article · International Journal of Diabetes and Endocrinology
This study examined the relationships between glycated haemoglobin, known as HbA1c, and lipid biomarkers among 70 individuals with type 2 diabetes and 67 non-diabetic controls at the Central Hospital of Yaoundé. The analysis revealed that metabolic patterns differ between the two groups, with diabetic patients showing lipid profiles tied closely to obesity and inflammation. In non-diabetic individuals, HbA1c displayed a significant inverse correlation with HDL cholesterol that was independent of blood glucose levels. For diabetic individuals, HbA1c correlated positively with fasting blood glucose. A predictive model incorporating HbA1c, HDL cholesterol, C-reactive protein, age, education level, and marital status demonstrated strong diagnostic accuracy for type 2 diabetes, with high sensitivity and specificity. Furthermore, adjusting for these factors yielded an optimal HbA1c diagnostic cut-off of 7.59 per cent, compared to an unadjusted threshold of 6.05 per cent.
Type 2 diabetes is a widespread condition that requires precise diagnostic and risk evaluation methods. By demonstrating that lipid biomarkers and systemic inflammation alter how HbA1c relates to diabetes status, this research shows that standard diagnostic cut-offs may benefit from adjustment. Considering multiple clinical and demographic markers alongside HbA1c can significantly improve detection accuracy.
The findings could support the development of multi-marker risk assessment software or diagnostic decision-support tools for healthcare providers. Such algorithms would combine clinical, demographic, and biochemical indicators, including HbA1c and lipid levels, to predict type 2 diabetes risk more accurately. The work is at an early observational stage based on a small hospital cohort, meaning substantial prospective validation is necessary before translation into certified clinical software.
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Introduction: Type 2 diabetes is a significant global health concern, necessitating a thorough understanding of its metabolic processes for effective management. The role of glycated hemoglobin (HbA1c) is crucial, particularly in relation to lipid biomarkers, which warrants exploration to enhance early detection and prediction of diabetes risk in individuals. Objective: This study aimed to explore the associations between HbA1c and lipid biomarkers in diabetic and non-diabetic individuals and to identify key predictors of type 2 diabetes. Methods: A case-control study at the Central Hospital of Yaoundé involved 70 type 2 diabetes patients and 67 non-diabetic controls. Data on sociodemographic characteristics, blood pressure, and biochemical markers were analyzed using Principal Component Analysis, Spearman’s rank correlation, multivariate linear and logistic regressions, and LASSO logistic regression. Results: The findings demonstrate a differential relationship between HbA1c and HDL-cholesterol in diabetic and non-diabetic groups, with diabetics exhibiting distinct metabolic profiles illustrated with lipid levels more closely associated with obesity and inflammation. Among non-diabetic participants, HbA1c was significantly inversely associated with HDL cholesterol (r = -0.337, p = 0.006), while in diabetic participants, it was positively associated with fasting blood glucose (r = 0.277, p = 0.023). Multivariate linear models indicated that the negative association between HDL cholesterol and HbA1c in non-diabetic participants was glycemia-independent. The predictive model identified HbA1c, age, education level, marital status, HDL cholesterol, and C-reactive protein as key predictors of type 2 diabetes, demonstrating high performance with a pseudo-R-square value of 0.8517, sensitivity of 94.03%, specificity of 96.97%, and an AUC of 0.9948. Notably, the adjusted cutoff value of HbA1c was 7.59%, significantly higher than the unadjusted value of 6.05% (t = 13.52, p = 0.001). Conclusion: The study shows a distinct relationship between HbA1c and HDL-cholesterol, linking diabetes to lipid levels, obesity, and inflammation. These findings emphasize context-specific HbA1c interpretation for better diabetes risk prediction and management.
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DOI: 10.11648/j.ijde.20251001.11
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