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Updated: Mar 9, 2026

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
Published on: July 5, 2022
Assessment of causality between serum gamma-glutamyltransferase and type 2 diabetes mellitus using publicly available
Raymond Noordam1, Roelof Aj Smit1,2, Iris Postmus1
1Department of Internal Medicine, Section Gerontology and Geriatrics.
Background:
In observational studies, high levels of gamma-glutamyltransferase (GGT) have been associated with a higher risk of type 2 diabetes mellitus (T2D). We aimed to assess whether this association is causal, using Mendelian randomization.
Methods:
A Mendelian randomization study was conducted using publicly available data from a genome-wide association study (GWAS) on T2D (12 171 cases of T2D and 56 862 controls), and additionally from GWAS on glycaemic traits ( N = 46 186) and HbA1c ( N = 46 368) in nondiabetic participants. Independent genetic variants (26 in total), identified in the largest GGT GWAS comprising studies of European ancestry, were used as genetic instruments. Inverse-variance weighted and MR-Egger regression analyses were used to estimate the effect of the combined genetic instrumental variables on T2D and glycaemic traits and HbA1c.
Results:
F-statistics of the 26 genetic instrumental variables, as a measure of instrumental strength, ranged from 23.4 ( ATP8B1 ) to 258.3 ( GGT1 ). Using inverse-variance analyses, we found no evidence of an association between the combined genetic instrumental variables for GGT and the risk of T2D, or glucose-, insulin- or HbA1c-levels. More specifically, a 10% higher genetically determined GGT was not associated with a higher risk of T2D (odds ratio: 0.99; 95% confidence interval: 0.95; 1.02). Results were similar for MR-Egger regression analyses, which did not show evidence for directional pleiotropy.
Conclusion:
The previously observed association between high levels of serum GGT and T2D in observational studies might not be causal. Likely, results from the observational studies can be explained by reverse causality and/or residual confounding.
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