Relationship between obesity and structural brain abnormality: Accumulated evidence from observational studies

The WFS1-ZnT3-Zn2+ Axis Regulates the Vicious Cycle of Obesity and Depression

Obesity, a growing global health concern, is closely linked to depression. However, the neural mechanism of association between obesity and depression remains poorly understood. In this study, neural-specific WFS1 deficiency exacerbates the vicious cycle of obesity and depression in mice fed a high-fat diet (HFD), positioning WFS1 as a crucial factor in this cycle. Through human pluripotent stem cells (hESCs) neural differentiation, it is demonstrated that WFS1 regulates Zn2+ homeostasis and the apoptosis of neural progenitor cells (NPCs) and cerebral organoids by inhibiting the zinc transporter ZnT3 under the situation of dysregulated lipid metabolism. Notably, riluzole regulates ZnT3 expression to maintain zinc homeostasis and protect NPCs from lipotoxicity-induced cell death. Importantly, riluzole, a therapeutic molecule targeting the nervous system, in vivo administration prevents HFD-induced obesity and associated depression. Thus, a WFS1-ZnT3-Zn2+ axis critical is demonstrated for the vicious cycle of obesity and depression and that riluzole may have the potential to reverse this process against obesity and depression.

Contributions of Cerebral White Matter Hyperintensities to Postural Instability in Aging With and Without Alcohol Use Disorder

BACKGROUND

Both postural instability and brain white matter hyperintensities (WMHs) are noted markers of normal aging and alcohol use disorder (AUD). Here, we questioned what variables contribute to the sway path-WMH relationship in individuals with AUD and healthy control participants.

METHODS

The data comprised 404 balance platform sessions, yielding sway path length and magnetic resonance imaging data acquired cross-sectionally or longitudinally in 102 control participants and 158 participants with AUD ages 25 to 80 years. Balance sessions were typically conducted on the same day as magnetic resonance imaging fluid-attenuated inversion recovery acquisitions, permitting WMH volume quantification. Factors considered in multiple regression analyses as potential contributors to the relationship between WMH volumes and postural instability were age, sex, socioeconomic status, education, pedal 2-point discrimination, systolic and diastolic blood pressure, body mass index, depressive symptoms, total alcohol consumed in the past year, and race.

RESULTS

Initial analysis identified diagnosis, age, sex, and race as significant contributors to observed sway path-WMH relationships. Inclusion of these factors as predictors in multiple regression analyses substantially attenuated the sway path-WMH relationships in both AUD and healthy control groups. Women, irrespective of diagnosis or race, had shorter sway paths than men. Black participants, irrespective of diagnosis or sex, had shorter sway paths than non-Black participants despite having modestly larger WMH volumes than non-Black participants, which is possibly a reflection of the younger age of the Black sample.

CONCLUSIONS

Longer sway paths were related to larger WMH volumes in healthy men and women with and without AUD. Critically, however, age almost fully accounted for these associations.

Association of obesity indicators with cognitive function among US adults aged 60 years and older: Results from NHANES

BACKGROUND

Midlife obesity is a significant risk factor for Alzheimer's disease, but the effects of obesity on cognitive function, either detrimental or beneficial, are controversial among older individuals. This study aims to assess this associations of body mass index (BMI) or waist circumference (WC) with cognitive function among United States older individuals.

METHODS

A cross-sectional research study was conducted utilizing data from the 2011 to 2014 National Health and Nutrition Examination Survey (NHANES). Initially, the study compared differences in cognitive function among the normal weight, overweight, and obese groups. Subsequently, we examined the relationships between BMI or WC and cognitive function using multivariate linear regression. Finally, structural equation models were constructed to assess the relationships among body shape, lifestyle, and cognitive function pathways.

RESULTS

The study included 2254 individuals. Obese subjects had lower scores in the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) word list learning tasks (CERAD-WL) (χ2 = 7.804, p = .020) and digit symbol substitution test (χ2 = 8.869, p = .012). The regression analysis showed that WC was negatively connected with the CERAD-WL score after adjusting for confounding factors (β = -.029, p = .045). Moreover, WC had a mediating effect on the path from lifestyle to cognition (CERAD-WL). However, there was no difference in the CERAD delayed recall score and the animal fluency test between the obese and the other groups.

