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Visceral adiposity index is a better predictor of unhealthy metabolic phenotype than traditional adiposity measures: results from a population-based study

  • Fabrícia Geralda Ferreira (a1), Leidjaira Lopes Juvanhol (a2), Danielle Cristina Guimarães da Silva (a3) and Giana Zarbato Longo (a2)



The present study aimed to investigate whether the visceral adiposity index (VAI) is an effective predictor to identify unhealthy metabolic phenotype by comparing normal-weight and overweight individuals.


A population-based cross-sectional study. Data were collected by interviews, anthropometric evaluation, dietetic, clinical and laboratory tests. The area under the receiver-operating characteristic curve (AUC) and prevalence ratio (PR), obtained from Poisson regression, were used to compare the predictive capacity of the obesity indicators evaluated (VAI, BMI, waist and neck circumference, waist-to-height and waist-to-hip ratios) and their association with the unhealthy metabolic phenotype. All analyses were stratified by sex and by nutritional status.


Viçosa, Minas Gerais, Brazil.


A total of 854 Brazilian adults (20–59 years old) of both sexes.


VAI was the best predictor for unhealthy metabolic phenotype among men (AUC = 0·865) and women (AUC = 0·843) at normal weight. VAI also had the best predictive capacity among overweight women (AUC = 0·903). Among overweight men, its accuracy (AUC = 0·830) was higher than that of waist-to-hip ratio. In the adjusted regression models, VAI was the indicator most strongly associated with the unhealthy metabolic phenotype, especially among those with normal weight (PR = 6·74; 95 % CI 3·15, 14·42 for men; PR = 7·14; 95 % CI 3·79, 13·44 for women).


VAI has better predictive capacity in detecting unhealthy metabolic phenotype than conventional anthropometric indicators, regardless of nutritional status and sex.


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1. Malik, VS, Willett, WC & Hu, FB (2013) Global obesity: trends, risk factors and policy implications. Nat Rev Endocrinol 20, 1327.
2. Nguyen, T & Lau, DCW (2012) The obesity epidemic and its impact on hypertension. Can J Cardiol 28, 326333.
3. Mirzaei, B, Abdi, H, Serahati, S et al. (2017) Cardiovascular risk in different obesity phenotypes over a decade follow-up: Tehran Lipid and Glucose Study. Atherosclerosis 258, 6571.
4. Karelis, AD, St-Pierre, DH, Conus, F et al. (2004) Metabolic and body composition factors in subgroups of obesity: what do we know? J Clin Endocrinol Metab 89, 25692575.
5. Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration (BMI Mediated Effects), Lu, Y, Hajifathalian, K et al. (2014) Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1·8 million participants. Lancet 383, 970983.
6. Ruderman, N, Chisholm, D & Pi-Sunyer, X (1998) The metabolically obese, normal-weight individual revisited. Diabetes 47, 699713.
7. Wildman, R, Muntner, P, Reynolds, K et al. (2008) The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004). Arch Intern Med 168, 16171624.
8. Hwang, Y-C, Hayashi, T, Fujimoto, WY et al. (2015) Visceral abdominal fat accumulation predicts the conversion of metabolically healthy obese subjects to an unhealthy phenotype. Int J Obes (Lond) 39, 13651370.
9. Kang, YM, Jung, CH, Cho, YK et al. (2015) Visceral adiposity index predicts the conversion of metabolically healthy obesity to an unhealthy phenotype. PLoS One 12, e0179635.
10. Kim, NH, Seo, JA, Cho, H et al. (2016) Risk of the development of diabetes and cardiovascular disease in metabolically healthy obese people: the Korean Genome and Epidemiology Study. Medicine (Baltimore) 95, e3384.
11. Kabat, GC, Wu, WY-Y, Bea, JW et al. (2017) Metabolic phenotypes of obesity: frequency, correlates and change over time in a cohort of postmenopausal women. Int J Obes (Lond) 41, 170177.
12. Aung, K, Lorenzo, C, Hinojosa, MA et al. (2014) Risk of developing diabetes and cardiovascular disease in metabolically unhealthy normal-weight and metabolically healthy obese individuals. J Clin Endocrinol Metab 99, 462468.
13. Phillips, CM, Dillon, C, Harrington, JM et al. (2013) Defining metabolically healthy obesity: role of dietary and lifestyle factors. PLoS One 8, e76188.
14. Du, T, Yu, X, Zhang, J et al. (2015) Lipid accumulation product and visceral adiposity index are effective markers for identifying the metabolically obese normal-weight phenotype. Acta Diabetol 52, 855863.
15. Samocha-Bonet, D, Dixit, VD, Kahn, CR et al. (2014) Metabolically healthy and unhealthy obese – the 2013 Stock Conference report. Obes Rev 15, 697708.
16. Amato, MC, Giordano, C, Galia, M et al. (2010) Visceral adiposity index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care 33, 920922.
17. Segheto, W, Silva, DCG, Coelho, FA et al. (2015) Body adiposity index and associated factors in adults: method and logistics of a population-based study. Nutr Hosp 32, 101109.
18. Barbosa, MB, Pereira, CV, da Cruz, DT et al. (2018) Prevalence and factors associated with alcohol and tobacco use among non-institutionalized elderly persons. Rev Bras Geriatr Gerontol 21, 125135.
19. Amato, MC & Giordano, C (2013) Clinical indications and proper use of visceral adiposity index. Nutr Metab Cardiovasc Dis 23, e31e32.
20. Matthews, DR, Hosker, JP, Rudenski, AS et al. (1985) Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, 412419.
21. World Health Organization (2000) Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation. WHO Technical Report Series no. 894. Geneva: WHO.
22. Ashwell, M & Hsieh, SD (2005) Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr 56, 303307.
23. Ben-noun, L, Sohar, E & Laor, A (2001) Neck circumference as a simple screening measure for identifying overweight and obese patients. Obes Res 9, 470477.
24. Jackson, AS, Pollock, ML & Ward, A (1980) Generalized equations for predicting body density of women. Med Sci Sports Exerc 12, 175181.
25. Jackson, AS & Pollock, ML (1978) Generalized equations for predicting body density of men. Br J Nutr 40, 497504.
26. Siri, WE (1956) The gross composition of the body. Adv Biol Med Phys 4, 239280.
27. Haskell, WL, Lee, I-M, Pate, RR et al. (2007) Physical activity and public health updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sport Exerc 39, 14231434.
28. da Silva, DCG, Segheto, W, de Lima, MFC et al. (2018) Using the method of triads in the validation of a food frequency questionnaire to assess the consumption of fatty acids in adults. J Hum Nutr Diet 31, 8595.
29. Janghorbani, M, Aminorroaya, A & Amini, M (2017) Comparison of different obesity indices for predicting incident hypertension. High Blood Press Cardiovasc Prev 24, 157166.
30. Janghorbani, M & Amini, M (2016) The visceral adiposity index in comparison with easily measurable anthropometric markers did not improve prediction of diabetes. Can J Diabetes 40, 393398.
31. Janghorbani, M, Salamat, MR, Amini, M et al. (2017) Risk of diabetes according to the metabolic health status and degree of obesity. Diabetes Metab Syndr Clin Res Rev S1871, 40214022.
32. Wu, S, Fisher-Hoch, SP, Reninger, B et al. (2016) Metabolic health has greater impact on diabetes than simple overweight/obesity in Mexican Americans. J Diabetes Res 2016, 4094876.
33. Ryoo, J-H, Park, SK, Ye, S et al. (2015) Estimation of risk for diabetes according to the metabolically healthy status stratified by degree of obesity in Korean men. Endocrine 50, 650658.
34. Ryoo, J-H, Park, SK, Oh, C-M et al. (2017) Evaluating the risk of hypertension according to the metabolic health status stratified by degree of obesity. J Am Soc Hypertens 11, 2027.
35. Pimentel, A de, C, Scorsatto, M, Moraes de Oliveira, GM et al. (2015) Characterization of metabolically healthy obese Brazilians and cardiovascular risk prediction. Nutrition 31, 827833.



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