Department of Dermato-Venereology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark,
Department of Bioengineering, Faculty of Engineering, Imperial College, London, United Kingdom.
Dermatology. 2021;237(4):513-520. doi: 10.1159/000514503. Epub 2021 Mar 17.
A growing body of evidence links various biomarkers to atopic dermatitis (AD). Still, little is known about the association of specific biomarkers to disease characteristics and severity in AD.
To explore the relationship between various immunological markers in the serum and disease severity in a hospital cohort of AD patients.
Outpatients with AD referred to the Department of Dermatology, Bispebjerg Hospital, Copenhagen, Denmark, were divided into groups based on disease severity (SCORAD). Serum levels of a preselected panel of immunoinflammatory biomarkers were tested for association with disease characteristics. Two machine learning models were developed to predict SCORAD from the measured biomarkers.
A total of 160 patients with AD were included; 53 (33.1%) with mild, 73 (45.6%) with moderate, and 34 (21.3%) with severe disease. Mean age was 29.2 years (range 6-70 years) and 84 (52.5%) were females. Numerous biomarkers showed a statistically significant correlation with SCORAD, with the strongest correlations seen for CCL17/thymus and activation-regulated chemokine (chemokine ligand-17/TARC) and CCL27/cutaneous T cell-attracting-chemokine (CTACK; Spearman R of 0.50 and 0.43, respectively, p < 0.001). Extrinsic AD patients were more likely to have higher mean SCORAD (p < 0.001), CCL17 (p < 0.001), CCL26/eotaxin-3 (p < 0.001), and eosinophil count (p < 0.001) than intrinsic AD patients. Predictive models for SCORAD identified CCL17, CCL27, serum total IgE, IL-33, and IL-5 as the most important predictors for SCORAD, but with weaker associations than single cytokines.
Specific immunoinflammatory biomarkers in the serum, mainly of the Th2 pathway, are correlated with disease severity in patients with AD. Predictive models identified biomarkers associated with disease severity but this finding warrants further investigation.
越来越多的证据将各种生物标志物与特应性皮炎（AD）联系起来。然而，对于特定生物标志物与 AD 疾病特征和严重程度的关联知之甚少。
探讨丹麦哥本哈根比斯加普比约格医院皮肤科门诊 AD 患者血清中各种免疫标志物与疾病严重程度的关系。
根据 SCORAD 将 AD 门诊患者分为疾病严重程度组。检测血清中预选免疫炎症生物标志物的水平，以评估其与疾病特征的相关性。建立两种机器学习模型，从所测生物标志物预测 SCORAD。
共纳入 160 例 AD 患者，其中轻度 53 例（33.1%），中度 73 例（45.6%），重度 34 例（21.3%）。平均年龄为 29.2 岁（6-70 岁），女性 84 例（52.5%）。许多生物标志物与 SCORAD 呈显著相关性，其中 CCL17/胸腺激活调节趋化因子（chemokine ligand-17/TARC）和 CCL27/皮肤 T 细胞吸引趋化因子（CTACK）相关性最强（Spearman R 分别为 0.50 和 0.43，均 P<0.001）。特应性皮炎患者的平均 SCORAD（P<0.001）、CCL17（P<0.001）、CCL26/嗜酸性粒细胞趋化因子-3（p<0.001）和嗜酸性粒细胞计数（p<0.001）均高于非特应性皮炎患者。用于预测 SCORAD 的模型确定 CCL17、CCL27、血清总 IgE、IL-33 和 IL-5 是与 SCORAD 最重要的预测因子，但与单个细胞因子相比，相关性较弱。
AD 患者血清中特定的免疫炎症生物标志物，主要是 Th2 途径，与疾病严重程度相关。预测模型确定了与疾病严重程度相关的生物标志物，但这一发现需要进一步研究。
