Qin Pei, Ho Frederick K, Celis-Morales Carlos A, Pell Jill P
School of Health and Wellbeing, University of Glasgow, Clarice Pears Building, 90 Byres Road, Glasgow, G12 8TB, UK.
Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China.
Cardiovasc Diabetol. 2025 Apr 15;24(1):162. doi: 10.1186/s12933-025-02721-9.
The associations between systemic inflammation biomarkers and cardiovascular disease (CVD) remain not well explored. This study aimed to investigate associations between different systemic inflammation biomarkers and incident CVD and main CVD subtypes - ischaemic heart disease (IHD), stroke, and heart failure - explore dose-response relationships, and compare their predictive performance.
This prospective cohort study included 423,701 UK Biobank participants free of CVD at baseline. Baseline neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and system inflammation response index (SIRI) were derived. Cox-proportional regression models were used to investigate the associations.
NLR, PLR, SII, and SIRI was positively and LMR was negatively associated with all four of the outcomes investigated. The relationships were non-linear for all biomarkers with CVD and were linear for NLR, SII, and SIRI and non-linear for LMR and PLR with IHD, stroke and heart failure. Compared with the more established biomarkers, all four of the novel biomarkers had statistically superior predictive performance for three of the outcomes investigated (CVD, IHD and heart failure) and three of them were superior at predicting stroke. Compared to a model of CVD prediction with classical risk factors (C-index = 0.702), discrimination was improved on the addition of inflammation markers for CVD (C-index change 0.0069, 95% CI 0.0033 to 0.0107), IHD (C-index change 0.0054, 95% CI 0.0013 to 0.0095), and heart failure (C-index change 0.0153, 95% CI 0.0089 to 0.0218).
There were independent and dose-response relationships between the novel systemic inflammation biomarkers and CVD outcomes. Addition of the inflammation biomarkers including novel inflammation biomarkers showed improved discrimination of the traditional risk prediction model. With accumulated evidence, these biomarkers should be considered for inclusion in risk tools and prevention.
全身炎症生物标志物与心血管疾病（CVD）之间的关联尚未得到充分研究。本研究旨在调查不同全身炎症生物标志物与新发CVD及主要CVD亚型——缺血性心脏病（IHD）、中风和心力衰竭之间的关联，探索剂量反应关系，并比较它们的预测性能。
这项前瞻性队列研究纳入了423701名英国生物银行的参与者，他们在基线时无CVD。计算基线中性粒细胞与淋巴细胞比率（NLR）、淋巴细胞与单核细胞比率（LMR）、血小板与淋巴细胞比率（PLR）、全身免疫炎症指数（SII）和全身炎症反应指数（SIRI）。采用Cox比例回归模型研究这些关联。
NLR、PLR、SII和SIRI与所有四项研究结果呈正相关，而LMR与所有四项研究结果呈负相关。所有生物标志物与CVD的关系均为非线性，NLR、SII和SIRI与IHD、中风和心力衰竭的关系为线性，而LMR和PLR与IHD、中风和心力衰竭的关系为非线性。与更成熟的生物标志物相比，所有四种新型生物标志物对三项研究结果（CVD、IHD和心力衰竭）具有统计学上更好的预测性能，其中三种在预测中风方面表现更优。与经典危险因素的CVD预测模型（C指数=0.702）相比，添加炎症标志物后，CVD（C指数变化0.0069，95%CI 0.0033至0.0107）、IHD（C指数变化0.0054，95%CI 0.0013至0.0095）和心力衰竭（C指数变化0.0153，95%CI 0.0089至0.0218）的辨别力得到改善。
新型全身炎症生物标志物与CVD结局之间存在独立的剂量反应关系。添加包括新型炎症生物标志物在内的炎症生物标志物可改善传统风险预测模型的辨别力。随着证据的积累，应考虑将这些生物标志物纳入风险工具和预防措施中。
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