Atopic dermatitis phenotypes based on cluster analysis of the Danish Skin Cohort
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Atopic dermatitis phenotypes based on cluster analysis of the Danish Skin Cohort. / Nymand, Lea; Nielsen, Mia Louise; Vittrup, Ida; Halling, Anne Sofie; Thomsen, Simon Francis; Egeberg, Alexander; Thyssen, Jacob P.
In: British Journal of Dermatology, Vol. 190, No. 2, 2024, p. 207-215.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Atopic dermatitis phenotypes based on cluster analysis of the Danish Skin Cohort
AU - Nymand, Lea
AU - Nielsen, Mia Louise
AU - Vittrup, Ida
AU - Halling, Anne Sofie
AU - Thomsen, Simon Francis
AU - Egeberg, Alexander
AU - Thyssen, Jacob P.
N1 - Publisher Copyright: © 2024 Blackwell Publishing Ltd. All rights reserved.
PY - 2024
Y1 - 2024
N2 - Background Despite previous attempts to classify atopic dermatitis (AD) into subtypes (e.g. extrinsic vs. intrinsic), there is a need to better understand specific phenotypes in adulthood. Objectives To identify, using machine learning (ML), adult AD phenotypes. Methods We used unsupervised cluster analysis to identify AD phenotypes by analysing different responses to predetermined variables (age of disease onset, severity, itch and skin pain intensity, flare frequency, anatomical location, presence and/or severity of current comorbidities) in adults with AD from the Danish Skin Cohort. Results The unsupervised cluster analysis resulted in five clusters where AD severity most clearly differed. We classified them as mild , mild-To-moderate , moderate , severe and very severe . The severity of multiple predetermined patient-reported outcomes was positively associated with AD, including an increased number of flare-ups and increased flare-up duration and disease severity. However, an increased severity of rhinitis and mental health burden was also found for the mild-To-moderate phenotype. Conclusions ML confirmed the use of disease severity for the categorization of phenotypes, and our cluster analysis provided novel detailed information about how flare patterns and duration are associated with AD disease severity.
AB - Background Despite previous attempts to classify atopic dermatitis (AD) into subtypes (e.g. extrinsic vs. intrinsic), there is a need to better understand specific phenotypes in adulthood. Objectives To identify, using machine learning (ML), adult AD phenotypes. Methods We used unsupervised cluster analysis to identify AD phenotypes by analysing different responses to predetermined variables (age of disease onset, severity, itch and skin pain intensity, flare frequency, anatomical location, presence and/or severity of current comorbidities) in adults with AD from the Danish Skin Cohort. Results The unsupervised cluster analysis resulted in five clusters where AD severity most clearly differed. We classified them as mild , mild-To-moderate , moderate , severe and very severe . The severity of multiple predetermined patient-reported outcomes was positively associated with AD, including an increased number of flare-ups and increased flare-up duration and disease severity. However, an increased severity of rhinitis and mental health burden was also found for the mild-To-moderate phenotype. Conclusions ML confirmed the use of disease severity for the categorization of phenotypes, and our cluster analysis provided novel detailed information about how flare patterns and duration are associated with AD disease severity.
U2 - 10.1093/bjd/ljad401
DO - 10.1093/bjd/ljad401
M3 - Journal article
C2 - 37850907
AN - SCOPUS:85183457360
VL - 190
SP - 207
EP - 215
JO - British Journal of Dermatology
JF - British Journal of Dermatology
SN - 0007-0963
IS - 2
ER -
ID: 381681235