Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. IPCC Climate Change 2022: Impacts, Adaptation and Vulnerability. (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).

  2. Cohen, J., Pfeiffer, K. & Francis, J. A. Warm Arctic episodes linked with increased frequency of extreme winter weather in the United States. Nat. Commun. 9, 869 (2018).

    Google Scholar 

  3. Singh, D. et al. Recent amplification of the North American winter temperature dipole. J. Geophys. Res. Atmos. 121, 9911–9928 (2016).

    Google Scholar 

  4. Ye, X. et al. Ambient temperature and morbidity: a review of epidemiological evidence. Environ. Health Perspect. 120, 19–28 (2012).

    Google Scholar 

  5. Vicedo-Cabrera, A. M. et al. The burden of heat-related mortality attributable to recent human-induced climate change. Nat. Clim. Change 11, 492–500 (2021).

    CAS  Google Scholar 

  6. Zhao, Q. et al. Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study. Lancet Planet. Health 5, e415–e425 (2021).

    Google Scholar 

  7. Smith, C. J. Pediatric thermoregulation: considerations in the face of global climate change. Nutrients 11, 2010 (2019).

    CAS  Google Scholar 

  8. Helldén, D. et al. Climate change and child health: a scoping review and an expanded conceptual framework. Lancet Planet. Health 5, e164–e175 (2021).

    Google Scholar 

  9. Sugg, M. M., Dixon, P. G. & Runkle, J. D. Crisis support-seeking behavior and temperature in the United States: is there an association in young adults and adolescents? Sci. Total Environ. 669, 400–411 (2019).

    CAS  Google Scholar 

  10. Basu, R., Gavin, L., Pearson, D., Ebisu, K. & Malig, B. Examining the association between apparent temperature and mental health-related emergency room visits in California. Am. J. Epidemiol. 187, 726–735 (2018).

    Google Scholar 

  11. Yoo, E.-H., Eum, Y., Roberts, J. E., Gao, Q. & Chen, K. Association between extreme temperatures and emergency room visits related to mental disorders: a multi-region time-series study in New York, USA. Sci. Total Environ. 792, 148246 (2021).

    CAS  Google Scholar 

  12. Delaney, S. et al. Childhood cognitive and behavioral effects of hot summer temperatures. ISEE Conf. Abstr. https://doi.org/10.1289/isee.2022.P-0574 (2022).

  13. Younan, D. et al. Long-term ambient temperature and externalizing behaviors in adolescents. Am. J. Epidemiol. 187, 1931–1941 (2018).

    Google Scholar 

  14. Cho, H. The effects of summer heat on academic achievement: a cohort analysis. J. Environ. Econ. Manage. 83, 185–196 (2017).

    Google Scholar 

  15. Graff Zivin, J., Hsiang, S. M. & Neidell, M. Temperature and human capital in the short and long run. J. Assoc. Environ. Resour. Econ. 5, 77–105 (2018).

    Google Scholar 

  16. Park, R. J. Hot temperature and high-stakes performance. J. Hum. Resour. 57, 400–434 (2022).

    Google Scholar 

  17. Hu, Z. & Li, T. Too hot to handle: the effects of high temperatures during pregnancy on adult welfare outcomes. J. Environ. Econ. Manage. 94, 236–253 (2019).

    Google Scholar 

  18. Isen, A., Rossin-Slater, M. & Walker, R. Relationship between season of birth, temperature exposure, and later life wellbeing. Proc. Natl Acad. Sci. USA 114, 13447–13452 (2017).

    CAS  Google Scholar 

  19. Tang, H. & Di, Q. The effect of prenatal exposure to climate anomaly on adulthood cognitive function and job reputation. Int. J. Environ. Res. Public Health 19, 2523 (2022).

    Google Scholar 

  20. Doell, K. C. et al. Leveraging neuroscience for climate change research. Nat. Clim. Change 13, 1288–1297 (2023).

    Google Scholar 

  21. Neumann, A. et al. White matter microstructure and the general psychopathology factor in children. J. Am. Acad. Child Adolesc. Psychiatry 59, 1285–1296 (2020).

    Google Scholar 

  22. Muetzel, R. L. et al. White matter integrity and cognitive performance in school-age children: a population-based neuroimaging study. NeuroImage 119, 119–128 (2015).

    Google Scholar 

  23. Gilmore, J. H., Knickmeyer, R. C. & Gao, W. Imaging structural and functional brain development in early childhood. Nat. Rev. Neurosci. 19, 123–137 (2018).

