Untargeted metabolomic profiling reveals molecular signatures associated with type 2 diabetes in Nigerians

Abstract

Background: Type 2 diabetes (T2D) has reached epidemic proportions globally, including in Africa. However, molecular studies to understand the pathophysiology of T2D remain scarce outside Europe and North America. The aims of this study are to use an untargeted metabolomics approach to identify: (a) metabolites that are differentially expressed between individuals with and without T2D and (b) a metabolic signature associated with T2D in a population of Sub-Saharan Africa (SSA).

Methods: A total of 580 adult Nigerians from the Africa America Diabetes Mellitus (AADM) study were studied. The discovery study included 310 individuals (210 without T2D, 100 with T2D). Metabolites in plasma were assessed by reverse phase, ultra-performance liquid chromatography and mass spectrometry (RP)/UPLC-MS/MS methods on the Metabolon Platform. Welch's two-sample t-test was used to identify differentially expressed metabolites (DEMs), followed by the construction of a biomarker panel using a random forest (RF) algorithm. The biomarker panel was evaluated in a replication sample of 270 individuals (110 without T2D and 160 with T2D) from the same study.

Results: Untargeted metabolomic analyses revealed 280 DEMs between individuals with and without T2D. The DEMs predominantly belonged to the lipid (51%, 142/280), amino acid (21%, 59/280), xenobiotics (13%, 35/280), carbohydrate (4%, 10/280) and nucleotide (4%, 10/280) super pathways. At the sub-pathway level, glycolysis, free fatty acid, bile metabolism, and branched chain amino acid catabolism were altered in T2D individuals. A 10-metabolite biomarker panel including glucose, gluconate, mannose, mannonate, 1,5-anhydroglucitol, fructose, fructosyl-lysine, 1-carboxylethylleucine, metformin, and methyl-glucopyranoside predicted T2D with an area under the curve (AUC) of 0.924 (95% CI: 0.845-0.966) and a predicted accuracy of 89.3%. The panel was validated with a similar AUC (0.935, 95% CI 0.906-0.958) in the replication cohort. The 10 metabolites in the biomarker panel correlated significantly with several T2D-related glycemic indices, including Hba1C, insulin resistance (HOMA-IR), and diabetes duration.

Conclusions: We demonstrate that metabolomic dysregulation associated with T2D in Nigerians affects multiple processes, including glycolysis, free fatty acid and bile metabolism, and branched chain amino acid catabolism. Our study replicated previous findings in other populations and identified a metabolic signature that could be used as a biomarker panel of T2D risk and glycemic control thus enhancing our knowledge of molecular pathophysiologic changes in T2D. The metabolomics dataset generated in this study represents an invaluable addition to publicly available multi-omics data on understudied African ancestry populations.

Keywords: Africans; Biomarkers; Metabolomics; Type 2 diabetes.

Fig. 1

Classification of differentially expressed metabolites in T2D by super pathways. A Pie chart of super pathways associated with differentially expressed metabolites. B Number of differentially expressed metabolites in T2D by super pathways. Y-axis represents the number of metabolites

Fig. 2

Box Plots of differentially expressed metabolites in the carbohydrate super pathway (glucose utilization) and associated metabolism pathways

Fig. 3

Examples of differentially expressed lipids in T2D and associated metabolism pathways. A. DEMs in fatty acid metabolism pathways (free fatty acids: from upper left to lower left, palmitate, eicosapentaenoate (EPA;20:5n3), stearate, docohexaenoate (DHA;22:6n3), 3-hydroxybutyrate (BHBA); far right: fatty acid metabolism implicating FFA differentially expressed in this study. B. Examples of differentially expressed monoacylglycerols and diacylglycerols (products of lipolysis) in T2D. Monoacylglycerols: Left to right, 1-linoleoylglycerol (18:2); 2-linoleoylglycerol (18:2); 1-linoleoyglycerol (18:3). Diacylglycerols: Left to right, linoleoyl- linoleoyl-glycerol (18:2/18:2); oleoyl- oleoyl-glycerol (18:1/18:1); oleoyl-linoleoyl-glycerol (18:1/18:2)

Fig. 4

Box plots of examples of differentially expressed metabolites in the primary and secondary bile acid synthesis metabolisms. Left panel: primary bile acids: glycocholate and taurocholate are increased in individuals with T2D compared to those without T2D. Middle panel: top diagram represents the primary and secondary bile acid synthesis pathway in the liver and the digestive lumen; the bottom represents the box plot of deoxycholate concentrations in individuals with T2D and without T2D. Right panel: Secondary bile acids, taurodeoxycholate and glycodeoxycholate are increased in individuals with T2D compared to those without T2D

Fig. 5

Box plots of differentially expressed branched chain amino acids (BCAA) and associated changes in key metabolites of BCAA catabolism. Top panel represents the most significantly increased BCAA in individuals with T2D vs. without T2D (left to right: leucine, valine, and isoleucine). Lower panel represents changes in intermediates and downstream metabolites in BCAA catabolism and the diagram of BCAA catabolism

Fig. 6

Analysis of biomarker panels for T2D based on ROC curve analyses. A ROC curve for the 10-metabolite biomarker panel in the discovery cohort. B Box plot of the predictive accuracy of the 10-metabolite biomarker panel in the discovery cohort. C Plot of the most important features of the 10-metabolite biomarker panel; 0 = non-T2D (individuals without T2D), 1 = T2D (individuals with T2D). D ROC curve for the 7-metabolite biomarker panel in the discovery cohort (panel restricted to non-established biomarkers). E ROC curve representing the replication of the identified biomarker panel in a different set of participants (replication cohort). F ROC curve representing the evaluation of the panel restricted to the non-established biomarkers in a different set of participants (replication cohort)

Fig. 7

Spearman correlation matrix between metabolites in the biomarker panel and clinical indexes of type 2 diabetes in the merged cohorts (discovery + replication). *Glucose measured as part of the biochemical panel. **Glucose measured as part of the untargeted metabolomics