Immunoinflammatory Biomarkers in Serum Are Associated with Disease Severity in Atopic Dermatitis.


Journal

Dermatology (Basel, Switzerland)
ISSN: 1421-9832
Titre abrégé: Dermatology
Pays: Switzerland
ID NLM: 9203244

Informations de publication

Date de publication:
2021
Historique:
received: 10 09 2020
accepted: 16 01 2021
pubmed: 18 3 2021
medline: 15 12 2021
entrez: 17 3 2021
Statut: ppublish

Résumé

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.

Sections du résumé

BACKGROUND BACKGROUND
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.
OBJECTIVE OBJECTIVE
To explore the relationship between various immunological markers in the serum and disease severity in a hospital cohort of AD patients.
METHODS METHODS
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.
RESULTS RESULTS
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.
CONCLUSIONS CONCLUSIONS
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.

Identifiants

pubmed: 33730733
pii: 000514503
doi: 10.1159/000514503
doi:

Substances chimiques

Biomarkers 0
CCL26 protein, human 0
Chemokine CCL17 0
Chemokine CCL26 0
Chemokine CCL27 0
Cytokines 0
IL33 protein, human 0
IL5 protein, human 0
Interleukin-33 0
Interleukin-5 0
Immunoglobulin E 37341-29-0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

513-520

Informations de copyright

© 2021 S. Karger AG, Basel.

Auteurs

Jesper Grønlund Holm (JG)

Department of Dermato-Venereology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark, jespergholm@gmail.com.

Guillem Hurault (G)

Department of Bioengineering, Faculty of Engineering, Imperial College, London, United Kingdom.

Tove Agner (T)

Department of Dermato-Venereology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark.

Maja Lisa Clausen (ML)

Department of Dermato-Venereology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark.

Sanja Kezic (S)

Coronel Institute, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.

Reiko J Tanaka (RJ)

Department of Bioengineering, Faculty of Engineering, Imperial College, London, United Kingdom.

Simon Francis Thomsen (SF)

Department of Dermato-Venereology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark.
Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.

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