CONCLUSIONS

Obese older adults exhibited impaired cognitive abilities in terms of learning and working memory performance. The impact of lifestyle on cognition was mediated by obesity-related anthropometric indices. Sleep, physical activity, and diet influenced the degree of obesity, which subsequently determined cognitive function. Prioritizing weight management in elderly people is crucial for safeguarding cognitive function.

Development and validation of a predictive model for severe white matter hyperintensity with obesity

Purpose

The purpose of the present study was to identify predictors of severe white matter hyperintensity (WMH) with obesity (SWO), and to build a prediction model for screening obese people with severe WMH without Nuclear Magnetic Resonance Imaging (MRI) examination.

Patients subjects and methods

From September 2020 to October 2021, 650 patients with WMH were recruited consecutively. The subjects were divided into two groups, SWO group and non-SWO group. Univariate and Logistic regression analysis were was applied to explore the potential predictors of SWO. The Youden index method was adopted to determine the best cut-off value in the establishment of the prediction model of SWO. Each parameter had two options, low and high. The score table of the prediction model and nomogram based on the logistic regression were constructed. Of the 650 subjects, 487 subjects (75%) were randomly assigned to the training group and 163 subjects (25%) to the validation group. By resampling the area under the curve (AUC) of the subject's operating characteristics and calibration curves 1,000 times, nomogram performance was verified. A decision curve analysis (DCA) was used to evaluate the nomogram's clinical usefulness. By resampling the area under the curve (AUC) of the subject's operating characteristics and calibration curves 1,000 times, nomogram performance was verified. A decision curve analysis (DCA) was used to evaluate the nomogram's clinical usefulness.

Results

Logistic regression demonstrated that hypertension, uric acid (UA), complement 3 (C3) and Interleukin 8 (IL-8) were independent risk factors for SWO. Hypertension, UA, C3, IL-8, folic acid (FA), fasting C-peptide (FCP) and eosinophil could be used to predict the occurrence of SWO in the prediction models, with a good diagnostic performance, Areas Under Curves (AUC) of Total score was 0.823 (95% CI: 0.760-0.885, p < 0.001), sensitivity of 60.0%, specificity of 91.4%. In the development group, the nomogram's AUC (C statistic) was 0.829 (95% CI: 0.760-0.899), while in the validation group, it was 0.835 (95% CI: 0.696, 0.975). In both the development and validation groups, the calibration curves following 1,000 bootstraps showed a satisfactory fit between the observed and predicted probabilities. DCA showed that the nomogram had great clinical utility.

Conclusion

Hypertension, UA, C3, IL-8, FA, FCP and eosinophil models had the potential to predict the incidence of SWO. When the total score of the model exceeded 9 points, the risk of SWO would increase significantly, and the nomogram enabled visualization of the patient's WMH risk. The application prospect of our models mainly lied in the convenient screening of SWO without MRI examination in order to detect SWO and control the WMH hazards early.

Childhood maltreatment influences adult brain structure through its effects on immune, metabolic, and psychosocial factors

Childhood maltreatment (CM) leads to a lifelong susceptibility to mental ill-health which might be reflected by its effects on adult brain structure, perhaps indirectly mediated by its effects on adult metabolic, immune, and psychosocial systems. Indexing these systemic factors via body mass index (BMI), C-reactive protein (CRP), and rates of adult trauma (AT), respectively, we tested three hypotheses: (H1) CM has direct or indirect effects on adult trauma, BMI, and CRP; (H2) adult trauma, BMI, and CRP are all independently related to adult brain structure; and (H3) childhood maltreatment has indirect effects on adult brain structure mediated in parallel by BMI, CRP, and AT. Using path analysis and data from N = 116,887 participants in UK Biobank, we find that CM is related to greater BMI and AT levels, and that these two variables mediate CM's effects on CRP [H1]. Regression analyses on the UKB MRI subsample (N = 21,738) revealed that greater CRP and BMI were both independently related to a spatially convergent pattern of cortical effects (Spearman's ρ = 0.87) characterized by fronto-occipital increases and temporo-parietal reductions in thickness. Subcortically, BMI was associated with greater volume, AT with lower volume and CPR with effects in both directions [H2]. Finally, path models indicated that CM has indirect effects in a subset of brain regions mediated through its direct effects on BMI and AT and indirect effects on CRP [H3]. Results provide evidence that childhood maltreatment can influence brain structure decades after exposure by increasing individual risk toward adult trauma, obesity, and inflammation.

