Evaluation of Serum Zinc as a Potential Biomarker of Dyslipidemia among Obese Adults


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Keywords

Serum zinc
Dyslipidemia
Obesity
Lipid Profile
Biomarker

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1.
Evaluation of Serum Zinc as a Potential Biomarker of Dyslipidemia among Obese Adults. The Insight [Internet]. 2025 Dec. 12 [cited 2025 Dec. 14];8(03):507-13. Available from: https://www.bdjournals.org/insight/article/view/798

Abstract

Background: Dyslipidemia is a common metabolic disorder among obese adults, and emerging evidence suggests that micronutrient imbalance, particularly serum zinc deficiency, may be linked to altered lipid metabolism. This study aimed to evaluate serum zinc as a potential biomarker of dyslipidemia among obese adults. Methods & Materials: This comparative cross-sectional study was conducted in the Department of Physiology at Sylhet MAG Osmani Medical College, Sylhet, Bangladesh, from July 2022 to June 2023. A total of 60 individuals attending various Outpatient Departments of Sylhet MAG Osmani Medical College Hospital were enrolled. Participants were divided into two groups: Group A (Obese individuals with BMI ≥ 25 kg/m²), and Group B (Healthy individuals with normal BMI). Results: The demographic characteristics were comparable between the groups (p > 0.05). Obese participants exhibited significantly higher TC, TG, and LDL-C levels and lower HDL-C levels compared to healthy controls (p < 0.01). Serum zinc was significantly reduced in Group A (10.15 ± 1.03 µmol/L) compared to Group B (13.13 ± 1.34 µmol/L; p < 0.001). Regression analyses revealed moderate linear correlations between serum zinc and TC (r = –0.503), TG (r = –0.591), HDL-C (r = 0.616), and LDL-C (r = –0.546), all of which were statistically significant (p < 0.001). Each lipid parameter significantly predicted serum zinc levels, with HDL-C showing the strongest predictive association. Conclusion: Serum zinc levels are significantly associated with lipid abnormalities in obese adults and may serve as a potential biomarker for dyslipidemia. Further longitudinal studies are recommended to validate its predictive utility.

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