Association Between Insulin-like Growth Factor-1 rs35767 Polymorphism and Type 2 Diabetes Mellitus Susceptibility: A Meta-Analysis

Abstract

Background: Insulin-like growth factor-1 (IGF-1) has been demonstrated to increase fatty acid β oxidation during fasting, and play an important role in regulating lipid metabolism and type 2 diabetes mellitus (T2DM). The rs35767 (T > C) polymorphism, a functional SNP was found in IGF-1 promoter, which may directly affect IGF-1 expression. However, the inconsistent findings showed on the IGF-1 rs35767 polymorphism and T2DM risk. Methods: We performed a comprehensive meta-analysis to estimate the association between the IGF-1 rs35767 and T2DM risk among four genetic models (the allele, additive, recessive and dominant models). Results: A total 49,587 T2DM cases and 97,906 NDM controls were included in the allele model, a total 2256 T2DM cases and 2228 NDM controls were included in the other three genetic models (the additive; recessive and dominant models). In overall analysis, the IGF-1 rs35767 was shown to be significantly associated with increased T2DM risk for the allele model (T vs. C: OR = 1.251, 95% CI: 1.082-1.447, p = 0.002), additive model (homozygote comparisons: TT vs. CC: OR = 2.433, 95% CI: 1.095-5.405, p = 0.029; heterozygote comparisons: TC vs. CC: OR = 1.623, 95% CI: 1.055-2.495, p = 0.027) and dominant model (TT + CT vs. CC: OR = 1.934, 95% CI: 1.148-3.257, p = 0.013) with random effects model. After omitting Gouda's study could reduce the heterogeneity, especially in the recessive model (TT vs. CC + CT: I² = 38.7%, p = 0.163), the fixed effects model for recessive effect of the T allele (TT vs. CC + CT) produce results that were of borderline statistical significance (OR = 1.206, 95% CI: 1.004-1.448, p = 0.045). And increasing the risk of T2DM in Uyghur population of subgroup for the allele model. Conclusion: The initial analyses that included all studies showed statistically significant associations between the rs35767 SNP and type 2 diabetes, but after removing the Gouda et al. study produced results that were mostly not statistically significant. Therefore, there is not enough evidence from the results of the meta-analysis to indicate that the rs35767 SNP has a statistically significant association with type 2 diabetes.

Keywords: insulin-like growth factor-1; meta-analysis; rs35767; susceptibility; type 2 diabete mellitus.

FIGURE 1

Flow chart of researches selection in the meta-analysis.

FIGURE 2

Meta-analysis with a random effects model for the association between the IGF-1 rs35767 and T2DM susceptibility. (A) Allele model, T vs. C. (B) Additive model (homozygote comparisons): TT vs. CC.(C) Additive model (heterozygote comparisons): TC vs. CC. (D) Recessive model, TT vs. CC + CT.(E) Dominant model, TT + CT vs. CC. OR: odds ratio, CI: confidence interval, I-squared: measure to quantify the degree of heterogeneity in meta-analyses.

FIGURE 3

Sensitivity analysis by iteratively removing one study at a time. (A) Allele model, T vs. C. (B) Additive model (homozygote comparisons): TT vs. CC. (C) Additive model (heterozygote comparisons): TC vs. CC. (D) Recessive model, TT vs. CC + CT. (E) Dominant model, TT + CT vs. CC.

FIGURE 4

Meta-analysis for the association between the IGF-1 rs35767 and T2DM susceptibility after omitting Gouda’s study. (A) Allele model, T vs. C (random effects model). (B) Additive model (homozygote comparisons): TT vs. CC. (random effects model). (C) Additive model (heterozygote comparisons): TC vs. CC (random effects model) (D) Recessive model, TT vs. CC + CT (fixed effects model) (E) Dominant model, TT + CT vs. CC (random effects model). OR: odds ratio, CI: confidence interval, I-squared: measure to quantify the degree of heterogeneity in meta-analyses.

FIGURE 5

The association between the IGF-1 rs35767 and T2DM susceptibility in the subgroup for the allele model (T vs. C) (A) XinJiang, China (fixed effects model) (B) Other provinces, China (random effects model) (C) Other countries (fixed effects model). OR: odds ratio, CI: confidence interval, I-squared: measure to quantify the degree of heterogeneity in meta-analyses.