White adipose tissue distribution and amount are associated with increased white matter connectivity

Obesity represents a significant public health concern and is linked to various comorbidities and cognitive impairments. Previous research indicates that elevated body mass index (BMI) is associated with structural changes in white matter (WM). However, a deeper characterization of body composition is required, especially considering the links between abdominal obesity and metabolic dysfunction. This study aims to enhance our understanding of the relationship between obesity and WM connectivity by directly assessing the amount and distribution of fat tissue. Whole-body magnetic resonance imaging (MRI) was employed to evaluate total adipose tissue (TAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT), while MR liver spectroscopy measured liver fat content in 63 normal-weight, overweight, and obese males. WM connectivity was quantified using microstructure-informed tractography. Connectome-based predictive modeling was used to predict body composition metrics based on WM connectomes. Our analysis revealed a positive dependency between BMI, TAT, SAT, and WM connectivity in brain regions involved in reward processing and appetite regulation, such as the insula, nucleus accumbens, and orbitofrontal cortex. Increased connectivity was also observed in cognitive control and inhibition networks, including the middle frontal gyrus and anterior cingulate cortex. No significant associations were found between WM connectivity and VAT or liver fat. Our findings suggest that altered neural communication between these brain regions may affect cognitive processes, emotional regulation, and reward perception in individuals with obesity, potentially contributing to weight gain. While our study did not identify a link between WM connectivity and VAT or liver fat, further investigation of the role of various fat depots and metabolic factors in brain networks is required to advance obesity prevention and treatment approaches.

Association between Body Mass Index and Brain Health in Adults: A 16-Year Population-Based Cohort and Mendelian Randomization Study

Background: The cumulative effect of body mass index (BMI) on brain health remains ill-defined. The effects of overweight on brain health across different age groups need clarification. We analyzed the effect of cumulative BMI on neuroimaging features of brain health in adults of different ages. Methods: This study was based on a multicenter, community-based cohort study. We modeled the trajectories of BMI over 16 years to evaluate cumulative exposure. Multimodality neuroimaging data were collected once for volumetric measurements of the brain macrostructure, white matter hyperintensity (WMH), and brain microstructure. We used a generalized linear model to evaluate the association between cumulative BMI and neuroimaging features. Two-sample Mendelian randomization analysis was performed using summary level of BMI genetic data from 681,275 individuals and neuroimaging genetic data from 33,224 individuals to analyze the causal relationships. Results: Clinical and neuroimaging data were obtained from 1,074 adults (25 to 83 years). For adults aged under 45 years, brain volume differences in participants with a cumulative BMI of >26.2 kg/m2 corresponded to 12.0 years [95% confidence interval (CI), 3.0 to 20.0] of brain aging. Differences in WMH were statistically substantial for participants aged over 60 years, with a 6.0-ml (95% CI, 1.5 to 10.5) larger volume. Genetic analysis indicated causal relationships between high BMI and smaller gray matter and higher fractional anisotropy in projection fibers. Conclusion: High cumulative BMI is associated with smaller brain volume, larger volume of white matter lesions, and abnormal microstructural integrity. Adults younger than 45 years are suggested to maintain their BMI below 26.2 kg/m2 for better brain health. Trial Registration: This study was registered on clinicaltrials.gov (Clinical Indicators and Brain Image Data: A Cohort Study Based on Kailuan Cohort; No. NCT05453877; https://clinicaltrials.gov/ct2/show/NCT05453877).

