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早发冠心病影响因素的研究进展OA

Advances in research on influencing factors for premature coronary heart disease

中文摘要英文摘要

冠心病(CHD)通常被视为老年性疾病,但近年来发病呈现明显的年轻化趋势.早发冠心病(PCHD)是指发病年龄男性<55岁、女性<65岁的CHD.鉴于部分PCHD患者的病因无法完全用传统危险因素解释,亟需深入探索其多因素交互机制及新型生物标志物.该文系统回顾了PCHD的遗传、炎症与代谢、环境及行为等多维度影响因素及其交互作用,并综述了肠道菌群、非编码RNA等新兴领域及新型生物标志物的研究进展.此外,该文还总结了PCHD相关风险预测模型的研究进展,分析了现有模型的局限性,并对未来整合多维度数据、利用机器学习算法构建精准预测模型的改进方向进行了探讨.最后,基于对上述多因素作用的综合分析,指出了当前研究的不足并对未来研究方向进行了展望,以期为PCHD的早期防治提供新思路,改善年轻人群的心血管健康水平.

Coronary heart disease(CHD)is traditionally considered a disease of the elderly;however,a sig-nificant trend toward a younger age of onset has been observed in recent years.Premature coronary heart dis-ease(PCHD)is defined as CHD occurring in men aged<55 years and women aged<65 years.Given that the etiology in some patients with PCHD cannot be fully explained by traditional risk factors,an in-depth ex-ploration of its multi-factorial interaction mechanisms and novel biomarkers is urgently required.This article systematically reviews the multi-dimensional risk factors for PCHD,including genetic,inflammatory,metabol-ic,environmental and behavioral aspects,as well as their interactions.It also summarizes research progress in emerging fields,such as gut microbiota and non-coding RNA,along with novel biomarkers.Furthermore,this review outlines advances in PCHD-related risk prediction models,analyzes the limitations of existing models,and discusses future directions for improvement,specifically regarding the integration of multi-dimensional da-ta and the utilization of machine learning algorithms to construct precise prediction models.Finally,based on a comprehensive analysis of the aforementioned multi-factorial interactions,this review highlights the limita-tions of current research and provides an outlook on future research directions,aiming to offer novel insights for the early prevention and treatment of PCHD and to improve cardiovascular health outcomes in younger populations.

郭晋田;李凌

山西医科大学第一临床医学院,山西 太原 030001山西医科大学第一医院医学检验科,山西 太原 030001

医药卫生

早发冠心病影响因素交互作用遗传炎症代谢环境暴露风险预测模型

premature coronary heart diseaseinfluencing factorinteractiongeneticsinflamma-tionmetabolismenvironmental exposurerisk prediction model

《检验医学与临床》 2026 (6)

858-864,7

山西省科技厅项目(2022D100062062016334405485775).

10.3969/j.issn.1672-9455.2026.06.022

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