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Metabolic Score for Visceral Fat: a novel predictor for the risk of type 2 diabetes mellitus

Published online by Cambridge University Press:  11 October 2021

Yifei Feng
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Xingjin Yang
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Yang Li
Affiliation:
Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
Yuying Wu
Affiliation:
Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
Minghui Han
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Ranran Qie
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Shengbing Huang
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Xiaoyan Wu
Affiliation:
Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
Yanyan Zhang
Affiliation:
Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
Jinli Zhang
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Huifang Hu
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Lijun Yuan
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Tianze Li
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Dechen Liu
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Fulan Hu
Affiliation:
Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
Ming Zhang
Affiliation:
Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
Yunhong Zeng
Affiliation:
Center for Health Management, The Affiliated Shenzhen Hospital of University of Chinese Academy of Sciences, Shenzhen, Guangdong, People’s Republic of China
Xinping Luo
Affiliation:
Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, People’s Republic of China
Jie Lu
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Liang Sun
Affiliation:
Department of Social Medicine and Health Service Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Dongsheng Hu*
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
Yang Zhao*
Affiliation:
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
*
*Corresponding author: Dongsheng Hu, email dongshenghu563@126.com and Yang Zhao, email yzhao20@zzu.edu.cn
*Corresponding author: Dongsheng Hu, email dongshenghu563@126.com and Yang Zhao, email yzhao20@zzu.edu.cn

Abstract

To investigate the association between the Metabolic Score for Visceral Fat (METS-VF) and risk of type 2 diabetes mellitus (T2DM) and compare the predictive value of the METS-VF for T2DM incidence with other obesity indices in Chinese people. A total of 12 237 non-T2DM participants aged over 18 years from the Rural Chinese Cohort Study of 2007–2008 were included at baseline and followed up during 2013–2014. The cox proportional hazards regression was used to calculate hazard ratios (HR) and 95 % CI for the association between baseline METS-VF and T2DM risk. Restricted cubic splines were used to model the association between METS-VF and T2DM risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the ability of METS-VF to predict T2DM incidence. During a median follow-up of 6·01 (95 % CI 5·09, 6·06) years, 837 cases developed T2DM. After adjusting for potential confounding factors, the adjusted HR for the highest v. lowest METS-VF quartile was 5·97 (95 % CI 4·28, 8·32), with a per 1-sd increase in METS-VF positively associated with T2DM risk. Positive associations were also found in the sensitivity and subgroup analyses, respectively. A significant nonlinear dose–response association was observed between METS-VF and T2DM risk for all participants (Pnonlinearity = 0·0347). Finally, the AUC value of METS-VF for predicting T2DM was largest among six indices. The METS-VF may be a reliable and applicable predictor of T2DM incidence in Chinese people regardless of sex, age or BMI.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society

