Sex differences in brain functional connectivity of hippocampus in mild cognitive impairment

Sex differences on tau, astrocytic, and neurodegenerative plasma biomarkers

BackgroundSex differences have consistently been identified on autopsy, neuroimaging, and cerebrospinal fluid outcomes related to Alzheimer's disease (AD), but the exact mechanisms for these associations are unclear. Blood-based biomarkers are practical alternatives for the investigation of mechanisms of AD, in addition to accurate disease detection and monitoring.ObjectiveThe objective of this study was to examine sex differences across a panel of blood-based plasma biomarkers in participants with and without cognitive impairment due to AD.MethodsPlasma samples were collected from 567 participants from across the AD diagnostic continuum (i.e., normal cognition (NC), mild cognitive impairment (MCI), and dementia) and analyzed for glial fibrillary acidic protein (GFAP), neurofilament light (NfL), phosphorylated tau at threonine 181 (p-tau181), and total tau (t-tau). Baseline and longitudinal analyses evaluated for any significant associations between sex and AD-related plasma biomarkers.ResultsFemales were found to have higher plasma GFAP compared to males at baseline regardless of cognitive diagnosis. Among those with AD dementia, females were also found to have higher NfL levels compared to males. Longitudinal analyses found that higher plasma NfL at baseline was associated with an increased risk of worsening AD dementia status only in females. No significant findings were observed for p-tau181 or t-tau.ConclusionsThis study found significant sex differences in plasma biomarkers of GFAP and NfL. Further research is needed to better understand the underlying mechanisms mediating these differences.

Resting-state neural dynamics changes in older adults with post-COVID syndrome and the modulatory effect of cognitive training and sex

Post-COVID syndrome manifests with numerous neurological and cognitive symptoms, the precise origins of which are still not fully understood. As females and older adults are more susceptible to developing this condition, our study aimed to investigate how post-COVID syndrome alters intrinsic brain dynamics in older adults and whether biological sex and cognitive training might modulate these effects, with a specific focus on older females. The participants, aged between 60 and 75 years, were divided into three experimental groups: healthy old female, post-COVID old female and post-COVID old male. They underwent an adaptive task-switching training protocol. We analysed multiscale entropy and spectral power density of resting-state EEG data collected before and after the training to assess neural signal complexity and oscillatory power, respectively. We found no difference between post-COVID females and males before training, indicating that post-COVID similarly affected both sexes. However, cognitive training was effective only in post-COVID females and not in males, by modulating local neural processing capacity. This improvement was further evidenced by comparing healthy and post-COVID females, wherein the latter group showed increased finer timescale entropy (1-30 ms) and higher frequency band power (11-40 Hz) before training, but these differences disappeared following cognitive training. Our results suggest that in older adults with post-COVID syndrome, there is a pronounced shift from more global to local neural processing, potentially contributing to accelerated neural aging in this condition. However, cognitive training seems to offer a promising intervention method for modulating these changes in brain dynamics, especially among females.

Graph Neural Network Learning on the Pediatric Structural Connectome

PURPOSE

Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has proven useful for studying cognitive function and impairment. While graph neural networks (GNNs), specifically graph convolutional networks (GCNs), have gained popularity lately for their effectiveness in learning on graph data, achieving strong performance in adult sex classification tasks, their application to pediatric populations remains unexplored. We seek to characterize the capacity for GNN models to learn connectomic patterns on pediatric data through an exploration of training techniques and architectural design choices.

METHODS

Two datasets comprising an adult BRIGHT dataset (N = 147 Hodgkin's lymphoma survivors and N = 162 age similar controls) and a pediatric Human Connectome Project in Development (HCP-D) dataset (N = 135 healthy subjects) were utilized. Two GNN models (GCN simple and GCN residual), a deep neural network (multi-layer perceptron), and two standard machine learning models (random forest and support vector machine) were trained. Architecture exploration experiments were conducted to evaluate the impact of network depth, pooling techniques, and skip connections on the ability of GNN models to capture connectomic patterns. Models were assessed across a range of metrics including accuracy, AUC score, and adversarial robustness.

RESULTS

GNNs outperformed other models across both populations. Notably, adult GNN models achieved 85.1% accuracy in sex classification on unseen adult participants, consistent with prior studies. The extension of the adult models to the pediatric dataset and training on the smaller pediatric dataset were sub-optimal in their performance. Using adult data to augment pediatric models, the best GNN achieved comparable accuracy across unseen pediatric (83.0%) and adult (81.3%) participants. Adversarial sensitivity experiments showed that the simple GCN remained the most robust to perturbations, followed by the multi-layer perceptron and the residual GCN.

