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  • Clinical Research Article
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Early life factors and structural brain network in children with overweight/obesity: The ActiveBrains project

Abstract

Background

The aims of this study were to investigate the association of early life factors, including birth weight, birth length, and breastfeeding practices, with structural brain networks; and to test whether structural brain networks associated with early life factors were also associated with academic performance in children with overweight/obesity (OW/OB).

Method

96 children with OW/OB aged 8-11 years (10.03 ± 1.16) from the ActiveBrains project were included. Early life factors were collected from birth records and reported by parents as weight, height, and months of breastfeeding. T1-weighted images were used to identify structural networks using a non-negative matrix factorization (NNMF) approach. Academic performance was evaluated by the Woodcock-Muñoz standardized test battery.

Results

Birth weight and birth length were associated with seven networks involving the cerebellum, cingulate gyrus, occipital pole, and subcortical structures including hippocampus, caudate nucleus, putamen, pallidum, nucleus accumbens, and amygdala. No associations were found for breastfeeding practices. None of the networks linked to birth weight and birth length were linked to academic performance.

Conclusions

Birth weight and birth length, but not breastfeeding, were associated with brain structural networks in children with OW/OB. Thus, early life factors are related to brain networks, yet a link with academic performance was not observed.

Impact

  • Birth weight and birth length, but not breastfeeding, were associated with several structural brain networks involving the cerebellum, cingulate gyrus, occipital pole, and subcortical structures including hippocampus, caudate, putamen, pallidum, accumbens and amygdala in children with overweight/obesity, playing a role for a normal brain development.

  • Despite no academic consequences, other behavioral consequences should be investigated. Interventions aimed at improving optimal intrauterine growth and development may be of importance to achieve a healthy brain later in life.

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Fig. 1: Structural brain network associated to birth weight and birth length after FDR correction.

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Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to the participant consent form but are available from the corresponding author upon reasonable request.

References

  1. Short, A. K. & Baram, T. Z. Early-life adversity and neurological disease: age-old questions and novel answers. Nat. Rev. Neurol. 15, 657–669 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Solis-Urra, P. et al. Early life factors, gray matter brain volume and academic performance in overweight/obese children: the activebrains project. Neuroimage 202, 116130 (2019).

    Article  PubMed  Google Scholar 

  3. Koshiyama, D. et al. Association between duration of breastfeeding based on maternal reports and dorsal and ventral striatum and medial orbital gyrus volumes in early adolescence. Neuroimage 220, 117083 (2020).

    Article  PubMed  Google Scholar 

  4. Eikenes, L. et al. Being born small for gestational age reduces white matter integrity in adulthood: a prospective cohort study. Pediatr. Res. 72, 649–654 (2012).

    Article  PubMed  Google Scholar 

  5. Solis-Urra, P. et al. Early life factors and white matter microstructure in children with overweight and obesity: the activebrains project. Clin. Nutr. 41, 40–48 (2022).

    Article  PubMed  Google Scholar 

  6. Niu, W. et al. Breastfeeding improves dynamic reorganization of functional connectivity in preterm infants: a temporal brain network study. Med. Biol. Eng. Comput. 58, 2805–2819 (2020).

    Article  PubMed  Google Scholar 

  7. Nassar, R. et al. Gestational age is dimensionally associated with structural brain network abnormalities across development. Cereb. Cortex 29, 2102–2114 (2019).

    Article  PubMed  Google Scholar 

  8. Alexander-Bloch, A., Giedd, J. N. & Bullmore, E. Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14, 322–336 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sotiras, A., Resnick, S. M. & Davatzikos, C. Finding imaging patterns of structural covariance via non-negative matrix factorization. Neuroimage 108, 1–16 (2015).

    Article  PubMed  Google Scholar 

  10. Nosarti, C. et al. Structural covariance in the cortex of very preterm adolescents: a voxel-based morphometry study. Hum. Brain Mapp. 32, 1615–1625 (2011).

    Article  PubMed  Google Scholar 

  11. Scheinost, D. et al. Alterations in anatomical covariance in the prematurely born. Cereb. Cortex 27, 534–543 (2017).

    PubMed  Google Scholar 

  12. Blesa, M. et al. Early breast milk exposure modifies brain connectivity in preterm infants. Neuroimage 184, 431–439 (2019).

    Article  PubMed  Google Scholar 

  13. Chen, V. C. et al. Brain structural networks and connectomes: the brain-obesity interface and its impact on mental health. Neuropsychiatr. Dis. Treat. 14, 3199–3208 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Augustijn, M. et al. Structural connectivity and weight loss in children with obesity: a study of the “connectobese. Int J. Obes. 43, 2309–2321 (2019).

