Association between METS-IR and Prediabetes or Type 2 Diabetes Mellitus among Elderly Subjects in China: A Large-Scale Population-Based Study

Int J Environ Res Public Health. 2023 Jan 6;20(2):1053. doi: 10.3390/ijerph20021053.

Abstract

The metabolic score for insulin resistance (METS-IR) was recently proposed as a non-insulin-based, novel index for assessing insulin resistance (IR) in the Western population. However, evidence for the link between METS-IR and prediabetes or type 2 diabetes mellitus (T2DM) among the elderly Chinese population was still limited. We aimed to investigate the associations between METS-IR and prediabetes or T2DM based on large-scale, cross-sectional, routine physical examination data. In a total of 18,112 primary care service users, an increased METS-IR was independently associated with a higher prevalence of prediabetes (adjusted odds ratio [aOR] = 1.457, 95% confidence interval [CI]: 1.343 to 1.581, p < 0.001) and T2DM (aOR = 1.804, 95%CI: 1.720 to 1.891, p < 0.001), respectively. The aOR for prediabetes in subjects with the highest quartile of METS-IR was 3.060-fold higher than that in those with the lowest quartile of METS-IR. The aOR for T2DM in subjects with the highest quartile of METS-IR was 6.226-fold higher than that in those with the lowest quartile of METS-IR. Consistent results were obtained in subgroup analyses. Our results suggested that METS-IR was significantly associated with both prediabetes and T2DM. The monitoring of METS-IR may add value to early identification of individuals at risk for glucose metabolism disorders in primary care.

Keywords: METS-IR; T2DM; cross-sectional study; insulin resistance; prediabetes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Cross-Sectional Studies
  • Diabetes Mellitus, Type 2* / epidemiology
  • Humans
  • Insulin Resistance*
  • Metabolic Syndrome* / epidemiology
  • Prediabetic State* / epidemiology
  • Risk Assessment

Grants and funding

This research was funded by the National Natural Science Foundation of China [grant number 72061137002] and the Basic and Applied Basic Research Foundation of Guangdong Province [grant number 2019A1515011381].