High body mass index, brain metabolism and connectivity: an unfavorable effect in elderly females

The 'Behavioral Balance Model': A new perspective on the aetiology and therapy of obesity

Obesity is a debilitating disease of global proportions that necessitates refined, concept-driven therapeutic approaches. Policy makers, the public and even health care professionals, but also individuals with obesity harbour many misconceptions regarding this disease, which leads to prejudice, negative attitudes, stigmatization, discrimination, self-blame, and failure to provide and finance adequate medical care. Decades of intensive, successful scientific research on obesity have only had a very limited effect on this predicament. We propose a science-based, easy-to-understand conceptual model that synthesizes the complex pathogenesis of obesity including biological, psychological, social, economic and environmental aspects with the aim to explain and communicate better the nature of obesity and currently available therapeutic modalities. According to our integrative 'Behavioral Balance Model', 'top-down cognitive control' strategies are implemented (often with limited success) to counterbalance the increased 'bottom-up drive' to gain weight, which is triggered by biological, psycho-social and environmental mechanisms in people with obesity. Besides offering a deeper understanding of obesity, the model also highlights why there is a strong need for multimodal therapeutic approaches that may not only increase top-down control but also reduce a pathologically increased bottom-up drive.

Spatiotemporal profile of altered neural reactivity to food images in obesity: Reward system is altered automatically and predicts efficacy of weight loss intervention

Introduction

Obesity presents a significant public health problem. Brain plays a central role in etiology and maintenance of obesity. Prior neuroimaging studies have found that individuals with obesity exhibit altered neural responses to images of food within the brain reward system and related brain networks. However, little is known about the dynamics of these neural responses or their relationship to later weight change. In particular, it is unknown if in obesity, the altered reward response to food images emerges early and automatically, or later, in the controlled stage of processing. It also remains unclear if the pretreatment reward system reactivity to food images is predictive of subsequent weight loss intervention outcome.

Methods

In this study, we presented high-calorie and low-calorie food, and nonfood images to individuals with obesity, who were then prescribed lifestyle changes, and matched normal-weight controls, and examined neural reactivity using magnetoencephalography (MEG). We performed whole-brain analysis to explore and characterize large-scale dynamics of brain systems affected in obesity, and tested two specific hypotheses: (1) in obese individuals, the altered reward system reactivity to food images occurs early and automatically, and (2) pretreatment reward system reactivity predicts the outcome of lifestyle weight loss intervention, with reduced activity associated with successful weight loss.

Results

We identified a distributed set of brain regions and their precise temporal dynamics that showed altered response patterns in obesity. Specifically, we found reduced neural reactivity to food images in brain networks of reward and cognitive control, and elevated reactivity in regions of attentional control and visual processing. The hypoactivity in reward system emerged early, in the automatic stage of processing (< 150 ms post-stimulus). Reduced reward and attention responsivity, and elevated neural cognitive control were predictive of weight loss after six months in treatment.

Discussion

In summary, we have identified, for the first time with high temporal resolution, the large-scale dynamics of brain reactivity to food images in obese versus normal-weight individuals, and have confirmed both our hypotheses. These findings have important implications for our understanding of neurocognition and eating behavior in obesity, and can facilitate development of novel integrated treatment strategies, including tailored cognitive-behavioral and pharmacological therapies.

The orbitofrontal cortex, food reward, body weight and obesity

In primates including humans, the orbitofrontal cortex is the key brain region representing the reward value and subjective pleasantness of the sight, smell, taste and texture of food. At stages of processing before this, in the insular taste cortex and inferior temporal visual cortex, the identity of the food is represented, but not its affective value. In rodents, the whole organisation of reward systems appears to be different, with reward value reflected earlier in processing systems. In primates and humans, the amygdala is overshadowed by the great development of the orbitofrontal cortex. Social and cognitive factors exert a top-down influence on the orbitofrontal cortex, to modulate the reward value of food that is represented in the orbitofrontal cortex. Recent evidence shows that even in the resting state, with no food present as a stimulus, the liking for food, and probably as a consequence of that body mass index, is correlated with the functional connectivity of the orbitofrontal cortex and ventromedial prefrontal cortex. This suggests that individual differences in these orbitofrontal cortex reward systems contribute to individual differences in food pleasantness and obesity. Implications of how these reward systems in the brain operate for understanding, preventing and treating obesity are described.