    CAS  Google Scholar 

  24. Kooijman, M. N. et al. The Generation R Study: design and cohort update 2017. Eur. J. Epidemiol. 31, 1243 (2016).

    Google Scholar 

  25. De Ridder, K., Lauwaet, D. & Maiheu, B. UrbClim—a fast urban boundary layer climate model. Urban Clim. 12, 21–48 (2015).

    Google Scholar 

  26. Jenkinson, M. & Chappell, M. Introduction to Neuroimaging Analysis (Oxford Univ. Press, 2018).

  27. Gasparrini, A., Armstrong, B. & Kenward, M. G. Distributed lag non-linear models. Stat. Med. 29, 2224–2234 (2010).

    CAS  Google Scholar 

  28. Tobías, A., Armstrong, B. & Gasparrini, A. Investigating uncertainty in the minimum mortality temperature: methods and application to 52 Spanish cities. Epidemiology 28, 72–76 (2017).

    Google Scholar 

  29. Dubois, J. et al. The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience 276, 48–71 (2014).

    CAS  Google Scholar 

  30. Lebel, C. & Deoni, S. The development of brain white matter microstructure. NeuroImage 182, 207–218 (2018).

    Google Scholar 

  31. Geng, X. et al. Quantitative tract-based white matter development from birth to age two years. Neuroimage 61, 542–557 (2012).

    Google Scholar 

  32. Stephens, R. L. et al. White matter development from birth to 6 years of age: a longitudinal study. Cereb. Cortex 30, 6152–6168 (2020).

    Google Scholar 

  33. Dubois, J., Hertz-Pannier, L., Dehaene-Lambertz, G., Cointepas, Y. & Le Bihan, D. Assessment of the early organization and maturation of infants’ cerebral white matter fiber bundles: a feasibility study using quantitative diffusion tensor imaging and tractography. NeuroImage 30, 1121–1132 (2006).

    CAS  Google Scholar 

  34. Grotheer, M. et al. White matter myelination during early infancy is linked to spatial gradients and myelin content at birth. Nat. Commun. 13, 997 (2022).

    CAS  Google Scholar 

  35. Yu, Q. et al. Differential white matter maturation from birth to 8 years of age. Cereb. Cortex 30, 2674–2690 (2020).

    Google Scholar 

  36. Gilmore, J. H. et al. Early postnatal development of corpus callosum and corticospinal white matter assessed with quantitative tractography. AJNR Am. J. Neuroradiol. 28, 1789–1795 (2007).

    CAS  Google Scholar 

  37. Qiu, D., Tan, L.-H., Zhou, K. & Khong, P.-L. Diffusion tensor imaging of normal white matter maturation from late childhood to young adulthood: voxel-wise evaluation of mean diffusivity, fractional anisotropy, radial and axial diffusivities, and correlation with reading development. NeuroImage 41, 223–232 (2008).

    Google Scholar 

  38. Andre, Q. R., Geeraert, B. L. & Lebel, C. Brain structure and internalizing and externalizing behavior in typically developing children and adolescents. Brain Struct. Funct. 225, 1369–1378 (2020).

    Google Scholar 

  39. Cardenas-Iniguez, C. et al. Direct and indirect associations of widespread individual differences in brain white matter microstructure with executive functioning and general and specific dimensions of psychopathology in children. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 7, 362–375 (2022).

    Google Scholar 

  40. Figley, C. R. et al. Potential pitfalls of using fractional anisotropy, axial diffusivity, and radial diffusivity as biomarkers of cerebral white matter microstructure. Front. Neurosci. 15, 799576 (2022).

    Google Scholar 

  41. Jeurissen, B., Leemans, A., Tournier, J.-D., Jones, D. K. & Sijbers, J. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum. Brain Mapp. 34, 2747–2766 (2013).

    Google Scholar 

  42. Mulder, P., Dalla Longa, F. & Straver, K. Energy poverty in the Netherlands at the national and local level: a multi-dimensional spatial analysis. Energy Res. Soc. Sci. 96, 102892 (2023).

    Google Scholar 

  43. Islam, S. N. & Winkel, J. Climate Change and Social Inequality (UN Department of Economic and Social Affairs, 2017); https://doi.org/10.18356/2c62335d-en

  44. Jessel, S., Sawyer, S. & Hernández, D. Energy, poverty, and health in climate change: a comprehensive review of an emerging literature. Front. Public Health 7, 357 (2019).

    Google Scholar 

  45. Bronson, S. L. & Bale, T. L. The placenta as a mediator of stress effects on neurodevelopmental reprogramming. Neuropsychopharmacology 41, 207–218 (2016).

    Google Scholar 

  46. Zhang, X. et al. Association between thermal comfort and cortisol depends on the air temperature and exposure time. Build. Environ. 233, 110073 (2023).

