather than BMI determines metabolic risk. Int J Cardiol, 166:111-7.
3. Marra M, Sammarco R, De Lorenzo A, et al (2019). Assessment of Body Composition in Health and Disease Using Bioelectrical Impedance Analysis (BIA) and Dual Energy X-Ray Absorptiometry (DXA): A Critical Overview. Contrast Media Mol Imaging, 2019:3548284.
4. Jia A, Xu S, Ming J, et al (2018). Body fat percentage cutoffs for risk of cardiometabolic abnormalities in the Chinese adult population: a nationwide study. Eur J Clin Nutr, 72:728-35.
5. Macek P, Biskup M, Terek-Derszniak M, et al (2020). Optimal Body Fat Percentage Cut-Off Values in Predicting the Obesity-Related Cardiovascular Risk Factors: A Cross-Sectional Cohort Study. Diabetes Metab Syndr Obes, 13:1587-97.
6. Delavari A, Forouzanfar MH, Alikhani S, et al (2009). First nationwide study of the prevalence of the metabolic syndrome and optimal cutoff points of waist circumference in the Middle East: the national survey of risk factors for noncommunicable diseases of Iran. Diabetes Care, 32:1092-7.
7. Romero-Corral A, Somers VK, Sierra-Johnson J, et al (2008). Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond), 32:959-66.
8. Ward LC (2019). Bioelectrical impedance analysis for body composition assessment: reflections on accuracy, clinical utility, and standardisation. Eur J Clin Nutr, 73:194-199.
9. Azizi F, Ghanbarian A, Momenan AA, et al (2009). Prevention of non-communicable disease in a population in nutrition transition: Tehran Lipid and Glucose Study phase II. Trials, 10:5.
10. Alberti KG, Eckel RH, Grundy SM, et al (2009). Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation, 120:1640-5.
11. Liu P, Ma F, Lou H, et al (2013). The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome. BMC Public Health, 13:629.
12. Nyangasa MA, Buck C, Kelm S, et al (2019). Association between cardiometabolic risk factors and body mass index, waist circumferences and body fat in a Zanzibari cross-sectional study. BMJ Open, 9:e025397.
13. Bosy-Westphal A, Geisler C, Onur S, et al (2006). Value of body fat mass vs anthropometric obesity indices in the assessment of metabolic risk factors. Int J Obes (Lond), 30:475-83.
14. Lee K, Song YM, Sung J (2008). Which obesity indicators are better predictors of metabolic risk?: healthy twin study. Obesity (Silver Spring), 16:834-40.
15. Kobayashi J, Murano S, Kawamura I, et al (2006). The relationship of percent body fat by bioelectrical impedance analysis with blood pressure, and glucose and lipid parameters. J Atheroscler Thromb, 13:221-6.
16. Vanavanan S, Srisawasdi P, Rochanawutanon M, et al (2018). Performance of body mass index and percentage of body fat in predicting cardiometabolic risk factors in Thai adults. Diabetes Metab Syndr Obes, 11:241-53.
17. Nagaya T, Yoshida H, Takahashi H, et al (1999). Body mass index (weight/height2) or percentage body fat by bioelectrical impedance analysis: which variable better reflects serum lipid profile? Int J Obes Relat Metab Disord, 23:771-4.
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Issue | Vol 51 No 4 (2022) | |
Section | Original Article(s) | |
DOI | https://doi.org/10.18502/ijph.v51i4.9246 | |
Keywords | ||
Body composition Bioelectrical impedance analysis (BIA) Cardio-metabolic risk factor |
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