Evidence map›Paper›PMID 42209668›Full record

ArticleScientific reports2026

Long-term stability of the triglyceride-glucose index in cancer patient Cohort.

Yulei Xie, Xin Chen, Liang Xie, Yinxu Wang

Abstract read
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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yulei XieDepartment of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
Xin ChenDepartment of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
Liang XieDepartment of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
Yinxu WangDepartment of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China. 34089681@qq.com.

Funding

High-level Talent Research Initiation Fund Project of the Clinical Medical College and Affiliated Hospital of North Sichuan Medical College 2025GC016Sichuan Medical Association Chronic Disease (Taige) Special Research Project 2025TG04
6 · The paper itself

Abstract

The triglyceride-glucose (TyG) index is a simple surrogate marker for insulin resistance (IR), but its longitudinal stability and clinical predictive value in patients with cancer remain poorly understood. To evaluate whether cancer and anti-tumor therapies affect the longitudinal stability of the TyG index over 24 months and to identify its core influencing factors. This retrospective study analyzed 538 cancer patients from a single-center database (2015-2020) who completed five TyG index measurements over 24 months. Longitudinal stability was quantified using repeated measures analysis of variance (RM-ANOVA) and intraclass correlation coefficients (ICC). Binary logistic regression models were employed to explore factors associated with different TyG levels. A longitudinal K-means clustering algorithm was used to identify trajectory differences, and multivariable logistic regression models determined the independent predictive factors. The TyG index demonstrated excellent longitudinal stability over 24 months (F = 5.38, P < 0.001; average ICC = 0.890; Pearson r = 0.54-0.71). Binary logistic regression analysis showed that body mass index (BMI) was the core factor independently associated with a high TyG index (Q4 > 8.79). K-means clustering identified three parallel longitudinal trajectories (Stable-Low, Stable-Medium, and Stable-High risk). Multivariable logistic regression models indicated that the high-risk trajectory was significantly associated with BMI (OR = 1.25, 95% CI 1.10-1.41, P < 0.001) and diabetes mellitus, whereas advanced cancer stage (OR = 0.95, 95% CI 0.53-1.70, P = 0.862) and cachexia (OR = 0.90, 95% CI 0.45-1.80, P = 0.767) showed no significant effect. The TyG index in cancer patients exhibited a long-term stable trend over 24 months. Severe IR may represent a metabolic phenotype closely associated with fat accumulation, and it may be independent of catabolic states such as cancer stage and cachexia.

Indexed as

Blood GlucoseNeoplasmsTriglyceridesAgedBiomarkersBody Mass IndexFemaleHumansInsulin ResistanceLongitudinal StudiesMaleMiddle AgedRetrospective StudiesBiomarkersBlood GlucoseTriglyceridescancerdiabetes mellitusinsulin resistancelongitudinal cohortpredictive modeltriglyceride-glucose index

Identifiers

PMID42209668
PMCPMC13448520

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.