Background

Atopic dermatitis (AD) is a highly heterogeneous disease, both clinically and biologically, whereas patients are still being treated according to a “one-size-fits-all” approach. Stratification of patients into biomarker-based endotypes is important for future development of personalized therapies.

Objective

Our aim was to confirm previously defined serum biomarker-based patient clusters in a new cohort of patients with AD.

Methods

A panel of 143 biomarkers was measured by using Luminex technology in serum samples from 146 patients with severe AD (median Eczema Area and Severity Index = 28.3; interquartile range = 25.2-35.3). Principal components analysis followed by unsupervised k-means cluster analysis of the biomarker data was used to identify patient clusters. A prediction model was built on the basis of a previous cohort to predict the 1 of the 4 previously identified clusters to which the patients of our new cohort would belong.

Results

Cluster analysis identified 4 serum biomarker–based clusters, 3 of which (clusters B, C, and D) were comparable to the previously identified clusters. Cluster A (33.6%) could be distinguished from the other clusters as being a “skin-homing chemokines/IL-1R1–dominant” cluster, whereas cluster B (18.5%) was a “T_H1/T_H2/T_H17-dominant” cluster, cluster C (18.5%) was a “T_H2/T_H22/PARC-dominant” cluster, and cluster D (29.5%) was a “T_H2/eosinophil-inferior” cluster. Additionally, by using a prediction model based on our previous cohort we accurately assigned the new cohort to the 4 previously identified clusters by including only 10 selected serum biomarkers.

Conclusion

We confirmed that AD is heterogeneous at the immunopathologic level and identified 4 distinct biomarker-based clusters, 3 of which were comparable with previously identified clusters. Cluster membership could be predicted with a model including 10 serum biomarkers.

中文翻译：

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基于血清生物标志物的特应性皮炎中多种内型的确认。

背景

特应性皮炎（AD）在临床和生物学上都是高度异质性疾病，而患者仍按照“一刀切”的方法进行治疗。将患者分层为基于生物标记的内型对于个性化疗法的未来发展很重要。

目的

我们的目的是在新的AD患者队列中确认先前定义的基于血清生物标志物的患者群。

方法

使用Luminex技术从146名严重AD（中度湿疹面积和严重程度指数= 28.3；四分位间距= 25.2-35.3）的患者的血清样本中测量了143种生物标记物。使用主成分分析，然后对生物标记数据进行无监督的k均值聚类分析来识别患者聚类。在先前队列的基础上建立了一个预测模型，以预测我们新队列的患者所属的四个先前确定的簇中的1个。

结果

聚类分析确定了4个基于血清生物标志物的聚类，其中3个（聚类B，C和D）与之前确定的聚类相当。群集A（33.6％）可与其他群集区别为“归巢趋化因子/ IL-1R1为主”群集，而群集B（18.5％）为“ T _H 1 / T _H 2 / T _H” 17-显性”簇，簇C（18.5％）是一个‘T _ħ 2 / T _ħ 22 / PARC主导’集群和集群d（29.5％）是一个‘T _ħ 2 /嗜酸性粒细胞劣’群集。此外，通过使用基于我们先前队列的预测模型，我们仅包括10个选定的血清生物标志物，即可将新队列准确地分配给4个先前确定的簇。

结论

我们确认，AD在免疫病理学水平上是异质的，并确定了4个不同的基于生物标志物的簇，其中3个与先前鉴定的簇相当。可以用包含10个血清生物标志物的模型预测簇成员。