    Article  CAS  Google Scholar 

  15. Kharabian Masouleh, S. et al. Gray matter structural networks are associated with cardiovascular risk factors in healthy older adults. J. Cereb. Blood Flow. Metab. 38, 360–372 (2018).

    Article  PubMed  Google Scholar 

  16. Beyer, F. et al. A metabolic obesity profile is associated with decreased gray matter volume in cognitively healthy older adults. Front. Aging Neurosci. 11, 202 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Gill, N., Gjelsvik, A., Mercurio, L. Y. & Amanullah, S. Childhood obesity is associated with poor academic skills and coping mechanisms. J. Pediatr. 228, 278–284 (2020).

  18. Cole, T. J. & Lobstein, T. Extended international (Iotf) body mass index cut‐offs for thinness. Overweight Obes. Pediatr. Obes. 7, 284–294 (2012).

    CAS  PubMed  Google Scholar 

  19. Cadenas-Sanchez, C. et al. An exercise-based randomized controlled trial on brain, cognition, physical health and mental health in overweight/obese children (activebrains project): rationale, design and methods. Contemp. Clin. Trials 47, 315–324 (2016).

    Article  PubMed  Google Scholar 

  20. Woodcock, R. W., McGrew, K. S. & Mather, N. Woodcock-Johnson® Iii Nu Tests of Achievement (Riverside Rolling Meadows, IL, 2001).

  21. Solis-Urra, P. et al. Early life factors and hippocampal functional connectivity in children with overweight/obesity. Pediatr. Obes. 18, e12998 (2023).

    Article  PubMed  Google Scholar 

  22. Moore, S. A. et al. Enhancing a somatic maturity prediction model. Med Sci. Sports Exerc. 47, 1755–1764 (2015).

    Article  PubMed  Google Scholar 

  23. Huppertz, C. et al. The effects of parental education on exercise behavior in childhood and youth: a study in Dutch and Finnish twins. Scand. J. Med Sci. Sports 27, 1143–1156 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. Esteban-Cornejo, I. et al. A whole brain volumetric approach in overweight/obese children: examining the association with different physical fitness components and academic performance. the activebrains project. Neuroimage 159, 346–354 (2017).

    Article  PubMed  Google Scholar 

  25. Leger, L. A., Mercier, D., Gadoury, C. & Lambert, J. The multistage 20 metre shuttle run test for aerobic fitness. J. Sports Sci. 6, 93–101 (1988).

    Article  CAS  PubMed  Google Scholar 

  26. Sotiras, A. et al. Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion. Proc. Natl Acad. Sci. USA 114, 3527–3532 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ortega, F. B. et al. Effects of an exercise program on brain health outcomes for children with overweight or obesity: the activebrains randomized clinical trial. JAMA Netw. Open 5, e2227893 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc.: Ser. B (Methodol.) 57, 289–300 (1995).

    Google Scholar 

  29. Taylor, H. G. et al. Brain volumes in adolescents with very low birth weight: effects on brain structure and associations with neuropsychological outcomes. Dev. Neuropsychol. 36, 96–117 (2011).

    Article  PubMed  Google Scholar 

  30. Herrmann, M. J., Tesar, A. K., Beier, J., Berg, M. & Warrings, B. Grey matter alterations in obesity: a meta-analysis of whole-brain studies. Obes. Rev. 20, 464–471 (2019).

    Article  PubMed  Google Scholar 

  31. Smucny, J. et al. Brain structure predicts risk for obesity. Appetite 59, 859–865 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Mueller, K. et al. Overweight and obesity are associated with neuronal injury in the human cerebellum and hippocampus in young adults: a combined MRI, serum marker and gene expression study. Transl. Psychiatry 2, e200 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Chen, E. Y., Eickhoff, S. B., Giovannetti, T. & Smith, D. V. Obesity is associated with reduced orbitofrontal cortex volume: a coordinate-based meta-analysis. Neuroimage Clin. 28, 102420 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Pascoe, M. J., Melzer, T. R., Horwood, L. J., Woodward, L. J. & Darlow, B. A. Altered grey matter volume, perfusion and white matter integrity in very low birthweight adults. Neuroimage Clin. 22, 101780 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Cecil, K. M. et al. Decreased brain volume in adults with childhood lead exposure. PLoS Med. 5, e112 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Amodio, D. M. & Frith, C. D. Meeting of minds: the medial frontal cortex and social cognition. Nat. Rev. Neurosci. 7, 268–277 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Zhang, Y. et al. Connectivity-based parcellation of the human posteromedial cortex. Cereb. Cortex 24, 719–727 (2014).

