Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease.

Bioinformatics Biomarkers Epigenetics Multiomics Precision health Prenatal exposures

Journal

Environment international
ISSN: 1873-6750
Titre abrégé: Environ Int
Pays: Netherlands
ID NLM: 7807270

Informations de publication

Date de publication:
03 Aug 2024
Historique:
received: 16 11 2023
revised: 24 06 2024
accepted: 31 07 2024
medline: 12 8 2024
pubmed: 12 8 2024
entrez: 11 8 2024
Statut: aheadofprint

Résumé

Precision Health aims to revolutionize disease prevention by leveraging information across multiple omic datasets (multi-omics). However, existing methods generally do not consider personalized environmental risk factors (e.g., environmental pollutants). To develop and apply a precision health framework which combines multiomic integration (including early, intermediate, and late integration, representing sequential stages at which omics layers are combined for modeling) with mediation approaches (including high-dimensional mediation to identify biomarkers, mediation with latent factors to identify pathways, and integrated/quasi-mediation to identify high-risk subpopulations) to identify novel biomarkers of prenatal mercury induced metabolic dysfunction-associated fatty liver disease (MAFLD), elucidate molecular pathways linking prenatal mercury with MAFLD in children, and identify high-risk children based on integrated exposure and multiomics data. This prospective cohort study used data from 420 mother-child pairs from the Human Early Life Exposome (HELIX) project. Mercury concentrations were determined in maternal or cord blood from pregnancy. Cytokeratin 18 (CK-18; a MAFLD biomarker) and five omics layers (DNA Methylation, gene transcription, microRNA, proteins, and metabolites) were measured in blood in childhood (age 6-10 years). Each standard deviation increase in prenatal mercury was associated with a 0.11 [95% confidence interval: 0.02-0.21] standard deviation increase in CK-18. High dimensional mediation analysis identified 10 biomarkers linking prenatal mercury and CK-18, including six CpG sites and four transcripts. Mediation with latent factors identified molecular pathways linking mercury and MAFLD, including altered cytokine signaling and hepatic stellate cell activation. Integrated/quasi-mediation identified high risk subgroups of children based on unique combinations of exposure levels, omics profiles (driven by epigenetic markers), and MAFLD. Prenatal mercury exposure is associated with elevated liver enzymes in childhood, likely through alterations in DNA methylation and gene expression. Our analytic framework can be applied across many different fields and serve as a resource to help guide future precision health investigations.

Sections du résumé

BACKGROUND BACKGROUND
Precision Health aims to revolutionize disease prevention by leveraging information across multiple omic datasets (multi-omics). However, existing methods generally do not consider personalized environmental risk factors (e.g., environmental pollutants).
OBJECTIVE OBJECTIVE
To develop and apply a precision health framework which combines multiomic integration (including early, intermediate, and late integration, representing sequential stages at which omics layers are combined for modeling) with mediation approaches (including high-dimensional mediation to identify biomarkers, mediation with latent factors to identify pathways, and integrated/quasi-mediation to identify high-risk subpopulations) to identify novel biomarkers of prenatal mercury induced metabolic dysfunction-associated fatty liver disease (MAFLD), elucidate molecular pathways linking prenatal mercury with MAFLD in children, and identify high-risk children based on integrated exposure and multiomics data.
METHODS METHODS
This prospective cohort study used data from 420 mother-child pairs from the Human Early Life Exposome (HELIX) project. Mercury concentrations were determined in maternal or cord blood from pregnancy. Cytokeratin 18 (CK-18; a MAFLD biomarker) and five omics layers (DNA Methylation, gene transcription, microRNA, proteins, and metabolites) were measured in blood in childhood (age 6-10 years).
RESULTS RESULTS
Each standard deviation increase in prenatal mercury was associated with a 0.11 [95% confidence interval: 0.02-0.21] standard deviation increase in CK-18. High dimensional mediation analysis identified 10 biomarkers linking prenatal mercury and CK-18, including six CpG sites and four transcripts. Mediation with latent factors identified molecular pathways linking mercury and MAFLD, including altered cytokine signaling and hepatic stellate cell activation. Integrated/quasi-mediation identified high risk subgroups of children based on unique combinations of exposure levels, omics profiles (driven by epigenetic markers), and MAFLD.
CONCLUSIONS CONCLUSIONS
Prenatal mercury exposure is associated with elevated liver enzymes in childhood, likely through alterations in DNA methylation and gene expression. Our analytic framework can be applied across many different fields and serve as a resource to help guide future precision health investigations.

Identifiants

pubmed: 39128376
pii: S0160-4120(24)00516-6
doi: 10.1016/j.envint.2024.108930
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

108930

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Jesse A Goodrich (JA)

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States. Electronic address: jagoodri@usc.edu.

Hongxu Wang (H)

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States.

Qiran Jia (Q)

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States.

Nikos Stratakis (N)

Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain.

Yinqi Zhao (Y)

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States.

Léa Maitre (L)

Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain.

Mariona Bustamante (M)

Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain.

Marina Vafeiadi (M)

Department of Social Medicine Faculty of Medicine, University of Crete, Heraklion, Greece.

Max Aung (M)

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States.

Sandra Andrušaitytė (S)

Department of Environmental Sciences, Vytauto Didžiojo Universitetas, Kaunas, Lithuania.

Xavier Basagana (X)

Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain.

Shohreh F Farzan (SF)

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States.

Barbara Heude (B)

Université de Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), National Research Institute for Agriculture, Food and Environment, Centre of Research in Epidemiology and Statistics, Paris, France.

Hector Keun (H)

Department of Surgery & Cancer and Department of Metabolism Digestion & Reproduction Imperial College London, London, United Kingdom.

Rob McConnell (R)

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States.

Tiffany C Yang (TC)

Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom.

Alexandros P Siskos (AP)

Department of Surgery & Cancer and Department of Metabolism Digestion & Reproduction Imperial College London, London, United Kingdom.

Jose Urquiza (J)

Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain.

Damaskini Valvi (D)

Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.

Nerea Varo (N)

Laboratory of Biochemistry, University Clinic of Navarra, Pamplona, Spain.

Line Småstuen Haug (L)

Norwegian Institute of Public Health, Oslo, Norway.

Bente M Oftedal (BM)

Norwegian Institute of Public Health, Oslo, Norway.

Regina Gražulevičienė (R)

Department of Environmental Sciences, Vytauto Didžiojo Universitetas, Kaunas, Lithuania.

Claire Philippat (C)

University Grenoble Alpes, Institut National de la Santé et de la Recherche Médicale (INSERM) U 1209, CNRS UMR 5309, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences, 38000 Grenoble, France.

John Wright (J)

Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom.

Martine Vrijheid (M)

Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain.

Leda Chatzi (L)

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States.

David V Conti (DV)

Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States.

Classifications MeSH