Associations of hearing loss and structural changes in specific cortical regions: a Mendelian randomization study

INTRODUCTION

Previous studies have suggested a correlation between hearing loss (HL) and cortical alterations, but the specific brain regions that may be affected are unknown.

METHODS

Genome-wide association study (GWAS) data for 3 subtypes of HL phenotypes, sensorineural hearing loss (SNHL), conductive hearing loss, and mixed hearing loss, were selected as exposures, and GWAS data for brain structure-related traits were selected as outcomes. The inverse variance weighted method was used as the main estimation method.

RESULTS

Negative associations were identified between genetically predicted SNHL and brain morphometric indicators (cortical surface area, cortical thickness, or volume of subcortical structures) in specific brain regions, including the bankssts (β = -0.006 mm, P = 0.016), entorhinal cortex (β = -4.856 mm2, P = 0.029), and hippocampus (β = -24.819 cm3, P = 0.045), as well as in brain regions functionally associated with visual perception, including the pericalcarine (β = -10.009 cm3, P = 0.013).

CONCLUSION

Adaptive changes and functional remodeling of brain structures occur in patients with genetically predicted HL. Brain regions functionally associated with auditory perception, visual perception, and memory function are the main brain regions vulnerable in HL.

Association of variability in body size with neuroimaging metrics of brain health: a population-based cohort study

Background

The relationship between the fluctuation in body size and brain health is poorly understood. This study aimed to examine the associations of long-term variability in body mass index (BMI) and waist-to-hip ratio (WHR) with neuroimaging metrics that approximate brain health.

Methods

This cohort study recruited 1114 participants aged 25-83 years from a multicenter, community-based cohort study in China. We modeled the BMI and WHR trajectories of participants during 2006-2018 and assessed the BMI and WHR variability (direction and speed of change) by calculating the slope. Generalized linear models were applied to investigate the associations of BMI and WHR variability with MRI markers of brain tissue volume, white matter microstructural integrity, white matter hyperintensity (WMH), and cerebral small vessel disease (CSVD).

Findings

Progressive weight gain during follow-up was associated with lower global fractional anisotropy (beta = -0.18, 95% confidence interval [CI] -0.34 to -0.02), higher mean diffusivity (beta = 0.15, 95% CI 0.01-0.30) and radial diffusivity (beta = 0.17, 95% CI 0.02-0.32). Weight loss was also associated with a lower burden of periventricular WMH (beta = -0.26, 95% CI -0.48 to -0.03) and a lower risk of moderate-to-severe basal ganglia enlarged perivascular spaces (BG-EPVS, odds ratio [OR] = 0.41, 95% CI 0.20-0.83). Among overweight populations, weight loss was linked with smaller volumes of WMH (beta = -0.47, 95% CI -0.79 to -0.15), periventricular WMH (beta = -0.57, 95% CI -0.88 to -0.26), and deep WMH (beta = -0.36, 95% CI -0.69 to -0.03), as well as lower risk of CSVD (OR = 0.22, 95% CI 0.08-0.62), lacune (OR = 0.12, 95% CI 0.01-0.91) and moderate-to-severe BG-EPVS (OR = 0.24, 95% CI 0.09-0.61). In adults with central obesity, WHR loss was positively associated with larger gray matter volume (beta = 0.50, 95% CI 0.11-0.89), hippocampus volume (beta = 0.62, 95% CI 0.15-1.09), and parahippocampal gyrus volume (beta = 0.85, 95% CI 0.34-1.37). The sex-stratification and age-stratification analyses revealed similar findings with the main results, with the pattern of associations significantly presented in the individuals at mid-life and late-life.

Interpretation

Long-term stability of BMI level is essential for maintaining brain health. Progressive weight gain is associated with impaired white matter microstructural integrity. Weight and WHR losses are associated with improved general brain health. Our results contribute to a better understanding of the integrated associations between variations in obesity measures and brain health.

Funding

This study was supported by grants No. 62171297 (Han Lv) and 61931013 (Zhenchang Wang) from the National Natural Science Foundation of China, No. 7242267 from the Beijing Natural Science Foundation (Han Lv), and No. [2015] 160 from the Beijing Scholars Program (Zhenchang Wang).

Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention

Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.

Modifiable dementia risk factors and AT(N) biomarkers: findings from the EPAD cohort

Introduction

Modifiable risk factors account for a substantial proportion of Alzheimer's disease (AD) cases and we currently have a discrete AT(N) biomarker profile for AD biomarkers: amyloid (A), p-tau (T), and neurodegeneration (N). Here, we investigated how modifiable risk factors relate to the three hallmark AT(N) biomarkers of AD.

Methods

Participants from the European Prevention of Alzheimer's Dementia (EPAD) study underwent clinical assessments, brain magnetic resonance imaging, and cerebrospinal fluid collection and analysis. Generalized additive models (GAMs) with penalized regression splines were modeled in the AD Workbench on the NTKApp.

Results

A total of 1,434 participants were included (56% women, 39% APOE ε4+) with an average age of 65.5 (± 7.2) years. We found that modifiable risk factors of less education (t = 3.9, p < 0.001), less exercise (t = 2.1, p = 0.034), traumatic brain injury (t = -2.1, p = 0.036), and higher body mass index (t = -4.5, p < 0.001) were all significantly associated with higher AD biomarker burden.

Discussion

This cross-sectional study provides further support for modifiable risk factors displaying neuroprotective associations with the characteristic AT(N) biomarkers of AD.

Palmitic acid, but not other long-chain saturated fatty acids, increases S100B protein and TNF-α secretion by astrocytes

Obesity is a health problem that involves fat accumulation in adipose and other tissues and causes cell dysfunction. Long-chain saturated fatty acids can induce and propagate inflammation, which may also contribute to the brain alterations found in individuals with obesity. Fatty acids accumulate in astrocytes in situations of blood‒brain barrier disruption, such as inflammatory conditions. Furthermore, the increase in tumor necrosis factor-alpha (TNF-α) and S100 calcium-binding protein B (S100B) secretion is considered an essential component of the inflammatory response. We hypothesize that through their action on astrocytes, long-chain saturated fatty acids mediate some of the brain alterations observed in individuals with obesity. Here, we investigate the direct effect of long-chain fatty acids on astrocytes. Primary astrocyte cultures were incubated for 24 hours with myristic, palmitic, stearic, linoleic, or α-linolenic acids (25-100 µM). All saturated fatty acids tested led to an increase in TNF-α secretion, but only palmitic acid, one of the most common fatty acids, increased S100B secretion, indicating that S100B secretion is probably not caused in response to TNF-α release. Palmitic acid also caused nuclear migration of nuclear factor kappa B. Long-chain saturated fatty acids did not alter cell viability or redox status. In conclusion, long-chain saturated fatty acids can alter astrocytic homeostasis and may contribute to brain disorders associated with obesity, such as neuroinflammation.

Cardiometabolic health across menopausal years is linked to white matter hyperintensities up to a decade later

Introduction

The menopause transition is associated with several cardiometabolic risk factors. Poor cardiometabolic health is further linked to microvascular brain lesions, which can be detected as white matter hyperintensities (WMHs) using T2-FLAIR magnetic resonance imaging (MRI) scans. Females show higher risk for WMHs post-menopause, but it remains unclear whether changes in cardiometabolic risk factors underlie menopause-related increase in brain pathology.

Methods

In this study, we assessed whether cross-sectional measures of cardiometabolic health, including body mass index (BMI) and waist-to-hip ratio (WHR), blood lipids, blood pressure, and long-term blood glucose (HbA1c), as well as longitudinal changes in BMI and WHR, differed according to menopausal status at baseline in 9,882 UK Biobank females (age range 40-70 years, n premenopausal = 3,529, n postmenopausal = 6,353). Furthermore, we examined whether these cardiometabolic factors were associated with WMH outcomes at the follow-up assessment, on average 8.78 years after baseline.

