Correlation between TyG index and its modified index with breast cancer risk in women a study based on NHANES database and real-world data.

OBJECTIVE

To evaluate the association between the triglyceride-glucose (TyG) index and its modified indices with the risk of female breast cancer using data from the National Health and Nutrition Examination Survey (NHANES) database and to validate these findings with real-world data.

METHODS

Based on 1,086 women from the 2015 to 2016 NHANES cycle, the participants were divided into a breast cancer group (n = 32) and a non-breast cancer group (n = 1,054). The TyG index was calculated using the fasting glucose and triglyceride levels. TyG-BMI, TyG-WC, and TyG-WHtR were derived by incorporating the BMI, waist circumference, and height, respectively. Univariate and multivariate logistic regression analyses were used to assess the association between these indices and the risk of breast cancer. A nomogram was developed and its diagnostic performance was evaluated using calibration curves and ROC analysis. External validation was performed using real-world data through confusion matrix analysis.

RESULTS

Weighted analysis of the NHANES data revealed significantly higher median values of age, TyG, TyG-BMI, and TyG-WC in the breast cancer group than in the non-breast cancer group (P < 0.05). Inclusion of these variables in the multivariate logistic regression analysis identified age, TyG, TyG-BMI, and TyG-WC as factors associated with increased odds of breast cancer (P < 0.05). A nomogram model constructed using these factors indicated that advanced age, high TyG level, high TyG-BMI, and low TyG-WC were associated with increased odds of breast cancer (P < 0.05). Calibration curves for the training and testing sets of the nomogram prediction model demonstrated good agreement between the predicted and observed probabilities, with AUC values of 0.807 (95% confidence interval [CI]: 0.719-0.894) and 0.798 (95% CI: 0.702-0.894), respectively. External validation using real-world data showed that all metrics in the confusion matrix of the nomogram model exceeded 70%, indicating a good predictive performance.

CONCLUSION

Advanced age, elevated TyG level, high TyG-BMI, and low TyG-WC were associated with increased odds of breast cancer. The clinical application of this model may facilitate the early identification of patients at a high risk of breast cancer.