Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study
Shiying Li1, Iulian Dragan2, Van Du T Tran2
1Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, Canada.
Frontiers in Endocrinology
|March 21, 2024
Summary
Researchers identified two distinct type 2 diabetes (T2D) patient subgroups using plasma lipidomics and proteomics. These subgroups differ in disease severity, offering potential new prognostic markers for T2D management.
Area of Science:
- Biochemistry
- Genomics
- Metabolomics
Background:
- Type 2 diabetes (T2D) exhibits significant individual variability in onset, progression, and outcomes.
- Multi-omics analyses hold promise for understanding these differences and enabling personalized T2D treatments.
Purpose of the Study:
- To group T2D patients based solely on plasma -omics data using an unsupervised approach.
- To identify distinct patient subgroups and their associated molecular signatures.
Main Methods:
- Analyzed circulating plasma lipidomic and proteomic data from two independent cohorts (DCS and GoDARTS).
- Employed Similarity Network Fusion for network analysis.
- Utilized Logistic and Cox regression modeling to link -omic profiles with clinical characteristics.
Main Results:
- Separated 1,134 subjects into two T2D subgroups based on lipid and protein levels.
- Subgroups differed significantly in glycaemic deterioration, insulin sensitivity, and secretion.
- Key molecular signatures included triacylglycerols, sphingomyelin, testican-1, and interleukin 18 receptor.
Conclusions:
- Successfully identified two T2D patient subgroups with differing disease severity using unsupervised network-based fusion of plasma -omics data.
- The identified molecular signatures offer insights into T2D pathogenesis.
- These signatures may serve as novel prognostic markers for T2D.
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