Results

Postmenopausal females showed higher levels of baseline blood lipids (HDL β  = 0.14, p < 0.001, LDL β  = 0.20, p < 0.001, triglycerides β  = 0.12, p < 0.001) and HbA1c (β  = 0.24, p < 0.001) compared to premenopausal women, beyond the effects of age. Over time, BMI increased more in the premenopausal compared to the postmenopausal group (β  = -0.08, p < 0.001), while WHR increased to a similar extent in both groups (β  = -0.03, p = 0.102). The change in WHR was however driven by increased waist circumference only in the premenopausal group. While the group level changes in BMI and WHR were in general small, these findings point to distinct anthropometric changes in pre- and postmenopausal females over time. Higher baseline measures of BMI, WHR, triglycerides, blood pressure, and HbA1c, as well as longitudinal increases in BMI and WHR, were associated with larger WMH volumes (β range = 0.03-0.13, p ≤ 0.002). HDL showed a significant inverse relationship with WMH volume (β  = -0.27, p < 0.001).

Discussion

Our findings emphasise the importance of monitoring cardiometabolic risk factors in females from midlife through the menopause transition and into the postmenopausal phase, to ensure improved cerebrovascular outcomes in later years.

Characterizing causal relationships of visceral fat and body shape on multiple sclerosis risk

BACKGROUND

Epidemiologic studies have established obesity as a risk factor for multiple sclerosis (MS). These studies relied on body-mass index (BMI) and body size silhouettes as the primary measures of obesity. Unfortunately, the causal mechanisms through which obesity confers MS risk are not yet known.

OBJECTIVES

To investigate the causal effects of multiple specific measures of body fat on MS risk in populations of European descent, using Mendelian randomization (MR).

METHODS

MR is a genetic instrumental variable analysis utilizing genome-wide association (GWA) summary statistics to infer causality between phenotypes. MR analyses were performed to investigate the relationships between seven measures of body fat (BMI, waist-hip ratio, visceral adipose tissue [VAT], subcutaneous adipose tissue, and arm-, leg-, and trunk-fat to total body fat ratio) and MS risk.

RESULTS

Only BMI and VAT were significantly associated with MS risk in separate MR analyses (βBMI=0.27, pBMI<0.001; βVAT=0.28, pVAT=0.006). High correlation between BMI and VAT instruments suggest that two-sample MR associations for BMI and VAT likely capture the same causal mechanisms.

CONCLUSIONS

BMI and VAT were causally associated with MS risk in European populations, though their effects do not appear independent, suggesting overlap in the role of overall body mass and visceral obesity in MS pathogenesis.

Sex differences in the effects of high fat diet on underlying neuropathology in a mouse model of VCID

BACKGROUND

Damage to the cerebral vasculature can lead to vascular contributions to cognitive impairment and dementia (VCID). A reduction in blood flow to the brain leads to neuropathology, including neuroinflammation and white matter lesions that are a hallmark of VCID. Mid-life metabolic disease (obesity, prediabetes, or diabetes) is a risk factor for VCID which may be sex-dependent (female bias).

METHODS

We compared the effects of mid-life metabolic disease between males and females in a chronic cerebral hypoperfusion mouse model of VCID. C57BL/6J mice were fed a control or high fat (HF) diet starting at ~ 8.5 months of age. Three months after diet initiation, sham or unilateral carotid artery occlusion surgery (VCID model) was performed. Three months later, mice underwent behavior testing and brains were collected to assess pathology.

RESULTS

We have previously shown that in this VCID model, HF diet causes greater metabolic impairment and a wider array of cognitive deficits in females compared to males. Here, we report on sex differences in the underlying neuropathology, specifically white matter changes and neuroinflammation in several areas of the brain. White matter was negatively impacted by VCID in males and HF diet in females, with greater metabolic impairment correlating with less myelin markers in females only. High fat diet led to an increase in microglia activation in males but not in females. Further, HF diet led to a decrease in proinflammatory cytokines and pro-resolving mediator mRNA expression in females but not males.

CONCLUSIONS

The current study adds to our understanding of sex differences in underlying neuropathology of VCID in the presence of a common risk factor (obesity/prediabetes). This information is crucial for the development of effective, sex-specific therapeutic interventions for VCID.