    Google Scholar 

  47. Stoye, D. Q. et al. Maternal cortisol is associated with neonatal amygdala microstructure and connectivity in a sexually dimorphic manner. eLife 9, e60729 (2020).

    CAS  Google Scholar 

  48. Sheikh, H. I. et al. Links between white matter microstructure and cortisol reactivity to stress in early childhood: evidence for moderation by parenting. NeuroImage Clin. 6, 77–85 (2014).

    Google Scholar 

  49. Cantet, J. M., Yu, Z. & Ríus, A. G. Heat stress-mediated activation of immune–inflammatory pathways. Antibiotics 10, 1285 (2021).

    CAS  Google Scholar 

  50. Lian, S. et al. Impact of prenatal cold stress on placental physiology, inflammatory response, and apoptosis in rats. Oncotarget 8, 115304–115314 (2017).

    Google Scholar 

  51. Dubner, S. E. et al. White matter microstructure and cognitive outcomes in relation to neonatal inflammation in 6-year-old children born preterm. NeuroImage Clin. 23, 101832 (2019).

    Google Scholar 

  52. Favrais, G. et al. Systemic inflammation disrupts the developmental program of white matter. Ann. Neurol. 70, 550–565 (2011).

    CAS  Google Scholar 

  53. Galland, B. C., Taylor, B. J., Elder, D. E. & Herbison, P. Normal sleep patterns in infants and children: a systematic review of observational studies. Sleep. Med. Rev. 16, 213–222 (2012).

    Google Scholar 

  54. Smithson, L. et al. Shorter sleep duration is associated with reduced cognitive development at two years of age. Sleep. Med. 48, 131–139 (2018).

    Google Scholar 

  55. Morales-Muñoz, I. et al. Sleep during infancy, inhibitory control and working memory in toddlers: findings from the FinnBrain cohort study. Sleep. Sci. Pract. 5, 13 (2021).

    Google Scholar 

  56. Hysing, M., Sivertsen, B., Garthus-Niegel, S. & Eberhard-Gran, M. Pediatric sleep problems and social–emotional problems. a population-based study. Infant Behav. Dev. 42, 111–118 (2016).

    Google Scholar 

  57. Mulder, T. A. et al. Childhood sleep disturbances and white matter microstructure in preadolescence. J. Child Psychol. Psychiatry 60, 1242–1250 (2019).

    Google Scholar 

  58. Pittner, K. et al. Sleep across the first year of life is prospectively associated with brain volume in 12-months old infants. Neurobiol. Sleep Circadian Rhythms 14, 100091 (2023).

    Google Scholar 

  59. Chevance, G. et al. A systematic review of ambient heat and sleep in a warming climate. Sleep. Med. Rev. 75, 101915 (2024).

    Google Scholar 

  60. Berger, S. E. et al. The impact of extreme summer temperatures in the United Kingdom on infant sleep: implications for learning and development. Sci. Rep. 13, 10061 (2023).

    CAS  Google Scholar 

  61. Okamoto-Mizuno, K. & Mizuno, K. Effects of thermal environment on sleep and circadian rhythm. J. Physiol. Anthropol. 31, 14 (2012).

    Google Scholar 

  62. Harrison, F. et al. Weather and children’s physical activity; how and why do relationships vary between countries? Int. J. Behav. Nutr. Phys. Act. 14, 74 (2017).

    Google Scholar 

  63. Valkenborghs, S. R. et al. The impact of physical activity on brain structure and function in youth: a systematic review. Pediatrics 144, e20184032 (2019).

    Google Scholar 

  64. Nguyen, J. L., Schwartz, J. & Dockery, D. W. The relationship between indoor and outdoor temperature, apparent temperature, relative humidity, and absolute humidity. Indoor Air 24, 103–112 (2014).

    CAS  Google Scholar 

  65. Rovers, V., Niessink, R., Loonen, P., van der Wal, A. & Matthijssen, E. Energievraag van Ruimtekoeling in Woningen (TNO, 2021); https://repository.tno.nl/SingleDoc?docId=54007

  66. Starting Strong II: Early Childhood Education and Care (OECD, 2006).

  67. Keeley, S. Climate Reanalysis (ECMWF, 2022); https://www.ecmwf.int/en/research/climate-reanalysis

  68. Lauwaet, D. et al. Assessing the current and future urban heat island of Brussels. Urban Clim. 15, 1–15 (2016).

    Google Scholar 

  69. Lauwaet, D. et al. Detailed urban heat island projections for cities worldwide: dynamical downscaling CMIP5 global climate models. Climate 3, 391–415 (2015).

