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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 (P nonlinearity = 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
Full Papers
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society

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