Confirmation of multiple endotypes in atopic dermatitis based on serum biomarkers
Authors: Daphne S. Bakker, Stefan Nierkens, Edward F. Knol, Barbara Giovannone, Eveline M. Delemarre, Jorien van der Schaft, Femke van Wijk, Marjolein S. de Bruin-Weller, Julia Drylewicz, Judith L. Thijs
Journal: Journal of Allergy and Clinical Immunology (2020)

BackgroundAtopic 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.ObjectiveOur aim was to confirm previously defined serum biomarker-based patient clusters in a new cohort of patients with AD.MethodsA 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.ResultsCluster 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 "TH1/TH2/TH17-dominant" cluster, cluster C (18.5%) was a "TH2/TH22/PARC-dominant" cluster, and cluster D (29.5%) was a "TH2/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.ConclusionWe 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. 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. Our aim was to confirm previously defined serum biomarker-based patient clusters in a new cohort of patients with AD. 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. 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 "TH1/TH2/TH17-dominant" cluster, cluster C (18.5%) was a "TH2/TH22/PARC-dominant" cluster, and cluster D (29.5%) was a "TH2/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. 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.