No abstract

Iron overload is increasingly being connected to insulin resistance in Type 2 Diabetes Mellitus (T2DM) patients. Free iron causes the assembly of reactive oxygen species that invariably steer the body’s homeostasis towards oxidative stress-mediated diabetic complications. This study aims to assess the serum iron, total iron binding capacity (TIBC), and percentage transferrin saturation (Tsat) of 150 subjects divided into three groups (I,II,III) of 50. Healthy individuals (controls) constituted Group I. Group II consisted of T2DM patients with optimal glycaemic control. T2DM patients with suboptimal glycaemic control formed group III. Mean serum free iron concentration was 105.34 ± 3.5, 107.33 ± 3.45, and 125.58 ± 3.45 μg/dL in Group I, Group II, and Group III, respectively. Mean serum TIBC concentration in Group I, Group II, and Group III was 311.39 ± 5.47, 309.63 ± 6.1, and 284.2 ± 3.18μg/dL, respectively. Mean serum transferrin saturation (%) in Group I, Group II, and Group III was 34.17 ± 1.21, 35.02 ± 1.2, and 44.39 ± 1.07, respectively. The difference between TIBC, mean serum free iron concentration, and transferrin saturation between Group I and Group III (for all, p values <0.001), as well as between Group II and Group III (p values 0.0012, 0.0015, and <0.0001, respectively) was statistically significant. The fasting plasma glucose values of Groups II and III were significantly higher than those of Group I, (p < 0.0001). Glycated haemoglobin (HbA1c) values were also shown to increase from Group I to II and then III, and the increase was highly significant (all p values <0.0001). Thus, decreased glycaemic control and an increase in the glycation of haemoglobin was the key to elevation in serum iron values and alterations in other parameters. However, a significant correlation was absent between serum iron and HbA1c (r = 0.05) and transferrin saturation (r = 0.0496) in Group III.

Background: Diabetes is among the most prevalent diseases worldwide, of all the affected individuals a significant proportion of the population remains undiagnosed because of a lack of specific symptoms early in this disorder and inadequate diagnostics. Diabetes and its associated sequela, i.e., comorbidity are associated with microvascular and macrovascular complications. As diabetes is characterized by an altered metabolism of key metabolites and regulatory pathways. Metabolic phenotyping can provide us with a better understanding of the unique set of regulatory perturbations that predispose to diabetes and its associated comorbidities. Methodology: The present study utilizes the analytical platform NMR spectroscopy coupled with Random Forest statistical analysis to identify the discriminatory metabolites of diabetes (DB) and diabetes-related comorbidity (DC) along with the healthy control (HC) subjects. A combined and pairwise analysis was performed, between the serum samples of HC (n=50), and DB (n=38), and DC (n=35) individuals to identify the discriminatory metabolites responsible for class separation. The perturbed metabolites were further rigorously validated using t-test, AUROC analysis to examine the statistical significance of the identified metabolites. Results: The DB and DC patients were well discriminated from HC. However, 15 metabolites were found to be significantly perturbed in DC patients compared to DB, the identified panel of metabolites are TCA cycle (succinate, citrate), methylamine metabolism (trimethylamine, methylamine, betaine), -intermediates; energy metabolites (glucose, lactate, pyruvate); and amino acids (valine, arginine, glutamate, methionine, proline and threonine). The metabolites were further used to identify the perturbed metabolic pathway and correlation of metabolites in DC patients. Conclusion: The 1H NMR metabolomics may prove a promising technique to differentiate and predict diabetes and its comorbidities on their onset or progression by determining the altered levels of the metabolites in serum.