Journal of Diabetes Investigation (Jun 2024)

Interaction effect between low birthweight and resistin gene rs1862513 variant on insulin resistance and type 2 diabetes mellitus in adulthood: Toon Genome Study

  • Ayaka Yoshida,
  • Yasunori Takata,
  • Yasuharu Tabara,
  • Koutatsu Maruyama,
  • Shota Inoue,
  • Haruhiko Osawa,
  • Takashi Sugiyama

DOI
https://doi.org/10.1111/jdi.14163
Journal volume & issue
Vol. 15, no. 6
pp. 725 – 735

Abstract

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Abstract Aims/Introduction Gene–environment interactions are considered to critically influence type 2 diabetes mellitus development; however, the underlying mechanisms and specific interactions remain unclear. Given the increasing prevalence of low birthweight (LBW) influenced by the intrauterine environment, we sought to investigate genetic factors related to type 2 diabetes development in individuals with LBW. Materials and Methods The interaction between 20 reported type 2 diabetes susceptibility genes and the development of type 2 diabetes in LBW (<2,500 g) individuals in a population‐based Japanese cohort (n = 1,021) was examined by logistic regression and stratified analyses. Results Logistic regression analyses showed that only the G/G genotype at the rs1862513 locus of the resistin gene (RETN), an established initiator of insulin resistance, was closely related to the prevalence of type 2 diabetes in individuals with LBW. Age, sex and current body mass index‐adjusted stratified analyses showed a significant interaction effect of LBW and the RETN G/G genotype on fasting insulin, homeostatic model assessment 2‐insulin resistance, Matsuda index and the prevalence of type 2 diabetes (all P‐values for interaction <0.05). The adjusted odds ratio for type 2 diabetes in the LBW + G/G genotype group was 7.33 (95% confidence interval 2.43–22.11; P = 0.002) compared with the non‐LBW + non‐G/G genotype group. Similar results were obtained after excluding the influence of malnutrition due to World War II. Conclusions Simultaneous assessment of LBW and the RETN G/G genotype can more accurately predict the risk of future type 2 diabetes than assessing each of these factors alone, and provide management strategies, including early lifestyle intervention in LBW population.

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