    Google Scholar 

  70. García-Díez, M. et al. Advantages of using a fast urban boundary layer model as compared to a full mesoscale model to simulate the urban heat island of Barcelona. Geosci. Model Dev. 9, 4439–4450 (2016).

    Google Scholar 

  71. Klein Tank, A. M. G. et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol. 22, 1441–1453 (2002).

    Google Scholar 

  72. Cornes, R. C., Van Der Schrier, G., Van Den Besselaar, E. J. M. & Jones, P. D. An ensemble version of the E‐OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).

    Google Scholar 

  73. White, T. et al. Paediatric population neuroimaging and the Generation R Study: the second wave. Eur. J. Epidemiol. 33, 99–125 (2018).

    Google Scholar 

  74. White, T. et al. Pediatric population-based neuroimaging and the Generation R Study: the intersection of developmental neuroscience and epidemiology. Eur. J. Epidemiol. 28, 99–111 (2013).

    Google Scholar 

  75. Dall’Aglio, L., Xu, B., Tiemeier, H. & Muetzel, R. L. Longitudinal associations between white matter microstructure and psychiatric symptoms in youth. J. Am. Acad. Child Adolesc. Psychiatry S0890-8567, 00322 (2023).

    Google Scholar 

  76. Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. FSL. NeuroImage 62, 782–790 (2012).

    Google Scholar 

  77. Shrier, I. & Platt, R. W. Reducing bias through directed acyclic graphs. BMC Med. Res. Methodol. 8, 70 (2008).

    Google Scholar 

  78. Rakesh, D., Whittle, S., Sheridan, M. A. & McLaughlin, K. A. Childhood socioeconomic status and the pace of structural neurodevelopment: accelerated, delayed, or simply different? Trends Cogn. Sci. 27, 833–851 (2023).

    Google Scholar 

  79. Knol, F. Statusontwikkeling van wijken in Nederland 1998–2010. Neth. Inst. Soc. Res. SCP https://doi.org/10.48592/657 (2012).

  80. Rhew, I. C., Vander Stoep, A., Kearney, A., Smith, N. L. & Dunbar, M. D. Validation of the Normalized Difference Vegetation Index as a measure of neighborhood greenness. Ann. Epidemiol. 21, 946–952 (2011).

    Google Scholar 

  81. Feyisa, G. L., Dons, K. & Meilby, H. Efficiency of parks in mitigating urban heat island effect: an example from Addis Ababa. Landsc. Urban Plan. 123, 87–95 (2014).

    Google Scholar 

  82. Dadvand, P. et al. The association between lifelong greenspace exposure and 3-dimensional brain magnetic resonance imaging in Barcelona schoolchildren. Environ. Health Perspect. 126, 027012 (2018).

    Google Scholar 

  83. Mackay, D. F. et al. Month of conception and learning disabilities: a record-linkage study of 801,592 children. Am. J. Epidemiol. 184, 485–493 (2016).

    Google Scholar 

  84. López-Vicente, M. et al. White matter microstructure correlates of age, sex, handedness and motor ability in a population-based sample of 3031 school-age children. NeuroImage 227, 117643 (2021).

    Google Scholar 

  85. Honaker, J., King, G. & Blackwell, M. Amelia: a program for missing data. J. Stat. Soft. https://doi.org/10.18637/jss.v045.i07 (2022).

  86. Weisskopf, M. G., Sparrow, D., Hu, H. & Power, M. C. Biased exposure–health effect estimates from selection in cohort studies: are environmental studies at particular risk? Environ. Health Perspect. 123, 1113–1122 (2015).

    CAS  Google Scholar 

  87. Weuve, J. et al. Accounting for bias due to selective attrition: the example of smoking and cognitive decline. Epidemiology 23, 119 (2012).

    Google Scholar 

  88. R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2023).

  89. van Buuren, S. & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations. J. Stat. Soft. https://doi.org/10.18637/jss.v045.i03 (2011).

  90. Gasparrini, A. & Armstrong, B. Distributed lag non-linear models in R: the package dlnm. J. Stat. Softw. https://doi.org/10.18637/jss.v043.i08 (2013).

  91. Granés, L. et al. Code for ‘Early life cold and heat exposure impacts white matter development in children’. CORA https://doi.org/10.34810/data1294 (2024).

Download references

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.

Author information

Authors and Affiliations

Authors

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.

Corresponding author

Correspondence to Mònica Guxens.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Kai Chen, Chunshui Yu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Supplementary information

Supplementary Information

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

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-024-02027-w

This article is cited by

Search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene