Association Between Glycated Hemoglobin and Lipid Biomarkers in Diabetic and Non-diabetic Populations for Type 2 Diabetes Detection

<i>Introduction</i>: 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. <i>Objective</i>: 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. <i>Methods</i>: 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. <i>Results</i>: 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). <i>Conclusion</i>: 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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Journal
International Journal of Diabetes and Endocrinology
Published
2025-02-10
DOI
https://doi.org/10.11648/j.ijde.20251001.11
Primary Topic
Diabetes Management and Research
Type
article
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article

Association Between Glycated Hemoglobin and Lipid Biomarkers in Diabetic and Non-diabetic Populations for Type 2 Diabetes Detection

Ismael Ngnie‐Teta, Choumessi Tchewonpi Aphrodite, Lifoter Kenneth Navti, Edouard Akono Nantia et al.
International Journal of Diabetes and Endocrinology
Diabetes Management and Research
article

Association Between Glycated Hemoglobin and Lipid Biomarkers in Diabetic and Non-diabetic Populations for Type 2 Diabetes Detection

Ismael Ngnie‐Teta, Choumessi Tchewonpi Aphrodite, Lifoter Kenneth Navti, Edouard Akono Nantia, Brice Ulrich Saha Foudjo, Jerry Kenmoe, Daliane Latsap
article en

Abstract

<i>Introduction</i>: 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. <i>Objective</i>: 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. <i>Methods</i>: 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. <i>Results</i>: 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). <i>Conclusion</i>: 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.

International Journal of Diabetes and EndocrinologyVol. 10(1)
University of Bamenda (CM), Catholic University of Central Africa (CM)
Openalex Percentile: Top 94%
Diabetes Management and Research
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