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IntroductionGestational Diabetes Mellitus (GDM) is a metabolic disorder marked by Q10 hyperglycemia that can negatively affect both mothers and newborns. The increasing prevalence of GDM and the limitations associated with the standard diagnostic test highlight the urgent need for early screening strategies that promote timely interventions.MethodsThis study aims to investigate the metabolic profile associated with GDM through an untargeted metabolomic analysis using mass spectrometry (MS)- based omics. Serum samples were collected from 40 pregnant women at weeks 24–28 of gestation based on the 2-h 75-g oral glucose tolerance test (OGTT); 50% were diagnosed with GDM (n = 20), and the remaining were considered a control group.Results and discussionThe results showed distinct metabolic differences between women with GDM and those without, with 222 significantly dysregulated metabolites, 120 up- and 102 downregulated in GDM compared to the control group. Key metabolic pathways, such as tryptophan, inositol phosphate, phenylalanine, and histidine metabolism, were notably dysregulated in GDM. The study also found that specific metabolites, like N-Acetylproline and Serylmethionine, with area under the curve (AUC) of 0.978 and 0.968, respectively, showed high accuracy in distinguishing between GDM and non-GDM women. This study would enhance our understanding of metabolic alterations in GDM and could contribute to early prediction and management strategies.

ObjectiveChronic hyperglycemia induces pathogenic changes in the vascular endothelium and leads to the development of microvascular complications in patients with type 2 diabetes mellitus. Early identification of markers of diabetes complications may help to minimize the risk of the development and progression of microvascular complications.MethodsThis follow-up study was conducted in type 2 diabetic cohort aged between 30-70 years. Out of 160 eligible participants, 70 of them completed follow-up. Levels of cell adhesion molecules and selectins (VCAM-1, ICAM-1, E-selectin, L-selectin and P-selectin) at baseline and follow-up were measured using Randox Evidence biochip analyzer (UK). Development of microvascular complications (diabetic neuropathy, retinopathy and nephropathy) was evaluated.ResultsDuring the follow-up (2 years, median), 31 (44.3%) developed diabetic neuropathy, 10 (14.3%) developed diabetic retinopathy and, 27 (38.6%) developed diabetic nephropathy. A significant difference in levels of cell adhesion molecules and selectins were found in type 2 diabetic patients with and without microvascular complications. Multiple logistic regression analysis reveals that baseline level of VCAM-1 is significantly associated with microvascular complications; diabetic neuropathy(p=0.028), retinopathy (p=0.007) and nephropathy(p=

Metformin is a widely prescribed medication for the treatment of type 2 diabetes mellitus (T2DM). It possesses effective roles in various disorders, including cancer, dyslipidemia, and obesity. However, the underlying mechanisms of metformin's multiple benefits are not fully understood. Herein, a mass spectrometry-based untargeted metabolomics approach was used to investigate the metabolic changes associated with the administration of a single dose of metformin in the plasma of 26 healthy subjects at five-time points; pre-dose, before the maximum concentration of metformin (Cmax), Cmax, after Cmax, and 36 h post-dose. A total of 111 metabolites involved in various biochemical processes were perturbed, with branched-chain amino acid (BCAA) being the most significantly altered pathway. Additionally, the Pearson similarity test revealed that 63 metabolites showed a change in their levels dependent on metformin level. Out of these 63, the level of 36 metabolites was significantly altered by metformin. Significantly altered metformin-dependent metabolites, including hydroxymethyl uracil, propionic acid, glycerophospholipids, and eicosanoids, pointed to fundamental biochemical processes such as lipid network signaling, energy homeostasis, DNA lesion repair mechanisms, and gut microbiota functions that could be linked to the multiple beneficial roles of metformin. Thus, the distinctive metabolic pattern linked to metformin administration can be used as a metabolic signature to predict the potential effect and mechanism of actions of new chemical entities during drug development.

Metformin is widely used in the treatment of Type 2 Diabetes Mellitus (T2DM). However, it is known to have beneficial effects in many other conditions, including obesity and cancer. In this study, we aimed to investigate the metabolic effect of metformin in T2DM and its impact on obesity. A mass spectrometry (MS)-based metabolomics approach was used to analyze samples from two cohorts, including healthy lean and obese control, and lean as well as obese T2DM patients on metformin regimen in the last 6 months. The results show a clear group separation and sample clustering between the study groups due to both T2DM and metformin administration. Seventy-one metabolites were dysregulated in diabetic obese patients (30 up-regulated and 41 down-regulated), and their levels were unchanged with metformin administration. However, 30 metabolites were dysregulated (21 were up-regulated and 9 were down-regulated) and then restored to obese control levels by metformin administration in obese diabetic patients. Furthermore, in obese diabetic patients, the level of 10 metabolites was dysregulated only after metformin administration. Most of these dysregulated metabolites were dipeptides, aliphatic amino acids, nucleic acid derivatives, and urea cycle components. The metabolic pattern of 62 metabolites was persistent, and their levels were affected by neither T2DM nor metformin in obesity. Interestingly, 9 metabolites were significantly dysregulated between lean and obese cohorts due to T2DM and metformin regardless of the obesity status. These include arginine, citrulline, guanidoacetic acid, proline, alanine, taurine, 5-hydroxyindoleacetic acid, and 5-hydroxymethyluracil. Understanding the metabolic alterations taking place upon metformin treatment would shed light on possible molecular targets of metformin, especially in conditions like T2DM and obesity.