CONCLUSIONS

These findings underscore the potential of GNNs in advancing our understanding of sex-specific neurological development and disorders and highlight the importance of data augmentation in overcoming challenges associated with small pediatric datasets. Further, they highlight relevant tradeoffs in the design landscape of connectomic GNNs. For example, while the simpler GNN model tested exhibits marginally worse accuracy and AUC scores in comparison to the more complex residual GNN, it demonstrates a higher degree of adversarial robustness.

Cognitive impairment caused by compromised hepatic ketogenesis is prevented by endurance exercise

Extensive research has demonstrated endurance exercise to be neuroprotective. Whether these neuroprotective benefits are mediated, in part, by hepatic ketone production remains unclear. To investigate the role of hepatic ketone production on brain health during exercise, healthy 6-month-old female rats underwent viral knockdown of the rate-limiting enzyme in the liver that catalyses the first reaction in ketogenesis: 3-hydroxymethylglutaryl-CoA synthase 2 (HMGCS2). Rats were then subjected to either a bout of acute exercise or 4 weeks of chronic treadmill running (5 days/week) and cognitive behavioural testing. Acute exercise elevated ketone plasma concentration 1 h following exercise. Hepatic HMGCS2 knockdown, verified by protein expression, reduced ketone plasma concentration 1 h after acute exercise and 48 h after chronic exercise. Proteomic analysis and enrichment of the frontal cortex revealed hepatic HMGCS2 knockdown reduced markers of mitochondrial function 1 h after acute exercise. HMGCS2 knockdown significantly reduced state 3 complex I + II respiration in isolated mitochondria from the frontal cortex after chronic exercise. Spatial memory and protein markers of synaptic plasticity were significantly reduced by HMGCS2 knockdown. These deficiencies were prevented by chronic endurance exercise training. In summary, these are the first data to propose that hepatic ketogenesis is required to maintain cognition and mitochondrial function, irrespective of training status, and that endurance exercise can overcome neuropathology caused by insufficient hepatic ketogenesis. These results establish a mechanistic link between liver and brain health that enhance our understanding of how peripheral tissue metabolism influences brain health. KEY POINTS: Decades of literature demonstrate endurance exercise to be neuroprotective. Whether neuroprotective benefits are mediated, in part, by hepatic ketone production remains unclear. This study provides the first set of data that suggest hepatic ketogenesis is required to maintain cognition, synaptic plasticity and mitochondrial function. These data indicate endurance exercise can protect against cognitive decline caused by compromised hepatic ketogenesis. These results establish a mechanistic link between liver and brain function, prompting further investigation of how hepatic metabolism influences brain health.

Sex differences in interacting genetic and functional connectivity biomarkers in Alzheimer's disease

As of 2023, it is estimated that 6.7 million individuals in the United States live with Alzheimer's disease (AD). Prior research indicates that AD disproportionality affects females; females have a greater incidence rate, perform worse on a variety of neuropsychological tasks, and have greater total brain atrophy. Recent research shows that hippocampal functional connectivity differs by sex and may be related to the observed sex differences in AD, and apolipoprotein E (ApoE) ε4 carriers have reduced hippocampal functional connectivity. The purpose of this study was to determine if the ApoE genotype plays a role in the observed sex differences in hippocampal functional connectivity in Alzheimer's disease. The resting state fMRI and T2 MRI of individuals with AD (n = 30, female = 15) and cognitively normal individuals (n = 30, female = 15) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed using the functional connectivity toolbox (CONN). Our results demonstrated intrahippocampal functional connectivity differed between those without an ε4 allele and those with at least one ε4 allele in each group. Additionally, intrahippocampal functional connectivity differed only by sex when Alzheimer's participants had at least one ε4 allele. These results improve our current understanding of the role of the interacting relationship between sex, ApoE genotype, and hippocampal function in AD. Understanding these biomarkers may aid in the development of sex-specific interventions for improved AD treatment.

Structural and functional sex differences in medial temporal lobe subregions at midlife

BACKGROUND

Research has increasingly recognized sex differences in aging and Alzheimer's Disease (AD) susceptibility. However, sex effects on the medial temporal lobe (MTL), a crucial region affected by aging and AD, remain poorly understood when it comes to the intricacies of morphology and functional connectivity. This study aimed to systematically analyze structural and functional connectivity among MTL subregions, which are known to exhibit documented morphological sex differences, during midlife, occurring before the putative pivotal age of cerebral decline. The study sought to explore the hypothesis that these differences in MTL subregion volumes would manifest in sex-related functional distinctions within the broader brain network.

METHODS

201 cognitively unimpaired adults were included and stratified into four groups according to age and sex (i.e., Women and Men aged 40-50 and 50-60). These participants underwent comprehensive high-resolution structural MRI as well as resting-state functional MRI (rsfMRI). Utilizing established automated segmentation, we delineated MTL subregions and assessed morphological differences through an ANOVA. Subsequently, the CONN toolbox was employed for conducting ROI-to-ROI and Fractional Amplitude of Low-Frequency Fluctuations (fALFF) analyses to investigate functional connectivity within the specific MTL subregions among these distinct groups.

