全身性炎症指数在冠状动脉粥样硬化评估中的研究进展

Research Progress of Systemic Inflammatory Indices in the Assessment of Coronary Atherosclerosis

摘要: 炎症在冠状动脉粥样硬化(CAD)的发生发展中发挥核心作用，但传统炎症标志物如hs-CRP存在特异性不足等局限。基于血常规衍生的全身性炎症指数，如中性粒细胞–淋巴细胞比值(NLR)、血小板–淋巴细胞比值(PLR)、全身免疫炎症指数(SII)、系统性炎症反应指数(SIRI)、泛免疫炎症值(PIV，亦称系统性炎症聚集指数AISI)，因其简便、经济、可重复等优势，成为研究热点。本文系统回顾上述指数在CAD评估中的研究进展。证据表明，NLR与PLR研究充分但截断值不统一；孟德尔随机化研究未支持NLR与CAD的因果关系。SII作为综合指数，在预测CAD严重程度、预后及PCI术后并发症(造影剂肾病、无复流)方面证据最为丰富，且能预测CAD患者的新发癌症风险。SIRI、PIV/AISI等新兴指数纳入单核细胞，更全面反映炎症网络，其中PIV在预测STEMI患者院内MACE及冠脉狭窄程度方面优于SII、NLR和PLR，AISI同样可独立预测CAD患者术后长期死亡。年龄可显著调节PLR与CAD的关系——年轻患者中高PLR呈负相关。NLR的预后价值独立于糖尿病状态，但糖尿病合并高NLR (>5)的患者术后1年MACE风险最高。此外，SII与NLR在造影剂肾病、抑郁症状与MACE的联合预测、以及肝纤维化指数FIB-4的联合应用等领域也显示出临床价值。新近研究还证实，将炎症指数与传统风险模型(如FRS、SCORE2)或代谢指标(如TyG指数)联合，可进一步提高对CAD患者预后的预测能力；PHR等新型指数在CABG术后MACE预测中也展现出潜力；hs-CRP、NLR与血小板反应性三者联合可显著提升STEMI患者MACE预测效能；Meta分析进一步确认SII对CAD患者MACE、全因死亡、心血管死亡、心肌梗死和卒中的预测价值；在合并CKD的CAD患者中，AISI同样独立预测全因及心血管死亡。综合性炎症指数有望成为新型风险评估工具，但截断值不统一、因果证据缺乏等问题亟待解决，未来需结合遗传学证据及标准化研究推动临床转化。此外，将这些指数从研究推向临床实践还面临分析前/中质量控制缺失、截断值标准化路径不清、作为治疗靶点的证据不足等具体障碍，亟需系统性对策研究。

Abstract: Inflammation plays a central role in the development and progression of coronary artery disease (CAD). However, traditional inflammatory markers such as high-sensitivity C-reactive protein (hs-CRP) have limitations including limited specificity. Systemic inflammatory indices derived from routine blood counts—such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune-inflammation value (PIV, also termed aggregate index of systemic inflammation, AISI)—have emerged as research hotspots due to their simplicity, cost-effectiveness, and reproducibility. This article systematically reviews the research progress of these indices in CAD assessment. Evidence indicates that NLR and PLR are well-studied but lack unified cut-off values; Mendelian randomization studies have not supported a causal relationship between NLR and CAD. SII, as a comprehensive index, has the most extensive evidence in predicting CAD severity, prognosis, and post-PCI complications (contrast-induced nephropathy, no-reflow), and can also predict new-onset cancer risk in CAD patients. Emerging indices such as SIRI and PIV/AISI incorporate monocytes, more comprehensively reflecting the inflammatory network. Among them, PIV demonstrates superior predictive value for in-hospital major adverse cardiovascular events (MACE) and severity of coronary stenosis in STEMI patients compared with SII, NLR, and PLR, while AISI independently predicts long-term post-operative mortality in CAD patients. Age significantly modulates the relationship between PLR and CAD—with a negative association observed in younger patients. The prognostic value of NLR is independent of diabetes status, but patients with diabetes and high NLR (>5) face the highest 1-year post-operative MACE risk. Additionally, SII and NLR show clinical value in contrast-induced nephropathy, combined prediction of depressive symptoms and MACE, and combined use with the liver fibrosis index FIB-4. Recent studies have further confirmed that combining inflammatory indices with traditional risk models (e.g., FRS, SCORE2) or metabolic indicators (e.g., TyG index) can enhance prognostic prediction in CAD patients; novel indices such as PHR also show potential in predicting post-CABG MACE; and the combination of hs-CRP, NLR, and platelet reactivity significantly improves MACE prediction in STEMI patients. Meta-analyses further confirm the predictive value of SII for MACE, all-cause mortality, cardiovascular mortality, myocardial infarction, and stroke in CAD patients. In CAD patients with concomitant CKD, AISI independently predicts all-cause and cardiovascular mortality. Comprehensive inflammatory indices hold promise as novel risk assessment tools; however, issues such as non-uniform cut-off values and lack of causal evidence urgently need resolution. Future efforts should integrate genetic evidence and standardized research to facilitate clinical translation. Furthermore, translating these indices from research to clinical practice faces specific obstacles, including lack of pre-analytical/analytical quality control, unclear pathways for standardizing cut-off values, and insufficient evidence as therapeutic targets, all of which require systematic countermeasures.