Evidence map›Paper›PMID 41631367›Full record

ArticleCNS neuroscience & therapeutics2026

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 and 2 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in CNS neuroscience & therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Le ChenDepartment of Neurosurgery, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China.
Jiaming WangCenter for Pituitary Tumor Surgery, Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Ailiang ZengDepartment of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Farhana AkterFaculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts, USA.
Shanshan WangSchool of Basic Medical Sciences, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China.
Shitong LiuSchool of Basic Medical Sciences, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China.
Weiyu HuDepartment of Neurosurgery, Pituitary Tumor Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Shun YaoCenter for Pituitary Tumor Surgery, Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.ORCID 0000-0003-1727-4078
Konstantinos MargetisDepartment of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Zongming WangDepartment of Neurosurgery, Pituitary Tumor Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Haipeng LiuNational Medical Research Association, Leicester, UK.ORCID 0000-0002-4212-2503
Xin WangDepartment of Neurosurgery, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China.ORCID 0009-0000-5158-5967

Funding

Guangdong Basic and Applied Basic Research Foundation 2024A1515011697Guangdong Medical Association Clinical Research Special Fund 2024HY-A6003Guangdong Province Administration of Traditional Chinese Medicine Project 20231210Guangdong Province Key Technologies R&D Program for "Brain Science and Brain-like Intelligence Research" 2023B0303020002Key Clinical Technique of Guangzhou 2023P-ZD18
6 · The paper itself

Abstract

purposeDifferentiating 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.

methodsIn 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.

resultsSGSTs 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).

conclusionThe 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.

Indexed as

AdenomaGrowth Hormone-Secreting Pituitary AdenomaTriglyceridesUric AcidAdultCohort StudiesFemaleHumansInsulin-Like Growth Factor IMaleMiddle AgedPredictive Value of TestsRetrospective StudiesInsulin-Like Growth Factor ITriglyceridesUric Acidgranulation subtypesmachine learningsomatotroph tumorstriglycerideuric acid

Identifiers

PMID41631367
PMCPMC12865497

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