Background and objectives: The visceral adiposity index (VAI), estimating visceral adiposity dysfunction through a simple formula, could serve as a useful tool for identifying individuals at higher cardiometabolic risk. Its relationship with insulin resistance (IR), assessed using the homeostasis model assessment of IR (HOMA-IR), and metabolic syndrome (MetS) components remains unclear. The study aimed to investigate the association of VAI with both HOMA-IR and MetS. Materials and Methods: After undergoing anthropometric and biochemical studies, 783 individuals were divided into three groups according to a number of present MetS components. The VAI cut-offs signaling MetS and HOMA-IR were determined by maximizing the sum of the sensitivity and specificity. Correlation analysis was performed to explore the associations between VAI and other tested parameters. A logistic stepwise regression analysis was applied to identify statistically significant determinants of HOMA-IR. Given the variability of reference values, two thresholds of HOMA-IR were applied, namely 2.0 and 3.8. Results: VAI increased significantly between the groups with a rising number of MetS components. The VAI cut-off for MetS was 2.37, with a sensitivity of 0.86 and a specificity of 0.78. The same cut-off point identified subjects with HOMA-IR = 3.8, with a sensitivity of 0.79 and a specificity of 0.66. The VAI cut-off for HOMA-IR = 2.0 was 1.89, with a sensitivity of 0.74 and a specificity of 0.68. The strongest correlations of VAI were noted with HOMA-IR (r = 0.51) and insulin (r = 0.49), respectively, while the strongest correlation of HOMA-IR was with waist circumference (r = 0.54). Not one of the routine parameters was a significant predictor in the regression analysis. Conclusions: The obtained results show an existing association of VAI with HOMA-IR. The high sensitivity and specificity of the cut-offs may allow the application of VAI in common clinical practice.

3rd Department of Internal Medicine Nephrology Rheumatology and Endocrinology University Hospital Olomouc and Faculty of Medicine and Dentistry Palacký University Olomouc 77900 Olomouc Czech Republic

Department of Biophysics Faculty of Medicine and Dentistry Palacký University Olomouc 77515 Olomouc Czech Republic

Department of Occupational Medicine University Hospital Olomouc and Faculty of Medicine and Dentistry Palacký University Olomouc 77900 Olomouc Czech Republic

Department of Public Health Faculty of Medicine and Dentistry Palacký University Olomouc 77515 Olomouc Czech Republic

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Paniagua J.A. Nutrition, insulin resistance and dysfunctional adipose tissue determine the different components of metabolic syndrome. World J. Diabetes. 2016;7:483–514. doi: 10.4239/wjd.v7.i19.483. PubMed DOI PMC

World Health Organization The Top 10 Causes of Death. [(accessed on 19 June 2019)]; Available online: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death.

Kassi E., Pervanidou P., Kaltsas G., Chrousos G. Metabolic syndrome: Definitions and controversies. BMC Med. 2011;9:48. doi: 10.1186/1741-7015-9-48. PubMed DOI PMC

Xu H., Li X., Adams H., Kubena K., Guo S. Etiology of Metabolic syndrome and dietary intervention. Int. J. Mol. Sci. 2019;20:128. doi: 10.3390/ijms20010128. PubMed DOI PMC

Eckel N., Li Y., Kuxhaus O., Stefan N., Hu F.B., Schulze M.B. Transition from metabolic healthy to unhealthy phenotypes and association with cardiovascular disease risk across BMI categories in 90,257 women (the Nurses’ Health Study): 30-year follow-up from a prospective cohort study. Lancet Diabetes Endocrinol. 2018;6:714–724. doi: 10.1016/S2213-8587(18)30137-2. PubMed DOI

Kramer C.K., Zinman B., Retnakaran R. Are metabolically healthy overweight and obesity benign conditions? A systematic review and meta-analysis. Ann. Intern. Med. 2013;159:758–769. doi: 10.7326/0003-4819-159-11-201312030-00008. PubMed DOI

Liu C., Wang C., Guan S., Liu H., Wu X., Zhang Z., Gu X., Zhang Y., Zhao Y., Tse L.A., et al. The prevalence of metabolically healthy and unhealthy obesity according to different criteria. Obes. Facts. 2019;12:78–90. doi: 10.1159/000495852. PubMed DOI PMC

Wallace T.M., Levy J.C., Matthews D.R. Use and abuse of HOMA modeling. Diabetes Care. 2004;27:1487–1495. doi: 10.2337/diacare.27.6.1487. PubMed DOI

Amato M.C., Pizzolanti G., Torregrossa V., Misiano G., Milano S., Giordano G. Visceral adiposity index (VAI) is predictive of an altered adipokine profile in patients with type 2 diabetes. PLoS ONE. 2014;9:e91969. doi: 10.1371/journal.pone.0091969. PubMed DOI PMC

Alberti K.G.M.M., Eckel R.H., Grundy S.M., Zimmet P.Z., Cleeman J.I., Donato K.A., Fruchart J.C., James W.P.T., Loria C.M., Smith S.C. 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. 2009;120:1640–1645. doi: 10.1161/CIRCULATIONAHA.109.192644. PubMed DOI

