Serum Testosterone and Atherogenic Indices as an independent Risk Factor for Predicting the Severity of Coronary Artery Disease: A Cross-sectional Study from North Eastern Region of India
Authors: Ashem Niketa , Shaini Laikangbam , Singh Thokchom Sachindeba , Laikhuram Priya , Laishram Nikkita , Phom Ongchu , Konyak N Penjei , Chongtham Sangeeta 
Published: December 2024

Introduction: Coronary Artery Disease (CAD) is one of the primary causes of mortality worldwide. Dyslipidaemia is associated with the development of Atherosclerotic Cardiovascular Disease (ASCVD). Atherogenic indices are emerging lipid parameters related to atherosclerosis and CAD. There is a limited amount of data regarding the relationship between CAD severity, serum testosterone, and lipid indices, especially in the northeastern region of India.

Aim: To determine the relationship between serum testosterone and atherogenic indices as independent risk factors for the severity of CAD.

Materials and Methods: This cross-sectional study was conducted in the Department of Biochemistry in collaboration with the Department of Cardiology at the Regional Institute of Medical Sciences, Imphal, Manipur, India, from January 2021 to October 2022, consisting of 70 male patients with CAD who underwent coronary angiography and 70 male patients without CAD. Serum total and free Testosterone (TT and fT), Total Cholesterol (TC), Triglycerides (TG), High-density Lipoprotein (HDL), and Low-density Lipoprotein (LDL) were assessed. Atherogenic indices, including the Atherogenic Index of Plasma (AIP), Atherogenic Index (AI), and Castelli Risk Indices I and II (CRI-I and II), as well as the Triglyceride Glucose Index (TyG), were calculated using conventional lipid parameters and glucose, respectively. Binary logistic regression analysis was performed to examine the association of the atherogenic indices and testosterone with the severity of CAD.

Results: The mean age was found to be 63.4±11.8 years in cases and 60.8±9.1 years in controls. The median (interquartile range) of AI was 5.03 (4.2, 5.9) vs 3.04 (2.2, 3.6), p<0.001; AIP was 0.39 (0.36, 0.44) vs 0.14 (0.006, 0.24), p<0.001; CRI-I was 6.03 (5.22, 6.91) vs 4.04 (3.2, 4.61), p<0.001; CRI-II was 3.91 (3.3, 4.7) vs 2.31 (1.6, 2.78), p<0.001; and the TyG index was 4.84 (4.68, 4.96) vs 3.82 (3.7, 3.9), p<0.001. These values were significantly higher in the CAD group compared to the non CAD group. Total Testosterone (TT) was 2.05±1.1 ng/mL vs 4.93±0.65 ng/mL, p<0.001, and fT was (3.6±2.6 vs 15.7±4.7, p<0.001) were significantly lower in cases compared to controls. The Spearman’s correlation analysis showed that AI (r=0.446, p<0.001), AIP (r=0.518, p<0.001), CRI-I (r=0.446, p<0.001), CRI-II (r=0.406, p=0.001), TGL/HDL-C (r=0.502, p<0.001), and the TyG index (r=0.305, p<0.010) were positively correlated with the Gensini score. The binary logistic regression analysis indicated that AI (OR: 3.08, 95% CI: 1.70- 5.57, p<0.001), AIP (OR: 2.54, 95% CI: 1.84-3.78, p<0.001), CRI-I (OR: 3.07, 95% CI: 1.65-5.57, p<0.001), CRI-II (OR: 3.17, 95% CI: 1.64-6.10, p<0.001), and TyG (OR: 1.7, 95% CI: 1.01-1.98, p=0.009) were independent risk predictors of the severity of CAD after adjustment for confounders. Additionally, TT (OR: 1.417, CI: 1.24-1.70) and fT (OR: 1.67, CI: 1.12-1.98) were also found to be independent risk predictors of the severity of CAD.

Conclusion: The AI, AIP, CRI-I, CRI-II, TyG, TT, and fT were independent predictors of the severity of CAD and could serve as potential biomarkers for CAD risk assessment. To specifically explain the diagnostic use of these novel indices in the early diagnosis of ASCVDs and CAD incidence, long-term prospective cohort surveys must be designed.