Multiomics Biomarker Clusters for Chronic Kidney Disease in Type 2 Diabetes

Abstract

Background: Microvascular complications are common in diabetes mellitus (DM). Approximately 30% of the patients with DM developed chronic kidney disease (CKD), also known as diabetic kidney disease (DKD). DKD is associated with higher mortality risk before progressing to kidney failure. The integration of electronic health records (EHRs) and high-throughput multiomics data has shown great potential in personalised medicine. This thesis aims to identify biomarker clusters (corresponding to underlying systemic factors) of CKD progression in type 2 diabetes (T2D) using EHR-linked Scottish cohorts (GoDARTS, SUMMIT, and RHAPSODY).

Methods: Data included routinely-measured biochemistry variables and prescribing information in EHR; lifestyle factors and DM-related risk factors in GoDARTS; proteomics and metabolomics data of stage 3 CKD in SUMMIT; and proteomics, metabolomics, and lipidomics data of early CKD in RHAPSODY. Kidney function trajectory was estimated based on serum creatinine levels. Standard statistical models, dimensionality reduction methods, and functional enrichment analyses were applied.

Results: We identified three main biomarker clusters corresponding to albuminuria, DM, and cardiovascular disease (CVD). Other less significant biomarker clusters could be associated with a variety of stage-specific symptoms such as diabetic retinopathy (DR), smoking status, potassium level, infection, and mucous membrane inflammation. In addition, the functional analysis highlighted the importance of inflammation, endothelial dysfunction, and lipid metabolism.

Conclusions: The biomarker clusters improved the prediction accuracy by incorporating both strong and weak signals from individual biomarkers. The clusters could also serve as a framework for interpreting the underlying pathophysiology. The holistic approach identified a comprehensive set of prescriptions and biomarkers, which warrant further investigations.

Date of Award	2022
Original language	English
Supervisor	Colin Palmer (Supervisor) & Samira Bell (Supervisor)