Metabolite signatures as new severity biomarkers for type 2 diabetes mellitus and its complications

Prof. Dr. Pramod Wangikar
Indian Institute of Technology (IIT)
Mumbai | India
Part of the Symposium:
Metabolomics India 2023 - Clinical applications
Type 2 Diabetes mellitus (T2DM) is a progressive metabolic disorder that can develop into life-threatening complications such as diabetes-associated kidney (DKD) and cardiovascular (DCD) diseases. Although several metabolomics-based studies have been reported on T2DM, no new blood tests have been developed for the severity of diabetes or its complications. Here we present our results with metabolite signatures as biomarker rather than individual metabolites. Blood samples were drawn from a cohort of Indian patients from four groups: healthy volunteers, and the T2DM, DKD, and DCD. The study was approved by the Institute Ethics Committee. Untargeted metabolomics was performed using LCMS and GCMS and the data was processed using our own, AI/ML-based software platform. We identified over 200 metabolites that were significantly altered in T2DM, DKD, and DCD conditions compared to the healthy group. Metabolites from different classes including acylcarnitines, glucogenic amino acids, cholines, fatty acids, bile acids and several uremic toxins were significantly altered in T2DM, DKD and DCD patients compared to the healthy group. Importantly, a subset of patients showed significantly greater change in a number of metabolites, which led us to the analysis of metabolite signatures. To exemplify, the signature of uremic toxins was dramatically altered in a subset of DKD patients who had an advanced disease. Further, the signature of uremic toxins was also altered in a subset of T2DM patients, who should be monitored for asymptomatic, early stage DKD. Another important signature was that of acyl carnitines, many of which get downregulated in a subset of diabetics, which may indicate possible mitochondrial dysfunction. Further, sugars other than glucose show alterations, which may have implications in certain complications of diabetes. We will also discuss our LCMS data analysis pipeline that makes use of deep neural networks to reduce noise, redundant features and provide clean, reliable and quantifiable data thus resulting in significant saving of time required for data analysis.
Prof. Dr. Wangikar @LinkedIn: / pramodwangikar
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