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

medRxiv

Abstract

Abstract Aims/hypothesis There 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. Methods We 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. Results In 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. Conclusion The 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.

Authors 6

1. Wenyi Wang corresponding Aachen Department of Human Genetics

   Leiden University Medical Center

   Affiliation as printed

   Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands; (W.W); (K.W.v.D)

   (K.W.v.D)

   (W.W)

   Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands

2. Ephrem Baraki Tesfay corresponding Aachen

   Leiden University Medical Center

   Affiliation as printed

   Department of Internal Medicine, Section of Gerontology and Geriatrics; Leiden University Medical Center, Leiden, Netherlands; (E.B.T); (R.N.); (D.v.H.)

   (D.v.H.)

   (E.B.T)

   (R.N.)

   Department of Internal Medicine, Section of Gerontology and Geriatrics

   Leiden University Medical Center, Leiden, Netherlands

3. Ko Willems van Dijk corresponding Aachen Department of Human Genetics Department of Internal Medicine Division Endocrinology Leiden Laboratory for Experimental Vascular Medicine

   Leiden University Medical Center

   Affiliation as printed

   Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands; (W.W); (K.W.v.D)

   Department of Internal Medicine, Division Endocrinology, Leiden University Medical Center, Leiden, the Netherlands ; (K.W.v.D)

   Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands; (K.W.v.D)

   (K.W.v.D)

   (W.W)

   Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands

   Department of Internal Medicine, Division Endocrinology, Leiden University Medical Center, Leiden, the Netherlands

   Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands

4. Andrzej Bartke corresponding

   SIU Medicine

   Affiliation as printed

   Department of Internal Medicine, Southern Illinois University School of Medicine, Springfield, Illinois, USA; (A.B)

   (A.B)

   Department of Internal Medicine, Southern Illinois University School of Medicine, Springfield, Illinois, USA

5. Diana van Heemst corresponding Aachen

   Leiden University Medical Center

   Affiliation as printed

   Department of Internal Medicine, Section of Gerontology and Geriatrics; Leiden University Medical Center, Leiden, Netherlands; (E.B.T); (R.N.); (D.v.H.)

   (D.v.H.)

   (E.B.T)

   (R.N.)

   Department of Internal Medicine, Section of Gerontology and Geriatrics

   Leiden University Medical Center, Leiden, Netherlands

6. Raymond Noordam corresponding Aachen

   Leiden University Medical Center

   Affiliation as printed

   Department of Internal Medicine, Section of Gerontology and Geriatrics; Leiden University Medical Center, Leiden, Netherlands; (E.B.T); (R.N.); (D.v.H.)

   (D.v.H.)

   (E.B.T)

   (R.N.)

   Department of Internal Medicine, Section of Gerontology and Geriatrics

   Leiden University Medical Center, Leiden, Netherlands