Evidence mapPaperPMID 41247866Full record

ArticleAnnals of medicine2025

C-reactive protein-triglyceride glucose index in predicting three-vessel coronary artery disease risk: a retrospective study using machine learning approaches.

Ling Hou, Yuanhong Li, Qianfei Liu

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Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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3 · Its place in the literature

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6 citing papers in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Ling HouCardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, Hubei Province, People's Republic of China.
Yuanhong LiCardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, Hubei Province, People's Republic of China.
Qianfei LiuDepartment of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, Hubei Province, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThree-vessel coronary artery disease (TVD) is a severe subtype of coronary heart disease, strongly associated with inflammation and metabolic dysfunction. The C-reactive protein-triglyceride glucose index (CTI), an integrated measure of inflammation and metabolism, may serve as a critical predictor of TVD risk. This study evaluates the predictive utility of CTI for TVD.

methodsThis single-center retrospective study compared clinical characteristics, laboratory indices, and inflammation- and metabolism-related markers between TVD and non-TVD groups. Feature selection using the Boruta algorithm and LASSO regression identified key predictors for constructing a TVD risk model. SHAP analysis provided model interpretability and evaluated the relative importance of predictive factors. Predefined subgroup analyses by sex and diabetes status were conducted using multivariable logistic regression, with interaction terms tested to assess the consistency of CTI's predictive effect across subgroups.

resultsTraditional risk factors, including gender, age, hypertension, diabetes, and smoking, were significantly more prevalent in the TVD group. The CTI index was markedly elevated in this group and emerged as a critical predictor. Boruta and LASSO identified CTI, age, and gender as primary predictors. SHAP analysis confirmed the prominent role of CTI, with age and gender also contributing significantly to risk prediction. CTI demonstrated the strongest positive impact on TVD risk assessment. Subgroup analyses showed CTI was significantly associated with TVD in men and diabetic patients, with no significant interaction by sex or diabetes, indicating a generally stable predictive effect.

conclusionCTI is a reliable predictor of TVD with immediate value for improving current risk assessment and patient stratification. Beyond its present clinical relevance, CTI also holds potential for future applications in early screening and long-term management.

Indexed as

Blood GlucoseCoronary Artery DiseaseC-Reactive ProteinMachine LearningTriglyceridesAgedBiomarkersFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesRisk AssessmentRisk FactorsBiomarkersBlood GlucoseC-Reactive ProteinTriglyceridesC-reactive protein–triglyceride glucose indexinflammationinsulin resistancethree-vessel coronary artery diseaseXGBoost

Identifiers

PMID41247866
PMCPMC12624966

What Socratic holds

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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 Socratic graph.