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. 2013 Oct 1:79:129-37.
doi: 10.1016/j.neuroimage.2013.04.075. Epub 2013 Apr 29.

Competing physiological pathways link individual differences in weight and abdominal adiposity to white matter microstructure

Timothy D Verstynen  1 , Andrea Weinstein, Kirk I Erickson, Lei K Sheu, Anna L Marsland, Peter J Gianaros
Affiliations

Competing physiological pathways link individual differences in weight and abdominal adiposity to white matter microstructure

Timothy D Verstynen et al. Neuroimage. .

Abstract

Being overweight or obese is associated with reduced white matter integrity throughout the brain. It is not yet clear which physiological systems mediate the association between inter-individual variation in adiposity and white matter. We tested whether composite indicators of cardiovascular, lipid, glucose, and inflammatory factors would mediate the adiposity-related variation in white matter microstructure, measured with diffusion tensor imaging on a group of neurologically healthy adults (N=155). A composite factor representing adiposity (comprised of body mass index and waist circumference) was associated with smaller fractional anisotropy and greater radial diffusivity throughout the brain, a pattern previously linked to myelin structure changes in non-human animal models. A similar global negative association was found for factors representing inflammation and, to a lesser extent, glucose regulation. In contrast, factors for blood pressure and dyslipidemia had positive associations with white matter in isolated brain regions. Taken together, these competing influences on the diffusion signal were significant mediators linking adiposity to white matter and explained up to fifty-percent of the adiposity-white matter variance. These results provide the first evidence for contrasting physiological pathways, a globally distributed immunity-linked negative component and a more localized vascular-linked positive component, that associate adiposity to individual differences in the microstructure of white matter tracts in otherwise healthy adults.

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Conflict of interest statement

Conflict of Interest: None of the authors report any conflicts of interest.

Figures

Figure 1
Figure 1
Global distributions of the regression coefficients showing the relationship between each latent variable and fractional anisotropy (FA) across all white matter voxels. Black distributions show the observed values. Grey distributions show the estimated null distribution determined from bootstrapped simulations. PDF, probability density function; CDF, cumulative distribution function.
Figure 2
Figure 2
The spatial distribution of positive (red voxels) and negative (blue voxels) associations between each latent variable and FA. Clusters are thresholded to a minimum of 20 connected voxels and a cluster-wise false discovery rate of 0.05. The z-plane coordinates of each slice, in MNI space, are presented at the bottom. A) Adiposity; B) BP, Blood Pressure factor; C) Dyslp, Dyslipidemia factor; D) Inflam, Inflammation factor; E) Glucos, Glucose regulation factor.
Figure 3
Figure 3
A) Illustration of the indirect pathway analysis for all four factors tested. Percentages show the number of voxels with statistically significant (p < 0.025) indirect pathways, and depicted by the weight of the arrows. Light gray arrows show average positive effects, while dark gray shows average negative effects. The upper bound of the chance 95% confidence interval is 26.54%. The mean and standard deviation for the indirect pathway coefficients is shown adjacent to each pathway label. Same factor labels as Figure 2. B) Percent of voxels with significant Adiposity-FA associations. Error bars show 95% confidence interval from 20 random simulations, followed by the detection rate for the original regression model (*) and the direct pathway model (Δ) after accounting for the indirect physiological pathways.
Figure 4
Figure 4
Distribution of indirect pathways coefficients. Dashed lines on the color bars show the median coefficient values for significant positive and negative effects. Labeling conventions are the same as Figure 2.
Figure 5
Figure 5
Distributions of indirect pathway coefficients across all white matter voxels. Black distributions show across all voxels. Blue distributions show voxels with significant (p<0.025, bootstrapped) negative effects, while red distributions show significant positive effects.
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