Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes

Abstract

Aims/hypothesis Identification of novel biomarkers for type 2 diabetes and their genetic determinants could lead to improved understanding of causal pathways and improve risk prediction. Methods In this study, we used data from non-targeted metabolomics performed using liquid chromatography coupled with tandem mass spectrometry in three Swedish cohorts (Uppsala Longitudinal Study of Adult Men [ULSAM], n = 1138; Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS], n = 970; TwinGene, n = 1630). Metabolites associated with impaired fasting glucose (IFG) and/or prevalent type 2 diabetes were assessed for associations with incident type 2 diabetes in the three cohorts followed by replication attempts in the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort (n = 855). Assessment of the association of metabolite-regulating genetic variants with type 2 diabetes was done using data from a meta-analysis of genome-wide association studies. Results Out of 5961 investigated metabolic features, 1120 were associated with prevalent type 2 diabetes and IFG and 70 were annotated to metabolites and replicated in the three cohorts. Fifteen metabolites were associated with incident type 2 diabetes in the four cohorts combined (358 events) following adjustment for age, sex, BMI, waist circumference and fasting glucose. Novel findings included associations of higher values of the bile acid deoxycholic acid and monoacylglyceride 18:2 and lower concentrations of cortisol with type 2 diabetes risk. However, adding metabolites to an existing risk score improved model fit only marginally. A genetic variant within the CYP7A1 locus, encoding the rate-limiting enzyme in bile acid synthesis, was found to be associated with lower concentrations of deoxycholic acid, higher concentrations of LDL-cholesterol and lower type 2 diabetes risk. Variants in or near SGPP1, GCKR and FADS1/2 were associated with diabetes-associated phospholipids and type 2 diabetes. Conclusions/interpretation We found evidence that the metabolism of bile acids and phospholipids shares some common genetic origin with type 2 diabetes. Access to research materials Metabolomics data have been deposited in the Metabolights database, with accession numbers MTBLS93 (TwinGene), MTBLS124 (ULSAM) and MTBLS90 (PIVUS).

Metabolomics analyses in non-diabetic middle-aged individuals reveal metabolites impacting early glucose disturbances and insulin sensitivity

“…To date, several amino acids, sugar metabolites, and lipids have been associated with T2D risk in observational studies and causality has been investigated using Mendelian Randomization (Mahendran et al 2013a, b;Tillin et al 2015;Wurtz et al 2012Wurtz et al , 2013Mook-Kanamori et al 2014;t Hart et al 2018;Guasch-Ferre et al 2016;Liu et al 2017;Wang et al 2017;Fall et al 2016;Rawat et al 2019;Newsholme et al 2005). The present study replicates these observational findings in participants without diabetes mellitus, indicating that alterations in plasma metabolites levels in relation to perturbed glucose metabolism are already present in the non-diabetic population.…”

“…Metabolomics offers the possibility to comprehensively measure a broad range of metabolites in tissues and biological fluids. Multiple observational and causal association studies revealed metabolites such as phospholipids, triacylglycerols, ketone bodies, sphingomyelins, acyl-carnitines and organic acids to be linked to future risk of T2D onset (Mahendran et al 2013a, b;Tillin et al 2015;Wurtz et al 2012Wurtz et al , 2013Mook-Kanamori et al 2014;t Hart et al 2018;Guasch-Ferre et al 2016;Liu et al 2017;Wang et al 2017;Fall et al 2016).…”

Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes

“…Others have also incorporated genetics to streamline the identification of unknown metabolites (37)(38)(39). A recent multistep approach (37) used a GWAS of known and unknown metabolites and correlation analysis of known metabolites with unknown ones, and then used published metabolic pathway information to guide the construction of networks that displayed these relationships as a function of metabolite class.…”

Precision medicine in diabetes: an opportunity for clinical translation

“…For instance, a study that pooled data from four European cohorts found that CYP7A1, which encodes the rate-limiting enzyme in bile acid synthesis, was associated with lower concentrations of deoxycholic acid and higher T2D risk. 78 In addition, this study also identified variants in or near the genes encoding sphingosine-1-phosphate phosphatase 1 (SGPP1), glucokinase regulator (GCKR), and fatty acid desaturase 1 and 2 (FADS1/2) that were associated with diabetesassociated phospholipids and T2D risk. Finally, using Mendelian randomization in combination with plasma metabolomics suggested a causal role for lower levels of palmitoleic acid and oleic acid on insulin resistance.…”