Body mass index is associated with smaller medial temporal lobe volume in those at risk for Alzheimer's disease

Sex-Specific Association of Body Mass Index with Hippocampal Subfield Volume and Cognitive Function in Non-Demented Chinese Older Adults

Recent research suggests a possible association between midlife obesity and an increased risk of dementia in later life. However, the underlying mechanisms remain unclear. Little is known about the relationship between body mass index (BMI) and hippocampal subfield atrophy. In this study, we aimed to explore the associations between BMI and hippocampal subfield volumes and cognitive function in non-demented Chinese older adults. Hippocampal volumes were assessed using structural magnetic resonance imaging. Cognitive function was evaluated using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). A total of 66 participants were included in the final analysis, with 35 females and 31 males. We observed a significant correlation between BMI and the hippocampal fissure volume in older females. In addition, there was a negative association between BMI and the RBANS total scale score, the coding score, and the story recall score, whereas no significant correlations were observed in older males. In conclusion, our findings revealed sex-specific associations between BMI and hippocampal subfield volumes and cognitive performance, providing valuable insights into the development of effective interventions for the early prevention of cognitive decline.

The Association of Body Mass Index with Cognition and Alzheimer's Disease Biomarkers in the Elderly with Different Cognitive Status: A Study from the Alzheimer's Disease Neuroimaging Initiative Database

Background

The association of body mass index (BMI) with cognition and Alzheimer's disease (AD) biomarkers of the elderly remains inconclusive.

Objective

To investigate the relationship between BMI and cognition as well as AD biomarkers in the elderly with different cognitive status.

Methods

Participants with cognitively normal (CN) were included as the CN group. Participants with mild cognitive impairment and mild dementia were included as the cognitive impairment (CI) group. The relationship between BMI and AD biomarkers (cerebrospinal fluid Aβ42 and p-tau181, hippocampal volume [HV]), global cognition (Mini-Mental State Examination [MMSE]), memory, and executive function were explored.

Results

In the CI group, BMI was associated with MMSE (β= 0.03, p = 0.009), Aβ42 (β= 0.006, p = 0.029), p-tau181/Aβ42 ratio (β= -0.001, p = 0.011), and HV (β= 0.05, p < 0.001). However in the CN group, BMI exhibited associations with p-tau181 (β= 0.012, p = 0.014) and memory composite score (β= -0.04, p = 0.038), but not with p-tau181/Aβ42 ratio and HV. Moreover, mediation analysis showed that in the CI group, the positive effect of BMI on HV and MMSE score was partially mediated by diastolic blood pressure.

Conclusion

The association of BMI with cognition and AD biomarkers varies across different cognitive status. In particular, a lower BMI was associated with worse cognition, higher Aβ burden, and lower HV in individuals with CI. Clinical practice should strengthen the monitoring and management of BMI in patients with AD.

Thirty Risk Factors for Alzheimer's Disease Unified by a Common Neuroimmune-Neuroinflammation Mechanism

One of the major obstacles confronting the formulation of a mechanistic understanding for Alzheimer's disease (AD) is its immense complexity-a complexity that traverses the full structural and phenomenological spectrum, including molecular, macromolecular, cellular, neurological and behavioural processes. This complexity is reflected by the equally complex diversity of risk factors associated with AD. However, more than merely mirroring disease complexity, risk factors also provide fundamental insights into the aetiology and pathogenesis of AD as a neurodegenerative disorder since they are central to disease initiation and subsequent propagation. Based on a systematic literature assessment, this review identified 30 risk factors for AD and then extended the analysis to further identify neuroinflammation as a unifying mechanism present in all 30 risk factors. Although other mechanisms (e.g., vasculopathy, proteopathy) were present in multiple risk factors, dysfunction of the neuroimmune-neuroinflammation axis was uniquely central to all 30 identified risk factors. Though the nature of the neuroinflammatory involvement varied, the activation of microglia and the release of pro-inflammatory cytokines were a common pathway shared by all risk factors. This observation provides further evidence for the importance of immunopathic mechanisms in the aetiopathogenesis of AD.

Obesity reduces hippocampal structure and function in older African Americans with the APOE-ε4 Alzheimer's disease risk allele

Introduction

Excess body weight and Alzheimer's disease (AD) disproportionately affect older African Americans. While mid-life obesity increases risk for AD, few data exist on the relationship between late-life obesity and AD, or how obesity-based and genetic risk for AD interact. Although the APOE-ε4 allele confers a strong genetic risk for AD, it is unclear if late-life obesity poses a greater risk for APOE-ε4 carriers compared to non-carriers. Here we assessed: (1) the influence of body mass index (BMI) (normal; overweight; class 1 obese; ≥ class 2 obese) on cognitive and structural MRI measures of AD risk; and (2) the interaction between BMI and APOE-ε4 in older African Americans.