Body Mass Index Decrease Has a Distinct Association with Alzheimer's Disease Pathophysiology in APOE ɛ4 Carriers and Non-Carriers

BACKGROUND

Body mass index (BMI) changes may be related to Alzheimer's disease (AD) alterations, but it is unclear how the apolipoprotein E ɛ4 (APOE ɛ4) allele affects their association.

OBJECTIVE

To explore the association of BMI changes with AD pathologies in APOE ɛ4 carriers and non-carriers.

METHODS

In 862 non-demented ADNI participants with≥2 BMI measurements, we investigated the relationships between BMI slopes and longitudinal changes in amyloid-β (Aβ) accumulation, neurodegeneration and cognition, and follow-up tau deposition in different Aβ and APOE ɛ4 statuses.

RESULTS

In Aβ+ APOE ɛ4 non-carriers, faster BMI declines were associated with faster rates of Aβ accumulation (standardized β (βstd) = -0.29, p = 0.001), AD meta regions of interest (metaROI) hypometabolism (βstd = 0.23, p = 0.026), memory declines (βstd = 0.17, p = 0.029), executive function declines (βstd = 0.19, p = 0.011), and marginally faster Temporal-metaROI cortical thinning (βstd = 0.15, p = 0.067) and higher follow-up Temporal-metaROI tau deposition (βstd = -0.17, p = 0.059). Among Aβ- individuals, faster BMI decreases were related to faster Aβ accumulation (βstd = -0.25, p = 0.023) in APOE ɛ4 carriers, whereas predicted faster declines in memory and executive function in both APOE ɛ4 carriers (βstd = 0.25, p = 0.008; βstd = 0.32, p = 0.001) and APOE ɛ4 non-carriers (βstd = 0.11, p = 0.030; βstd = 0.12, p = 0.026).

CONCLUSIONS

This study highlights the significance of tracking BMI data in older adults by providing novel insights into how body weight fluctuations and APOE ɛ4 interact with AD pathology and cognitive decline.

Gender differences in the correlation between body mass index and cognitive impairment among the community-dwelling oldest-old in China: a cross-sectional study

OBJECTIVE

This study investigates gender differences in the correlation between body mass index (BMI) and cognitive impairment among Chinese community-dwelling oldest-old.

SETTING

Twenty-three provinces in China. Participants' mini-mental state examination (MMSE) scores <24 were considered cognitive impairment. Furthermore, the assessment standards of BMI status were classified into four categories: obese (BMI >30), overweight (25≤BMI≤30), normal (18.5≤BMI<25) and underweight (BMI <18.5).

PARTICIPANTS

A total of 9218 older adults (age 80+) were included from the 2018 wave of Chinese Longitudinal Healthy Longevity Study.

METHODS

Cognitive impairment, BMI and other covariates consisted of the sociodemographic variables, health behaviours and health status were collected. Cognitive impairment was assessed by the MMSE. Inverse probability weighting procedure was adopted to deal with bias due to dropout.Logistic regression was conducted to examine the correlation between BMI and cognitive impairment.

RESULTS

Among 9218 respondents, 3837 were males. Overall, the percentage of participants with cognitive impairment was 44.7%, with 32.1% among males and 53.7% among females. After controlling for other variables, males who were either overweight or underweight and females who were underweight were found to have higher risk of cognitive impairment among the oldest-old. Age, education, economic status, physical activity, activities of daily living, hypertension as well as heart disease were the predicting factors of cognitive impairment.

CONCLUSIONS

The relationship between BMI and cognitive impairment differs between male and female oldest-old, suggesting that we should pay attention to different BMI groups and adopt precise prevention strategies based on gender.

