Grupo de Investigación de Reumatología (GIR), INIBIC-Hospital Universitario A Coruña, SERGAS, A Coruña, Spain.
Grupo de Reumatología y Salud, Departamento de Fisioterapia y Medicina, Centro Interdisciplinar de Química e Bioloxía (CICA), Universidad de A Coruña, A Coruña, Spain.
Ann Rheum Dis. 2024 Apr 11;83(5):661-668. doi: 10.1136/ard-2023-225090.
OBJECTIVE: Early diagnosis of knee osteoarthritis (KOA) in asymptomatic stages is essential for the timely management of patients using preventative strategies. We develop and validate a prognostic model useful for predicting the incidence of radiographic KOA (rKOA) in non-radiographic osteoarthritic subjects and stratify individuals at high risk of developing the disease. METHODS: Subjects without radiographic signs of KOA according to the Kellgren and Lawrence (KL) classification scale (KL=0 in both knees) were enrolled in the OA initiative (OAI) cohort and the Prospective Cohort of A Coruña (PROCOAC). Prognostic models were developed to predict rKOA incidence during a 96-month follow-up period among OAI participants based on clinical variables and serum levels of the candidate protein biomarkers APOA1, APOA4, ZA2G and A2AP. The predictive capability of the biomarkers was assessed based on area under the curve (AUC), and internal validation was performed to correct for overfitting. A nomogram was plotted based on the regression parameters. Model performance was externally validated in the PROCOAC. RESULTS: 282 participants from the OAI were included in the development dataset. The model built with demographic, anthropometric and clinical data (age, sex, body mass index and WOMAC pain score) showed an AUC=0.702 for predicting rKOA incidence during the follow-up. The inclusion of ZA2G, A2AP and APOA1 data significantly improved the model's sensitivity and predictive performance (AUC=0.831). The simplest model, including only clinical covariates and ZA2G and A2AP serum levels, achieved an AUC=0.826. Both models were internally cross-validated. Predictive performance was externally validated in an independent dataset of 100 individuals from the PROCOAC (AUC=0.713). CONCLUSION: A novel prognostic model based on common clinical variables and protein biomarkers was developed and externally validated to predict rKOA incidence over a 96-month period in individuals without any radiographic signs of disease. The resulting nomogram is a useful tool for stratifying high-risk populations and could potentially lead to personalised medicine strategies for treating OA.
目的：在无症状阶段早期诊断膝骨关节炎（KOA）对于及时采用预防策略管理患者至关重要。我们开发并验证了一种预测模型，用于预测无放射学骨关节炎（OA）受试者的放射学 KOA（rKOA）发生率，并对疾病高风险个体进行分层。
方法：根据 Kellgren 和 Lawrence（KL）分类量表（双侧膝关节 KL=0），无放射学 KOA 迹象的受试者被纳入 OA 倡议（OAI）队列和拉科鲁尼亚前瞻性队列（PROCOAC）。根据临床变量和候选蛋白生物标志物 APOA1、APOA4、ZA2G 和 A2AP 的血清水平，为 OAI 参与者开发了预测 96 个月随访期间 rKOA 发生率的预测模型。基于曲线下面积（AUC）评估生物标志物的预测能力，并进行内部验证以纠正过度拟合。根据回归参数绘制了列线图。在 PROCOAC 中进行了模型的外部验证。
结果：纳入 OAI 的 282 名参与者进入开发数据集。基于人口统计学、人体测量学和临床数据（年龄、性别、体重指数和 WOMAC 疼痛评分）构建的模型显示，预测随访期间 rKOA 发生率的 AUC=0.702。纳入 ZA2G、A2AP 和 APOA1 数据显著提高了模型的敏感性和预测性能（AUC=0.831）。仅包含临床协变量和 ZA2G 和 A2AP 血清水平的最简单模型，其 AUC=0.826。两个模型均进行了内部交叉验证。在来自 PROCOAC 的 100 名独立参与者的数据集进行了外部验证（AUC=0.713）。
结论：我们开发了一种基于常见临床变量和蛋白质生物标志物的新型预测模型，并在没有任何疾病放射学迹象的个体中进行了外部验证，以预测 96 个月内 rKOA 的发生率。由此产生的列线图是对高危人群进行分层的有用工具，并可能为 OA 的个体化医学策略提供依据。
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