CORRELATION OF DIABETIC PARAMETER (HBA1C) WITH OXIDATIVE STRESS PARAMETER (MDA) IN TYPE 2 DIABETES MELLITUS PATIENTS
Background: Type 2 diabetes mellitus (T2DM) is a growing public health concern globally, characterized by chronic hyperglycemia, dyslipidemia, and increased oxidative stress. These metabolic alterations are key contributors to the development of long-term complications. Aim: This study compared glycemic control, lipid proles, serum protein levels, and oxidative stress markers between patients with T2DM and healthy controls. Materials & methods: A total of 60 participants were recruited, including 30 healthy controls and 30 patients with T2DM. Data on age, gender, body mass index (BMI), and family history of T2DM were recorded. Blood glucose levels, HbA1c, lipid proles (total cholesterol, triglycerides, LDL, HDL), total serum proteins, and oxidative stress marker (MDA) were measured and compared between the two groups. Results: The diabetic group exhibited signicantly higher blood glucose levels (14.24mmol/dL) and HbA1c (10.55%) compared to controls (6.27mmol/dL and 5.54%, respectively; p < 0.001*). Total cholesterol, triglycerides, and LDL levels were elevated in the diabetic group, while total serum proteins were lower. Oxidative stress, as measured by MDA levels, was signicantly higher in the diabetic group (0.305nmol/mg protein) compared to controls (0.176nmol/mg protein). Conclusion: Patients with T2DM demonstrated poorer glycemic control, lipid dysregulation, and increased oxidative stress compared to healthy controls. These ndings highlight the need for comprehensive management of metabolic and oxidative stress parameters in patients with diabetes to prevent complications.
Authors
- Syeda Ayesha Siddiqua (ORCID: https://orcid.org/0000-0002-9557-7958)
- Arooba Fatima (ORCID: https://orcid.org/0009-0005-4712-5809)
- Abdul Mohammed (ORCID: https://orcid.org/0000-0003-0791-596X)
- Rimsha Rimsha
- Fnu Rukhayya
Institutions
- Shadan Hospital and Institute of Medical Sciences (IN)
Publication Details
- Journal
- GLOBAL JOURNAL FOR RESEARCH ANALYSIS
- Published
- 2024-10-15
- DOI
- https://doi.org/10.36106/gjra/2806912
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00