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References

Fox, CS, Massaro, JM, Hoffmann, U, et al. (2007) Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation 116, 3948.CrossRefGoogle ScholarPubMed
International Diabetic Federation (2019) IDF Diabetes Atlas, 9th ed. https://diabetesatlas.org/en/ (accessed November 2019).Google Scholar
Festa, A, Williams, K, D’Agostino, R, et al. (2006) The natural course of beta-cell function in nondiabetic and diabetic individuals: the insulin resistance atherosclerosis study. Diabetes 55, 11141120.CrossRefGoogle ScholarPubMed
Vos, T, Flaxman, AD, Naghavi, M, et al. (2012) Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 21632196.CrossRefGoogle ScholarPubMed
World Health Organization (2014) Global Status Report on Noncommunicable Diseases. http://www.who.int/nmh/publications/ncd-status-report-2014/en/ (accessed December 2017).Google Scholar
Bello-Chavolla, OY, Antonio-Villa, NE, Vargas-Vázquez, A, et al. (2020) Metabolic Score for Visceral Fat (METS-VF), a novel estimator of intra-abdominal fat content and cardio-metabolic health. Clin Nutr 39, 16131621.CrossRefGoogle Scholar
Kapoor, N, Jiwanmall, SA, Nandyal, MB, et al. (2020) Metabolic Score for Visceral Fat (METS-VF) estimation: a novel cost-effective obesity indicator for visceral adipose tissue estimation. Diabetes Metab Syndrome Obes: Target Ther 13, 32613267.CrossRefGoogle ScholarPubMed
Liu, XZ, Chen, DS, Xu, X, et al. (2020) Longitudinal associations between metabolic score for visceral fat and hyperuricemia in non-obese adults. Nutr Metab Cardiovasc Dis 30, 17511757.CrossRefGoogle ScholarPubMed
Zhang, M, Zhao, Y, Sun, L, et al. (2020) Cohort profile: the rural Chinese cohort study. Int J Epidemiol 50, 723724l.CrossRefGoogle Scholar
Han, C, Liu, Y, Sun, X, et al. (2017) Prediction of a new body shape index and body adiposity estimator for development of type 2 diabetes mellitus: the Rural Chinese Cohort Study. Br J Nutr 118, 771776.CrossRefGoogle ScholarPubMed
Craig, CL, Marshall, AL, Sjöström, M, et al. (2003) International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35, 13811395.CrossRefGoogle ScholarPubMed
Perloff, D, Grim, C, Flack, J, et al. (1993) Human blood pressure determination by sphygmomanometry. Circulation 88, 24602470.CrossRefGoogle ScholarPubMed
Bairaktari, E, Hatzidimou, K, Tzallas, C, et al. (2000) Estimation of LDL cholesterol based on the Friedewald formula and on apo B levels. Clin Biochem 33, 549555.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Krakauer, NY & Krakauer, JC (2012) A new body shape index predicts mortality hazard independently of body mass index. PLOS ONE 7, e39504.CrossRefGoogle ScholarPubMed
Expert Committee on the Diagnosis and Classification of Diabetes Mellitus (2003) Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 26, S5S20.CrossRefGoogle Scholar
Stefan, N (2020) Causes, consequences, and treatment of metabolically unhealthy fat distribution. Lancet Diabetes Endocrinol 8, 616627.CrossRefGoogle ScholarPubMed
Liu, J, Fan, D, Wang, X, et al. (2020) Association of two novel adiposity indicators with visceral fat area in type 2 diabetic patients: novel adiposity indexes for type 2 diabetes. Medicine 99, e20046.CrossRefGoogle ScholarPubMed
Omura-Ohata, Y, Son, C, Makino, H, et al. (2019) Efficacy of visceral fat estimation by dual bioelectrical impedance analysis in detecting cardiovascular risk factors in patients with type 2 diabetes. Cardiovasc Diabetol 18, 137.CrossRefGoogle ScholarPubMed
Chen, P, Hou, X, Hu, G, et al. (2018) Abdominal subcutaneous adipose tissue: a favorable adipose depot for diabetes? Cardiovasc Diabetol 17, 93.CrossRefGoogle ScholarPubMed
Vega, GL, Adams-Huet, B, Peshock, R, et al. (2006) Influence of body fat content and distribution on variation in metabolic risk. J Clin Endocrinol Metab 91, 44594466.CrossRefGoogle ScholarPubMed
Yuan, S & Larsson, SC (2020) An atlas on risk factors for type 2 diabetes: a wide-angled Mendelian randomisation study. Diabetologia 63, 23592371.CrossRefGoogle ScholarPubMed
Yoon, KH, Lee, JH, Kim, JW, et al. (2006) Epidemic obesity and type 2 diabetes in Asia. Lancet 368, 16811688.CrossRefGoogle ScholarPubMed
Sjöström, LV (1992) Morbidity of severely obese subjects. Am J Clin Nutr 55, 508s515s.CrossRefGoogle ScholarPubMed
Shuster, A, Patlas, M, Pinthus, JH, et al. (2012) The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis. Br J Radiol 85, 110.CrossRefGoogle ScholarPubMed
