Body Mass Index Relates to Regional White Matter Microstructure in Sex-Specific Manners
Poster No:
1608
Submission Type:
Abstract Submission
Authors:
Tiril Pedersen Gurholt1, Dani Beck2, Irene Voldsbekk3, Nadine Parker4, Daniel Askeland-Gjerde1, Ann-Marie de Lange5, Dennis van der Meer4, Ivan Maximov6, Ida Sønderby7, Lars Westlye8, Ole Andreassen9
Institutions:
1Oslo University Hospital, Oslo, Oslo, 2Diakonhjemmet Hospital, Oslo, Oslo, 3Norwegian Centre for Mental Disorders Research, Oslo, Oslo, 4University of Oslo, Oslo, Oslo, 5University of Lausanne, Lausanne, Switzerland, 6Western Norway University of Applied Sciences, Bergen, Bergen, 7Oslo University Hospital, Oslo, Norway, 8Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway, 9NORMENT, Oslo, Norway
First Author:
Co-Author(s):
Lars Westlye
Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital
Oslo, Norway
Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital
Oslo, Norway
Introduction:
Sex differences are observed in brain disorders [Arnold et al., 2023], white matter microstructure [Ritchie et al., 2018], and body shape and fat distribution [Tchernof and Després, 2013]. Indications of sex-related differences in how obesogenic traits (e.g., body mass index (BMI)) relate to white matter microstructure exists [Kroll et al., 2020], including higher brain age in men than women at higher body fat [Subramaniapillai et al., 2021]. Yet, our knowledge of sex-related differences between obesogenic traits and regional white matter remains limited. Here, we investigate whether and how BMI relates to white matter microstructure in sex-specific patterns to enhance our understanding of the role of sex in somatic and brain health.
Methods:
We included n=40,517 UK Biobank participants (52.1% women) with diffusion magnetic resonance imaging (MRI), clinical, and demographic data. On average, women were slightly younger (aged 63.7±7.6 years) than men (aged 64.9±7.9 years) and had lower cardiometabolic risk, including lower BMI (women: 25.9±4.7; men: 26.9±3.8).
We used an optimized pipeline to process raw diffusion MRI scans, derive diffusion maps of fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), and mean diffusivity (MD), extract FSL tract-based spatial statistics diffusion metrics, and for quality control [Maximov et al., 2019; Maximov et al., 2021]. We included 27 regions of interest (ROIs) from the Johns Hopkins University atlas [Mori et al., 2008] (Fig. 1).
We performed statistical analyses in R (v-4.2.1). We used multiple linear regression to investigate sex differences in how BMI relates to regional white matter by (i) investigating for BMI-by-sex interaction effects (adjusted for main BMI effect), followed by (ii) sex-stratified analyses while adjusting for age, age2, sex, self-identified ethnic ancestry (European/non-European), self-reported cardiometabolic factors, and assessment site. We derived r effect sizes from t-statistics and used Bonferroni correction (p≤ 0.0005).
We used an optimized pipeline to process raw diffusion MRI scans, derive diffusion maps of fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), and mean diffusivity (MD), extract FSL tract-based spatial statistics diffusion metrics, and for quality control [Maximov et al., 2019; Maximov et al., 2021]. We included 27 regions of interest (ROIs) from the Johns Hopkins University atlas [Mori et al., 2008] (Fig. 1).
We performed statistical analyses in R (v-4.2.1). We used multiple linear regression to investigate sex differences in how BMI relates to regional white matter by (i) investigating for BMI-by-sex interaction effects (adjusted for main BMI effect), followed by (ii) sex-stratified analyses while adjusting for age, age2, sex, self-identified ethnic ancestry (European/non-European), self-reported cardiometabolic factors, and assessment site. We derived r effect sizes from t-statistics and used Bonferroni correction (p≤ 0.0005).
·Figure 1: Extracted regions of interest.
Results:
The analyses revealed significant BMI-by-sex interaction effects on regional white matter phenotypes for 12.5% FA, 45.8% AD, 58.3% RD, and 68.8% MD ROIs (|r| effect sizes [0.02, 0.06] and p-values [0.0005, 1.4e-32]). The main effect of BMI was also significant for 68.8% FA, 68.8% AD, 39.6% RD, and 62.5% MD ROIs (|r| effect sizes [0.02, 0.15] and p-values [0.0003, 5.5e-202]). Additionally, the main effect of sex was significant for most ROIs: 83.7% FA, 89.6% AD, 85.4% RD, and 91.7% MD ROIs (|r| effect sizes [0.02, 0.26] and p-values [0.0002, 0]).
Stratified by sex, we observed distinct sex-specific association patterns between BMI and white matter phenotypes (Fig. 2). The pattern for women and men was similar for FA but differed significantly for AD, RD, and MD, reflecting the above interaction analysis. Women showed lower negative effect sizes across AD, RD, and MD at higher BMI for brainstem tracts than men. For multiple non-brainstem tracts, men showed higher positive effect sizes across AD, RD, and MD at higher BMI than women. Overall, BMI was significantly associated with white matter in women for 68.8% FA, 83.3% AD, 45.8% RD, and 64.6% MD ROIs (|r| values [0.03, 0.19] and p-values [0.0003, 6.9e-196]). For men, BMI was significantly associated with white matter for 75.0% FA, 66.7% AD, 66.7% RD, and 60.4% MD ROIs (|r| values [0.03, 0.18] and p-values [0.0004, 1.1e-136]).
Stratified by sex, we observed distinct sex-specific association patterns between BMI and white matter phenotypes (Fig. 2). The pattern for women and men was similar for FA but differed significantly for AD, RD, and MD, reflecting the above interaction analysis. Women showed lower negative effect sizes across AD, RD, and MD at higher BMI for brainstem tracts than men. For multiple non-brainstem tracts, men showed higher positive effect sizes across AD, RD, and MD at higher BMI than women. Overall, BMI was significantly associated with white matter in women for 68.8% FA, 83.3% AD, 45.8% RD, and 64.6% MD ROIs (|r| values [0.03, 0.19] and p-values [0.0003, 6.9e-196]). For men, BMI was significantly associated with white matter for 75.0% FA, 66.7% AD, 66.7% RD, and 60.4% MD ROIs (|r| values [0.03, 0.18] and p-values [0.0004, 1.1e-136]).
·Figure 2: Significant sex differences in how BMI relates to white matter microstructure.
Conclusions:
Our results show that sex is an essential factor when investigating obesogenic traits with white matter microstructure. The results indicate that sex-related body shape and fat distribution differences are reflected in sex-related white matter differences. It is also insufficient only to study FA as AD, RD, and MD better capture regional sex-related variation. Understanding the observed sex differences in how BMI relates to white matter microstructure might be important for disentangling sex-specific brain and somatic disease risk and outcomes.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Lifespan Development:
Lifespan Development Other
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 1
Keywords:
ADULTS
Brainstem
Cerebellum
Cerebrovascular Disease
Degenerative Disease
MRI
NORMAL HUMAN
White Matter
Other - Obesity
1|2Indicates the priority used for review
Provide references using author date format
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