    Article  PubMed  Google Scholar 

  38. Bouyssi-Kobar, M. et al. Regional microstructural organization of the cerebral cortex is affected by preterm birth. Neuroimage Clin. 18, 871–880 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Leung, M. P., Thompson, B., Black, J., Dai, S. & Alsweiler, J. M. The effects of preterm birth on visual development. Clin. Exp. Optom. 101, 4–12 (2018).

    Article  PubMed  Google Scholar 

  40. Draganski, B. et al. Evidence for segregated and integrative connectivity patterns in the human basal ganglia. J. Neurosci. 28, 7143–7152 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Volpe, J. J. Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances. Lancet Neurol. 8, 110–124 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Alanko, O. et al. Reading and math abilities of Finnish school beginners born very preterm or with very low birth weight. Learn. Individ. Differ. 54, 173–183 (2017).

    Article  Google Scholar 

  43. Leijon, I., Ingemansson, F., Nelson, N., Samuelsson, S. & Wadsby, M. Children with a very low birth weight showed poorer reading skills at eight years of age but caught up in most areas by the age of 10. Acta Paediatr. 107, 1937–1945 (2018).

    Article  PubMed  Google Scholar 

  44. van Ginkel, C. D. et al. Retrospective observational cohort study regarding the effect of breastfeeding on challenge-proven food allergy. Eur. J. Clin. Nutr. 72, 557–563 (2018).

    Article  PubMed  Google Scholar 

  45. Walsh, K. et al. Maternal prenatal stress phenotypes associated with fetal neurodevelopment and birth outcomes. Proc. Natl Acad. Sci. USA 116, 23996–24005 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kristjansson, A. L. et al. Maternal smoking during pregnancy and academic achievement of offspring over time: a registry data-based cohort study. Prev. Med. 113, 74–79 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank all the families participating in the ActiveBrains. We also acknowledge everyone who helped with the data collection and all of the members involved in the field-work for their effort, enthusiasm, and support. This work is part of Ph.D. Thesis conducted in the Biomedicine Doctoral Studies of the University of Granada, Spain.

Funding

This study was supported by the Spanish Ministry of Economy and Competitiveness (DEP2013-47540, DEP2016-79512-R, and DEP2017-91544-EXP), the European Regional Development Fund (ERDF), the European Commission (No 667302) and the Alicia Koplowitz Foundation. This study was partially funded by the UGR Research and Knowledge Transfer Fund (PPIT) 2016, Excellence Actions Program. Units of Scientific Excellence; Scientific Unit of Excellence on Exercise and Health (UCEES) and by the Regional Government of Andalusia, Regional Ministry of Economy, Knowledge, Entreprises and University and European Regional Development Fund (ERDF), ref. SOMM17/6107/UGR. In addition, this study was further supported by the SAMID III network, RETICS, funded by the PN I + D + I 2017–2021 (Spain). P.S.-U. is supported by a grant from ANID/BECAS Chile/72180543 and though a Margarita Salas grant from the Spanish Ministry Universities. MR-A was supported by the Alicia Koplowitz Foundation. AVG is funded by an Australian NHMRC Investigator Leadership Grant (2009464). I.E.-C. is supported by the Spanish Ministry of Economy and Competitiveness (RTI2018-095284-J-100) and the Ministry of Science and Innovation (RyC2019-027287-1).

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Authors

Contributions

P.S.-U. was involved in the conception and design of the paper, analysis, interpretation of data, and drafting the manuscript. I.E.-C and F.B.O. were involved in the conception and design of the paper/project, interpretation of data, investigation (experiments), and critical revision. M.R.-A., J.V.-R., K.I.E., A.V.-G., and A.C. were involved in critical revision. All authors have approved the final version of the manuscript.

Corresponding authors

Correspondence to Patricio Solis-Urra or Irene Esteban-Cornejo.

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The authors declare no competing interests.

Ethics approval and consent to participate

The study was approved by the Committee for Research Involving Human Subjects at the University of Granada (Reference: 848, February 2014). All parents were informed about the study objective and written informed consent following the Declaration of Helsinki.

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Solis-Urra, P., Rodriguez-Ayllon, M., Verdejo-Román, J. et al. Early life factors and structural brain network in children with overweight/obesity: The ActiveBrains project. Pediatr Res (2023). https://doi.org/10.1038/s41390-023-02923-5

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