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Cohort profile: Celiac disease genomic, environmental, microbiome and metabolome study; a prospective longitudinal birth cohort study of children at-risk for celiac disease.

Maureen M LeonardVictoria KenyonFrancesco ValituttiRita Pennacchio-HarringtonPasqua PiemonteseRuggiero FrancavillaLorenzo NorsaTiziana PassaroMarco CroccoMaria Elisabetta BaldassarreChiara Maria TrovatoAlessio Fasanonull null
Published in: PloS one (2023)
The Celiac Disease Genomic, Environmental, Microbiome and Metabolomic (CDGEMM) study is an international prospective birth cohort in children at-risk of developing celiac disease (CD). The CDGEMM study has been designed to take a multi-omic approach to predicting CD onset in at-risk individuals. Participants are required to have a first-degree family member with biopsy diagnosed CD and must be enrolled prior to the introduction of solid food. Participation involves providing blood and stool samples longitudinally over a period of five years as well as answering questionnaires related to the participant, their family, and environment. Recruitment and data collection have been ongoing since 2014. As of 2022 we have a total of 554 participants and the average age of the cohort is 56.4 months. A total of 54 participants have developed positive antibodies for CD and 31 have confirmed CD. Approximately 80% of the 54 participants with CD have developed it by 3 years of age. To date we have identified several microbial strains, pathways, and metabolites occurring in increased abundance and detected before CD onset, which have previously been linked to autoimmune and inflammatory conditions while others occurred in decreased abundance before CD onset and are known to have anti-inflammatory effects. Our ongoing analysis includes expanding our metagenomic and metabolomic analyses, evaluating environmental risk factors linked to CD onset, and mechanistic studies investigating how alterations in the microbiome and metabolites may protect against or contribute to CD development.
Keyphrases
  • celiac disease
  • nk cells
  • young adults
  • escherichia coli
  • machine learning
  • physical activity
  • multiple sclerosis
  • oxidative stress
  • dna methylation
  • big data
  • microbial community
  • cross sectional
  • artificial intelligence