Methods

Seventy cognitively normal older African American participants (Mage = 69.50 years; MBMI = 31.01 kg/m2; 39% APOE-ε4 allele carriers; 86% female) completed anthropometric measurements, physical assessments, saliva collection for APOE-ε4 genotyping, cognitive testing, health and lifestyle questionnaires, and structural neuroimaging [volume/surface area (SA) for medial temporal lobe subregions and hippocampal subfields]. Covariates included age, sex, education, literacy, depressive symptomology, and estimated aerobic fitness.

Results

Using ANCOVAs, we observed that individuals who were overweight demonstrated better hippocampal cognitive function (generalization of learning: a sensitive marker of preclinical AD) than individuals with normal BMI, p = 0.016, ηp2 = 0.18. However, individuals in the obese categories who were APOE-ε4 non-carriers had larger hippocampal subfield cornu Ammonis region 1 (CA1) volumes, while those who were APOE-ε4 carriers had smaller CA1 volumes, p = 0.003, ηp2 = 0.23.

Discussion

Thus, being overweight by BMI standards may preserve hippocampal function, but obesity reduces hippocampal structure and function in older African Americans with the APOE-ε4 Alzheimer's disease risk allele.

Obesity-Associated Neurodegeneration Pattern Mimics Alzheimer's Disease in an Observational Cohort Study

BACKGROUND

Excess weight in adulthood leads to health complications such as diabetes, hypertension, or dyslipidemia. Recently, excess weight has also been related to brain atrophy and cognitive decline. Reports show that obesity is linked with Alzheimer's disease (AD)-related changes, such as cerebrovascular damage or amyloid-β accumulation. However, to date no research has conducted a direct comparison between brain atrophy patterns in AD and obesity.

OBJECTIVE

Here, we compared patterns of brain atrophy and amyloid-β/tau protein accumulation in obesity and AD using a sample of over 1,300 individuals from four groups: AD patients, healthy controls, obese otherwise healthy individuals, and lean individuals.

METHODS

We age- and sex-matched all groups to the AD-patients group and created cortical thickness maps of AD and obesity. This was done by comparing AD patients with healthy controls, and obese individuals with lean individuals. We then compared the AD and obesity maps using correlation analyses and permutation-based tests that account for spatial autocorrelation. Similarly, we compared obesity brain maps with amyloid-β and tau protein maps from other studies.

RESULTS

Obesity maps were highly correlated with AD maps but were not correlated with amyloid-β/tau protein maps. This effect was not accounted for by the presence of obesity in the AD group.

CONCLUSION

Our research confirms that obesity-related grey matter atrophy resembles that of AD. Excess weight management could lead to improved health outcomes, slow down cognitive decline in aging, and lower the risk for AD.

Lobular Difference in Heritability of Brain Atrophy among Elderly Japanese: A Twin Study

Background and Objectives: Brain atrophy is related to cognitive decline. However, the heritability of brain atrophy has not been fully investigated in the Eastern Asian population. Materials and Methods: Brain imaging of 74 Japanese twins registered in the Osaka University Twin Registry was conducted with voxel-based morphometry SPM12 and was processed by individual voxel-based morphometry adjusting covariates (iVAC) toolbox. The atrophy of the measured lobes was obtained by comparing the focal volume to the average of healthy subjects. Classical twin analysis was used to measure the heritability of its z-scores. Results: The heritability of brain atrophy ranged from 0.23 to 0.97, depending upon the lobes. When adjusted to age, high heritability was reported in the frontal, frontal-temporal, and parietal lobes, but the heritability in other lobes was lower than 0.70. Conclusions: This study revealed a relatively lower heritability in brain atrophy compared to other ethnicities. This result suggests a significant environmental impact on the susceptibility of brain atrophy the Japanese. Therefore, environmental factors may have more influence on the Japanese than in other populations.

