Triglyceride-Glucose Index Versus Glycated Albumin: Diagnostic Tools for Early Detection of Insulin Resistance and Pre-Diabetes

Background: Early detection of insulin resistance is essential for preventing type 2 diabetes and cardiovascular disease. Conventional methods like HOMA-IR rely on insulin assays, which may not be readily available in all clinical settings. This study explores the diagnostic potential of the TriglycerideGlucose (TyG) Index and Glycated Albumin (GA) as accessible, non-insulinbased markers. Methods: A cross-sectional analysis was conducted on 120 adults aged 18–60 years. Anthropometric measurements and fasting blood samples were collected. Insulin resistance was determined using HOMA-IR, with a cut-off value >2.5. ROC analysis was performed to evaluate the predictive ability of TyG and GA, and multivariate regression was used to identify independent predictors. Results: Among the participants, 25% were classified as insulin resistant. The TyG Index showed a significant correlation with HOMA-IR (r = 0.26, p = 0.005), whereas GA did not (r = – 0.12, p = 0.196). ROC analysis revealed a moderate predictive value for TyG (AUC = 0.691), while GA showed poor discrimination (AUC = 0.588). Multivariate analysis identified BMI and TyG as significant independent predictors of insulin resistance, with Glycated albumin contributing minimally. Conclusion: The TyG Index demonstrates promise as a practical, non-insulin-dependent marker for identifying insulin resistance, particularly in resource-limited settings. While Glycated Albumin remains useful for glycemic assessment, it may not reliably indicate insulin resistance in this population.

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Publication Details

Journal
Batna Journal of Medical Sciences (BJMS)
Published
2025-08-25
DOI
https://doi.org/10.48087/bjmsoa.2025.12301
Primary Topic
Diabetes, Cardiovascular Risks, and Lipoproteins
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article
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article

Triglyceride-Glucose Index Versus Glycated Albumin: Diagnostic Tools for Early Detection of Insulin Resistance and Pre-Diabetes

Subhasis Mukherjee, Sandip Kumar KUNDU, Kamala Kanta Parhi, Sanjay Ghosh et al.
Batna Journal of Medical Sciences (BJMS)
Diabetes, Cardiovascular Risks, and Lipoproteins
article

Triglyceride-Glucose Index Versus Glycated Albumin: Diagnostic Tools for Early Detection of Insulin Resistance and Pre-Diabetes

Subhasis Mukherjee, Sandip Kumar KUNDU, Kamala Kanta Parhi, Sanjay Ghosh, Kamal Gupta, Aashish Choudhary
article en

Abstract

Background: Early detection of insulin resistance is essential for preventing type 2 diabetes and cardiovascular disease. Conventional methods like HOMA-IR rely on insulin assays, which may not be readily available in all clinical settings. This study explores the diagnostic potential of the TriglycerideGlucose (TyG) Index and Glycated Albumin (GA) as accessible, non-insulinbased markers. Methods: A cross-sectional analysis was conducted on 120 adults aged 18–60 years. Anthropometric measurements and fasting blood samples were collected. Insulin resistance was determined using HOMA-IR, with a cut-off value >2.5. ROC analysis was performed to evaluate the predictive ability of TyG and GA, and multivariate regression was used to identify independent predictors. Results: Among the participants, 25% were classified as insulin resistant. The TyG Index showed a significant correlation with HOMA-IR (r = 0.26, p = 0.005), whereas GA did not (r = – 0.12, p = 0.196). ROC analysis revealed a moderate predictive value for TyG (AUC = 0.691), while GA showed poor discrimination (AUC = 0.588). Multivariate analysis identified BMI and TyG as significant independent predictors of insulin resistance, with Glycated albumin contributing minimally. Conclusion: The TyG Index demonstrates promise as a practical, non-insulin-dependent marker for identifying insulin resistance, particularly in resource-limited settings. While Glycated Albumin remains useful for glycemic assessment, it may not reliably indicate insulin resistance in this population.

Batna Journal of Medical Sciences (BJMS)Vol. 12(3)
Openalex Percentile: Top 66%
Diabetes, Cardiovascular Risks, and Lipoproteins
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