AUTHOR=Cai Shanshan , Xing Hongquan , Wang Yihan , Yang Weichang , Luo Hongdan , Ye Xiaoqun 

TITLE=The association between triglyceride glucose-body mass index and overall survival in postoperative patient with lung cancer

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1528644

DOI=10.3389/fendo.2025.1528644

ISSN=1664-2392

ABSTRACT=ObjectiveLung cancer continues to be one of the leading causes of cancer-related mortality, and the identification of effective prognostic markers is crucial for enhancing post-surgical outcomes. The present study was designed to investigate the association between the triglyceride-glucose body mass index (TyG-BMI) and postoperative overall survival (OS) rates in patients undergoing lung cancer surgery, while also evaluating its potential prognostic value for predicting postoperative outcomes.MethodsThis study conducted a retrospective look at the data sourced from lung cancer patients undergone surgical procedures at the Second Affiliated Hospital of Nanchang University between 2016 and 2022. By dividing patients by TyG-BMI, the correlation between TyG-BMI and OS was determined via Cox regression modeling, Lasso regression, and Kaplan-Meier survival analyses. The link between TyG-BMI and OS regarding the dose-response was scrutinized by restricted cubic spline (RCS) analysis. A dynamic prognostic nomogram model based on TyG-BMI and other clinical factors was developed and validated.ResultsThe survival rates showed a significant variation between those with low and high TyG-BMI values, with the low TyG-BMI group having significantly better survival rates (P = 0.012). Multivariate analysis confirmed that smoking, pathological type, lymph node metastasis, N stage, and TyG-BMI were independent prognostic factors for OS. The nomogram model demonstrated robust predictive performance, achieving AUC values of 0.77, 0.81, and 0.86 for predicting OS at 24, 48, and 72 months, respectively, outperforming traditional TNM staging. Calibration and decision curve analyses further confirmed the model’s predictive accuracy and clinical utility.ConclusionTyG-BMI is a valuable prognostic biomarker for assessing survival outcomes in lung cancer patients post-surgery. The predictive model based on TyG-BMI provides a valuable tool for the prognosis assessment of lung cancer. These findings need to be further validated, and the potential mechanism between TyG-BMI and lung cancer prognosis needs to be further investigated.