Differential impact of body mass index and leptin on baseline and longitudinal positron emission tomography measurements of the cerebral metabolic rate for glucose in amnestic mild cognitive impairment

Introduction

Several studies have suggested that greater adiposity in older adults is associated with a lower risk of Alzheimer's disease (AD) related cognitive decline, some investigators have postulated that this association may be due to the protective effects of the adipose tissue-derived hormone leptin. In this study we sought to demonstrate that higher body mass indices (BMIs) are associated with greater baseline FDG PET measurements of the regional cerebral metabolic rate for glucose (rCMRgl), a marker of local neuronal activity, slower rCMRgl declines in research participants with amnestic mild cognitive impairment (aMCI). We then sought to clarify the extent to which those relationships are attributable to cerebrospinal fluid (CSF) or plasma leptin concentrations.

Materials and methods

We used baseline PET images from 716 73 ± 8 years-old aMCI participants from the AD Neuroimaging Initiative (ADNI) of whom 453 had follow up images (≥6 months; mean follow up time 3.3 years). For the leptin analyses, we used baseline CSF samples from 81 of the participants and plasma samples from 212 of the participants.

Results

As predicted, higher baseline BMI was associated with greater baseline CMRgl measurements and slower declines within brain regions preferentially affected by AD. In contrast and independently of BMI, CSF, and plasma leptin concentrations were mainly related to less baseline CMRgl within mesocorticolimbic brain regions implicated in energy homeostasis.

Discussion

While higher BMIs are associated with greater baseline CMRgl and slower declines in persons with aMCI, these associations appear not to be primarily attributable to leptin concentrations.

Cerebral 18 F-fluorodeoxyglucose metabolism alteration of reward- and motivation-related regions in groups of different BMI classifications

OBJECTIVE

This study explored the relationship between BMI and regional cerebral glucose metabolism and explicitly detected regions with significant differences in cerebral metabolism using positron emission tomography (PET)/magnetic resonance imaging in the resting state.

METHODS

Corresponding PET images acquired from 220 participants were sorted into four groups according to Asian BMI standards: underweight, normal weight, overweight, and obesity. Pearson correlation coefficient analysis was performed to assess the association between BMI and standard uptake value. The regional cerebral glucose metabolism was measured in the fasted state. The PET images were analyzed using statistical parameter maps. One-way ANOVA was used to explore differences in the standard uptake value as an indicator of regional cerebral glucose metabolism.

RESULTS

This study found that lower cerebral glucose metabolism in reward- and motivation-related regions was accompanied by more severe obesity and that regional cerebral glucose metabolism activities were negatively correlated with BMI. In addition, more severe obesity was accompanied by a larger range of areas with significant differences independent of current dietary status.

CONCLUSIONS

These findings suggest that the reward and motivation circuits may be a factor regulating energy balance and influencing the degree of obesity.

Does acute exercise benefit emotion regulation? Electrophysiological evidence from affective ratings and implicit emotional effects on cognition

Negative affect impacts cognition, and sometimes may interfere with cognitive function. Furthermore, emotion regulation is thought to play an important role in easing the suffering from negative affect. However, whether acute exercise could ease the emotional interference caused by unconscious affect on cognitive control, remains to be investigated. To test this, we used behavioral measures combined with event-related potentials (ERPs) to specifically investigate (i) the impacts of negative affect evoked by implicit cues on conflict inhibition (Flanker task), and (ii) whether acute exercise could mitigate these effects. Furthermore, we examined (iii) the impact of acute exercise on frontal alpha asymmetry as an index of cognitive emotional down-regulation to emotional stressors. Forty young women (age range from 18 to 26) were randomly assigned to either a control group (n = 20) or an exercise group (n = 20), and a repeated-measures design with a space of one week between measures was conducted. Results demonstrated that negative Flanker trials produced larger N1 amplitude but smaller N200 amplitude than neutral trials; furthermore, acute exercise could mitigate emotional effects on N1. However, significant effects of acute exercise on the resting and responding frontal alpha asymmetry were not found. The distinct direction of the impacts of negative affect on cognition are discussed together with implications about the effects of attention allocation on exercise-enhanced emotion regulation.

Abnormal functional connectivity in the right dorsal anterior insula associated with cognitive dysfunction in patients with type 2 diabetes mellitus

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a chronic disease with a high incidence worldwide. T2DM can cause cognitive impairment, but its neuropathological basis is unclear. A variety of neuropsychiatric studies have found that abnormal functional connectivity (FC) in the central executive network (CEN), default-mode network (DMN), and salience network (SN) may be the neuropathological basis of cognitive dysfunction. The right dorsal anterior insula (dAI) is the core SN area. It plays an important role in regulating the CEN and the DMN. However, few studies have explored the relationship between cognitive impairment and FC among the right dAI, CEN, and DMN in patients with T2DM.

