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炎症蛋白组:在一项横断面研究中探索外周动脉疾病诊断的诊断见解

Inflammatory Protein Panel: Exploring Diagnostic Insights for Peripheral Artery Disease Diagnosis in a Cross-Sectional Study.

作者信息

Li Ben, Nassereldine Rakan, Shaikh Farah, Younes Houssam, AbuHalimeh Batool, Zamzam Abdelrahman, Abdin Rawand, Qadura Mohammad

机构信息

Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, ON M5B 1W8, Canada.

Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada.

出版信息

Diagnostics (Basel). 2024 Aug 24;14(17):1847. doi: 10.3390/diagnostics14171847.

Abstract

Cytokine-induced neutrophil chemoattractant 1 (CINC-1), a cluster of differentiation 95 (CD95), fractalkine, and T-cell immunoglobulin and mucin domain 1 (TIM-1) are circulating proteins known to be involved in inflammation. While their roles have been studied in neurological conditions and cardiovascular diseases, their potential as peripheral artery disease (PAD) biomarkers remain unexplored. We conducted a cross-sectional diagnostic study using data from 476 recruited patients (164 without PAD and 312 with PAD). Plasma levels of CINC-1, CD95, fractalkine, and TIM-1 were measured at baseline. A PAD diagnosis was established at recruitment based on clinical exams and investigations, defined as an ankle-brachial index < 0.9 or toe-brachial index < 0.67 with absent/diminished pedal pulses. Using 10-fold cross-validation, we trained a random forest algorithm, incorporating clinical characteristics and biomarkers that showed differential expression in PAD versus non-PAD patients to predict a PAD diagnosis. Among the proteins tested, CINC-1, CD95, and fractalkine were elevated in PAD vs. non-PAD patients, forming a 3-biomarker panel. Our predictive model achieved an AUROC of 0.85 for a PAD diagnosis using clinical features and this 3-biomarker panel. By combining the clinical characteristics with these biomarkers, we developed an accurate predictive model for a PAD diagnosis. This algorithm can assist in PAD screening, risk stratification, and guiding clinical decisions regarding further vascular assessment, referrals, and medical/surgical management to potentially improve patient outcomes.

摘要

细胞因子诱导的中性粒细胞趋化因子1(CINC-1)、分化簇95(CD95)、 fractalkine和T细胞免疫球蛋白黏蛋白结构域1(TIM-1)是已知参与炎症反应的循环蛋白。虽然它们在神经系统疾病和心血管疾病中的作用已得到研究,但其作为外周动脉疾病(PAD)生物标志物的潜力仍未被探索。我们使用476名招募患者(164名无PAD和312名有PAD)的数据进行了一项横断面诊断研究。在基线时测量了CINC-1、CD95、 fractalkine和TIM-1的血浆水平。在招募时根据临床检查和调查确定PAD诊断,定义为踝臂指数<0.9或趾臂指数<0.67且足背动脉搏动消失/减弱。使用10倍交叉验证,我们训练了一种随机森林算法,纳入了在PAD患者与非PAD患者中表现出差异表达的临床特征和生物标志物,以预测PAD诊断。在测试的蛋白质中,PAD患者的CINC-1、CD95和 fractalkine水平升高,形成了一个由三种生物标志物组成的检测组。我们的预测模型使用临床特征和这个由三种生物标志物组成的检测组对PAD诊断的曲线下面积(AUROC)为0.85。通过将临床特征与这些生物标志物相结合,我们开发了一种用于PAD诊断的准确预测模型。该算法可协助进行PAD筛查、风险分层,并指导有关进一步血管评估、转诊以及医疗/手术管理的临床决策,以潜在地改善患者预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/716e/11394143/65cb57bd5a0e/diagnostics-14-01847-g001.jpg

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