A randomized controlled trial examining the effects of behavioral weight loss treatment on hippocampal volume and neurocognition

BACKGROUND/PURPOSE

Obesity in midlife is an established risk factor for dementia. In middle-aged adults, elevated body mass index (BMI) is associated with lower neurocognition and smaller hippocampal volumes. It is unclear whether behavioral weight loss (BWL) can improve neurocognition. The purpose of this study was to evaluate whether BWL, compared to wait list control (WLC), improved hippocampal volume and neurocognition. We also examined if baseline hippocampal volume and neurocognition were associated with weight loss.

METHODS

We randomly assigned women with obesity (N=61; mean±SD age=41.1±9.9 years; BMI=38.6±6.2 kg/m²; and 50.8% Black) to BWL or WLC. Participants completed assessments at baseline and follow-up including T1-weighted structural magnetic resonance imaging scans and the National Institutes of Health (NIH) Toolbox Cognition Battery.

RESULTS

The BWL group lost 4.7±4.9% of initial body weight at 16 to 25 weeks, which was significantly more than the WLC group which gained 0.2±3.5% (p<0.001). The BWL and WLC groups did not differ significantly in changes in hippocampal volume or neurocognition (ps>0.05). Baseline hippocampal volume and neurocognition scores were not significantly associated with weight loss (ps>0.05).

CONCLUSIONS AND IMPLICATIONS

Contrary to our hypothesis, we found no overall benefit of BWL relative to WLC on hippocampal volumes or cognition in young- and middle-aged women. Baseline hippocampal volume and neurocognition were not associated with weight loss.

Relationship Between Weight-Change Patterns and Cognitive Function: A Retrospective Study

BACKGROUND

Obesity has been linked to cognitive impairment. However, how changes in body mass index (BMI) over the life course influence cognitive function remains unclear.

OBJECTIVE

The influence of distinct weight-change patterns from young adulthood to midlife and late adulthood on cognitive function in older adults was explored.

METHODS

A total of 5,809 individuals aged≥60 years were included and categorized into four groups on the basis of BMI change patterns. Cognitive function was assessed using four cognition tests in the baseline survey. The relationship between the weight-change patterns and cognition was evaluated using regression models.

RESULTS

In comparison with participants who remained at non-obese, those moving from the non-obese to obese weight-change pattern from young (25 years of age) to middle adulthood showed lower Digit Symbol Substitution Test (DSST) scores (β= -1.28; 95% confidence interval [CI]: -2.24 to -0.32). A non-obese to obese change pattern from age 25 years of age to 10 years before baseline was associated with a higher risk of DSST impairment (odds ratio = 1.40; 95% CI: 1.09 to 1.79). In comparison with participants whose heaviest weight was recorded after 60 years of age, those with the heaviest weight between 18 and 40 years of age had lower DSST scores (β= -1.46; 95% CI: -2.77 to -1.52).

CONCLUSION

Our results suggest that the transition from the non-obese to obese category in early adulthood and appearance of the heaviest weight between 18 and 40 years of age are associated with lower cognitive function in later life.

Metabolic profile-based subgroups can identify differences in brain volumes and brain iron deposition

AIMS

To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia.

MATERIALS AND METHODS

Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self-organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition.

RESULTS

In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high-density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (βstandardized -0.20, 95% confidence interval [CI] -0.24 to -0.16), HV (βstandardized -0.09, 95% CI -0.13 to -0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C-reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized -0.15, 95% CI -0.16 to -0.14) and HV (βstandardized -0.11, 95% CI -0.12 to -0.10), and between BP and WMH volume (βstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).

CONCLUSIONS

Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.

