The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

Atopic dermatitis trajectories to age 8 years in the GUSTO cohort.

Atopic dermatitis trajectories to age 8 years in the GUSTO cohort.
Atopic dermatitis trajectories to age 8 years in the GUSTO cohort.
Background:
The heterogeneity of childhood atopic dermatitis (AD) underscores the need to understand latent phenotypes that may inform risk stratification and disease prognostication.

Objective:
To identify AD trajectories across the first 8 years of life and investigate risk factors associated with each trajectory and their relationships with other comorbidities.

Methods:
Data were collected prospectively from 1152 mother-offspring dyads in the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort from ages 3 months to 8 years. AD was defined based on parent-reported doctor's diagnosis. An unsupervised machine learning technique was used to determine AD trajectories.

Results:
Three AD trajectories were identified as follows: early-onset transient (6.3%), late-onset persistent (6.3%) and early-onset persistent (2.1%), alongside a no AD/reference group (85.2%). Early-onset transient AD was positively associated with male gender, family history of atopy, house dust mite sensitization and some measures of wheezing. Early-onset persistent AD was associated with antenatal/intrapartum antibiotic use, food sensitization and some measures of wheezing. Late-onset persistent AD was associated with a family history of atopy, some measures of house dust mite sensitization and some measures of allergic rhinitis and wheezing.

Conclusion and Clinical Relevance:
Three AD trajectories were identified in this birth cohort, with different risk factors and prognostic implications. Further work is needed to understand the molecular and immunological origins of these phenotypes.
atopic dermatitis, machine learning, rhinitis, trajectories, wheezing
0954-7894
1195-1206
Suaini, Noor H. A.
8ddd1497-88c3-4840-8733-473947bbe5f9
Yap, Gaik Chin
280a12f8-89d9-4f25-92a7-3b56cc8a5b99
Tung, Bui Do Phuong
9b3cf69d-1fa1-439e-a4e7-ea3f4892f549
Loo, Evelyn X.L.
c21376cb-00e3-4c16-b6e4-1a81b66a7604
Goh, Anne Eng Neo
9d869111-5368-420f-97dc-336f052bef41
Teoh, Oon Hoe
1f6973b2-81c6-4749-bef0-d4f09a7ce738
Tan, Kok Hian
672ae6c4-d4c8-4b1b-8512-efec36431503
Godfrey, Keith
0931701e-fe2c-44b5-8f0d-ec5c7477a6fd
Lee, Bee Wah
81ec0089-b824-4835-b908-1fc8e9f62249
Shek, Lynette Pei-Chi
ff5b44bf-5ab5-4249-8cf1-21751a4f6ae8
van Bever, Hugo
967ee912-3ad6-4c7b-ab6c-bb50914ea687
Chong, Yap-Seng
7043124b-e892-4d4b-8bb7-6d35ed94e136
Tham, Elizabeth Huiwen
e22014ec-8242-478a-aafc-e4177164f814
Suaini, Noor H. A.
8ddd1497-88c3-4840-8733-473947bbe5f9
Yap, Gaik Chin
280a12f8-89d9-4f25-92a7-3b56cc8a5b99
Tung, Bui Do Phuong
9b3cf69d-1fa1-439e-a4e7-ea3f4892f549
Loo, Evelyn X.L.
c21376cb-00e3-4c16-b6e4-1a81b66a7604
Goh, Anne Eng Neo
9d869111-5368-420f-97dc-336f052bef41
Teoh, Oon Hoe
1f6973b2-81c6-4749-bef0-d4f09a7ce738
Tan, Kok Hian
672ae6c4-d4c8-4b1b-8512-efec36431503
Godfrey, Keith
0931701e-fe2c-44b5-8f0d-ec5c7477a6fd
Lee, Bee Wah
81ec0089-b824-4835-b908-1fc8e9f62249
Shek, Lynette Pei-Chi
ff5b44bf-5ab5-4249-8cf1-21751a4f6ae8
van Bever, Hugo
967ee912-3ad6-4c7b-ab6c-bb50914ea687
Chong, Yap-Seng
7043124b-e892-4d4b-8bb7-6d35ed94e136
Tham, Elizabeth Huiwen
e22014ec-8242-478a-aafc-e4177164f814

Suaini, Noor H. A., Yap, Gaik Chin, Tung, Bui Do Phuong, Loo, Evelyn X.L., Goh, Anne Eng Neo, Teoh, Oon Hoe, Tan, Kok Hian, Godfrey, Keith, Lee, Bee Wah, Shek, Lynette Pei-Chi, van Bever, Hugo, Chong, Yap-Seng and Tham, Elizabeth Huiwen (2021) Atopic dermatitis trajectories to age 8 years in the GUSTO cohort. Clinical & Experimental Allergy, 51 (9), 1195-1206. (doi:10.1111/cea.13993).

Record type: Article

Abstract

Background:
The heterogeneity of childhood atopic dermatitis (AD) underscores the need to understand latent phenotypes that may inform risk stratification and disease prognostication.

Objective:
To identify AD trajectories across the first 8 years of life and investigate risk factors associated with each trajectory and their relationships with other comorbidities.

Methods:
Data were collected prospectively from 1152 mother-offspring dyads in the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort from ages 3 months to 8 years. AD was defined based on parent-reported doctor's diagnosis. An unsupervised machine learning technique was used to determine AD trajectories.

Results:
Three AD trajectories were identified as follows: early-onset transient (6.3%), late-onset persistent (6.3%) and early-onset persistent (2.1%), alongside a no AD/reference group (85.2%). Early-onset transient AD was positively associated with male gender, family history of atopy, house dust mite sensitization and some measures of wheezing. Early-onset persistent AD was associated with antenatal/intrapartum antibiotic use, food sensitization and some measures of wheezing. Late-onset persistent AD was associated with a family history of atopy, some measures of house dust mite sensitization and some measures of allergic rhinitis and wheezing.

Conclusion and Clinical Relevance:
Three AD trajectories were identified in this birth cohort, with different risk factors and prognostic implications. Further work is needed to understand the molecular and immunological origins of these phenotypes.

Text
GUSTO_AD_Trajectories_revised_V3.7_cleaned_090721 - Accepted Manuscript
Restricted to Repository staff only until 26 July 2022.
Request a copy

More information

Accepted/In Press date: 21 July 2021
e-pub ahead of print date: 26 July 2021
Published date: September 2021
Keywords: atopic dermatitis, machine learning, rhinitis, trajectories, wheezing

Identifiers

Local EPrints ID: 450720
URI: http://eprints.soton.ac.uk/id/eprint/450720
ISSN: 0954-7894
PURE UUID: 675cf1eb-06bf-4f41-9576-a43827829e07
ORCID for Keith Godfrey: ORCID iD orcid.org/0000-0002-4643-0618

Catalogue record

Date deposited: 09 Aug 2021 16:31
Last modified: 26 Nov 2021 02:35

Export record

Altmetrics

Contributors

Author: Noor H. A. Suaini
Author: Gaik Chin Yap
Author: Bui Do Phuong Tung
Author: Evelyn X.L. Loo
Author: Anne Eng Neo Goh
Author: Oon Hoe Teoh
Author: Kok Hian Tan
Author: Keith Godfrey ORCID iD
Author: Bee Wah Lee
Author: Lynette Pei-Chi Shek
Author: Hugo van Bever
Author: Yap-Seng Chong
Author: Elizabeth Huiwen Tham

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×