A Postmortem Study of Frontal and Temporal Gyri Thickness and Cell Number in Human Obesity

Diffusion time-related structure-function coupling reveals differential association with inter-individual variations in body mass index

Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated that inter-individual variations in BMI are associated with altered brain structure and function. However, the mechanism underlying the alteration of structure-function correspondence according to BMI is under-investigated. In this study, we studied structural and functional connectivity derived from diffusion MRI tractography and inter-regional correlations of functional MRI time series, respectively. We combined the structural and functional connectivity information using the Riemannian optimization approach. First, the low-dimensional principal eigenvectors (i.e., gradients) of the structural connectivity were generated by applying diffusion map embedding with varying diffusion times. A transformation was identified so that the structural and functional embeddings share the same coordinate system, and subsequently, the functional connectivity matrix was simulated. Then, we generated gradients from the simulated functional connectivity matrix. We found the most apparent cortical hierarchical organization differentiating between low-level sensory and higher-order transmodal regions in the middle of the diffusion time, indicating that the hierarchical organization of the brain may reflect the intermediate mechanisms of mono- and polysynaptic communications. Associations between the functional gradients and BMI were strongest when the hierarchical structure was the most evident. Moreover, the gradient-BMI association map was related to the microstructural features, and the findings indicated that the BMI-related structure-function coupling was significantly associated with brain microstructure, particularly in higher-order transmodal areas. Finally, transcriptomic association analysis revealed the potential biological underpinnings specifying gene enrichment in the striatum, hypothalamus, and cortical cells. Our findings provide evidence that structure-function correspondence is strongly coupled with BMI when hierarchical organization is the most apparent and that the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI.

Genetic Insights into Obesity and Brain: Combine Mendelian Randomization Study and Gene Expression Analysis

As a major public-health concern, obesity is imposing an increasing social burden around the world. The link between obesity and brain-health problems has been reported, but controversy remains. To investigate the relationship among obesity, brain-structure changes and diseases, a two-stage analysis was performed. At first, we used the Mendelian-randomization (MR) approach to identify the causal relationship between obesity and cerebral structure. Obesity-related data were retrieved from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and the UK Biobank, whereas the cortical morphological data were from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Further, we extracted region-specific expressed genes according to the Allen Human Brian Atlas (AHBA) and carried out a series of bioinformatics analyses to find the potential mechanism of obesity and diseases. In the univariable MR, a higher body mass index (BMI) or larger visceral adipose tissue (VAT) was associated with a smaller global cortical thickness (pBMI = 0.006, pVAT = 1.34 × 10-4). Regional associations were found between obesity and specific gyrus regions, mainly in the fusiform gyrus and inferior parietal gyrus. Multivariable MR results showed that a greater body fat percentage was linked to a smaller fusiform-gyrus thickness (p = 0.029) and precuneus surface area (p = 0.035). As for the gene analysis, region-related genes were enriched to several neurobiological processes, such as compound transport, neuropeptide-signaling pathway, and neuroactive ligand-receptor interaction. These genes contained a strong relationship with some neuropsychiatric diseases, such as Alzheimer's disease, epilepsy, and other disorders. Our results reveal a causal relationship between obesity and brain abnormalities and suggest a pathway from obesity to brain-structure abnormalities to neuropsychiatric diseases.

Immunohistochemical analysis of caspase expression in the brains of individuals with obesity or overweight

Mechanisms underlying the negative effects of obesity on the brain are still unknown. Obesity is associated with oxidative stress in the brain and neuroinflammation that promotes neurodegenerative diseases. Chronic low-grade neuroinflammation in obesity could be associated with lower volumes of gray matter and lower neuronal density. If neuroinflammation mediated by the expression of cytokines and chemokines leads to apoptosis, this can be assessed by examining caspase expression. The aim of this study was to compare the expression of caspases in the 16 brains of donors with obesity/overweight (n = 8; Body Mass Index [BMI] = 31.6 ± 4.35 kg/m²; 2 females; Age = 52.9 ± 4.76 years) and normal weight (n = 8; BMI = 21.8 ± 1.5 kg/m²; 3 females; Age = 37.8 ± 19.2 years). Sixteen human brain samples were processed. Serial paraffin sections were examined by anti-caspase immunochemistry (caspase-3, caspase-4, caspase-6, caspase-1, caspase-8, and caspase-9 antibodies). Postmortem samples of cerebral cortex tissue were captured as photomicrographs and the images obtained were analyzed using ImageJ software to obtain the percentage of positive caspase expression. Nonparametric Mann-Whitney U tests were performed to compare caspase expression between samples from donors with obesity/overweight and normal weight. Taking into consideration the immunohistochemistry results, the Search Tool for the Retrieval of Interacting Genes was used to model molecular interactions. Results showed that brain samples from individuals with obesity/overweight exhibited significantly greater values of positive expression for Caspase-1 (U = 16.5, p = 0.05, Cohen d = 0.89) and -8 (U = 15, p = 0.03, Cohen d = 0.99) than those from donors with normal weight. This study contributes to the knowledge about the inflammatory effects of obesity/overweight on brain, suggesting the activation of the alternative inflammasome pathway in which interact caspase-1 and -8.

