CNS neuroscience & therapeutics · 2026
Preoperative Metabolic Predictors of Granulation Subtypes in Somatotroph Tumors: A Multicenter Retrospective Cohort Study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Chen Le, Wang Jiaming, Zeng Ailiang, Akter Farhana, Wang Shanshan, Liu Shitong, Hu Weiyu, Yao Shun, Margetis Konstantinos, Wang Zongming, Liu Haipeng, Wang Xin
Аннотация
PURPOSE: Differentiating between sparsely granulated and densely granulated somatotroph tumors (SGSTs and DGSTs) currently relies on postoperative immunohistochemistry. This study aimed to evaluate whether triglyceride (TG), uric acid (UA), and their composite TG-UA index [ln(TG × 1000/UA)] could serve as preoperative indicators for distinguishing granulation subtypes of somatotroph tumors.
METHODS: In this multicenter retrospective cohort study, 230 patients with somatotroph tumors were analyzed. Logistic regression and generalized additive models assessed associations and potential nonlinear associations between metabolic indicators and granulation subtypes. Predictive performance was compared between models using UA and TG separately and those using the TG-UA index.
RESULTS: SGSTs were associated with significantly higher TG, growth hormone, insulin-like growth factor 1, and TG-UA index values. The TG-UA index remained an independent predictor of the SGST subtype (OR = 1.514, p = 0.014). Predictive performance was similar between models (p = 0.108).
CONCLUSION: The TG-UA index is a promising noninvasive biomarker for identifying the SGST subtype in somatotroph tumors. Although limited by its retrospective design and lack of long-term data, this study provides a foundation for future prospective validation.
METHODS: In this multicenter retrospective cohort study, 230 patients with somatotroph tumors were analyzed. Logistic regression and generalized additive models assessed associations and potential nonlinear associations between metabolic indicators and granulation subtypes. Predictive performance was compared between models using UA and TG separately and those using the TG-UA index.
RESULTS: SGSTs were associated with significantly higher TG, growth hormone, insulin-like growth factor 1, and TG-UA index values. The TG-UA index remained an independent predictor of the SGST subtype (OR = 1.514, p = 0.014). Predictive performance was similar between models (p = 0.108).
CONCLUSION: The TG-UA index is a promising noninvasive biomarker for identifying the SGST subtype in somatotroph tumors. Although limited by its retrospective design and lack of long-term data, this study provides a foundation for future prospective validation.