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Population-based body-brain mapping links brain morphology with anthropometrics and body composition.

ABSTRACT: Understanding complex body-brain processes and the interplay between adipose tissue and brain health is important for understanding comorbidity between psychiatric and cardiometabolic disorders. We investigated associations between brain structure and anthropometric and body composition measures using brain magnetic resonance imaging (MRI; n = 24,728) and body MRI (n = 4973) of generally healthy participants in the UK Biobank. We derived regional and global measures of brain morphometry using FreeSurfer and tested their association with (i) anthropometric measures, and (ii) adipose and muscle tissue measured from body MRI. We identified several significant associations with small effect sizes. Anthropometric measures showed negative, nonlinear, associations with cerebellar/cortical gray matter, and brain stem structures, and positive associations with ventricular volumes. Subcortical structures exhibited mixed effect directionality, with strongest positive association for accumbens. Adipose tissue measures, including liver fat and muscle fat infiltration, were negatively associated with cortical/cerebellum structures, while total thigh muscle volume was positively associated with brain stem and accumbens. Regional investigations of cortical area, thickness, and volume indicated widespread and largely negative associations with anthropometric and adipose tissue measures, with an opposite pattern for thigh muscle volume. Self-reported diabetes, hypertension, or hypercholesterolemia were associated with brain structure. The findings provide new insight into physiological body-brain associations suggestive of shared mechanisms between cardiometabolic risk factors and brain health. Whereas the causality needs to be determined, the observed patterns of body-brain relationships provide a foundation for understanding the underlying mechanisms linking psychiatric disorders with obesity and cardiovascular disease, with potential for the development of new prevention strategies.

SUBMITTER: Gurholt TP

PROVIDER: S-EPMC8131380 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Total and regional body composition are strongly correlated with metabolic syndrome and have been estimated non-invasively from 3D optical scans using linear parameterizations of body shape and linear regression models. Prior works produced accurate and precise predictions on many, but not all, body composition targets relative to the reference dual X-Ray absorptiometry (DXA) measurement. Here, we report the effects of replacing linear models with nonlinear parameterization and regression models on the precision and accuracy of body composition estimation in a novel application of deep 3D convolutional graph networks to human body composition modeling. We assembled an ensemble dataset of 4286 topologically standardized 3D optical scans from four different human body shape databases, DFAUST, CAESAR, Shape Up! Adults, and Shape Up! Kids and trained a parameterized shape model using a graph convolutional 3D autoencoder (3DAE) in lieu of linear PCA. We trained a nonlinear Gaussian process regression (GPR) on the 3DAE parameter space to predict body composition via correlations to paired DXA reference measurements from the Shape Up! scan subset. We tested our model on a set of 424 randomly withheld test meshes and compared the effects of nonlinear computation against prior linear models. Nonlinear GPR produced up to 20% reduction in prediction error and up to 30% increase in precision over linear regression for both sexes in 10 tested body composition variables. Deep shape features produced 6-8% reduction in prediction error over linear PCA features for males only and a 4-14% reduction in precision error for both sexes. Our best performing nonlinear model predicting body composition from deep features outperformed prior work using linear methods on all tested body composition prediction metrics in both precision and accuracy. All coefficients of determination (R2) for all predicted variables were above 0.86. We show that GPR is a more precise and accurate method for modeling body composition mappings from body shape features than linear regression. Deep 3D features learned by a graph convolutional autoencoder only improved male body composition accuracy but improved precision in both sexes. Our work achieved lower estimation RMSEs than all previous work on 10 metrics of body composition.

Project description:BackgroundWe aimed to examine maternal metabolic correlates of neonate body composition, and the potential mediating effect of the placenta.MethodsData were collected throughout pregnancy and at delivery. An oral glucose tolerance test (OGTT) was conducted in order to diagnose or rule out gestational diabetes mellitus (GDM). Maternal weight and blood pressure were taken and hypertension and gestational weight gain (GWG) were defined. Gestational age, birth weight (BW) and weight to length ration (WLR) were recorded. Photographs were taken of the placenta, and the widths and lengths were measured digitally. Body composition was analysed via air displacement plethysmography or dual-energy x-ray absorptiometry. Mediation models were conducted to determine the mediation effect of the placental variables on the relationships between maternal health variables and neonate outcomes. Next, interaction terms were added to models to determine how maternal and placental variables interacted in their effect on neonate outcomes.ResultsA total of n = 280 women were included in the analysis. Majority were overweight or obese. Fourteen percent of women developed GDM during pregnancy, 5% had hypertension during pregnancy, 32% were HIV positive, and 32% had anemia. For the birth weight outcome, coefficients of BMI were attenuated by the addition of placental variables (Model 1 β=18.66 vs Model 2 β=16.40). Similar patterns were evident for GWG and hypertension, and for the WLR outcome. In all cases the addition of the placental variables attenuated associations between maternal exposures and neonatal outcomes, yet the level of significance did not change. Inclusion of interaction terms reversed the direction of the relationships between hypertension and BW and WLR, and between GWG and WLR.ConclusionThe placenta buffers some harmful effects of obesity, GWG, and hypertension on neonate size, and placental efficiency interacted with most maternal risk factors to either counteract, or attenuate relationships with neonate size at birth. However the placenta was unable to completely counteract the negative effect of excess nutrient supply on in utero growth.

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Population-based body-brain mapping links brain morphology with anthropometrics and body composition.

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