Observational studyJournal of the European Academy of Dermatology and Venereology : JEADV2026
Multi-omics profiling of chronic immune-mediated skin diseases: SKINERGY protocol and strategic evaluation.
Observational study in Journal of the European Academy of Dermatology and Venereology : JEADV, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe Dutch flagship project Next Generation ImmunoDermatology (NGID) aims to profile five chronic immune-mediated inflammatory skin diseases: atopic dermatitis (AD), plaque psoriasis (PSO), hidradenitis suppurativa (HS), chronic spontaneous urticaria (CSU) and cutaneous lupus erythematosus (CLE) in comparison with cutaneous T-cell lymphoma subtype mycosis fungoides (MF) and healthy volunteers. Within NGID, a clinical study entitled: 'SKIN disease profiling by an Exploratory, pRospective, biomarker study in dermatoloGY practice (SKINERGY)' will be conducted as a multicentre, parallel-cohort, open-label, observational, longitudinal basket study.
objectivesObjectives include evaluation of disease-related characteristics in comparison to those of healthy volunteers and evaluation of biomarkers for disease stratification and (targeted) treatment response in patients in a real-world clinical setting. Additionally, differences and similarities in disease characteristics between diseases, changes over time, and profiles of responders versus non-responders will be evaluated.
methodsPatients with AD (N = 120), PSO (N = 160), HS (N = 80), CSU (N = 120) and CLE (N = 120) will be enrolled in groups of N ≤ 40 patients per treatment. Matched healthy volunteers (N = 120) and the MF cohort (N = 120) will serve as control groups. Assessments include blood sampling, skin punch biopsies, tape stripping, skin swabs, (multimodal) imaging, tele-health and patient- and physician-reported outcomes. This manuscript describes the study protocol prior to data collection and its strategic evaluation of multi-omics profiling. Patient advocacy groups co-defined the research agenda and contributed to study design and informed consent document development, ensuring alignment with patients' needs and real-world relevance.
resultsSKINERGY will generate a machine learning-ready dataset with information about changes in various biomarkers over time, including histology, metabolomics, spatial proteomics, transcriptomics, lipidomics, microbiomics, imaging biomarkers, tele-health, patient-reported outcome measures (PROMs) and clinical parameters.
conclusionsIdentified biomarker profiles within SKINERGY may guide targeted treatment selection, enhance targeted therapeutic response in clinical practice and improve understanding of disease pathology in chronic immune-mediated skin diseases.
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