Yang Fan, Sang Weihua, Liu Yongqing, Wang Jun
Department of Orthopedics, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Front Med (Lausanne). 2025 Jul 18;12:1624527. doi: 10.3389/fmed.2025.1624527. eCollection 2025.
Rheumatoid arthritis (RA) is a chronic inflammatory disorder that leads to joint damage, cartilage and bone destruction, and functional disability. The C-Reactive Protein to Albumin Ratio (CAR), an emerging biomarker reflecting systemic inflammation and nutritional status, has demonstrated prognostic value in various diseases. However, its utility in predicting clinical outcomes in RA patients remains underexplored, warranting further investigation to assess its potential role in disease management and prognosis. This cross-sectional study investigates the potential relationship between CAR and RA in United States adults, develops a clinical prediction model, and validates its effectiveness.
To investigate the association between the CAR and RA using data from the National Health and Nutrition Examination Survey (NHANES).
This large-scale, cross-sectional study analyzed data from the NHANES database between 1999 and 2018 (excluding 2011-2014). The CAR was calculated as the ratio of C-reactive protein (CRP) to albumin (ALB) levels. RA status was identified through self-reported questionnaire data. Weighted multivariate regression models and subgroup analyses were used to examine the association between CAR and RA. Restricted cubic splines (RCS) were employed to evaluate potential non-linear relationships, and sensitivity analyses were conducted to assess the robustness of the results. Least absolute shrinkage and selection operator (LASSO) were utilized for variable selection in the prediction model. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curve analysis were applied to assess the predictive performance of the models.
This study included a total of 20,733 patients, among whom 1,744 individuals (4.95%) were diagnosed with RA. After controlling for all covariates, the results of multivariate logistic regression analysis indicated a statistically significant correlation between higher Ln(CAR) levels and the increased incidence of RA (OR:1.77 (95% CI, 1.39-2.25); < 0.001). The interaction test results showed that there was no statistically significant influence in this specific association. RCS regression modeling demonstrated a linear relationship between Ln-CAR and RA risk. After variable screening, we constructed an RA prediction model incorporating CAR, and the results were visualized using a nomogram. The area under the curve (AUC) was 0.749 (95% CI, 0.738-0.760), and DCA indicated that the model holds clinical significance.
These findings suggest that CAR may serve as a promising inflammatory biomarker for predicting the presence of RA. In the RA prediction model incorporating CAR, we validated the effectiveness and clinical utility of this model, providing evidence that CAR can serve as a biomarker for RA risk prediction. Further prospective studies are warranted to validate its clinical utility in RA risk stratification and management.
类风湿关节炎（RA）是一种慢性炎症性疾病，可导致关节损伤、软骨和骨质破坏以及功能残疾。C反应蛋白与白蛋白比值（CAR）是一种反映全身炎症和营养状况的新兴生物标志物，已在多种疾病中显示出预后价值。然而，其在预测RA患者临床结局方面的作用仍未得到充分探索，需要进一步研究以评估其在疾病管理和预后中的潜在作用。这项横断面研究调查了美国成年人中CAR与RA之间的潜在关系，开发了一种临床预测模型，并验证了其有效性。
利用国家健康与营养检查调查（NHANES）的数据，研究CAR与RA之间的关联。
这项大规模横断面研究分析了1999年至2018年（不包括2011 - 2014年）NHANES数据库中的数据。CAR计算为C反应蛋白（CRP）与白蛋白（ALB）水平的比值。通过自我报告的问卷数据确定RA状态。使用加权多变量回归模型和亚组分析来检验CAR与RA之间的关联。采用受限立方样条（RCS）评估潜在的非线性关系，并进行敏感性分析以评估结果的稳健性。在预测模型中使用最小绝对收缩和选择算子（LASSO）进行变量选择。应用决策曲线分析（DCA）和受试者工作特征（ROC）曲线分析来评估模型的预测性能。
本研究共纳入20,733例患者，其中1,744例（4.95%）被诊断为RA。在控制所有协变量后，多变量逻辑回归分析结果表明，较高的Ln（CAR）水平与RA发病率增加之间存在统计学显著相关性（OR：1.77（95%CI，1.39 - 2.25）；P < 0.001）。交互检验结果表明，在这种特定关联中没有统计学显著影响。RCS回归模型显示Ln - CAR与RA风险之间存在线性关系。经过变量筛选，我们构建了一个包含CAR的RA预测模型，并使用列线图将结果可视化。曲线下面积（AUC）为0.749（95%CI，0.738 - 0.760），DCA表明该模型具有临床意义。
这些发现表明，CAR可能是预测RA存在的一种有前景的炎症生物标志物。在包含CAR的RA预测模型中，我们验证了该模型的有效性和临床实用性，提供了证据表明CAR可作为RA风险预测的生物标志物。需要进一步的前瞻性研究来验证其在RA风险分层和管理中的临床实用性。
