Causal Effects of Overall and Abdominal Obesity on Insulin Resistance and the Risk of Type 2 Diabetes Mellitus: A Two-Sample Mendelian Randomization Study

Abstract

Overall and abdominal obesity were significantly associated with insulin resistance and type 2 diabetes mellitus (T2DM) risk in observational studies, though these associations cannot avoid the bias induced by confounding effects and reverse causation. This study aimed to test whether these associations are causal, and it compared the causal effects of overall and abdominal obesity on T2DM risk and glycemic traits by using a two-sample Mendelian randomization (MR) design. Based on summary-level statistics from genome-wide association studies, the instrumental variables for body mass index (BMI), waist-to-hip ratio (WHR), and WHR adjusted for BMI (WHRadjBMI) were extracted, and the horizontal pleiotropy was analyzed using MR–Egger regression and the MR–pleiotropy residual sum and outlier (PRESSO) method. Thereafter, by using the conventional MR method, the inverse-variance weighted method was applied to assess the causal effect of BMI, WHR, and WHRadjBMI on T2DM risk, Homeostatic model assessment of insulin resistance (HOMA-IR), fasting insulin, fasting glucose, and Hemoglobin A1c (HbA1c). A series of sensitivity analyses, including the multivariable MR (diastolic blood pressure, systolic blood pressure, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol as covariates), MR–Egger regression, weighted median, MR–PRESSO, and leave-one-out method, were conducted to test the robustness of the results from the conventional MR. Despite the existence of horizontal pleiotropy, consistent results were found in the conventional MR results and sensitivity analyses, except for the association between BMI and fasting glucose, and WHRadjBMI and fasting glucose. Each one standard deviation higher BMI was associated with an increased T2DM risk [odds ratio (OR): 2.741; 95% confidence interval (CI): 2.421–3.104], higher HbA1c [1.054; 1.04–1.068], fasting insulin [1.202; 1.173–1.231], and HOMA-IR [1.221; 1.187–1.255], similar to findings for causal effect of WHRadjBMI on T2DM risk [1.993; 1.704–2.33], HbA1c [1.061; 1.042–1.08], fasting insulin [1.102; 1.068–1.136], and HOMA-IR [1.127; 1.088–1.167]. Both BMI ( P = 0.546) and WHRadjBMI ( P = 0.443) were unassociated with fasting glucose in the multivariable MR analysis. In conclusion, overall and abdominal obesity have causal effects on T2DM risk and insulin resistance but no causal effect on fasting glucose. Individuals can substantially reduce their insulin resistance and T2DM risk through reduction of body fat mass and modification of body fat distribution.

Hyperinsulinemia in Obesity, Inflammation, and Cancer

“…The INS locus is complex, and how these polymorphisms affect insulin secretion remain unclear, but are beginning to be investigated. Mendelian randomization studies support a causal role for genetically driven glucose-stimulated hyperinsulinemia in obesity [ 98 ], although studies also support a causal contribution of BMI to fasting insulin and insulin resistance [ 99 ]. It is likely that there is bi-directional causality and the results depend on which single-nucleotide polymorphisms and which anthropomorphic traits are chosen for analysis.…”

Anthropometric and adiposity indicators and risk of type 2 diabetes: systematic review and dose-response meta-analysis of cohort studies

“…Finally, because of the observational design of the studies, residual confounding cannot be excluded and should be considered when interpreting the results. Recent mendelian randomisation studies, however, suggested a casual effect of general and central adiposity on the risk of type 2 diabetes 5556…”

“…Recent mendelian randomisation studies, however, suggested a casual effect of general and central adiposity on the risk of type 2 diabetes. 55 56 …”

The Role of Different Indexes of Adiposity and Body Composition for the Identification of Metabolic Syndrome in Women with Obesity

“…In a recent paper, it was suggested that the body mass index (BMI), which is the most used index in clinical practice, could be considered a useful clinical threshold to help the decision [ 4 ]. However, BMI does not discriminate fat mass from fat-free mass, or the percentage of fat mass and its distribution, which in turn are considered the most important predictors of cardio-metabolic risk [ 5 , 6 ].…”