METHODS

Resting-state functional magnetic resonance imaging was used to investigate FC between the right dAI and the CEN and DMN in 44 patients with T2DM and 41 sex-, age-, and education-matched healthy controls, as well as its relationship with clinical/cognitive variables.

RESULTS

In patients with T2DM, FC between the right dAI and multiple brain regions of the CEN and DMN was generally decreased, and FC strength between the right dAI and the inferior frontal gyrus negatively correlated with trail making test A score (r = -0.421, p = 0.004).

CONCLUSIONS

Patients with T2DM exhibit abnormal FC between the right dAI and the CEN and DMN. This may be one of the neuromechanisms of cognitive impairment in patients with T2DM. In addition, reduced FC between the right dAI and the right inferior frontal gyrus may be related to abnormal attention regulation in patients with T2DM.

Turning the clock forward: New pharmacological and non pharmacological targets for the treatment of obesity

AIMS

Obesity and its main metabolic complication, type 2 diabetes, have attained the status of a global pandemic; there is need for novel strategies aimed at treating obesity and preventing the development of diabetes. A healthy diet and exercise are basic for treatment of obesity but often not enough. Pharmacotherapy can be helpful in maintaining compliance, ameliorating obesity-related health risks, and improving quality of life. In the last two decades, the knowledge of central and peripheral mechanisms underlying homeostatic and hedonic aspects of food intake has significantly increased. Dysregulation of one or more of these components could lead to obesity.

DATA SYNTHESIS

In order to better understand how potential innovative treatment options can affect obesity, homeostatic and reward mechanisms that regulate energy balance has been firstly illustrated. Then, an overview of potential therapeutic targets for obesity, distinguished according to the level of regulation of feeding behavior, has been provided. Moreover, several non-drug therapies have been recently tested in obesity, such as non-invasive neurostimulation: Transcranial Magnetic Stimulation or Transcranial Direct Current Stimulation. All of them are promising for obesity treatment and are almost devoid of side effects, constituting a potential resource for the prevention of metabolic diseases.

CONCLUSIONS

The plethora of current anti-obesity therapies creates the unique challenge for physicians to customize the intervention, according to the specific obesity characteristics and the intervention side effect profiles; moreover, it allows multimodal approaches addressed to treat obesity and metabolic adaptation with complementary mechanisms.

Efficacy and acceptability of noninvasive brain stimulation interventions for weight reduction in obesity: a pilot network meta-analysis

BACKGROUND/OBJECTIVES

Obesity has recently been recognized as a neurocognitive disorder involving circuits associated with the reward system and the dorsolateral prefrontal cortex (DLPFC). Noninvasive brain stimulation (NIBS) has been proposed as a strategy for the management of obesity. However, the results have been inconclusive. The aim of the current network meta-analysis (NMA) was to evaluate the efficacy and acceptability of different NIBS modalities for weight reduction in participants with obesity.

METHODS

Randomized controlled trials (RCTs) examining NIBS interventions in patients with obesity were analyzed using the frequentist model of NMA. The coprimary outcome was change in body mass index (BMI) and acceptability, which was calculated using the dropout rate.

RESULTS

Overall, the current NMA, consisting of eight RCTs, revealed that the high-frequency repetitive transcranial magnetic stimulation (TMS) over the left DLPFC was ranked to be associated with the second-largest decrease in BMI and the largest decrease in total energy intake and craving severity, whereas the high-frequency deep TMS over bilateral DLPFC and the insula was ranked to be associated with the largest decrease in BMI.

CONCLUSION

This pilot study provided a "signal" for the design of more methodologically robust and larger RCTs based on the findings of the potentially beneficial effect on weight reduction in participants with obesity by different NIBS interventions.

