Distinct Molecular Signatures of Clinical Clusters in People With Type 2 Diabetes: An IMI-RHAPSODY Study

Abstract

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (N = 12,828), metabolomic (N = 2,945), lipidomic (N = 2,593), and proteomic (N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.

Novel Subgroups of Type 2 Diabetes Display Different Epigenetic Patterns That Associate With Future Diabetic Complications

“…Higher SIRD-MRS and MARD-MRS were associated with an increased risk of developing CVD and CKD, whereas a higher MOD-MRS was associated with lower risk for developing these two complications. Our results are supported by previous studies, where patients with SIRD had a higher risk of CKD and patients with MOD had a lower risk of CKD and coronary events ( 4 , 11 ). In the current study, we also found that the associations between patients with MOD and those with MARD and the risk for CVD and CKD were similar to the associations observed for MOD-MRS and MARD-MRS. On the other hand, SIRD-MRS and SIDD-MRS were able to predict CVD and/or CKD, whereas patients with SIRD and those with SIDD were not associated with the risk of these diseases, suggesting a potentially better ability of these MRSs on predicting complications compared with the T2D subgroups.…”

“…Our group and others have found epigenetic differences in tissues from patients with T2D versus control subjects ( 11 – 15 ), demonstrating that epigenetic mechanisms contribute to the pathogenesis of T2D. Moreover, there has been an increasing interest in identifying blood-based epigenetic biomarkers for risk assessment in patients with diabetes.…”

“…The subgroups have different patient characteristics and risk of diabetic complications ( 4 ). Differences in their genetic, metabolomic, and proteomic signatures further support that diverse etiologies exist between the subgroups ( 10 , 11 ). This reclassification may hence provide a better basis for understanding differences in patients with T2D, representing an important step toward precision medicine in diabetes.…”

A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

“…At present, the only study integrating genetic, metabolomic, lipidomic and proteomic data to compare diabetes subtypes was based on different clustering variables (age, BMI, HbA 1c , HDL-cholesterol, and random or fasting Cpeptide). SIRD showed the most distinct molecular signature, mostly related to insulin resistance, lipids and inflammation [50].…”

A roadmap to achieve pharmacological precision medicine in diabetes

“…These might include measures of adiposity, disease duration, beta cell function or insulin action: in this regard, a basal or stimulated C-peptide holds singular promise. In addition, agnostic explorations for such biomarkers as can be assessed by global metabolomic profiling are of intense interest [22][23][24].…”