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AFRICAN RESEARCH NEXUS

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biochemistry, genetics and molecular biology

Potential Predictive Role of Lipid Peroxidation Markers for Type 2 Diabetes in the Adult Tunisian Population

Canadian Journal of Diabetes, Volume 42, No. 3, Year 2018

Objectives: We evaluated the potential clinical relevance of malondialdehyde (MDA) and autoantibodies to copper oxidized low-density lipoprotein (CuOx-LDL) in type 2 diabetes occurrence. Methods: This cross-sectional study enrolled 69 normoglycemic subjects, 18 prediabetic patients and 108 type 2 diabetes patients. MDA concentration was assessed spectrophotometrically. Plasma IgG, IgA and IgM levels to CuOx-LDL were determined by ELISA. Results: Plasma MDA levels were considerably higher in obese, prediabetic and type 2 diabetes subjects compared to controls. In multiple linear regression analysis, both MDA and IgA to CuOx-LDL were significantly associated with glucose metabolism markers (p<0.05). Multiple logistic regression analyses showed that high plasma MDA and IgA to CuOx-LDL were independent risk factors for type 2 diabetes (OR 1.196, 95% CI: 1.058 to 1.353; p=0.004; OR 1.626, 95% CI: 1.066 to 2.481; p=0.024; respectively). Importantly, elevated IgA to CuOx-LDL predicted incident diabetes in patients with prediabetes (OR 2.321, 95% CI:1.063 to 5.066; p=0.035). From stratified analyses by body mass index (BMI), both MDA and IgA to CuOx-LDL remained independent predictors of type 2 diabetes occurrence in non-obese subjects (p<0.05). More interesting, elevated IgA to CuOx-LDL levels could be predictors of type 2 diabetes in obese prediabetic subjects (p=0.044). Conversely, neither IgG nor IgM to CuOx-LDL was associated with glucose metabolism markers, obesity or type 2 diabetes. Conclusions: Plasma MDA and IgA to CuOx-LDL were significantly associated with blood markers of glucose metabolism. High levels of MDA and IgA to CuOx-LDL could independently predict type 2 diabetes development in normoglycemia and prediabetic subjects.
Statistics
Citations: 15
Authors: 14
Affiliations: 5
Identifiers
Research Areas
Noncommunicable Diseases
Study Design
Cross Sectional Study
Study Approach
Quantitative