Repetitive deep TMS for the reduction of body weight: Bimodal effect on the functional brain connectivity in "diabesity"

BACKGROUND AND AIMS

Deep repetitive Transcranial Magnetic Stimulation (deep rTMS) over the bilateral insula and prefrontal cortex (PFC) can promote weight-loss in obesity, preventing cardiometabolic complications as Type 2 Diabetes (T2D). To investigate the changes in the functional brain integration after dTMS, we conducted a resting-state functional connectivity (rsFC) study in obesity.

METHODS AND RESULTS

This preliminary study was designed as a randomized, double-blind, sham-controlled study: 9 participants were treated with high-frequency stimulation (realTMS group), 8 were sham-treated (shamTMS group). Out of the 17 enrolled patients, 6 were affected by T2D. Resting-state fMRI scans were acquired at baseline (T0) and after the 5-week intervention (T1). Body weight was measured at three time points [T0, T1, 1-month follow-up visit (FU1)]. A mixed-model analysis showed a significant group-by-time interaction for body weight (p = .04), with a significant decrease (p < .001) in the realTMS group. The rsFC data revealed a significant increase of degree centrality for the realTMS group in the medial orbitofrontal cortex (mOFC) and a significant decrease in the occipital pole.

CONCLUSION

An increase of whole-brain functional connections of the mOFC, together with the decrease of whole-brain functional connections with the occipital pole, may reflect a brain mechanism behind weight-loss through a diminished reactivity to bottom-up visual-sensory processes in favor of increased reliance on top-down decision-making processes.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov NCT03009695.

Brain Glucose Metabolism in Health, Obesity, and Cognitive Decline-Does Insulin Have Anything to Do with It? A Narrative Review

Imaging brain glucose metabolism with fluorine-labelled fluorodeoxyglucose ([¹⁸F]-FDG) positron emission tomography (PET) has long been utilized to aid the diagnosis of memory disorders, in particular in differentiating Alzheimer’s disease (AD) from other neurological conditions causing cognitive decline. The interest for studying brain glucose metabolism in the context of metabolic disorders has arisen more recently. Obesity and type 2 diabetes—two diseases characterized by systemic insulin resistance—are associated with an increased risk for AD. Along with the well-defined patterns of fasting [¹⁸F]-FDG-PET changes that occur in AD, recent evidence has shown alterations in fasting and insulin-stimulated brain glucose metabolism also in obesity and systemic insulin resistance. Thus, it is important to clarify whether changes in brain glucose metabolism are just an epiphenomenon of the pathophysiology of the metabolic and neurologic disorders, or a crucial determinant of their pathophysiologic cascade. In this review, we discuss the current knowledge regarding alterations in brain glucose metabolism, studied with [¹⁸F]-FDG-PET from metabolic disorders to AD, with a special focus on how manipulation of insulin levels affects brain glucose metabolism in health and in systemic insulin resistance. A better understanding of alterations in brain glucose metabolism in health, obesity, and neurodegeneration, and the relationships between insulin resistance and central nervous system glucose metabolism may be an important step for the battle against metabolic and cognitive disorders.

Brain Molecular Connectivity in Neurodegenerative Conditions

Positron emission tomography (PET) allows for the in vivo assessment of early brain functional and molecular changes in neurodegenerative conditions, representing a unique tool in the diagnostic workup. The increased use of multivariate PET imaging analysis approaches has provided the chance to investigate regional molecular processes and long-distance brain circuit functional interactions in the last decade. PET metabolic and neurotransmission connectome can reveal brain region interactions. This review is an overview of concepts and methods for PET molecular and metabolic covariance assessment with evidence in neurodegenerative conditions, including Alzheimer’s disease and Lewy bodies disease spectrum. We highlight the effects of environmental and biological factors on brain network organization. All of the above might contribute to innovative diagnostic tools and potential disease-modifying interventions.

