Synergistic impact of insulin resistance and hepatic fibrosis on cardiovascular events in patients with coronary artery disease: A machine learning subtyping and interaction analysis

IF 3.9 3区医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS

Nutrition Metabolism and Cardiovascular Diseases Pub Date : 2026-04-01 Epub Date: 2025-12-13 DOI:10.1016/j.numecd.2025.104520

Zhihao Zheng , Wanqing Sun , Han Xu , Yanjun Song , Jining He , Xiaohui Bian , Bowen Li , Chenxi Song , Rui Fu , Kefei Dou

{"title":"Synergistic impact of insulin resistance and hepatic fibrosis on cardiovascular events in patients with coronary artery disease: A machine learning subtyping and interaction analysis","authors":"Zhihao Zheng , Wanqing Sun , Han Xu , Yanjun Song , Jining He , Xiaohui Bian , Bowen Li , Chenxi Song , Rui Fu , Kefei Dou","doi":"10.1016/j.numecd.2025.104520","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and aims</h3><div>Insulin resistance (IR) and hepatic fibrosis are significant yet underexplored synergistic risk factors for cardiovascular events in coronary artery disease (CAD). We investigated the interaction between the triglyceride-glucose (TyG) index and liver fibrosis scores (FIB-4, BARD) for risk prediction.</div></div><div><h3>Methods and results</h3><div>Within a prospective cohort of 14,660 CAD patients, we performed a data-driven phenotypic stratification using K-means clustering—an unsupervised machine learning algorithm—to deconvolute heterogeneous metabolic profiles. Subsequently, Cox regression models were employed to evaluate associations of these phenotypes, along with continuous and tertiled TyG, FIB-4, and BARD scores, with incident cardiovascular events (a composite of cardiovascular mortality, nonfatal myocardial infarction, or stroke). The analysis identified three mechanistically distinct metabolic subtypes. During a median follow-up of 3 years, 463 cardiovascular events occurred (overall event rate: 3.16 %). The “metabolic-fibrosis mixed” subtype exhibited the highest risk (adjusted HR = 1.71, 95 %CI:1.34–2.19), with an event rate of 4.40 % (183/4159) compared to 2.43 % (116/4782) in the low-risk subtype. Both the TyG index (HR = 1.52, 95 %CI:1.40–1.65) and BARD score were independent predictors. A significant multiplicative interaction existed between TyG and BARD (P = 0.041). Additive interaction analysis confirmed synergy, with a relative excess risk (RERI) of 1.42 when both biomarkers were elevated. Risk escalated nonlinearly once TyG exceeded 9.0, potentiated by fibrosis.</div></div><div><h3>Conclusion</h3><div>IR and hepatic fibrosis synergistically increase cardiovascular risk in CAD patients. Combining TyG and BARD scores with metabolic subtyping enhances risk stratification, potentially guiding targeted interventions.</div></div>","PeriodicalId":49722,"journal":{"name":"Nutrition Metabolism and Cardiovascular Diseases","volume":"36 4","pages":"Article 104520"},"PeriodicalIF":3.9000,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nutrition Metabolism and Cardiovascular Diseases","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0939475325006751","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/12/13 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Background and aims

Insulin resistance (IR) and hepatic fibrosis are significant yet underexplored synergistic risk factors for cardiovascular events in coronary artery disease (CAD). We investigated the interaction between the triglyceride-glucose (TyG) index and liver fibrosis scores (FIB-4, BARD) for risk prediction.

Methods and results

Within a prospective cohort of 14,660 CAD patients, we performed a data-driven phenotypic stratification using K-means clustering—an unsupervised machine learning algorithm—to deconvolute heterogeneous metabolic profiles. Subsequently, Cox regression models were employed to evaluate associations of these phenotypes, along with continuous and tertiled TyG, FIB-4, and BARD scores, with incident cardiovascular events (a composite of cardiovascular mortality, nonfatal myocardial infarction, or stroke). The analysis identified three mechanistically distinct metabolic subtypes. During a median follow-up of 3 years, 463 cardiovascular events occurred (overall event rate: 3.16 %). The “metabolic-fibrosis mixed” subtype exhibited the highest risk (adjusted HR = 1.71, 95 %CI:1.34–2.19), with an event rate of 4.40 % (183/4159) compared to 2.43 % (116/4782) in the low-risk subtype. Both the TyG index (HR = 1.52, 95 %CI:1.40–1.65) and BARD score were independent predictors. A significant multiplicative interaction existed between TyG and BARD (P = 0.041). Additive interaction analysis confirmed synergy, with a relative excess risk (RERI) of 1.42 when both biomarkers were elevated. Risk escalated nonlinearly once TyG exceeded 9.0, potentiated by fibrosis.

Conclusion

IR and hepatic fibrosis synergistically increase cardiovascular risk in CAD patients. Combining TyG and BARD scores with metabolic subtyping enhances risk stratification, potentially guiding targeted interventions.

查看原文本刊更多论文

胰岛素抵抗和肝纤维化对冠心病患者心血管事件的协同影响：机器学习亚型和相互作用分析

背景和目的：胰岛素抵抗（IR）和肝纤维化是冠状动脉疾病（CAD）心血管事件的重要但尚未被充分探索的协同危险因素。我们研究了甘油三酯-葡萄糖（TyG）指数与肝纤维化评分（FIB-4， BARD）之间的相互作用，用于风险预测。方法和结果：在14660名CAD患者的前瞻性队列中，我们使用K-means聚类（一种无监督机器学习算法）进行了数据驱动的表型分层，以解卷积异质性代谢谱。随后，采用Cox回归模型来评估这些表型以及连续和间断的TyG、FIB-4和BARD评分与心血管事件（心血管死亡率、非致死性心肌梗死或中风的组合）的相关性。分析确定了三种机制上不同的代谢亚型。在中位随访3年期间，发生了463例心血管事件（总事件发生率：3.16%）。“代谢-纤维化混合”亚型表现出最高的风险（校正HR = 1.71, 95% CI:1.34-2.19），事件发生率为4.40%(183/4159)，而低风险亚型的事件发生率为2.43%（116/4782）。TyG指数（HR = 1.52, 95% CI:1.40-1.65）和BARD评分均为独立预测因子。TyG与BARD之间存在显著的乘法交互作用（P = 0.041）。加性相互作用分析证实了协同作用，当两种生物标志物升高时，相对超额风险（RERI）为1.42。一旦TyG超过9.0，风险非线性上升，纤维化增强。结论：IR和肝纤维化可协同增加冠心病患者的心血管风险。将TyG和BARD评分与代谢亚型相结合可以增强风险分层，可能指导有针对性的干预措施。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Nutrition Metabolism and Cardiovascular Diseases 医学-内分泌学与代谢

CiteScore

6.80

自引率

2.60%

发文量

332

审稿时长

57 days

期刊介绍： Nutrition, Metabolism & Cardiovascular Diseases is a forum designed to focus on the powerful interplay between nutritional and metabolic alterations, and cardiovascular disorders. It aims to be a highly qualified tool to help refine strategies against the nutrition-related epidemics of metabolic and cardiovascular diseases. By presenting original clinical and experimental findings, it introduces readers and authors into a rapidly developing area of clinical and preventive medicine, including also vascular biology. Of particular concern are the origins, the mechanisms and the means to prevent and control diabetes, atherosclerosis, hypertension, and other nutrition-related diseases.