Metabolite Biomarkers for Type 2 Diabetes (T2D)

The Challenge: Type 2 Diabetes (T2D) Has a Heterogeneous Etiology

Type 2 diabetes (T2D) is a highly heterogeneous, multifactorial condition affecting over 400 million people worldwide. T2D’s heterogeneous etiology influences its progression, treatment, and complications. Therefore, a study recently conducted a data-driven cluster analysis in European individuals with T2D and stratified subjects into four clusters representing T2D subtypes: severe insulin-deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes.¹

Given that clinical discrepancies between racial/ethnic groups have been confirmed, a more thorough validation of different populations is still warranted. Furthermore, individualized therapies targeting the underlying pathophysiology of T2D should be a primary goal in treating these patients. In this study, metabolomics helped characterize the broader spectrum of physiological perturbations associated with T2D to enable subtype-specific individualization of therapies.

Metabolon Insight: Metabolomics Characterizes T2D Subtypes

The Metabolon Global Discovery Panel was used in this study to profile plasma samples from an Arab population with T2D (n = 420) and without T2D (n = 1735).² The goal was to discover distinctive alterations in metabolic components of signaling pathways underlying different T2D subtypes.

The Solution: Elucidating T2D Subtype-Specific Metabolites

Here, the clustering approach was applied to subjects with T2D from an Arab population. Further, the team used nontargeted metabolomics and affinity proteomics profiling to identify cluster-specific physiological and biochemical processes. This study identified 214 proteins and 194 metabolites associated with T2D. In relation to plasma metabolites, this study confirmed previous findings that associate T2D with sugars (glucose, mannose, 1,5-anhydroglucitol (1,5-AG), etc.), branched-chain amino acids (BCAAs), and several lipids and markers of kidney function.

Cluster-specific signatures of proteins and metabolites were established, revealing T2D subtype-specific molecular mechanisms. As in the case of metabolites, subjects in the SIDD group had the lowest 1,5-AG levels of all other T2D subtypes. Blood sugars (eg, mannose, glucose, and fructose), cortisone, and cortisol were considerably higher in the SIDD cluster, indicating dysregulated glucose metabolism and many chronic complications of T2D. Furthermore, the SIDD cluster had lower levels of gamma-glutamyl amino acids, indicating perturbed glutathione metabolism. The levels of two sphingomyelin species were also lower in the SIDD cluster. Downregulated sphingolipid metabolism can affect insulin sensitivity. The SIRD cluster had elevated levels of 12,13-DiHOME, which can be used as an alternative energy source for SIRD individuals given their potentially limited access to glucose due to insulin resistance. The MOD cluster had high levels of hydroxyasparagine, 5-(galactosylhydroxy)-L-lysine, and 7-alpha-hydroxy-3-oxo-4-cholestenoate (7-Hoca), which play a role in lipid metabolism and obesity. The MARD cluster was the healthiest of the four T2D subtypes, with metabolomic profiles most similar to the controls.

The Benefit: Improving Type 2 Diabetes Diagnosis and Therapeutic Strategies and Efficacy

This group translated the T2D subtypes to an Arab population and discovered distinct molecular signatures to further elucidate these T2D subtypes’ etiology. The research team identified many T2D subtype-specific metabolite and protein signatures that could help identify pathways involved in the etiology of T2D and enable more personalized treatment approaches. Metabolites supported as potentially causal for T2D in this data could be particularly interesting as novel therapeutic targets. These metabolites could potentially help diagnose the correct T2D subtype to improve care and outcomes for individuals with T2D. Monitoring these metabolites in patients with T2D via targeted metabolomic profiling may inform the efficacy of new therapeutic interventions.