Loss of pons-to-hypothalamic white matter tracks in brainstem obesity

Hypothalamic control of body fat mass by food intake: The key to understanding why obesity should be treated as a disease

BACKGROUND

Hypothalamic centres have been recognized to play a central role in body weight regulation for nearly 70 years.

AIMS

In this review, we will explore the current undersanding of the role the hypothalamus plays in controlling food intake behaviours.

MATERIALS AND METHODS

Review of relevant literature from PubMed searches and review article citations.

RESULTS

Beginning with autopsy studies showing destructive hypothalamic lesions in patients manifesting hyperphagia and rapid weight gain, followed by animal lesioning studies pinpointing adjacent hypothalamic sites as the 'satiety' centre and the 'feeding' centre of the brain, the neurocircuitry that governs our body weight is now understood to consist of a complex, interconnected network, including the hypothalamus and extending to cortical sites, reward centres and brainstem. Neurons in these sites receive afferent signals from the gastrointestinal tract and adipose tissue indicating food availability, calorie content, as well as body fat mass.

DISCUSSION

Integration of these complex signals leads to modulation of the two prime effector systems that defend a body fat mass set point: food intake and energy expenditure.

CONCLUSION

Understanding the hypothalamic control of food intake forms the foundation for understanding and managing obesity as a chronic disease.

Food go/no-go training alters neural circuits for food evaluation for appetite reduction

Food go/no-go training has been traditionally categorized as a type of inhibitory training that decreases the desire for high-calorie food consumption. This training requires participants to either respond or withhold their responses to presented items with go cues or food items with no-go cues, respectively. Recent findings have suggested that this training may devalue food items associated with no-go cues instead of facilitating inhibitory control, leading to reduced food consumption. We thus hypothesized that food go/no-go training would alter the brain response to food items with no-go cues in food evaluation regions. To examine this hypothesis, we conducted a repeated measures functional magnetic resonance imaging using food images in healthy participants, who underwent 3 weeks of food go/no-go training (n = 26) using high- and low-calorie food items paired with no-go cues (no-go food) and go cues (go food), respectively, and control training (n = 24). The food go/no-go training reduced the ratings for the desire to eat no-go foods and increased the ratings for go foods. The reduction in no-go food rating was positively associated with a decrease in daily snack intake. The neural responses in the food evaluation regions increased for go foods. Moreover, the functional connectivity of those regions was altered. The food go/no-go training did not decrease impulsivity traits or increase restrained eating, which are associated with inhibitory control. Overall, food go/no-go training influenced the brain regions associated with food evaluation, thus devaluating no-go foods and reducing the daily snack intake. Accordingly, food go/no-go training could promote healthier food choices.

Brain-gut-microbiome interactions in obesity and food addiction

Normal eating behaviour is coordinated by the tightly regulated balance between intestinal and extra-intestinal homeostatic and hedonic mechanisms. By contrast, food addiction is a complex, maladaptive eating behaviour that reflects alterations in brain-gut-microbiome (BGM) interactions and a shift of this balance towards hedonic mechanisms. Each component of the BGM axis has been implicated in the development of food addiction, with both brain to gut and gut to brain signalling playing a role. Early-life influences can prime the infant gut microbiome and brain for food addiction, which might be further reinforced by increased antibiotic usage and dietary patterns throughout adulthood. The ubiquitous availability and marketing of inexpensive, highly palatable and calorie-dense food can further shift this balance towards hedonic eating through both central (disruptions in dopaminergic signalling) and intestinal (vagal afferent function, metabolic endotoxaemia, systemic immune activation, changes to gut microbiome and metabolome) mechanisms. In this Review, we propose a systems biology model of BGM interactions, which incorporates published reports on food addiction, and provides novel insights into treatment targets aimed at each level of the BGM axis.

