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A Mendelian Randomization Study of Metabolite Profiles, Fasting Glucose, and Type 2 Diabetes.

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journal contribution
posted on 2018-07-24, 09:34 authored by Jun Liu, JJan Bert van Klinken, Sabina Semiz, Ko Willems van Dijk, Aswin Verhoeven, Thomas Hankemeier, Amy C. Harms, Eric Sijbrands, Nuala A. Sheehan, Cornelia M. van Duijn, Ayşe Demirkan
Mendelian randomization (MR) provides us the opportunity to investigate the causal paths of metabolites in type 2 diabetes and glucose homeostasis. We developed and tested an MR approach based on genetic risk scoring for plasma metabolite levels, utilizing a pathway-based sensitivity analysis to control for nonspecific effects. We focused on 124 circulating metabolites that correlate with fasting glucose in the Erasmus Rucphen Family (ERF) study (n = 2,564) and tested the possible causal effect of each metabolite with glucose and type 2 diabetes and vice versa. We detected 14 paths with potential causal effects by MR, following pathway-based sensitivity analysis. Our results suggest that elevated plasma triglycerides might be partially responsible for increased glucose levels and type 2 diabetes risk, which is consistent with previous reports. Additionally, elevated HDL components, i.e., small HDL triglycerides, might have a causal role of elevating glucose levels. In contrast, large (L) and extra large (XL) HDL lipid components, i.e., XL-HDL cholesterol, XL-HDL-free cholesterol, XL-HDL phospholipids, L-HDL cholesterol, and L-HDL-free cholesterol, as well as HDL cholesterol seem to be protective against increasing fasting glucose but not against type 2 diabetes. Finally, we demonstrate that genetic predisposition to type 2 diabetes associates with increased levels of alanine and decreased levels of phosphatidylcholine alkyl-acyl C42:5 and phosphatidylcholine alkyl-acyl C44:4. Our MR results provide novel insight into promising causal paths to and from glucose and type 2 diabetes and underline the value of additional information from high-resolution metabolomics over classic biochemistry.


The ERF study was supported by the Netherlands Consortium for Systems Biology, within the framework of the Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research. The ERF study as a part of the European Special Populations Research Network was supported by European Commission Sixth Framework Programme STRP grant no. 018947 (LSHG-CT-2006-01947) and also received funding from the European Community Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the program “Quality of Life and Management of the Living Resources” of Fifth Framework Programme (no. QLG2-CT-2002-01254), as well as FP7 project EUROHEADPAIN (no. 602633). High-throughput genetic analysis of the ERF data was supported by a joint grant from the Netherlands Organisation for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). High-throughput metabolomics measurements of the ERF study have been supported by Biobanking and BioMolecular resources Research Infrastructure the Netherlands (BBMRI-NL). Biocrates platform measurements were supported by the European Community's Seventh Framework Programme (FP7/2007–2013), ENGAGE Consortium, grant agreement HEALTH-F4-2007-201413. Lipidomics analysis was supported by the European Commission FP7 grant LipidomicNet (2007-202272). J.L., C.M.v.D., and A.D. have used exchange grants from the Personalized pREvention of Chronic DIseases consortium (PRECeDI). S.S. has been awarded the Erasmus Mundus–Western Balkans (ERAWEB) mobility program academic scholarship. A.D. is supported by a Veni grant (2015) from ZonMw.



Diabetes, 2017, 66 (11), pp. 2915-2926

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