Relationships between gray matter, body mass index, and waist circumference in healthy adults

Department of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, Los Angeles, California. .
Human Brain Mapping (Impact Factor: 6.92). 07/2013; 34(7). DOI: 10.1002/hbm.22021
Source: PubMed

ABSTRACT Obesity and overweight are often defined by the body mass index (BMI), which associates with metabolic and cardiovascular disease, and possibly with dementia as well as variations in brain volume. However, body fat distribution and abdominal obesity (as measured by waist circumference) is more strongly correlated with cardiovascular and metabolic risk than is BMI. While prior studies have revealed negative associations between gray matter tissue volumes and BMI, the relationship with respect to waist circumference remains largely unexplored. We therefore investigated the effects of both BMI and waist circumference on local gray matter volumes in a group of 115 healthy subjects screened to exclude physical or mental disorders that might affect the central nervous system. Results revealed significant negative correlations for both BMI and waist circumference where regional gray matter effects were largest within the hypothalamus and further encompassed prefrontal, anterior temporal and inferior parietal cortices, and the cerebellum. However, associations were more widespread and pronounced for waist circumference than BMI. Follow-up analyses showed that these relationships differed significantly across gender. While associations were similar for both BMI and waist circumference for males, females showed more extensive correlations for waist circumference. Our observations suggest that waist circumference is a more sensitive indicator than BMI, particularly in females, for potentially determining the adverse effects of obesity and overweight on the brain and associated risks to health. Hum Brain Mapp , 2012. © 2011 Wiley Periodicals, Inc.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective Neuroimaging studies have revealed abnormalities in brain structure, including the striatum, in obese people. In this study, the cellular and parenchymal basis for these findings in post-mortem brain tissue was investigated.Design and Methods Design-based (unbiased) stereology combined with histochemical and immunocytochemical staining was used to quantify total number of neurons and astrocytes in post-mortem striatal brain samples from nine obese (BMI 40.2 ± 6.1 kg/m2) and 8 lean (BMI 24.4 ± 1.0 kg/m2) donors. Total numbers of Nissl-stained neurons and GFAP-immunopositive astrocytes were counted in ten systematic-random sections starting from the frontal pole of the striatum.ResultsThere were no differences in mean total numbers of neurons (obese: 7.60 E+06; SD 2.50 E+06; lean: 7.85 E+06; SD 8.26 E+05; P < 0.78) or astrocytes (obese: 7.42 E+06; SD 2.27 E+06; lean: 7.43 E+06; SD 2.50 E+06; P < 0.99). A higher variance was found for number of neurons (P < 0.007) but not astrocytes (P < 0.72) in the obese group. Neuron/glia ratios were similar in both groups (obese: 1.07, SD 0.39; lean: 1.15, SD 0.37; P < 0.70) with an overall striatal neuron/glia ratio of 1.11 (SD 0.37) across the entire study population (n = 17).Conclusion No difference was found in the average numbers of neurons and astrocytes in the anterior striatum between lean and obese people. The morphological basis for structural brain changes in obesity requires further investigation.
    Obesity 01/2015; 23(1). DOI:10.1002/oby.20897 · 4.39 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Structural brain imaging studies have shown that obesity is associated with widespread reductions in gray matter (GM) volume. Although the body mass index (BMI) is an easily accessible anthropometric measure, substantial health problems are more related to specific body fat compartments, like visceral adipose tissue (VAT). We investigated cortical thickness measures in a group of 72 healthy subjects (BMI range 20–35 kg/m2, age range 19–50 years). Multiple regression analyses were performed using VAT and BMI as predictors and age, gender, total surface area and education as confounds. BMI and VAT were independently associated with reductions in cortical thickness in clusters comprising the left lateral occipital area, the left inferior temporal cortex, and the left precentral and inferior parietal area, while the right insula, the left fusiform gyrus and the right inferior temporal area showed a negative correlation with VAT only. In addition, we could show significant reductions in cortical thickness with increasing VAT adjusted for BMI in the left temporal cortex. We were able to detect widespread cortical thinning in a young to middle-aged population related to BMI and VAT; these findings show close resemblance to studies focusing on GM volume differences in diabetic patients. This may point to the influence of VAT related adverse effects, like low-grade inflammation, as a potentially harmful factor in brain integrity already in individuals at risk of developing diabetes, metabolic syndromes and arteriosclerosis.
    01/2014; 6. DOI:10.1016/j.nicl.2014.09.013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69% accuracy in discriminating overweight from normal weight. In both brain signatures regions of the reward, salience, executive control and emotional arousal networks were associated with lower morphological values in overweight individuals compared to normal weight individuals, while the opposite pattern was seen for regions of the somatosensory network. 1. An increased BMI (i.e., overweight subjects) is associated with distinct changes in gray-matter and fiber density of the brain. 2. Classification algorithms based on white-matter connectivity involving regions of the reward and associated networks can identify specific targets for mechanistic studies and future drug development aimed at abnormal ingestive behavior and in overweight/obesity.
    12/2015; 7:506-17. DOI:10.1016/j.nicl.2015.01.005

Florian Kurth