Evidence map›Paper›PMID 41877216›Full record

ArticleCardiovascular diabetology2026

Triglyceride-glucose index and mortality in critically ill patients with cardiovascular-kidney-metabolic syndrome stage 4.

Xueshi Yin, Shuaixian Han, Zhiming Zheng, Long Tang, Jianping Liu, Yongheng Zhang

Abstract read
In one paragraph

Article in Cardiovascular diabetology, 2026. 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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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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2 · The registry

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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

6 authors.

Xueshi YinDepartment of Cardiac and Great Vascular Surgery, Suining Central Hospital, Suining, 629000, Sichuan Province, China.
Shuaixian HanDepartment of Cardiac and Great Vascular Surgery, Suining Central Hospital, Suining, 629000, Sichuan Province, China.
Zhiming ZhengDepartment of Cardiac and Great Vascular Surgery, Suining Central Hospital, Suining, 629000, Sichuan Province, China.
Long TangDepartment of Cardiac and Great Vascular Surgery, Suining Central Hospital, Suining, 629000, Sichuan Province, China.
Jianping LiuDepartment of Cardiac and Great Vascular Surgery, Suining Central Hospital, Suining, 629000, Sichuan Province, China. 18928939@qq.com.
Yongheng ZhangDepartment of Cardiac and Great Vascular Surgery, Suining Central Hospital, Suining, 629000, Sichuan Province, China. mfqq_258383@sohu.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The triglyceride-glucose (TyG) index is a well-established surrogate marker of insulin resistance (IR). Previous studies have linked higher TyG levels to an increased risk of cardiovascular events in individuals with early-stage (0-3) Cardiovascular-Kidney-Metabolic Syndrome (CKM). However, its prognostic value in critically ill patients with CKM stage 4 remains unclear. In this retrospective study, we analyzed clinical data from critically ill CKM stage 4 patients in the MIMIC-IV database. The TyG index was calculated and patients were categorized into tertiles. Cox proportional hazards models and restricted cubic spline (RCS) analyses were used to evaluate the association between the TyG index and all-cause mortality. A total of 3,125 patients were included, of whom 65.22% were male.= The 1-year all-cause mortality was 34.18% overall (28.79% in Q1, 35.06% in Q2, and 38.68% in Q3; P < 0.05). In multivariable Cox regression, each 1-standard deviation increase in the TyG index was associated with a 23% higher risk of 1-year mortality (HR = 1.23, 95% CI 1.09-1.39). Compared with the Q1 group, patients in Q3 had a 30% higher 1-year mortality risk (HR = 1.30, 95% CI 1.08-1.55). RCS analysis showed a linear positive relationship between the TyG index and mortality (P for non-linearity > 0.05). These findings indicate that higher TyG levels are independently associated with increased short- and long-term mortality in critically ill patients with CKM stage 4. The TyG index may provide a simple indicator of acute metabolic disturbances and could support early risk stratification in this population. However, its role in clinical decision-making and potential utility in guiding interventions require confirmation in prospective studies.

Indexed as

Blood GlucoseCardio-Renal SyndromeMetabolic SyndromeTriglyceridesAgedBiomarkersCritical IllnessDatabases, FactualFemaleHumansInsulin ResistanceMaleMiddle AgedPredictive Value of TestsPrognosisProportional Hazards ModelsBiomarkersBlood GlucoseTriglyceridesCardiovascular-Kidney-Metabolic Syndrome (CKM)Critical IllnessInsulin ResistanceMortality RiskPrognostic BiomarkersTriglyceride-Glucose Index (TyG)

Identifiers

PMID41877216
PMCPMC13020180

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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.