Metabolomics profile in diabetes and blood glucose regulation: a systematic review
TL;DR
A consistent metabolic pattern was identified, characterized by alterations in amino acid metabolism notably branched chain and aromatic amino acids, lipid metabolism including ceramides, sphingolipids, fatty acids, and central energy pathways like the TCA cycle.
Abstract
Introduction Diabetes mellitus is a chronic, non-communicable metabolic disorder of global public health concern. Many studies utilize metabolomics to profile metabolites in individuals with diabetes. However, these studies have shown inconsistencies in identifying metabolite signatures associated with the disease without clearly defining how it influences glucose homeostasis. Discrepancies in analytical platforms and biomarker validation limit clinical translation of metabolomic biomarkers. This review aims to integrate existing evidence on metabolomic profiles in diabetes, and address challenges related to biomarker validation and method standardization. Methods The review was conducted in adherence with PRISMA 2020 guidelines, from PubMed and Google Scholar databases. Peer-reviewed English-language studies published between 2010 and 2026 were retrieved using predefined and related keywords. Following thorough screening, 188 studies were included. The review synthesizes diverse metabolomics evidence into a unified model, establishing a link between metabolic disruptions and regulation of blood glucose. Results Across the studies, a consistent metabolic pattern was identified, characterized by alterations in amino acid metabolism notably branched chain and aromatic amino acids, lipid metabolism including ceramides, sphingolipids, fatty acids, and central energy pathways like the TCA cycle. Discussion Rather than being present as isolated biomarkers, they form interconnected networks that collectively influence insulin sensitivity, β-cell function, and hepatic glucose. Also, while advancement of LC-MS, GC-MS, and NMR-based metabolomics led to expansion in the identification of biomarkers, variability in experimental design and inadequate method standardization persist as constraint in reproducibility. Metabolomics offers strong framework to understand diabetes and improve early diagnosis, risk prediction, and precision glucose management, but requires better standardization.