Duke Molecular Physiology Institute, Duke University, Durham, NC, United States; Department of Medicine, Duke University, Durham, NC, United States.
Duke Molecular Physiology Institute, Duke University, Durham, NC, United States.
Osteoarthritis Cartilage. 2024 Mar;32(3):329-337. doi: 10.1016/j.joca.2023.09.007. Epub 2023 Sep 19.
To better understand the pathogenesis of knee osteoarthritis (OA) through identification of serum diagnostics.
We conducted multiple reaction monitoring mass spectrometry analysis of 107 peptides in baseline sera of two cohorts: the Foundation for National Institutes of Health (NIH) (n = 596 Kellgren-Lawrence (KL) grade 1-3 knee OA participants); and the Johnston County Osteoarthritis Project (n = 127 multi-joint controls free of radiographic OA of the hands, hips, knees (bilateral KL=0), and spine). Data were split into (70%) training and (30%) testing sets. Diagnostic peptide and clinical data predictors were selected by random forest (RF); selection was based on association (p < 0.05) with OA status in multivariable logistic regression models. Model performance was based on area under the curve (AUC) of receiver operating characteristic (ROC) and precision-recall (PR) curves.
RF selected 23 peptides (19 proteins) and body mass index (BMI) as diagnostic of OA. BMI weakly diagnosed OA (ROC-AUC 0.57, PR-AUC 0.812) and only symptomatic OA cases. ACTG was the strongest univariable predictor (ROC-AUC 0.705, PR-AUC 0.897). The final model (8 serum peptides) was highly diagnostic (ROC-AUC 0.833, 95% confidence interval [CI] 0.751, 0.905; PR-AUC 0.929, 95% CI 0.876, 0.973) in the testing set and equally diagnostic of non-symptomatic and symptomatic cases (AUCs 0.830-0.835), and not significantly improved with addition of BMI. The STRING database predicted multiple high confidence interactions of the 19 diagnostic OA proteins.
No more than 8 serum protein biomarkers were required to discriminate knee OA from non-OA. These biomarkers lend strong support to the involvement and cross-talk of complement and coagulation pathways in the development of OA.
通过鉴定血清诊断标志物，更好地了解膝骨关节炎（OA）的发病机制。
我们对两个队列的基线血清进行了 107 个肽段的多重反应监测质谱分析：国立卫生研究院（NIH）基础队列（n=596 例 Kellgren-Lawrence（KL）分级 1-3 级膝 OA 参与者）和约翰斯顿县骨关节炎项目队列（n=127 例多关节对照者，无手部、髋关节、膝关节（双侧 KL=0）和脊柱的放射学 OA）。数据分为（70%）训练集和（30%）测试集。采用随机森林（RF）选择诊断肽和临床数据预测因子；基于多变量逻辑回归模型中与 OA 状态的关联（p<0.05）进行选择。模型性能基于接收器操作特征（ROC）和精确-召回（PR）曲线的曲线下面积（AUC）。
RF 选择了 23 个肽段（19 个蛋白）和体重指数（BMI）作为 OA 的诊断标志物。BMI 对 OA 具有较弱的诊断能力（ROC-AUC 0.57，PR-AUC 0.812），仅诊断有症状的 OA 病例。ACTG 是最强的单变量预测因子（ROC-AUC 0.705，PR-AUC 0.897）。最终模型（8 个血清肽段）在测试集中具有很高的诊断能力（ROC-AUC 0.833，95%置信区间[CI]0.751，0.905；PR-AUC 0.929，95%CI 0.876，0.973），对无症状和有症状的病例同样具有诊断能力（AUC 0.830-0.835），且添加 BMI 并不能显著提高诊断能力。STRING 数据库预测了 19 个诊断 OA 蛋白的多个高可信度相互作用。
不超过 8 个血清蛋白生物标志物即可区分膝 OA 与非 OA。这些生物标志物有力地支持补体和凝血途径的参与和串扰在 OA 的发生发展中起作用。
