Yang Li, He Shixin, Tang Li, Qin Xiao, Zheng Yan
Department of Medical Cosmetology, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China.
Front Immunol. 2025 Jul 31;16:1619490. doi: 10.3389/fimmu.2025.1619490. eCollection 2025.
Psoriasis is a chronic immune-mediated inflammatory skin disorder characterized by multifactorial pathogenesis. Recent studies have extensively highlighted the strong associations between psoriasis and various inflammatory markers, which are considered novel predictive tools for evaluating systemic inflammation.
Cross-sectional data from the NHANES were analyzed in this study. To assess model performance and generalizability, the dataset was randomly divided into 70% for training and 30% for validation. To address class imbalance in the training data, a hybrid resampling technique (SMOTEENN) was applied. Subsequently, nine classification algorithms were developed using the processed training set, including random forest, neural networks, XGBoost, k-nearest neighbors, gradient boosting, logistic regression, naïve Bayes, AdaBoost, and SVMs. The final gradient boosting was implemented via the gbm package in R, with hyperparameters selected from the default tuning grid of the caret framework. Inflammatory biomarkers with the highest classification utility were identified based on the predictions of the best-performing model.
A total of 22,908 participants were included in the final analysis. Gradient boosting (AUC: 0.629, 95% CI: 0.588-0.669) demonstrated the highest performance, followed closely by logistic regression (AUC: 0.627, 95% CI: 0.588-0.666). Among all the inflammatory markers, MLR exhibited the best classification performance, with an AUC value of 0.662 (95% CI: 0.640-0.683), followed by NLMR, with an AUC value of 0.661 (95% CI: 0.640-0.683). Other markers, including the NLR, dNLR, SII, SIRI, and PLR, had AUC values ranging from 0.658 to 0.661. The MLR had the highest relative importance score, demonstrating its critical role in the model's predictive performance for psoriasis classification. The NLR ranked second, followed by the SII and SIRI, which had moderate contributions, whereas the PLR contributed the least.
Among all the tested algorithms, the gradient boosting model achieved the best performance. Not only does it achieve the highest predictive accuracy, but it also excels in classification efficacy and feature importance analysis, highlighting key inflammatory markers such as the MLR, SII, and NLR. These markers are significant as reliable indicators for evaluating systemic inflammation and predicting the development of psoriasis, emphasizing their potential clinical applications.
银屑病是一种慢性免疫介导的炎症性皮肤病，其发病机制具有多因素性。最近的研究广泛强调了银屑病与各种炎症标志物之间的紧密关联，这些标志物被认为是评估全身炎症的新型预测工具。
本研究分析了美国国家健康与营养检查调查（NHANES）的横断面数据。为了评估模型性能和通用性，将数据集随机分为70%用于训练，30%用于验证。为了解决训练数据中的类别不平衡问题，应用了一种混合重采样技术（SMOTEENN）。随后，使用处理后的训练集开发了九种分类算法，包括随机森林、神经网络、XGBoost、k近邻、梯度提升、逻辑回归、朴素贝叶斯、AdaBoost和支持向量机。最终的梯度提升通过R语言中的gbm包实现，超参数从caret框架的默认调优网格中选择。根据表现最佳的模型的预测结果，确定了具有最高分类效用的炎症生物标志物。
最终分析共纳入22908名参与者。梯度提升（AUC：0.629，95%可信区间：0.588 - 0.669）表现最佳，紧随其后的是逻辑回归（AUC：0.627，95%可信区间：0.588 - 0.666）。在所有炎症标志物中，混合淋巴细胞反应（MLR）表现出最佳的分类性能，AUC值为0.662（95%可信区间：0.640 - 0.683），其次是中性粒细胞与淋巴细胞比值（NLMR），AUC值为0.661（95%可信区间：0.640 - 0.683）。其他标志物，包括中性粒细胞与淋巴细胞比值（NLR）、衍生中性粒细胞与淋巴细胞比值（dNLR）、全身免疫炎症指标（SII）、系统性免疫炎症反应指数（SIRI）和血小板与淋巴细胞比值（PLR），AUC值在0.658至0.661之间。MLR的相对重要性得分最高，表示其在银屑病分类模型预测性能中起关键作用。NLR排名第二，其次是SII和SIRI，它们的贡献适中，而PLR的贡献最小。
在所有测试算法中，梯度提升模型表现最佳。它不仅实现了最高的预测准确性，而且在分类效能和特征重要性分析方面也表现出色，突出了关键炎症标志物，如MLR、SII和NLR。这些标志物作为评估全身炎症和预测银屑病发展的可靠指标具有重要意义，强调了它们潜在的临床应用价值。