Obesity as a Risk Factor for Dementia and Alzheimer's Disease: The Role of Leptin

Obesity is a growing worldwide health problem, affecting many people due to excessive saturated fat consumption, lack of exercise, or a sedentary lifestyle. Leptin is an adipokine secreted by adipose tissue that increases in obesity and has central actions not only at the hypothalamic level but also in other regions and nuclei of the central nervous system (CNS) such as the cerebral cortex and hippocampus. These regions express the long form of leptin receptor LepRb, which is the unique leptin receptor capable of transmitting complete leptin signaling, and are the first regions to be affected by chronic neurocognitive deficits, such as mild cognitive impairment (MCI) and Alzheimer's Disease (AD). In this review, we discuss different leptin resistance mechanisms that could be implicated in increasing the risk of developing AD, as leptin resistance is frequently associated with obesity, which is a chronic low-grade inflammatory state, and obesity is considered a risk factor for AD. Key players of leptin resistance are SOCS3, PTP1B, and TCPTP whose signalling is related to inflammation and could be worsened in AD. However, some data are controversial, and it is necessary to further investigate the underlying mechanisms of the AD-causing pathological processes and how altered leptin signalling affects such processes.

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.

METHODS

We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.

RESULTS

Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

DISCUSSION

ADNI has had a profound impact in improving clinical trials for AD.

Body Mass Index and Polygenic Risk for Alzheimer's Disease Predict Conversion to Alzheimer's Disease

Body mass index (BMI) is a risk factor for Alzheimer's disease (AD) although the relationship is complex. Obesity in midlife is associated with increased risk for AD, whereas evidence supports both higher and lower BMI increasing risk for AD in late life. This study examined the influence of individual differences in genetic risk for AD to further clarify the relationship between late-life BMI and conversion to AD. Participants included 52 individuals diagnosed as having mild cognitive impairment (MCI) at baseline who converted to AD within 24 months and 52 matched MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. BMI was measured at baseline. Genetic risk for AD was assessed via genome-wide polygenic risk scores. Conditional logistic regression models were run to determine if BMI and polygenic risk predicted conversion to AD. Results showed an interaction between BMI and genetic risk, such that individuals with lower BMI and higher polygenic risk were more likely to convert to AD relative to individuals with higher BMI. These results remained significant after adjusting for cerebrospinal fluid biomarkers of AD. Exploratory sex-stratified analyses revealed this relationship only remained significant in males. These results show that higher genetic risk in the context of lower BMI predicts conversion to AD in the next 24 months, particularly among males. These findings suggest that genetic risk for AD in the context of lower BMI may serve as a prodromal risk factor for future conversion to AD.

Sarcopenia is associated with incident Alzheimer's dementia, mild cognitive impairment, and cognitive decline

OBJECTIVE

We examined whether sarcopenia is associated with the occurrence of late-life cognitive impairment.

METHODS

Nondemented older adults (N = 1175) underwent annual testing with 17 cognitive tests summarized as a global cognitive score. A composite sarcopenia score was constructed based on muscle mass measured with bioelectrical impedance and muscle function based on grip strength. Cox proportional hazard models were employed to examine associations of sarcopenia with incident Alzheimer's dementia (AD) and incident mild cognitive impairment (MCI). Linear mixed-effect models determined the association of sarcopenia with cognitive decline. All models controlled for age, sex, education, race, and height squared.

RESULTS

Average follow-up was 5.6 years. More severe sarcopenia at baseline was associated with a higher risk of incident AD (hazard ratio [HR], 1.50 [95% confidence interval 1.20-1.86]; p < 0.001) and of MCI (1.21 [1.01-1.45]; 0.04) and a faster rate of cognitive decline (estimate = -0.013; p = 0.01). Analyses of the individual components of sarcopenia showed that muscle function was associated with incident AD, incident MCI, and cognitive decline with and without a term for lean muscle mass in the model. In contrast, lean muscle mass was not associated with incident cognitive impairment or cognitive decline when a term for muscle function was included in the model.

CONCLUSIONS

Poor muscle function, but not reduced lean muscle mass, drives the association of sarcopenia with late-life cognitive impairment. Further work is needed to identify features of muscle structure, which may increase the specificity of sarcopenia for identifying older adults at risk for late-life cognitive impairment.

Polygenic Score Models for Alzheimer's Disease: From Research to Clinical Applications

The high prevalence of Alzheimer's disease (AD) among the elderly population and its lack of effective treatments make this disease a critical threat to human health. Recent epidemiological and genetics studies have revealed the polygenic nature of the disease, which is possibly explainable by a polygenic score model that considers multiple genetic risks. Here, we systemically review the rationale and methods used to construct polygenic score models for studying AD. We also discuss the associations of polygenic risk scores (PRSs) with clinical outcomes, brain imaging findings, and biochemical biomarkers from both the brain and peripheral system. Finally, we discuss the possibility of incorporating polygenic score models into research and clinical practice along with potential challenges.