Ashwell, M, Cole, TJ & Dixon, AK (1985) Obesity: new insight into the anthropometric classification of fat distribution shown by computed tomography. Br Med J 290, 16921694.CrossRefGoogle ScholarPubMed
Okorodudu, DO, Jumean, MF, Montori, VM, et al. (2010) Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes 34, 791799.CrossRefGoogle ScholarPubMed
Nevill, AM, Stewart, AD, Olds, T, et al. (2006) Relationship between adiposity and body size reveals limitations of BMI. Am J Phys Anthropol 129, 151156.CrossRefGoogle ScholarPubMed
Neeland, IJ, McGuire, DK, Eliasson, B, et al. (2015) Comparison of adipose distribution indices with gold standard body composition assessments in the EMPA-REG H2H SU Trial: a body composition sub-study. Diabetes Ther: Res Treat Educ Diabetes Relat Disord 6, 635642.CrossRefGoogle ScholarPubMed
Pou, KM, Massaro, JM, Hoffmann, U, et al. (2009) Patterns of abdominal fat distribution: the Framingham Heart Study. Diabetes Care 32, 481485.CrossRefGoogle ScholarPubMed
Chen, C, Xu, Y, Guo, ZR, et al. (2014) The application of visceral adiposity index in identifying type 2 diabetes risks based on a prospective cohort in China. Lipids Health Dis 13, 108.CrossRefGoogle ScholarPubMed
Deurenberg, P, Deurenberg-Yap, M & Guricci, S (2002) Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes Rev 3, 141146.CrossRefGoogle ScholarPubMed
Wang, B, Zhang, M, Liu, Y, et al. (2018) Utility of three novel insulin resistance-related lipid indices for predicting type 2 diabetes mellitus among people with normal fasting glucose in rural China. J Diabetes 10, 641652.CrossRefGoogle ScholarPubMed
Lim, U, Ernst, T, Buchthal, SD, et al. (2011) Asian women have greater abdominal and visceral adiposity than Caucasian women with similar body mass index. Nutr Diabetes 1, e6.CrossRefGoogle ScholarPubMed
Zhang, M, Zheng, L, Li, P, et al. (2016) 4-year trajectory of visceral adiposity index in the development of type 2 diabetes: a prospective cohort study. Ann Nutr Metab 69, 142149.CrossRefGoogle ScholarPubMed
Brundavani, V, Murthy, SR & Kurpad, AV (2006) Estimation of deep-abdominal-adipose-tissue (DAAT) accumulation from simple anthropometric measurements in Indian men and women. Eur J Clin Nutr 60, 658666.CrossRefGoogle ScholarPubMed
Lee, CG, Carr, MC, Murdoch, SJ, et al. (2009) Adipokines, inflammation, and visceral adiposity across the menopausal transition: a prospective study. J Clin Endocrinol Metab 94, 11041110.CrossRefGoogle ScholarPubMed
Neeland, IJ, Turer, AT, Ayers, CR, et al. (2012) Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA 308, 11501159.CrossRefGoogle ScholarPubMed
Boyko, EJ, Fujimoto, WY, Leonetti, DL, et al. (2000) Visceral adiposity and risk of type 2 diabetes: a prospective study among Japanese Americans. Diabetes Care 23, 465471.CrossRefGoogle ScholarPubMed
Wei, M, Gaskill, SP, Haffner, SM, et al. (1997) Waist circumference as the best predictor of noninsulin dependent diabetes mellitus (NIDDM) compared to body mass index, waist/hip ratio and other anthropometric measurements in Mexican Americans – a 7-year prospective study. Obes Res 5, 1623.CrossRefGoogle ScholarPubMed
Ohlson, LO, Larsson, B, Svärdsudd, K, et al. (1985) The influence of body fat distribution on the incidence of diabetes mellitus. 13·5 years of follow-up of the participants in the study of men born in 1913. Diabetes 34, 10551058.CrossRefGoogle Scholar
Lv, X, Zhou, W, Sun, J, et al. (2017) Visceral adiposity is significantly associated with type 2 diabetes in middle-aged and elderly Chinese women: a cross-sectional study. J Diabetes 9, 920928.CrossRefGoogle ScholarPubMed
Bays, HE (2011) Adiposopathy is ‘sick fat’ a cardiovascular disease? J Am Coll Cardiol 57, 24612473.CrossRefGoogle Scholar
Han, SJ, Boyko, EJ, Fujimoto, WY, et al. (2017) Low plasma adiponectin concentrations predict increases in visceral adiposity and insulin resistance. J Clin Endocrinol Metab 102, 46264633.CrossRefGoogle ScholarPubMed
Karlsson, T, Rask-Andersen, M, Pan, G, et al. (2019) Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease. Nat Med 25, 13901395.CrossRefGoogle ScholarPubMed
Neeland, IJ, Hughes, C, Ayers, CR, et al. (2017) Effects of visceral adiposity on glycerol pathways in gluconeogenesis. Metab Clin Exp 67, 8089.CrossRefGoogle ScholarPubMed
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