Association between cardiometabolic index and risk of testosterone deficiency in adult men: a cross-sectional study

Background Metabolic health is closely related to testosterone levels, and the cardiometabolic index (CMI) is a novel metabolic evaluation metric that encompasses obesity and lipid metabolism. However, there is currently a lack of research on the relationship between CMI and testosterone, which is the objective of this study. Methods This study utilized data from the National Health and Nutrition Examination Survey (NHANES) cycles from 2011 to 2016. Only adult males who completed physical measurements, lipid metabolism assessments, and testosterone measurements were included in the final analysis. The exposure variable CMI was analyzed both as a continuous variable and a categorical variable divided into quartiles. Testosterone was measured using the isotope dilution liquid chromatography-tandem mass spectrometry technique. Linear and logistic regression analyses were used to explore the relationship between CMI and total testosterone (TT) levels, as well as the risk of testosterone deficiency (TD). Smooth curve fittings were employed to visualize their linear relationships. Subgroup analyses were conducted to evaluate the stability of our results across different participant characteristics. Finally, ROC analysis was used to assess the performance of CMI in predicting TD. Results A total of 2,747 participants were included in the analysis, including 552 with TD (20.10%). The average CMI of the sample was 1.59 +/- 0.03, with TD participants having a higher CMI of 2.18 +/- 0.08 compared to non-TD participants at 1.46 +/- 0.03. Corresponding testosterone levels were 223.79 +/- 3.69 ng/dL and 508.36 +/- 5.73 ng/dL, respectively. After adjusting for all covariates, participants with higher CMI showed lower TT (beta = -23.84, 95% CI: -33.94, -13.74, p < 0.0001) and a higher risk of TD (OR = 1.26, 95% CI: 1.08, 1.48, p = 0.01). When CMI was categorized into quartiles with Q1 as the reference, participants in Q4 exhibited significantly lower TT (beta = -74.04, 95% CI: -106.01, -42.08, p < 0.0001) and a higher risk of TD (OR = 2.34, 95% CI: 1.18, 4.64, p = 0.02). Smooth curve fittings indicated a linear relationship between these variables. Subgroup analyses confirmed the stability of these associations across different population characteristics. ROC curve analysis demonstrated that CMI had good predictive performance for TD with a cut-off value of 1.126 and an AUC (95% CI) of 0.673 (0.649, 0.700). Conclusion CMI is associated with lower TT and a higher risk of TD, and it can predict the risk of TD. Using CMI for early detection and timely intervention could reduce the disease burden and promote reproductive health. Further prospective studies with large sample sizes are needed to validate these findings.