Evidence map›Paper›PMID 40271122›Full record

ArticleFrontiers in cardiovascular medicine2025

The relationship and differences in the triglyceride-glucose index and all-cause mortality in patients with coronary heart disease combined with cerebrovascular and other comorbidities: an analysis of the MIMIC-IV database.

Xiao Zeng, Yuping Liu, Ping Shuai, Peiyuan He, Xiaoli Liu

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Article in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xiao ZengOutpatient Department, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Yuping LiuDepartment of Health Management Center & Institute of Health Management, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Ping ShuaiDepartment of Health Management Center & Institute of Health Management, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Peiyuan He *Department of Health Management Center & Institute of Health Management, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Xiaoli Liu *Department of Health Management Center & Institute of Health Management, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to investigate the predictive capability of the triglyceride-glucose index (TyG index) for all-cause mortality among patients with coronary heart disease (CHD), particularly in those with cerebrovascular disease (CVD) and other comorbidities, based on the MIMIC-IV database. Methods: Using the ICD-9/10 coding standards, eligible CHD patients were identified from the MIMIC-IV database (version 3.0) with defined inclusion and exclusion criteria to ensure sample representativeness. Patients were categorized into CVD and other comorbidity groups. Data on mortality rates at 90 days, 1 year, and overall were collected, along with the TyG index and relevant covariates associated with survival risk. Baseline analyses, Spearman correlation, and restricted cubic splines (RCS) were employed to assess the nonlinear relationship between the TyG index and mortality. Kaplan-Meier curves and Cox proportional hazards models were utilized to evaluate survival risk. Results: A total of 1,872 CHD patients were included, with 578 having CVD and a mortality rate of 50.17%; 1,294 had other comorbidities with a mortality rate of 64.91%. RCS analysis indicated a nonlinear relationship between the TyG index and mortality risk. For patients with concurrent CVD, the lowest mortality risk occurred at a TyG index of 9.37 mmol/L, while for those with other comorbidities, the lowest risk was observed at 9.36 mmol/L. Cox regression analysis revealed a significant association between the TyG index and survival risk in all CHD patients ( Conclusion: The TyG index exhibits a nonlinear relationship with mortality risk in CHD patients, with elevated levels significantly increasing mortality risk in those with other comorbidities. These findings suggest that the TyG index may serve as a critical metabolic marker for prognostic evaluation in CHD patients, warranting further clinical attention.

Indexed as

all-cause mortalitycoronary heart diseaseMIMIC-IV databaserestricted cubic spline modeltriglyceride-glucose index

Identifiers

PMID40271122
PMCPMC12014732

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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.