RESULTS

Significant differences in volumetric measurements were found primarily between women aged 40-50 and men of all ages, in the posterior hippocampus (pHPC) and the parahippocampal (PHC) cortex (p < 0.001), and, to a lesser extent, between women aged 50-60 and men of all ages (p < 0.05). Other distinctions were observed, but no significant differences in connectivity patterns or fALFF scores were detected between these groups.

DISCUSSION

Despite notable sex-related morphological differences in the posterior HPC and PHC regions, women and men appear to share a common pattern of brain connectivity at midlife. Longitudinal analyses are necessary to assess if midlife morphological sex differences in the MTL produce functional changes over time and thus, their potential role in cerebral decline.

CLINICAL TRIAL NUMBER

Not applicable.

Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases

Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer's Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN.

Local functional connectivity abnormalities in mild cognitive impairment and Alzheimer's disease: A meta-analytic investigation using minimum Bayes factor activation likelihood estimation

Functional magnetic resonance imaging research employing regional homogeneity (ReHo) analysis has uncovered aberrant local brain connectivity in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) in comparison with healthy controls. However, the precise localization, extent, and possible overlap of these aberrations are still not fully understood. To bridge this gap, we applied a novel meta-analytic and Bayesian method (minimum Bayes Factor Activation Likelihood Estimation, mBF-ALE) for a systematic exploration of local functional connectivity alterations in MCI and AD brains. We extracted ReHo data via a standardized MEDLINE database search, which included 35 peer-reviewed experiments, 1,256 individuals with AD or MCI, 1,118 healthy controls, and 205 x-y-z coordinates of ReHo variation. We then separated the data into two distinct datasets: one for MCI and the other for AD. Two mBF-ALE analyses were conducted, thresholded at "very strong evidence" (mBF ≥ 150), with a minimum cluster size of 200 mm³. We also assessed the spatial consistency and sensitivity of our Bayesian results using the canonical version of the ALE algorithm. For MCI, we observed two clusters of ReHo decrease and one of ReHo increase. Decreased local connectivity was notable in the left precuneus (Brodmann area - BA 7) and left inferior temporal gyrus (BA 20), while increased connectivity was evident in the right parahippocampal gyrus (BA 36). The canonical ALE confirmed these locations, except for the inferior temporal gyrus. In AD, one cluster each of ReHo decrease and increase were found, with decreased connectivity in the right posterior cingulate cortex (BA 30 extending to BA 23) and increased connectivity in the left posterior cingulate cortex (BA 31). These locations were confirmed by the canonical ALE. The identification of these distinct functional connectivity patterns sheds new light on the complex pathophysiology of MCI and AD, offering promising directions for future neuroimaging-based interventions. Additionally, the use of a Bayesian framework for statistical thresholding enhances the robustness of neuroimaging meta-analyses, broadening its applicability to small datasets.

Sex differences in the relationship between depression and Alzheimer's disease-mechanisms, genetics, and therapeutic opportunities

Depression and Alzheimer's disease (AD) are prevalent neuropsychiatric disorders with intriguing epidemiological overlaps. Their interrelation has recently garnered widespread attention. Empirical evidence indicates that depressive disorders significantly contribute to AD risk, and approximately a quarter of AD patients have comorbid major depressive disorder, which underscores the bidirectional link between AD and depression. A growing body of evidence substantiates pervasive sex differences in both AD and depression: both conditions exhibit a higher incidence among women than among men. However, the available literature on this topic is somewhat fragmented, with no comprehensive review that delineates sex disparities in the depression-AD correlation. In this review, we bridge these gaps by summarizing recent progress in understanding sex-based differences in mechanisms, genetics, and therapeutic prospects for depression and AD. Additionally, we outline key challenges in the field, holding potential for improving treatment precision and efficacy tailored to male and female patients' distinct needs.

