The triglyceride–glucose body mass index paradox: dual metabolic effects on tumor aggressiveness and recurrence risk in 11 317 papillary thyroid carcinoma patients

OBJECTIVE: To investigate the linear and nonlinear relationships between the triglyceride-glucose body mass index (TyG-BMI) and aggressiveness and risk of recurrence in papillary thyroid carcinoma (PTC). METHODS: This retrospective single-center cohort study included 11 317 patients with PTC. The associations between the TyG-BMI and PTC aggressiveness as well as moderate-to-high recurrence risk were analyzed with binary logistic regression and odds ratios (ORs). Linear and nonlinear relationships between the TyG-BMI and these outcomes were evaluated with restricted cubic spline and smoothed curve-fitted logistic risk regression models. Key factors contributing to TyG-BMI prediction outcomes were weighted with machine learning algorithms. RESULTS: After adjusting for confounding factors, higher TyG-BMI was associated with a significantly increased risk for tumors with a maximum diameter of >1 cm (OR adjust = 1.35, P < 0.001), multifocality (OR adjust = 1.42, P < 0.001), and extrathyroidal extension (OR adjust = 1.53, P < 0.001). Conversely, higher TyG-BMI was associated with a significantly decreased risk for positive lymph nodes with a maximum diameter of >0.2 cm (OR adjust = 0.36, P < 0.001) and intermediate-to-high risk of PTC recurrence (OR adjust = 0.68, P < 0.001). TyG-BMI exhibited a linear relationship with the risk of tumors with a maximum diameter of >1 cm and multifocality, but a nonlinear relationship with extrathyroidal extension and intermediate-to-high recurrence risk of PTC. TyG-BMI showed a positive linear correlation with free triiodothyronine (FT3) and thyroglobulin (Tg); a negative linear correlation with free thyroxine (FT4), thyroid peroxidase antibody (TPOAb), and thyroglobulin antibody (TgAb); and no linear relationship with thyroid-stimulating hormone (TSH). Among the components of the TyG-BMI and potential confounding factors, machine learning algorithms consistently identified triglyceride (TG) level as the primary contributor when predicting PTC aggressiveness and intermediate-to-high risk of PTC recurrence. CONCLUSION: This study reveals complex relationships, both linear and nonlinear, between the TyG-BMI, PTC aggressiveness and intermediate-to-high risk of PTC recurrence, with TG playing a pivotal role within the TyG-BMI. There were linear correlations between the TyG-BMI and thyroid function.