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Early life cold and heat exposure impacts white matter development in children

Nature Climate Change volume 14, pages 760–766 (2024)Cite this article

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Abstract

Prenatal life and childhood represent periods that are vulnerable to environmental exposures. Both cold and heat may have negative impacts on children’s mental health and cognition, but the underlying neural mechanisms are unknown. Here, by a magnetic resonance imaging assessment of 2,681 children from the Netherlands Generation R birth cohort, we show that heat exposure during infancy and toddlerhood as well as cold exposure during pregnancy and infancy are associated with higher mean diffusivity at preadolescence, indicative of reduced myelination and maturation of white matter microstructure. No associations for fractional anisotropy were observed. Children living in poorer neighbourhoods were more vulnerable to cold and heat exposure. Our findings suggest that cold and heat exposure in periods of rapid brain development may have lasting impacts on children’s white matter microstructure, a risk that must be considered in the context of ongoing climate change.

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Fig. 1: Monthly variability in the 4 week mean temperature at participants’ place of residence for the study period (2002–2014).
Fig. 2: Adjusted associations between cold and heat exposure during early life and DTI parameters.
Fig. 3: Adjusted associations between cold and heat exposure during early life and mean diffusivity at 9–12 years of age in 12 white matter tracts.

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Data availability

The datasets generated and analysed during the current study are not publicly available due to legal and ethical regulations but may be made available upon request to the Director of the Generation R Study, Vincent Jaddoe (v.jaddoe@erasmusmc.nl), in accordance with the local, national and European Union regulations. The E-OBS dataset used for the temperature validation is available at the European Climate Assessment and Dataset website (https://www.ecad.eu) (ref. 72).

Code availability

The code to reproduce the analysis is available at the open repository ‘dataverse’ of the Consorci de Serveis Universitaris de Catalunya (CSUC) via https://doi.org/10.34810/data1294 (ref. 91). Statistical analyses were performed in R statistical software88, version 4.3.0; R Core Team (2023). Amelia package85 (version 1.8.1) was used for expectation-maximization imputation, ‘mice’ package89 was used for imputation in the inverse probability weighting calculation (version 3.16.0), and the ‘dlnm’ package90 (version 2.4.7) was used for the main analysis.

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Acknowledgements

The Generation R Study is conducted by the Erasmus Medical Center in close collaboration with the Faculty of Social Sciences of the Erasmus University Rotterdam, the Municipal Health Service Rotterdam area, Rotterdam, the Rotterdam Homecare Foundation, Rotterdam, and the Stichting Trombosedienst & Artsenlaboratorium Rijnmond (STAR-MDC), Rotterdam. We gratefully acknowledge the contribution of children and parents, general practitioners, hospitals, midwives and pharmacies in Rotterdam. The general design of the Generation R Study is made possible by financial support from the Erasmus Medical Center, Rotterdam; the Erasmus University Rotterdam; the Netherlands Organization for Health Research and Development (ZonMw); the Netherlands Organization for Scientific Research (NWO); and the Ministry of Health, Welfare and Sport. L.G. was funded by a Rio Hortega fellowship (CM22/00011) and M.G. by a Miguel Servet II fellowship (CPII18/00018) both awarded by the Spanish Institute of Health Carlos III. H.T. was supported by a grant of the Netherlands Organization for Scientific Research (NWO/ZonMW grant 016.VICI.170.200). Temperature estimations were done within the framework of a project funded by the Spanish Institute of Health Carlos III (PI20/01695 including FEDER funds, received by M.G). High-performance computing for neuroimage analysis was provided by the Dutch Organization for Scientific Research (NWO.2021.042, Snellius). We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu). J.B. gratefully acknowledge funding from the European Union’s Horizon 2020 and Horizon Europe research and innovation programmes under grant agreement Nos 865564 (European Research Council Consolidator Grant EARLY-ADAPT, https://www.early-adapt.eu/), 101069213 (European Research Council Proof-of-Concept HHS-EWS, https://forecaster.health) and 101123382 (European Research Council Proof-of-Concept FORECAST-AIR). We acknowledge support from the grant CEX2018-000806-S funded by MCIN/AEI/ 10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program. Additional support was received by AGAUR-Generalitat de Catalunya (2021-SGR-01017). The Institute of Neurosciences of the University of Barcelona is a María de Maeztu Unit of Excellence CEX2021-001159-M of the Ministry of Science and Innovation of Spain. Finally, we thank R. L. Muetzel for his helpful insights on diffusion tensor imaging and M. S. W. Kusters for her generous help with the creation of figures in FreeSurfer.