White matter fiber-specific degeneration in older adults with metabolic syndrome

OBJECTIVE

Metabolic syndrome (MetS) is defined as a complex of interrelated risk factors for type 2 diabetes and cardiovascular disease, including glucose intolerance, abdominal obesity, hypertension, and dyslipidemia. Studies using diffusion tensor imaging (DTI) have reported white matter (WM) microstructural abnormalities in MetS. However, interpretation of DTI metrics is limited primarily due to the challenges of modeling complex WM structures. The present study used fixel-based analysis (FBA) to assess the effect of MetS on the fiber tract-specific WM microstructure in older adults and its relationship with MetS-related measurements and cognitive and locomotor functions to better understand the pathophysiology of MetS.

METHODS

Fixel-based metrics, including microstructural fiber density (FD), macrostructural fiber-bundle cross-section (FC), and a combination of FD and FC (FDC), were evaluated in 16 healthy controls (no components of MetS; four men; mean age, 71.31 ± 5.06 years), 57 individuals with premetabolic syndrome (preMetS; one or two components of MetS; 29 men; mean age, 72.44 ± 5.82 years), and 46 individuals with MetS (three to five components of MetS; 27 men; mean age, 72.15 ± 4.97 years) using whole-brain exploratory FBA. Tract of interest (TOI) analysis was then performed using TractSeg across 14 selected WM tracts previously associated with MetS. The associations between fixel-based metrics and MetS-related measurements, neuropsychological, and locomotor function tests were also analyzed in individuals with preMetS and MetS combined. In addition, tensor-based metrics (i.e., fractional anisotropy [FA] and mean diffusivity [MD]) were compared among the groups using tract-based spatial statistics (TBSS) analysis.

RESULTS

In whole-brain FBA, individuals with MetS showed significantly lower FD, FC, and FDC compared with healthy controls in WM areas, such as the splenium of the corpus callosum (CC), corticospinal tract (CST), middle cerebellar peduncle (MCP), and superior cerebellar peduncle (SCP). Meanwhile, in fixel-based TOI, significantly reduced FD was observed in individuals with preMetS and MetS in the anterior thalamic radiation, CST, SCP, and splenium of the CC compared with healthy controls, with relatively greater effect sizes observed in individuals with MetS. Compared with healthy controls, significantly reduced FC and FDC were only demonstrated in individuals with MetS, including regions with loss of FD, inferior cerebellar peduncle, inferior fronto-occipital fasciculus, MCP, and superior longitudinal fasciculus part I. Furthermore, negative correlations were observed between FD and Brinkman index of cigarette consumption cumulative amount and between FC or FDC and the Trail Making Test (parts B-A), which is a measure of executive function, waist circumference, or low-density lipoprotein cholesterol. Finally, TBSS analysis revealed that FA and MD were not significantly different among all groups.

CONCLUSIONS

The FBA results demonstrate that substantial axonal loss and atrophy in individuals with MetS and early axonal loss without fiber-bundle morphological changes in those with preMetS within the WM tracts are crucial to cognitive and motor function. FBA also clarified the association between executive dysfunction, abdominal obesity, hyper-low-density lipoprotein cholesterolemia, smoking habit, and compromised WM neural tissue microstructure in MetS.

Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.

From Metabolic Syndrome to Neurological Diseases: Role of Autophagy

Metabolic syndrome is not a single pathology, but a constellation of cardiovascular disease risk factors including: central and abdominal obesity, systemic hypertension, insulin resistance (or type 2 diabetes mellitus), and atherogenic dyslipidemia. The global incidence of Metabolic syndrome is estimated to be about one quarter of the world population; for this reason, it would be desirable to better understand the underlying mechanisms involved in order to develop treatments that can reduce or eliminate the damage caused. The effects of Metabolic syndrome are multiple and wide ranging; some of which have an impact on the central nervous system and cause neurological and neurodegenerative diseases. Autophagy is a catabolic intracellular process, essential for the recycling of cytoplasmic materials and for the degradation of damaged cellular organelle. Therefore, autophagy is primarily a cytoprotective mechanism; even if excessive cellular degradation can be detrimental. To date, it is known that systemic autophagic insufficiency is able to cause metabolic balance deterioration and facilitate the onset of metabolic syndrome. This review aims to highlight the current state of knowledge regarding the connection between metabolic syndrome and the onset of several neurological diseases related to it. Furthermore, since autophagy has been found to be of particular importance in metabolic disorders, the probable involvement of this degradative process is assumed to be responsible for the attenuation of neurological disorders resulting from metabolic syndrome.