Cerebral Blood Flow Alterations and Obesity: A Systematic Review and Meta-Analysis

Background: Reduction in cerebral blood flow (CBF) plays an essential role in the cognitive impairment and dementia in obesity. However, current conclusions regarding CBF changes in patients with obesity are inconsistent. Objective: A systematic review and meta-analysis was performed to evaluate the relationship between obesity and CBF alterations. Methods: We systematically screened published cross-sectional and longitudinal studies focusing on the differences in CBF between obese and normal-weight individuals. Eighteen studies including 24,866 participants, of which seven articles reported longitudinal results, were evaluated in the present study. Results: The results of the meta-analysis showed that in cross-sectional studies, body mass index (BMI) was negatively associated with CBF (β= –0.31, 95% confidence interval [CI]: –0.44, –0.19). Moreover, this systematic review demonstrated that obese individuals showed global and regional reductions in the CBF and increased CBF in diverse functional areas of the frontal lobe, including the prefrontal cortex, left frontal superior orbital, right frontal mid-orbital cortex, and left premotor superior frontal gyrus. Conclusion: Our findings suggest that BMI, rather than waist circumference and waist-to-hip ratio, is inversely associated with CBF in cross-sectional studies. The CBF of obese individuals showed global and regional reductions, including the frontal lobe, temporal and parietal lobes, cerebellum, hippocampus, and thalamus.

Obesity and Neurocognitive Performance of Memory, Attention, and Executive Function

BACKGROUND

Obesity has been linked to an increased risk of dementia in the future. Obesity is known to affect core neural structures, such as the hippocampus, and frontotemporal parts of the brain, and is linked to memory, attention, and executive function decline. The overwhelming majority of the data, however, comes from high-income countries. In undeveloped countries, there is little evidence of a link between obesity and neurocognition. The aim of this study was to investigate the effects of BMI on the key cognitive functioning tasks of attention, memory, and executive function in a South African cohort.

METHODS

A total of 175 females (NW: BMI = 18.5-24.9 kg/m2 and OB: BMI > 30.0 kg/m2) aged 18-59 years (M = 28, SD = 8.87 years) completed tasks on memory, attention, and executive functioning.

RESULTS

There was a statistically significant difference between the groups. The participants who had a BMI corresponding with obesity performed poorly on the tasks measuring memory (p = 0.01), attention (p = 0.01), and executive function (p = 0.02) compared to the normal-weight group.

CONCLUSIONS

When compared to normal-weight participants, the findings confirm the existence of lowered cognitive performance in obese persons on tasks involving planning, decision making, self-control, and regulation. Further research into the potential underlying mechanism by which obesity impacts cognition is indicated.

Progressive Brain Structural Impairment Assessed via Network and Causal Analysis in Patients With Hepatitis B Virus-Related Cirrhosis

Objectives

This research amid to elucidate the disease stage-specific spatial patterns and the probable sequences of gray matter (GM) deterioration as well as the causal relationship among structural network components in hepatitis B virus-related cirrhosis (HBV-RC) patients.

Methods

Totally 30 HBV-RC patients and 38 healthy controls (HC) were recruited for this study. High-resolution T1-weighted magnetic resonance imaging and psychometric hepatic encephalopathy score (PHES) were evaluated in all participants. Voxel-based morphometry (VBM), structural covariance network (SCN), and causal SCN (CaSCN) were applied to identify the disease stage-specific GM abnormalities in morphology and network, as well as their causal relationship.

Results

Compared to HC (0.443 ± 0.073 cm3), the thalamus swelled significantly in the no minimal hepatic encephalopathy (NMHE) stage (0.607 ± 0.154 cm3, p <0.05, corrected) and further progressed and expanded to the bilateral basal ganglia, the cortices, and the cerebellum in the MHE stage (p < 0.05, corrected). Furthermore, the thalamus swelling had a causal effect on other parts of cortex-basal ganglia-thalamus circuits (p < 0.05, corrected), which was negatively correlated with cognitive performance (r = −0.422, p < 0.05). Moreover, the thalamus-related SCN also displayed progressive deterioration as the disease advanced in HBV-RC patients (p < 0.05, corrected).

Conclusion

Progressive deterioration of GM morphology and SCN exists in HBV-RC patients during advanced disease, displaying thalamus-related causal effects. These findings indicate that bilateral thalamus morphology as well as the thalamus-related network may serve as an in vivo biomarker for monitoring the progression of the disease in HBV-RC patients.

Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women

The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.