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Authors and Affiliations

  1. Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain

    Laura Granés & Carles Soriano-Mas

  2. ISGlobal, Barcelona, Spain

    Laura Granés, Esmée Essers, Joan Ballester, Sami Petricola & Mònica Guxens

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Laura Granés, Esmée Essers, Sami Petricola & Mònica Guxens

  4. Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Carlos III Health Institute, Madrid, Spain

    Laura Granés, Esmée Essers, Sami Petricola, Carmen Iñiguez & Mònica Guxens

  5. Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands

    Esmée Essers, Henning Tiemeier & Mònica Guxens

  6. Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA

    Henning Tiemeier

  7. Department of Statistics and Operational Research, University of Valencia, Valencia, Spain

    Carmen Iñiguez

  8. Epidemiology and Environmental Health Joint Research Unit, Jaume I University–University of Valencia–FISABIO, Valencia, Spain

    Carmen Iñiguez

  9. Spanish Consortium for Research on Mental Health (CIBERSAM), Carlos III Health Institute, Madrid, Spain

    Carles Soriano-Mas

  10. Department of Social Psychology and Quantitative Psychology, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

    Carles Soriano-Mas

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Contributions

L.G., C.S.-M. and M.G. were responsible for the conceptualization of the study. L.G., E.E., J.B., S.P., C.I. and M.G. were involved in the methodological design. S.P. was responsible for preparing the exposure data. L.G. conducted the formal analysis and produced the results. E.E. conducted a validation of the analysis. L.G., S.P. and C.I. worked on the visualization of results. L.G., E.E., J.B., H.T., C.S.-M. and M.G. were involved in the discussion and interpretation of results. L.G. wrote the original draft manuscript. E.E., J.B., S.P., H.T., C.I., C.S.-M. and M.G. revised and edited the manuscript. H.T., C.S.-M. and M.G. acquired the funding. C.S.-M. and M.G. supervised the project.

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Correspondence to Mònica Guxens.

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Extended data

Extended Data Fig. 1 Adjusted associations between cold and heat exposure during early life and mean diffusivity at 9–12 years, stratified by neighbourhood socioeconomic status.

(a) Lag-response curves for 1,336 children living in high socioeconomic status neighbourhoods (b) Lag-response curves for 1,345 children living in low socioeconomic status neighbourhoods. Lag-response curves are plotted at the 5th percentile of temperature distribution for cold exposure and at 95th percentile of temperature distribution for heat exposure. Beta coefficients (β) are displayed as dark grey dots, with their 95% confidence intervals as grey light vertical lines. Significant associations are colored blue for cold and red for heat. Associations were obtained from distributed lag non-linear models adjusted for maternal and partner’s age, national origin, educational level and body mass index; monthly household income and residential surrounding greenness level; maternal social class based on occupation, smoking habit, alcohol consumption, folic acid supplementation during pregnancy, parity and marital status; child’s sex, child’s age at the magnetic resonance imaging session and month of conception. SES, socioeconomic status.

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Supplementary Figs. 1–8, Tables 1–4 and Methods 1 and 2.

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Granés, L., Essers, E., Ballester, J. et al. Early life cold and heat exposure impacts white matter development in children. Nat. Clim. Chang. 14, 760–766 (2024). https://doi.org/10.1038/s41558-024-02027-w

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