Preoperative Metabolic Predictors of Granulation Subtypes in Somatotroph Tumors: A Multicenter Retrospective Cohort Study

Le Chen; Jiaming Wang; Ailiang Zeng; Farhana Akter; Shanshan Wang; Shitong Liu; Weiyu Hu; Shun Yao; Konstantinos Margetis; Zongming Wang; Haipeng Liu; Xin Wang

doi:10.1002/cns.70774

Preoperative Metabolic Predictors of Granulation Subtypes in Somatotroph Tumors: A Multicenter Retrospective Cohort Study

Le Chen
, Jiaming Wang
, Ailiang Zeng
, Farhana Akter
, Shanshan Wang
, Shitong Liu
, Weiyu Hu
, Shun Yao
, Konstantinos Margetis
, Zongming Wang
, Haipeng Liu
, Xin Wang

The First Affiliated Hospital of Guangdong Pharmaceutical University
Sun Yat-Sen University
Icahn School of Medicine at Mount Sinai
Harvard University
Guangdong Pharmaceutical University
National Medical Research Association
Santa Paula University

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Abstract

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. 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. 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). 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. [Abstract copyright: © 2026 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.]

Original language	English
Article number	e70774
Number of pages	9
Journal	CNS neuroscience & therapeutics
Volume	32
Issue number	2
Early online date	3 Feb 2026
DOIs	https://doi.org/10.1002/cns.70774
Publication status	E-pub ahead of print - 3 Feb 2026

Bibliographical note

© 2026 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Funding

This work was supported by the Guangdong Province Key Technologies R&D Program for “Brain Science and Brain‐like Intelligence Research” (2023B0303020002), Guangdong Basic and Applied Basic Research Foundation (2024A1515011697), Guangdong Province Administration of Traditional Chinese Medicine Project (20231210), Key Clinical Technique of Guangzhou (2023P‐ZD18), and Guangdong Medical Association Clinical Research Special Fund (No. 2024HY‐A6003).

Funders	Funder number
Guangdong Province Key Technologies R&D Program	2023B0303020002
Key Clinical Technique of Guangzhou	2023P‐ZD18
Basic and Applied Basic Research Foundation of Guangdong Province	2024A1515011697
Guangdong Province Administration of Traditional Chinese Medicine Project	20231210
Guangdong Medical Association Clinical Research Special Fund	2024HY‐A6003

Keywords

somatotroph tumors
Adult
Adenoma - metabolism - surgery
granulation subtypes
Humans
Young Adult
Retrospective Studies
uric acid
Middle Aged
Male
Aged
machine learning
triglyceride
Growth Hormone-Secreting Pituitary Adenoma - metabolism - surgery - diagnosis - pathology
Triglycerides - blood - metabolism
Female
Cohort Studies

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title = "Preoperative Metabolic Predictors of Granulation Subtypes in Somatotroph Tumors: A Multicenter Retrospective Cohort Study",

abstract = "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. 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. 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). 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. [Abstract copyright: {\textcopyright} 2026 The Author(s). CNS Neuroscience \& Therapeutics published by John Wiley \& Sons Ltd.]",

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author = "Le Chen and Jiaming Wang and Ailiang Zeng and Farhana Akter and Shanshan Wang and Shitong Liu and Weiyu Hu and Shun Yao and Konstantinos Margetis and Zongming Wang and Haipeng Liu and Xin Wang",

note = "{\textcopyright} 2026 The Author(s). CNS Neuroscience \& Therapeutics published by John Wiley \& Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.",

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AU - Akter, Farhana

AU - Wang, Shanshan

AU - Liu, Shitong

AU - Hu, Weiyu

AU - Yao, Shun

AU - Margetis, Konstantinos

AU - Wang, Zongming

AU - Liu, Haipeng

AU - Wang, Xin

N1 - © 2026 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

PY - 2026/2/3

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N2 - 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. 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. 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). 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. [Abstract copyright: © 2026 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.]

AB - 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. 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. 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). 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. [Abstract copyright: © 2026 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.]

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KW - machine learning

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KW - Triglycerides - blood - metabolism

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DO - 10.1002/cns.70774

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SN - 1755-5930

VL - 32

JO - CNS neuroscience & therapeutics

JF - CNS neuroscience & therapeutics

IS - 2

M1 - e70774

ER -

Preoperative Metabolic Predictors of Granulation Subtypes in Somatotroph Tumors: A Multicenter Retrospective Cohort Study

Abstract

Bibliographical note

Funding

Keywords

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