Evidence mapPaperPMID 41731494Full record

ArticleCardiovascular diabetology2026

The cholesterol, high-density lipoprotein, and glucose (CHG) index as a novel metabolic marker for predicting adverse outcomes in myocardial infarction survivors: insights from two large prospective cohorts.

Yanjun Song, Xinyue Chen, Zhen'ge Chang, Xiaohui Bian, Jining He, Bowen Li, Zhihao Zheng, Chunyue Wang, Zhangyu Lin, Chen Zhu and 2 more

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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Observational
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4 · The record

Corrections and comments

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.

Yanjun Song *Cardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xinyue Chen *Cardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhen'ge ChangCardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiaohui BianCardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jining HeCardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Bowen LiCardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhihao ZhengCardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chunyue WangCardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhangyu LinCardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chen ZhuCollege of Economics and Management, China Agricultural University, Beijing, 100083, China.
Rui FuEmergency and Critical Care Center, Fuwai Hospital, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 100037. fwfurui@163.com.
Kefei DouCardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. drdoukefei@126.com.

Funding

CAMS Innovation Fund for Medical Sciences 2021-I2M-1-008
6 · The paper itself

Abstract

backgroundPost-myocardial infarction (MI) patients remain at high risk of mortality and recurrent cardiovascular events. Metabolic disorders in patients after MI are closely related to high residual cardiovascular risk. The cholesterol, high-density lipoprotein, and glucose (CHG) index, calculated as Ln {[TC (mg/dL) × FBG (mg/dL)]/[2 × HDL-C (mg/dL)]}, is a recently proposed composite metabolic index. This study aimed to investigate the association between the CHG index and adverse outcomes in MI populations.

methodsThis study included two cohorts: 16,959 individuals with a history of MI from the UK Biobank and 6,253 post-MI patients with coronary artery disease from Fuwai Hospital. The primary endpoints in the UK Biobank cohort were all-cause mortality and cardiovascular mortality. In the Fuwai Hospital cohort, the primary endpoint was major adverse cardiovascular events (MACE, including all-cause mortality, non-fatal MI, and ischemia-mediated revascularization) and hard endpoint (including cardiovascular mortality and non-fatal MI). Cox proportional hazards models, Kaplan-Meier curves, and restricted cubic splines (RCS) were used to evaluate the associations between the CHG index and the endpoints. Time-dependent receiver operating characteristic (ROC) curves were employed to assess the predictive performance.

resultsIn the UK Biobank cohort (median follow-up of 13.4 years), after multivariate adjustment, compared to the Q1 of the CHG index, Q4 showed significantly increased risks of all-cause mortality (HR: 1.39, 95% CI: 1.33-1.41) and cardiovascular mortality (HR: 1.42, 95% CI: 1.14-1.74). In the Fuwai Hospital cohort (median follow-up of 3.1 years), the CHG Q4 group also demonstrated a significantly elevated risk of MACE (HR: 1.37, 95% CI: 1.17-1.61) and hard endpoint (HR: 1.87, 95% CI: 1.24-2.81). Kaplan-Meier curves showed significant separation in cumulative event rates across CHG quartiles in both cohorts (log-rank P < 0.05). RCS analyses demonstrated positive linear associations between CHG and all outcomes in both cohorts. Time-dependent ROC curves showed that the CHG index consistently outperformed the TyG index model in predicting adverse outcomes (all FDR-adjusted P < 0.05).

conclusionsIn two large independent cohorts of individuals with prior MI, the CHG index was independently associated with risks of adverse events. While its independent discriminative power is modest, the index serves as a valuable adjunctive tool that enhances risk reclassification, warranting further validation in prospective clinical settings to confirm its utility in secondary prevention.

Indexed as

Blood GlucoseCholesterol, HDLDyslipidemiasMyocardial InfarctionAgedBiomarkersChinaFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisProspective StudiesRisk AssessmentRisk FactorsBiomarkersBlood GlucoseCholesterol, HDLCholesterol, high-density lipoprotein, and glucose indexMetabolic syndromePrior myocardial infarctionTriglyceride-glucose indexUK Biobank

Identifiers

PMID41731494
PMCPMC12980928

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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.