Neonatal- maternal separation primes zymogenic cells in the rat gastric mucosa through glucocorticoid receptor activity

Neonatal- Maternal Separation (NMS) deprives mammals from breastfeeding and maternal care, influencing growth during suckling- weaning transition. In the gastric mucosa, Mist1 (encoded by Bhlha15 gene) and moesin organize the secretory apparatus for pepsinogen C in zymogenic cells. Our current hypothesis was that NMS would change corticosterone activity through receptors (GR), which would modify molecules involved in zymogenic cell differentiation in rats. We found that NMS increased corticosterone levels from 18 days onwards, as GR decreased in the gastric mucosa. However, as nuclear GR was detected, we investigated receptor binding to responsive elements (GRE) and observed an augment in NMS groups. Next, we demonstrated that NMS increased zymogenic population (18 and and 30 days), and targeted Mist1 and moesin. Finally, we searched for evolutionarily conserved sequences that contained GRE in genes involved in pepsinogen C secretion, and found that the genomic regions of Bhlha15 and PgC contained sites highly likely to be responsive to glucocorticoids. We suggest that NMS triggers GR- GRE to enhance the expression and to prime genes that organize cellular architecture in zymogenic population for PgC function. As pepsinogen C- pepsin is essential for digestion, disturbance of parenting through NMS might alter functions of gastric mucosa in a permanent manner.

Lower Working Memory Performance in Overweight and Obese Adolescents Is Mediated by White Matter Microstructure

OBJECTIVES

Elevated body mass index (BMI) is associated with deficits in working memory, reduced gray matter volume in frontal and parietal lobes, as well as changes in white matter (WM) microstructure. The current study examined whether BMI was related to working memory performance and blood oxygen level dependent (BOLD) activity, as well as WM microstructure during adolescence.

METHODS

Linear regressions with BMI and (1) verbal working memory BOLD signal, (2) spatial working memory BOLD signal, and (3) fractional anisotropy (FA), a measure of WM microstructure, were conducted in a sample of 152 healthy adolescents ranging in BMI.

RESULTS

BMI was inversely related to IQ and verbal and spatial working memory accuracy; however, there was no significant relationship between BMI and BOLD response for either verbal or spatial working memory. Furthermore, BMI was negatively correlated with FA in the left superior longitudinal fasciculus (SLF) and left inferior longitudinal fasciculus (ILF). ILF FA and IQ significantly mediated the relationship between BMI and verbal working memory performance, whereas SLF FA, but not IQ, significantly mediated the relationship between BMI and accuracy of both verbal and spatial working memory.

CONCLUSIONS

These findings indicate that higher BMI is associated with decreased FA in WM fibers connecting brain regions that support working memory, and that WM microstructural deficits may underlie inferior working memory performance in youth with higher BMI. Of interest, BMI did not show the same relationship with working memory BOLD activity, which may indicate that changes in brain structure precede changes in function.

Patterns of brain structural connectivity differentiate normal weight from overweight subjects

Background

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.

Aim

To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements.

Methods

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.

Results

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.

Conclusions

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.

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Multivariate analysis can be used to classify overweight from normal weight individuals.

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Anatomical connectivity achieved 97% accuracy in the classification algorithm.

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Greater connectivity was observed in extended reward and somatosensory regions.

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Morphological gray-matter achieved 69% accuracy in the classification algorithm.

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Lower morphological values were observed in regions of the extended reward network.

Obesity: pathophysiology and intervention

Obesity presents a major health hazard of the 21st century. It promotes co-morbid diseases such as heart disease, type 2 diabetes, obstructive sleep apnea, certain types of cancer, and osteoarthritis. Excessive energy intake, physical inactivity, and genetic susceptibility are main causal factors for obesity, while gene mutations, endocrine disorders, medication, or psychiatric illnesses may be underlying causes in some cases. The development and maintenance of obesity may involve central pathophysiological mechanisms such as impaired brain circuit regulation and neuroendocrine hormone dysfunction. Dieting and physical exercise offer the mainstays of obesity treatment, and anti-obesity drugs may be taken in conjunction to reduce appetite or fat absorption. Bariatric surgeries may be performed in overtly obese patients to lessen stomach volume and nutrient absorption, and induce faster satiety. This review provides a summary of literature on the pathophysiological studies of obesity and discusses relevant therapeutic strategies for managing obesity.