Novel insights into causal pathways in type 2 diabetes
A Mendelian Randomization Study of Metabolite Profile, Fasting Glucose and Type 2 Diabetes
Type 2 diabetes (T2DM) is associated with higher circulating concentrations of triglycerides and lower concentrations of HDL-C, as well as with dysregulation of phospholipids, branched-chain amino-acids, keto-acid metabolites and other metabolites such as acyl-carnitines [1-3]. Potential causal relationships between these associations have not been clarified in existing studies.
In this study, potential causal metabolic pathways in glucose homeostasis were investigated, using genetic predictors from published metabolite genome-wide association studies (GWAS), guided by pathway-based evidence to select instrumental variables. Subsequently, Mendelian randomization (MR) was performed between selected metabolic markers and glucose/T2DM.
The observational associations between metabolites and fasting glucose/T2DM were tested in the Erasmus Rucphen Family (ERF) study, a prospective family-based study in the Southwest of the Netherlands . In total, 562 metabolic markers including sub-fractions of lipoproteins, triglycerides, phospholipids, ceramides, amino-acids, acyl-carnitines and small intermediate compounds, were measured by five different metabolomics platforms. Metabolites associated with glucose in the ERF study (n = 124) were candidates for MR.
For each metabolite associated with glucose, a two-sample bi-directional MR was performed, and it was tested if genetically varying levels of a particular metabolite affect the risk for elevated glucose and T2DM, and if a genetically increased risk of T2DM or elevated glucose is associated with circulating levels of a particular metabolite.
- 124 metabolites significantly associated with fasting glucose (90 positive and 34 negative correlations) in the control population, consisting of 36 phospholipids, 20 triglycerides, 24 small molecular compounds and 44 lipoprotein particle sub-fractions. 112 of them also associated with T2DM.
- 34 Negative correlations were between glucose and alkyl-acyl and diacyl-phosphatidylcholines, mostly of the poly-unsaturated type, lysophosphatidylcholines, mostly of the saturated type and parts of the lipoprotein sub-fractions from LDL and HDL.
- 90 Positive correlations were between glucose and several phospholipids, phosphatidylethanolamines, and lysophosphatidylcholines, aminoacids and low-molecular weight compounds, in addition to lipid side-groups, and triglycerides.
- Small (S), extra-small (XS), medium (M) and XL-VLDL particles, as well as the total VLDL components, and to a lesser extent IDL and LDL-triglycerides, XS-LDL to M-LDL particle components, and the ApoA1 and triglyceride content of S-HDL particles correlated positively with fasting glucose in the non-diabetic population.
- Among the 20 eligible metabolite-glucose/T2DM sets, genetically decreased levels of eight metabolites associated significantly with fasting glucose (false discovery rate, FDR < 0.05). These include XL-HDL-C (FDR = 0.03), XL-HDL-phospholipids (FDR = 2.76 × 10-3), XS-VLDL-phospholipids (FDR = 0.04), XL-HDL-free-C (FDR = 0.01), L-HDL-C (FDR = 0.01), L-HDL-free-C (FDR = 2.76 × 10-3), HDL-C (FDR = 0.04), and IDL-phospholipids (FDR = 0.04). A causal role for IDL-phospholipids was not supported (FDR = 0.17).
- Pathway-based sensitivity analysis showed possibly causal roles for three additional metabolic markers, including S-VLDL-triglycerides (FDR = 0.04), S-HDL-triglycerides (FDR = 0.04), and plasma-triglycerides (FDR = 0.04).
- Genetic predisposition to T2DM is associated with lower levels of phosphatidylcholine alkyl-acyl 42:5 (FDR = 0.02) and phosphatidylcholine alkyl-acyl 44:4 (FDR = 0.02), and higher levels of alanine (FDR = 0.02).
Mendelian randomization provided evidence for potentially causal metabolic pathways of glucose homeostasis and T2DM. These findings indicate that an increase of large HDL particles might have a decreasing effect on glucose, while the opposite is the case with triglycerides and small HDL particles, suggesting them as targets for glucose management.