Evidence map›Paper›PMID 33655501›Full record

ArticleThe British journal of dermatology2021

Four childhood atopic dermatitis subtypes identified from trajectory and severity of disease and internally validated in a large UK birth cohort.

A R Mulick, K E Mansfield, R J Silverwood, A Budu-Aggrey, A Roberts, A Custovic, N Pearce, A D Irvine, L Smeeth, K Abuabara and 1 more

Open access · hybridAbstract read
In one paragraph

Article in The British journal of dermatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
4.3field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.

  1. Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2025
    Pooled it
  2. Pooled it
  3. Trial
  4. Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Observational
  12. Article
  13. The Janus face of Bifidobacterium in the development of atopic eczema: A role for compositional maturation.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025
    Article
  14. Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024
    Review
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Establishing subclasses of childhood eczema, their risk factors and prognosis.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2022
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 5 institutions in 3 countries.

A R MulickDepartment of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID 0000-0002-4009-2080
K E MansfieldDepartment of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID 0000-0002-2551-410X
R J SilverwoodCentre for Longitudinal Studies, Department of Social Science, University College London, London, UK.ORCID 0000-0002-2744-1194
A Budu-AggreyMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.ORCID 0000-0002-8911-2492
A RobertsNottingham Support Group for Carers of Children with Eczema, Nottingham, UK.
A CustovicNational Heart & Lung Institute, Imperial College London, London, UK.ORCID 0000-0001-5218-7071
N PearceDepartment of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID 0000-0002-9938-7852
A D IrvineClinical Medicine, Trinity College Dublin, Dublin 2, Ireland.ORCID 0000-0002-9048-2044
L SmeethDepartment of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID 0000-0002-9168-6022
K AbuabaraDepartment of Dermatology, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0002-7736-6946
S M LanganDepartment of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.ORCID 0000-0002-7022-7441
London School of Hygiene & Tropical Medicine · GBImperial College London · GBTrinity College Dublin · IEUniversity of Bristol · GBUniversity of California, San Francisco · US

Funding

Social, environmental, and epigenetic drivers of atopic dermatitis disease courseK23AR073915 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABUABARA, KATRINA ELAINE · 2018 to 2022
$881k
Medical Research Council G9815508Medical Research Council MC_PC_15018Medical Research Council MC_PC_19009NIAMS NIH HHS K23 AR073915Wellcome TrustWellcome Trust 205039/Z/16/ZWellcome Trust 217065/Z/19/Z
6 · The paper itself

Abstract

backgroundAtopic dermatitis (AD) disease activity and severity is highly variable during childhood. Early attempts to identify subtypes based on disease trajectory have assessed AD presence over time without incorporating severity.

objectivesTo identify childhood AD subtypes from symptom severity and trajectories, and determine associations with genetic risk factors, comorbidities and demographic and environmental variables.

methodsWe split data from children in the Avon Longitudinal Study of Parents and Children birth cohort into development and validation sets. To identify subtypes, we ran latent class analyses in the development set on AD symptom reports up to age 14 years. We regressed identified subtypes on nongenetic variables in mutually adjusted, multiply imputed (genetic: unadjusted, complete case) multinomial regression analyses. We repeated analyses in the validation set and report confirmed results.

resultsThere were 11 866 children who contributed to analyses. We identified one Unaffected/Rare class (66% of children) and four AD subtypes: Severe-Frequent (4%), Moderate-Frequent (7%), Moderate-Declining (11%) and Mild-Intermittent (12%). Symptom patterns within the first two subtypes appeared more homogeneous than the last two. Filaggrin (FLG) null mutations, an AD polygenic risk score (PRS), being female, parental AD and comorbid asthma were associated with higher risk for some or all subtypes; FLG, AD-PRS and asthma associations were stronger along a subtype gradient arranged by increasing severity and frequency; FLG and AD-PRS further differentiated some phenotypes from each other.

conclusionsConsidering severity and AD trajectories leads to four well-defined and recognizable subtypes. The differential associations of risk factors among and between subtypes is novel and requires further research.

Indexed as

Dermatitis, AtopicEczemaAdolescentChildChild, PreschoolFemaleFilaggrin ProteinsHumansInfantIntermediate Filament ProteinsLongitudinal StudiesMaleMutationSeverity of Illness IndexUnited KingdomFilaggrin ProteinsFLG protein, humanIntermediate Filament Proteins

Identifiers

PMID33655501
PMCPMC8410876
OpenAlexW3135049195

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.