Targeted and Untargeted GC–MS Metabolomics Coupled With Multivariate Chemometric Modeling Reveals Pioglitazone's Impact on Diabetic Rats

Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia and alterations in metabolic pathways. Herein, gas chromatography-mass spectrometry (GC-MS) metabolomics was employed to investigate the changes in serum metabolites of streptozotocin-induced diabetic rats in response to pioglitazone treatment. Both untargeted and targeted analyses were conducted to identify and quantify the key metabolic perturbations. Multivariate analysis modeling such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal projections to latent structures DA (OPLS-DA) revealed significant differences in the metabolic profiles among the diabetic, pioglitazone-treated, and control groups. A panel of 15 differentially altered metabolites was identified, of which a targeted quantification was performed of 6 key metabolites that were significantly modulated by pioglitazone treatment. These metabolites include two branched-chain amino acids (l-isoleucine, l-valine), two organic acids (lactic, beta-hydroxybutyric), and two fatty acids (palmitic, stearic). Interestingly, pathway analysis revealed the inability of pioglitazone to restore the levels of some metabolites, suggesting possible persistent metabolic alterations in diabetes even after treatment. This metabolic signature offers significant potential for clinical translation, serving as a biomarker panel for monitoring diabetes progression and therapeutic efficacy. Such comprehensive GC-MS metabolomics platform provides a robust analytical framework for accelerating diabetes drug discovery, enabling precise therapeutic monitoring, and facilitating personalized medicine approaches in diabetes management.