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Multi-omics architecture of childhood obesity and metabolic dysfunction uncovers biological pathways and prenatal determinants

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Listed:
  • Nikos Stratakis

    (Institute for Global Health (ISGlobal)
    Universitat Pompeu Fabra (UPF)
    CIBER Epidemiología y Salud Pública (CIBERESP))

  • Augusto Anguita-Ruiz

    (Institute for Global Health (ISGlobal)
    Universitat de Barcelona (UB)
    Institute of Health Carlos III (ISCIII))

  • Lorenzo Fabbri

    (Institute for Global Health (ISGlobal)
    Universitat Pompeu Fabra (UPF)
    CIBER Epidemiología y Salud Pública (CIBERESP))

  • Léa Maitre

    (Institute for Global Health (ISGlobal)
    Universitat Pompeu Fabra (UPF)
    CIBER Epidemiología y Salud Pública (CIBERESP))

  • Juan R. González

    (Institute for Global Health (ISGlobal)
    Universitat Pompeu Fabra (UPF)
    CIBER Epidemiología y Salud Pública (CIBERESP))

  • Sandra Andrusaityte

    (Vytautas Magnus University)

  • Xavier Basagaña

    (Institute for Global Health (ISGlobal)
    Universitat Pompeu Fabra (UPF)
    CIBER Epidemiología y Salud Pública (CIBERESP))

  • Eva Borràs

    (Universitat Pompeu Fabra (UPF)
    Barcelona Institute of Science and Technology)

  • Hector C. Keun

    (Imperial College London
    Hammersmith Hospital Campus)

  • Lida Chatzi

    (University of Southern California)

  • David V. Conti

    (University of Southern California)

  • Jesse Goodrich

    (University of Southern California)

  • Regina Grazuleviciene

    (Universitat de Barcelona (UB))

  • Line Småstuen Haug

    (Norwegian Institute of Public Health
    Norwegian Institute of Public Health)

  • Barbara Heude

    (Center for Research in Epidemiology and StatisticS (CRESS))

  • Wen Lun Yuan

    (Center for Research in Epidemiology and StatisticS (CRESS))

  • Rosemary McEachan

    (Bradford Teaching Hospitals NHS Foundation Trust)

  • Mark Nieuwenhuijsen

    (Institute for Global Health (ISGlobal)
    Universitat Pompeu Fabra (UPF)
    CIBER Epidemiología y Salud Pública (CIBERESP))

  • Eduard Sabidó

    (Universitat Pompeu Fabra (UPF)
    Barcelona Institute of Science and Technology)

  • Rémy Slama

    (Université Grenoble Alpes)

  • Cathrine Thomsen

    (Norwegian Institute of Public Health
    Norwegian Institute of Public Health)

  • Jose Urquiza

    (Institute for Global Health (ISGlobal)
    Universitat Pompeu Fabra (UPF)
    CIBER Epidemiología y Salud Pública (CIBERESP))

  • Theano Roumeliotaki

    (University of Crete)

  • Marina Vafeiadi

    (University of Crete)

  • John Wright

    (Bradford Teaching Hospitals NHS Foundation Trust)

  • Mariona Bustamante

    (Institute for Global Health (ISGlobal)
    Universitat Pompeu Fabra (UPF)
    CIBER Epidemiología y Salud Pública (CIBERESP))

  • Martine Vrijheid

    (Institute for Global Health (ISGlobal)
    Universitat Pompeu Fabra (UPF)
    CIBER Epidemiología y Salud Pública (CIBERESP))

Abstract

Childhood obesity poses a significant public health challenge, yet the molecular intricacies underlying its pathobiology remain elusive. Leveraging extensive multi-omics profiling (methylome, miRNome, transcriptome, proteins and metabolites) and a rich phenotypic characterization across two parts of Europe within the population-based Human Early Life Exposome project, we unravel the molecular landscape of childhood obesity and associated metabolic dysfunction. Our integrative analysis uncovers three clusters of children defined by specific multi-omics profiles, one of which characterized not only by higher adiposity but also by a high degree of metabolic complications. This high-risk cluster exhibits a complex interplay across many biological pathways, predominantly underscored by inflammation-related cascades. Further, by incorporating comprehensive information from the environmental risk-scape of the critical pregnancy period, we identify pre-pregnancy body mass index and environmental pollutants like perfluorooctanoate and mercury as important determinants of the high-risk cluster. Overall, our work helps to identify potential risk factors for prevention and intervention strategies early in the life course aimed at mitigating obesity and its long-term health consequences.

Suggested Citation

  • Nikos Stratakis & Augusto Anguita-Ruiz & Lorenzo Fabbri & Léa Maitre & Juan R. González & Sandra Andrusaityte & Xavier Basagaña & Eva Borràs & Hector C. Keun & Lida Chatzi & David V. Conti & Jesse Goo, 2025. "Multi-omics architecture of childhood obesity and metabolic dysfunction uncovers biological pathways and prenatal determinants," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56013-7
    DOI: 10.1038/s41467-025-56013-7
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    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Andrea Baccarelli & Dana C. Dolinoy & Cheryl Lyn Walker, 2023. "A precision environmental health approach to prevention of human disease," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Léa Maitre & Mariona Bustamante & Carles Hernández-Ferrer & Denise Thiel & Chung-Ho E. Lau & Alexandros P. Siskos & Marta Vives-Usano & Carlos Ruiz-Arenas & Dolors Pelegrí-Sisó & Oliver Robinson & Dan, 2022. "Multi-omics signatures of the human early life exposome," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. Habtamu B. Beyene & Corey Giles & Kevin Huynh & Tingting Wang & Michelle Cinel & Natalie A. Mellett & Gavriel Olshansky & Thomas G. Meikle & Gerald F. Watts & Joseph Hung & Jennie Hui & Gemma Cadby & , 2023. "Metabolic phenotyping of BMI to characterize cardiometabolic risk: evidence from large population-based cohorts," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
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