Ruopp M.D., Perkins N.J., Whitcomb B.W., Schisterman E.F. Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection. Biom. J. 2008;50:419–430. doi: 10.1002/bimj.200710415. PubMed DOI PMC

Tang Q., Li X., Song P., Xu L. Optimal cut-off values for the homeostasis model assessment of insulin resistance (HOMA-IR) and pre-diabetes screening: Developments in research and prospects for the future. Drug Discov. Ther. 2015;9:380–385. doi: 10.5582/ddt.2015.01207. PubMed DOI

Horakova D., Stepanek L., Nagelova R., Pastucha D., Azeem K., Kollarova H. Total and high-molecular-weight adiponectin levels and prediction of insulin resistance. Endokrynol. Pol. 2018;69:375–380. doi: 10.5603/EP.a2018.0035. PubMed DOI

Dobiasova M., Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: Correlation with lipoprotein particle size and esterification rate inapob-lipoprotein-depleted plasma (FERHDL) Clin. Biochem. 2001;34:583–588. doi: 10.1016/S0009-9120(01)00263-6. PubMed DOI

Pekgor S., Duran C., Berberoglu U., Eryilmaz M.A. The role of visceral adiposity index levels in predicting the presence of metabolic syndrome and insulin resistance in overweight and obese patients. Metab. Syndr. Relat. Disord. 2019;17:296–302. doi: 10.1089/met.2019.0005. PubMed DOI

Amato M.C., Giordano C., Pitrone M., Galluzzo A. Cut-off points of the visceral adiposity index (VAI) identifying a visceral adipose dysfunction associated with cardiometabolic risk in a Caucasian Sicilian population. Lipids Health Dis. 2011;10:183. doi: 10.1186/1476-511X-10-183. PubMed DOI PMC

Liu P.J., Ma F., Lou H.P., Chen Y. Visceral adiposity index is associated with pre-diabetes and type 2 diabetes mellitus in Chinese adults aged 20–50. Ann. Nutr. Metab. 2016;68:235–243. doi: 10.1159/000446121. PubMed DOI

Jabłonowska-Lietz B., Wrzosek M., Włodarczyk M., Nowicka G. New indexes of body fat distribution, visceral adiposity index, body adiposity index, waist-to-height ratio, and metabolic disturbances in the obese. Kardiol. Pol. 2017;75:1185–1191. doi: 10.5603/KP.a2017.0149. PubMed DOI

Krishna S.V.T., Kota S.K., Modi K.D. Glycemic variability: Clinical implications. Indian J. Endocrinol. Metab. 2013;17:611–619. doi: 10.4103/2230-8210.113751. PubMed DOI PMC

Li R., Li Q., Cui M., Ying Z., Li L., Zhong T., Huo Y., Xie P. Visceral adiposity index, lipid accumulation product and intracranial atherosclerotic stenosis in middle-aged and elderly Chinese. Sci. Rep. 2017;7:7951. doi: 10.1038/s41598-017-07811-7. PubMed DOI PMC

Randrianarisoa E., Lehn-Stefan A., Hieronimus A., Rietig R., Fritsche A., Machann J., Balletshofer B., Häring H.U., Stefan N., Rittig K. Visceral adiposity index as an independent marker of subclinical atherosclerosis in individuals prone to diabetes mellitus. J. Atheroscler. Thromb. 2019:47274. doi: 10.5551/jat.47274. PubMed DOI PMC

Biswas E., Choudhury A.K., Amin M.R., Khalequzzaman M., Chowdhury S., Kabir F.I., Sakib M.M., Mahabub E.E., Singha C.K. Visceral adiposity index score is the better predictor of clinical and coronary angiographic severity assessment than other adiposity indices in patients with acute coronary syndrome. Mymensingh Med. J. 2019;28:382–388. PubMed

Ma C.M., Liu X.L., Lu N., Wang R., Lu Q., Yin F.Z. Hypertriglyceridemic waist phenotype and abnormal glucose metabolism: A system review and meta-analysis. Endocrine. 2019;64:469–485. doi: 10.1007/s12020-019-01945-6. PubMed DOI

Marra N.F., Bechere Fernandes M.T., de Melo M.E., da Cruz R.M., Tess B.H. Fasting insulin resistance affects the prevalence of metabolically healthy obesity in Brazilian adolescents. Acta Paediatr. 2019;108:1295–1302. doi: 10.1111/apa.14684. PubMed DOI

Khawaja K.I., Mian S.A., Fatima A., Tahir G.M., Khan F.F., Burney S., Hasan A., Masud F. Phenotypic and metabolic dichotomy in obesity: Clinical, biochemical and immunological correlates of metabolically divergent obese phenotypes in healthy South Asian adults. Singap. Med. J. 2018;59:431–438. doi: 10.11622/smedj.2018019. PubMed DOI PMC