The impact of acute exercise on implicit cognitive reappraisal in association with left dorsolateral prefronta activation: A fNIRS study

Despite findings showing that acute exercise may help enhance emotion regulation, the neurophysiological mechanisms of these effects remain poorly understood. In this study, we examined whether acute exercise influences cognitive emotion regulation, and, in particular, an implicit cognitive reappraisal. Twenty sedentary young women were randomly assigned to either a control group (n = 10) or an exercise group (n = 10). Participants underwent an implicit cognitive reappraisal task twice, before and after the 30-min acute exercise or control, alongside functional near-infrared spectroscopy recordings (NIRS). The left dorsolateral prefrontal cortex (dlPFC) and left orbital frontal cortex (OFC) were activated during implicit cognitive reappraisal at baseline, but only the left dlPFC activation was linked with behavioral performance. Acute exercise enhanced the activation of these regions, reflective of the partial neural bases of implicit cognitive reappraisal, in the left dlPFC and left OFC, but did not alter the behavioral performance. Results also showed that acute exercise moderated the positive effect of left dlPFC activation on implicit cognitive reappraisal performance; specifically, this effect was stronger in the exercise group. In conclusion, the enhanced activation of the left dlPFC by acute exercise and the increased link between behavioral performance and its neural indices may point to acute exercise as a promoter of implicit cognitive reappraisal.

Body mass index and mild cognitive impairment among rural older adults in China: the moderating roles of gender and age

BACKGROUND

Evidence concerning the association between body mass index (BMI) and cognitive function among older people is inconsistent. This study aimed to investigate gender and age as moderators in association between BMI and mild cognitive impairment (MCI) among rural older adults.

METHODS

Data were derived from the 2019 Health Service for Rural Elderly Families Survey in Shandong, China. In total, 3242 people aged 60 years and above were included in the analysis. Multilevel mixed-effects logistic regression was used to examine the moderating roles of gender and age, then further to explore the relationship between BMI and MCI.

RESULTS

There were 601 (18.5%) participants with MCI. Compared with normal BMI group, low BMI group had a higher risk of MCI among older people [adjusted odds ratio (aOR) = 2.08, 95% confidence interval (CI): 1.26-3.44], women (aOR = 2.06, 95% CI: 1.35-3.12), or the older elderly aged ≥75 years old (aOR = 3.20, 95% CI: 1.34-7.45). This effect remained statistically significant among older women (aOR = 3.38, 95% CI: 1.69-6.73). Among older men, elevated BMI group had a higher risk of MCI (aOR = 2.32, 95% CI: 1.17-4.61) than normal BMI group.

CONCLUSIONS

Gender and age moderated the association between BMI and MCI among Chinese rural older adults. Older women with low BMI were more likely to have MCI, but older men with elevated BMI were more likely to have MCI. These findings suggest rural community managers strengthen the health management by grouping the weight of older people to prevent the risk of dementia.

Obesity impacts brain metabolism and structure independently of amyloid and tau pathology in healthy elderly

AIMS/HYPOTHESIS

Midlife obesity is a risk factor for dementia. We investigated the impact of obesity on brain structure, metabolism, and cerebrospinal fluid (CSF) core Alzheimer's disease (AD) biomarkers in healthy elderly.

METHODS

We selected controls from ADNI2 with CSF AD biomarkers and/or fluorodeoxyglucose positron emission tomography (FDG-PET) and 3T-MRI. We measured cortical thickness, FDG uptake, and CSF amyloid beta (Aβ)1-42, p-tau, and t-tau levels. We performed regression analyses between these biomarkers and body mass index (BMI).

RESULTS

We included 201 individuals (mean age 73.5 years, mean BMI 27.4 kg/m2). Higher BMI was related to less cortical thickness and higher metabolism in brain areas typically not involved in AD (family-wise error [FWE] <0.05), but not to AD CSF biomarkers. It is notable that the impact of obesity on brain metabolism and structure was also found in amyloid negative individuals.

CONCLUSIONS/INTERPRETATION

In the cognitively unimpaired elderly, obesity has differential effects on brain metabolism and structure independent of an underlying AD pathophysiology.

Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors

Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic information based on causal and/or statistical data and therefore reveal hidden dependencies between symptoms and illnesses. In this paper we give a detailed overview of the recent machine learning research and its applications for predicting cognitive diseases, especially the Alzheimer's disease, mild cognitive impairment and the Parkinson's disease. We survey different state-of-the-art methodological approaches, data sources and public data, and provide their comparative analysis. We conclude by identifying the open problems within the field that include an early detection of the cognitive diseases and inclusion of machine learning tools into diagnostic practice and therapy planning.