Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept.
Synovial fluid fingerprinting in end-stage knee osteoarthritis: a novel biomarker concept.
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DOI：
10.1302/2046-3758.99.bjr-2019-0192.r1
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发表时间：
2020-09
影响因子：
4.6
通讯作者：
Price A
中科院分区：
文献类型：
--
作者：
Jayadev C;Hulley P;Swales C;Snelling S;Collins G;Taylor P;Price A
骨关节炎（OA）的疾病修饰治疗的缺乏与合适的生物标志物的短缺有关。该研究将多分子滑液分析与机器学习相结合，为终末期膝关节OA（esOA）提供准确的诊断生物标志物模型。使用免疫测定法分析了esOA、非OA膝关节损伤和炎症性膝关节炎患者的滑液（SF）中的35种潜在标志物。偏最小二乘判别分析（PLS-DA）用于推导用于队列分类的生物标志物模型。生物标志物模型诊断esOA的能力通过对10名esOA患者的测试队列进行相同的广谱SF分析来验证。PLS-DA产生了一个精简的生物标志物模型，具有出色的灵敏度（95%），特异性（98.4%）和可靠性（97.4%）。八种生物标志物模型产生了esOA的指纹图谱，包括IIA型前胶原N-末端前肽（PIIANP）、金属蛋白酶组织抑制剂（TIMP）-1、具有血小板反应蛋白基序的去整合素和金属蛋白酶4（ADAMTS-4）、单核细胞趋化蛋白（MCP）-1、干扰素-γ-诱导蛋白-10（IP-10）和转化生长因子（TGF）-β3。受试者工作特征（ROC）分析显示了极好的判别准确性：esOA的曲线下面积（AUC）为0.970，膝关节损伤为0.957，炎性关节炎为1。所有10名验证测试患者通过生物标志物模型被正确分类为esOA（准确性100%;可靠性100%）。SF分析结合机器学习产生了一个部分验证的生物标志物模型，该模型具有队列特异性指纹，可以准确可靠地区分esOA与膝关节损伤和炎性关节炎，疗效几乎为100%。提出的研究结果和方法代表了一种新的生物标志物概念和潜在的诊断工具，可以在治疗试验中对疾病进行分期，并监测此类干预措施的疗效。引用这篇文章：骨关节研究2020;9（9）：623-632。
The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA. PLS-DA produced a streamlined biomarker model with excellent sensitivity (95%), specificity (98.4%), and reliability (97.4%). The eight-biomarker model produced a fingerprint for esOA comprising type IIA procollagen N-terminal propeptide (PIIANP), tissue inhibitor of metalloproteinase (TIMP)-1, a disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS-4), monocyte chemoattractant protein (MCP)-1, interferon-γ-inducible protein-10 (IP-10), and transforming growth factor (TGF)-β3. Receiver operating characteristic (ROC) analysis demonstrated excellent discriminatory accuracy: area under the curve (AUC) being 0.970 for esOA, 0.957 for knee injury, and 1 for inflammatory arthritis. All ten validation test patients were classified correctly as esOA (accuracy 100%; reliability 100%) by the biomarker model. SF analysis coupled with machine learning produced a partially validated biomarker model with cohort-specific fingerprints that accurately and reliably discriminated esOA from knee injury and inflammatory arthritis with almost 100% efficacy. The presented findings and approach represent a new biomarker concept and potential diagnostic tool to stage disease in therapy trials and monitor the efficacy of such interventions. Cite this article: Bone Joint Res 2020;9(9):623–632.