The atherogenic index of plasma and triglyceride-glucose index as promising predictors of overall and disease-free survival in postoperative breast cancer patients

TL;DR

This study finds that two blood markers, AIP and TyG, can predict survival outcomes in breast cancer patients after surgery.

Contribution

The study introduces AIP and TyG as novel, easily measurable biomarkers for predicting survival in postoperative breast cancer patients.

Findings

Elevated AIP and TyG are significantly associated with worse overall survival in breast cancer patients.

AIP and TyG outperformed other IR indices in predicting survival outcomes.

AIP and TyG showed consistent predictive power across different patient subgroups.

Abstract

Insulin resistance (IR) is closely linked to breast cancer development and prognosis. This study aimed to evaluate the prognostic value of four surrogate indices of IR, the atherogenic index of plasma (AIP), triglyceride-glucose (TyG) index, TyG index combined with body mass index (TyG-BMI), and metabolic score for IR (METS-IR), for overall survival (OS) and disease-free survival (DFS) in postoperative breast cancer patients.

This retrospective cohort study included 298 patients with primary breast cancer who underwent radical surgery. We used multivariable Cox regression, Kaplan-Meier analysis, restricted cubic splines, receiver operating characteristic (ROC) curves, and subgroup analyses to assess associations and predictive performance for 3-, 5-, and 10-year survival. Random survival forest analysis ranked the importance of IR-related variables.

In fully adjusted models, elevated AIP and TyG index were significantly associated with worse OS, with hazard ratios of 2.28 and 2.82, respectively, for the highest versus lowest tertiles. Per 1-standard-deviation (SD) increase in AIP and TyG was associated with a 50% and 55% increased risk of OS, respectively. For DFS, only the TyG index showed a statistically significant association in the tertile comparison. Notably, Kaplan-Meier analysis and multivariable Cox models consistently indicated significant associations for AIP and TyG with OS and DFS. The predictive performance of AIP and TyG as assessed by ROC analysis was similar to each other but superior to TyG-BMI and METS-IR across all time points. Random survival forest (RSF) analysis identified AIP and TyG as the most important variables for predicting OS and DFS, respectively. Subgroup analyses revealed consistent trends for AIP and TyG across different patient subgroups.

Our findings suggest that the AIP and TyG index are promising, easily obtainable biomarkers for risk stratification and long-term survival prediction in breast cancer patients after radical surgery.

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