Lipids in Health and Disease (Oct 2018)

Estimate of prevalent diabetes from cardiometabolic index in general Chinese population: a community-based study

  • Wen-Rui Shi,
  • Hao-Yu Wang,
  • Shuang Chen,
  • Xiao-Fan Guo,
  • Zhao Li,
  • Ying-Xian Sun

DOI
https://doi.org/10.1186/s12944-018-0886-2
Journal volume & issue
Vol. 17, no. 1
pp. 1 – 9

Abstract

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Abstract Background Cardiometabolic index (CMI) defines adiposity based on triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) ratio and waist-to-height ratio (WHtR). This newly proposed metric has been used to detect multiple cardiovascular risk factors, but data relative to diabetes in the general population are lacking. This study aims to validate CMI’s utility of discriminating diabetes and compares it with other indexes among general Chinese population. Methods Analyses were based on a cross-sectional study of 11,478 participants that underwent assessment of metabolic and anthropometric parameters in rural areas of northeastern China in 2013. CMI was calculated by TG/HDL-C × WHtR. Multivariate logistic regressions were performed to clarify CMI’s association with diabetes, ROC analyses were engaged to investigate CMI’s discriminating ability for diabetes. Results The prevalence of diabetes was 9.93% in males while 10.76% in females, and increased with CMI’s increment. After full adjustment, each SD increment of CMI had odds ratios (ORs) for diabetes of 1.471 (1.367–1.584) and 1.422 (1.315–1.539) in females and males, respectively. Compared with bottom categories of CMI, the top quartiles had ORs of 3.736 (2.783–5.015) in females and 3.697 (2.757–4.958) in males. The ROC results showed an excellent discriminating power of CMI (AUC: 0.702 for females, 0.664 for males). Conclusions An increasing CMI was correlated with higher odds of diabetes, supporting CMI as a useful and economic measure to screen and quantify diabetes in general Chinese population. Monitoring and promoting achievement of dyslipidemia and abdominal obesity based on CMI may improve subclinical and cardiovascular outcomes.

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