The association between body mass index and brain morphology in children: a population-based study

Cathelijne Steegers,Gwen Dieleman,Nadia Micali,Elisabet Blok,Jan Van Der Ende,Sander Lamballais,Meike Vernooij,Tonya White,Vincent Jaddoe,Pauline Jansen,Stefan Ehrlich,Fabio Bernardoni,Manon Hillegers

doi:10.1007/s00429-020-02209-0

Abstract

Brain morphology is altered in both anorexia nervosa and obesity. However, it is yet unclear if the relationship between Body Mass Index-Standard Deviation Score (BMI-SDS) and brain morphology exists across the BMI-SDS spectrum, or is present only in the extremes. The study involved 3160 9-to-11 year-old children (50.3% female) who participate in Generation R, a population-based study. Structural MRI scans were obtained from all children and FreeSurfer was used to quantify both global and surface-based measures of gyrification and cortical thickness. Body length and weight were measured to calculate BMI. Dutch growth curves were used to calculate BMI-SDS. BMI-SDS was analyzed continuously and in two categories (median split). The relationship between BMI-SDS (range − 3.82 to 3.31) and gyrification showed an inverted-U shape curve in children with both lower and higher BMI-SDS values having lower gyrification in widespread areas of the brain. BMI-SDS had a positive linear association with cortical thickness in multiple brain regions. This study provides evidence for an association between BMI-SDS and brain morphology in a large sample of children from the general population and suggests that a normal BMI during childhood is important for brain development. Future studies could determine whether lifestyle modifications optimize BMI-SDS result in return to more typical patterns of brain morphology.

Highlights

The saying ‘you are what you eat’ applies to our brains
Children were excluded if body mass index (BMI) was not assessed (n = 6), if they had dental braces (n = 27), if incidental findings were found in the brain that significantly altered brain morphology (n = 16), if images failed reconstructions or had insufficient quality (n = 707), or if the gyrification index could not be calculated (n = 16)
When fitting the mean local Gyrification (lGI) data to a quadratic polynomial in Body Mass Index-Standard Deviation Score (BMI-SDS) (Fig. 2) we found that the association was significant (B = − 0.003, p = 0.0042) in model 3

Summary

Introduction

The saying ‘you are what you eat’ applies to our brains. The brain is a unique organ, utilizing around 20% of the body’s energy (Herculano-Houzel 2011). The metabolism of the brain is over twice as high as in adulthood (Chugani 1998). Over the last decennia changes in eating behavior, with people choosing more unhealthy and higher caloric products, has resulted in a concomitant increase in body mass index (BMI) (Nagel et al 2009; WHO 2017). This has led to questions regarding the impact this can have on the developing brain. Eating behavior has been shown to impact psychosocial and physical health (Grieken et al 2013) and may influence brain morphology, but less is known about alterations in brain morphology associated with differences in BMI (Gustafson et al 2004; Raji et al 2010; Gregory et al 2016; White et al 2002; Dekkers et al 2019), which is influenced by caloric intake

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