Structural Brain Changes Associated with Overweight and Obesity

Obesity is a global health problem with a broad set of comorbidities, such as malnutrition, metabolic syndrome, diabetes, systemic hypertension, heart failure, and kidney failure. This review describes recent findings of neuroimaging and two studies of cell density regarding the roles of overnutrition-induced hypothalamic inflammation in neurodegeneration. These studies provided consistent evidence of smaller cortical thickness or reduction in the gray matter volume in people with overweight and obesity; however, the investigated brain regions varied across the studies. In general, bilateral frontal and temporal areas, basal nuclei, and cerebellum are more commonly involved. Mechanisms of volume reduction are unknown, and neuroinflammation caused by obesity is likely to induce neuronal loss. Adipocytes, macrophages of the adipose tissue, and gut dysbiosis in overweight and obese individuals result in the secretion of the cytokines and chemokines that cross the blood-brain barrier and may stimulate microglia, which in turn also release proinflammatory cytokines. This leads to chronic low-grade neuroinflammation and may be an important factor for apoptotic signaling and neuronal death. Additionally, significant microangiopathy observed in rat models may be another important mechanism of induction of apoptosis. Neuroinflammation in neurodegenerative diseases (such as Alzheimer's and Parkinson's diseases) may be similar to that in metabolic diseases induced by malnutrition. Poor cognitive performance, mainly in executive functions, in individuals with obesity is also discussed. This review highlights the neuroinflammatory and neurodegenerative mechanisms linked to obesity and emphasizes the importance of developing effective prevention and treatment intervention strategies for overweight and obese individuals.

Sugar in mind: Untangling a sweet and sour relationship beyond type 2 diabetes

It is widely recognised that type 2 diabetes (T2D) represents a major disease burden but it is only recently that its role in neurodegeneration has attracted more attention. This research has shown that T2D is associated with impaired cerebral health, cognitive decline and dementia. However, the impact on the brain of progressive metabolic changes associated with the pre-clinical development of the disease is less clear. The aim of this review is to comprehensively summarise how the emergence of risk factors and co-morbid conditions linked to the development of T2D impact cerebral health. Particular attention is directed at characterising how normal but elevated blood glucose levels in individuals without T2D contribute to neurodegenerative processes, and how the main risk factors for T2D including obesity, physical activity and diet modulate these effects. Where available, evidence from the animal and human literature is contrasted, and sex differences in risk and outcomes are highlighted.

Reductions in body weight and insulin resistance are not associated with changes in grey matter volume or cortical thickness during the PREVIEW study

INTRODUCTION

The effect of changes in body weight or insulin resistance on grey matter volume and cortical thickness change are unclear. The present observational study assessed effects of an 8-week weight loss period (≥8% of body weight), and a subsequent 22-month weight maintenance period on grey matter volume and cortical thickness.

METHODS

A total of 24 participants (12f/12 m; age 52.8 ± 10.6 years) with overweight/obesity and pre-diabetes were recruited. T1-weighted magnetic resonance imaging was used to determine grey matter volume and cortical thickness at baseline, after the weight loss period and after a medium to high dietary protein weight maintenance period.

RESULTS

At baseline, global grey matter volume was inversely associated with HOMA-IR, adjusted for sex and age (r = -0.42; p = .049). During the weight loss period participants decreased their BMI (32.1 ± 3.3 to 28.1 ± 2.8 kg/m², p < .01), body-fat (41.6 ± 6.4 to 35.0 ± 8.0%, p < .01) and insulin resistance (HOMA-IR: 4.0 ± 2.0 to 1.8 ± 0.9, p < .01). During the 22-month weight maintenance period, these parameters gradually increased again (BMI: 29.3 ± 3.8 kg/m²; body-fat: 37.8 ± 9.3%; HOMA-IR: 2.9 ± 1.4, p < .01). Global grey matter volume and cortical thickness did not change significantly during the weight loss or weight maintenance period. Changes in body weight, body-fat percentage or insulin sensitivity were not associated with changes in global grey matter volume.

CONCLUSION

In conclusion, we confirmed that global grey brain matter volume was inversely associated with insulin resistance at baseline, yet an intervention yielding a decrease in insulin resistance did not lead to changes in global grey brain matter volume or cortical thickness.

TRIAL REGISTRATION

The trial is registered with ClinicalTrials.gov, NCT01777893.