识别类风湿关节炎患者亚临床冠状动脉粥样硬化的新型生物标志物：探索性分析。
Identification of novel biomarkers for the prediction of subclinical coronary artery atherosclerosis in patients with rheumatoid arthritis: an exploratory analysis.
机构信息
Division of Rheumatology, Department of Medicine, Columbia University Irving Medical. Center/New York Presbyterian Hospital, New York, NY, USA.
Haus Bioceuticals, Oklahoma City, OK, USA.
出版信息
Arthritis Res Ther. 2023 Oct 30;25(1):213. doi: 10.1186/s13075-023-03196-3.
BACKGROUND
Cardiovascular (CV) risk estimation calculators for the general population underperform in patients with rheumatoid arthritis (RA). The purpose of this study was to identify relevant protein biomarkers that could be added to traditional CV risk calculators to improve the capacity of coronary artery calcification (CAC) prediction in individuals with RA. In a second step, we quantify the improvement of this prediction of CAC when these circulating biomarkers are added to standard risk scores.
METHODS
A panel of 141 serum and plasma proteins, which represent a broad base of both CV and RA biology, were evaluated and prioritized as candidate biomarkers. Of these, 39 proteins were selected and measured by commercial ELISA or quantitative mass spectroscopy in 561 individuals with RA in whom a measure of CAC and frozen sera were available. The patients were randomly split 50:50 into a training/validation cohort. Discrimination (using area under the receiver operator characteristic curves) and re-classification (through net reclassification improvement and integrated discrimination improvement calculation) analyses were performed first in the training cohort and replicated in the validation cohort, to estimate the increase in prediction accuracy for CAC using the ACA/AHA (American College of Cardiology and the American Heart Association) score with, compared to without, addition of these circulating biomarkers.
RESULTS
The model containing ACC/AHA score plus cytokines (osteopontin, cartilage glycoprotein-39, cystatin C, and chemokine (C-C motif) ligand 18) and plus quantitative mass spectroscopy biomarkers (serpin D1, paraoxonase, and clusterin) had a statistically significant positive net reclassifications index and integrated discrimination improvement for the prediction of CAC, using ACC/AHA score without any biomarkers as the reference category. These results were confirmed in the validation cohort.
CONCLUSION
In this exploratory analysis, the addition of several circulating CV and RA biomarkers to a standard CV risk calculator yielded significant improvements in discrimination and reclassification for the presence of CAC in individuals with RA.
背景
心血管（CV）风险估算计算器在类风湿关节炎（RA）患者中的表现不佳。本研究的目的是确定相关的蛋白质生物标志物，这些标志物可以添加到传统的 CV 风险计算器中，以提高 RA 患者冠状动脉钙化（CAC）的预测能力。在第二步中，我们量化了当这些循环生物标志物被添加到标准风险评分中时，对 CAC 预测的改善程度。
方法
评估并优先考虑了代表广泛 CV 和 RA 生物学的 141 种血清和血浆蛋白作为候选生物标志物。在 561 名有 CAC 测量值和冷冻血清的 RA 患者中，通过商业 ELISA 或定量质谱法测量了其中的 39 种蛋白质。这些患者被随机分为 50:50 的训练/验证队列。首先在训练队列中进行判别（使用接收者操作特征曲线下的面积）和重新分类（通过净重新分类改善和综合判别改善计算）分析，并在验证队列中进行复制，以估计使用 ACC/AHA（美国心脏病学院和美国心脏协会）评分，与不添加这些循环生物标志物相比，添加这些循环生物标志物对 CAC 预测准确性的提高。
结果
包含 ACC/AHA 评分加细胞因子（骨桥蛋白、软骨糖蛋白 39、胱抑素 C 和趋化因子（C-C 基序）配体 18）和加定量质谱生物标志物（丝氨酸蛋白酶抑制剂 D1、对氧磷酶和载脂蛋白）的模型对 CAC 的预测具有统计学上显著的正净重新分类指数和综合判别改善，使用 ACC/AHA 评分而不使用任何生物标志物作为参考类别。这些结果在验证队列中得到了证实。
结论
在这项探索性分析中，将几种循环 CV 和 RA 生物标志物添加到标准 CV 风险计算器中，显著提高了 RA 患者 CAC 存在的判别和重新分类能力。
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