Clustered Mendelian Randomization analyses identifies distinct and opposing pathways in the causal association between insulin-like growth factor-1 and type 2 diabetes mellitus

Aims/hypothesisThere is inconsistent evidence for the causal role of serum insulin-like growth factor-1 (IGF-1) concentration in the pathogenesis of type 2 diabetes. Here, we investigated the association between IGF-1 and type 2 diabetes using a combination of multivariable-adjusted and (clustered) Mendelian Randomization (MR) analyses in the UK Biobank. MethodsWe conducted Cox proportional hazard analyses in 451,232 European-ancestry individuals of the UK Biobank (55.3% women, mean age at recruitment 56.6 years), among which 13,247 individuals developed type 2 diabetes during up to 12 years of follow-up. In addition, we conducted two-sample MR analyses based on independent SNPs associated with IGF-1. Given the heterogeneity between the causal estimates of individual instruments (P-value for Q statistic=4.03e-145), we also conducted clustered MR analyses. Biological pathway analyses of the identified clusters were performed by overrepresentation analyses. ResultsIn the Cox proportional hazard models, with IGF-1 concentrations stratified in quintiles, we observed that participants in the lowest quintile had the highest relative risk of type 2 diabetes (HR: 1.31; CI: 1.23-1.39). In contrast, in the two-sample MR analyses, higher genetically-influenced IGF-1 was associated with a higher risk of type 2 diabetes. Based on the heterogeneous distribution of causal effect estimates, six clusters associated either with a lower or a higher risk of type 2 diabetes were identified. The main clusters in which a higher IGF-1 was associated with a lower risk of type 2 diabetes consisted of instruments mapping to genes in the growth-hormone signaling pathway, whereas the main clusters in which a higher IGF-1 was associated with a higher risk of type 2 diabetes consisted of instruments mapping to genes in pathways related to amino acid metabolism and genomic integrity. ConclusionThe IGF-1 associated SNPs used as genetic instruments in MR analyses showed a heterogeneous distribution of causal effect estimates on the risk of type 2 diabetes. This was likely explained by differences in the underlying molecular pathways that increase IGF-1 concentration and differentially mediate the effects of IGF-1 on type 2 diabetes.