The Granular Retrosplenial Cortex Is Necessary in Male Rats for Object-Location Associative Learning and Memory, But Not Spatial Working Memory or Visual Discrimination and Reversal, in the Touchscreen Operant Chamber

The retrosplenial cortex (RSC) is a hub of diverse afferent and efferent projections thought to be involved in associative learning. RSC shows early pathology in mild cognitive impairment and Alzheimer's disease (AD), which impairs associative learning. To understand and develop therapies for diseases such as AD, animal models are essential. Given the importance of human RSC in object-location associative learning and the success of object-location associative paradigms in human studies and in the clinic, it would be of considerable value to establish a translational model of object-location learning for the rodent. For this reason, we sought to test the role of RSC in object-location learning in male rats using the object-location paired-associates learning (PAL) touchscreen task. First, increased cFos immunoreactivity was observed in granular RSC following PAL training when compared with extended pretraining controls. Following this, RSC lesions following PAL acquisition were used to explore the necessity of the RSC in object-location associative learning and memory and two tasks involving only one modality: trial-unique nonmatching-to-location for spatial working memory and pairwise visual discrimination/reversal. RSC lesions impaired both memory for learned paired-associates and learning of new object-location associations but did not affect performance in either the spatial or visual single-modality tasks. These findings provide evidence that RSC is necessary for object-location learning and less so for learning and memory involving the individual modalities therein.

Multi-Head Graph Convolutional Network for Structural Connectome Classification

We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain-connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, thoroughly capturing representations from the input data. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.

The prevalence of mild cognitive impairment and dementia among rural dwellers: A systematic review and meta-analysis

The mild cognitive impairment (MCI) and dementia in rural areas are increasingly attracting public attention. However, their prevalence is still unclear. This study aims to reveal the distribution of MCI and dementia in rural areas. We systematically searched PubMed, Web of Science, Embase, and PsycINFO up to June 2023 for cohort and cross-sectional studies. Meta-analysis was conducted using random-effects models to evaluate the prevalence of MCI and dementia. Thirty-five studies with 16,936 participants met the inclusion criteria. The pooled prevalence of MCI and dementia was 27 % (n = 12, 95 %CI = 0.21-0.32, I2 = 99.5 %, P < 0.001) and 7 % (n = 27, 95 %CI = 0.05-0.08, I2 = 99.30 %, P < 0.001), respectively. Subgroup analyses revealed that aged 60 years or older [(MCI: 29 %, 95 %CI = 0.20-0.38, I2 = 99.7 %, P < 0.001), (dementia: 9 % (95 %CI = 0.06-0.12, I2 = 99 %, P < 0.001)], female [(MCI: 29 %, 95 %CI = 0.19-0.40, I2 = 99.3 %, P < 0.001), (dementia: 7 %, 95 % CI = 0.04-0.12, I2 = 98.66 %, P < 0.001)], a-MCI (19 %, 95 %CI = 0.12-0.26, I2 = 97.62 %, P < 0.001) and AD (4 %, 95 %CI = 0.02-0.05, I2 = 98.60 %, P < 0.001) showed higher prevalence. The prevalence of MCI and dementia in rural China was 23 % (95 %CI = 0.18-0.29, I2 = 99.5 %, P < 0.001) and 6 % (95 %CI = 0.04-0.08, I2 = 99.6 %, P < 0.001), respectively. Implementing cognitive impairment screening and intervention measures is necessary to improve the cognitive function of the rural population.

Sex difference in brain functional connectivity of hippocampus in Alzheimer's disease

Alzheimer's disease (AD), affecting nearly 6.5 million people, is the fifth leading cause of death in individuals 65 years or older in the USA. Prior research has shown that AD disproportionality affects females; females have a greater incidence rate, perform worse on a variety of neuropsychological tasks, and have greater total brain atrophy. Recent research has linked these sex differences to neuroimaging markers of brain pathology, such as hippocampal volumes. Specifically, research from our lab found that functional connectivity from the hippocampus to the precuneus cortex and brain stem was significantly stronger in males than in females with mild cognitive impairment. The aim of this study was to extend our understanding to individuals with AD and to determine if these potential sex-specific functional connectivity biomarkers extend through different disease stages. The resting state fMRI and T2 MRI of cognitively normal individuals (n = 32, female = 16) and individuals with AD (n = 32, female = 16) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed using the Functional Connectivity Toolbox (CONN). Our results demonstrate that males had a significantly stronger interhemispheric functional connectivity between the left and right hippocampus compared to females. These results improve our current understanding of the role of the hippocampus in sex differences in AD. Understanding the contribution of impaired functional connectivity sex differences may aid in the development of sex-specific precision medicine for improved AD treatment.

Multimodal diagnosis model of Alzheimer's disease based on improved Transformer

PURPOSE

Recent technological advancements in data acquisition tools allowed neuroscientists to acquire different modality data to diagnosis Alzheimer's disease (AD). However, how to fuse these enormous amount different modality data to improve recognizing rate and find significance brain regions is still challenging.

METHODS

The algorithm used multimodal medical images [structural magnetic resonance imaging (sMRI) and positron emission tomography (PET)] as experimental data. Deep feature representations of sMRI and PET images are extracted by 3D convolution neural network (3DCNN). An improved Transformer is then used to progressively learn global correlation information among features. Finally, the information from different modalities is fused for identification. A model-based visualization method is used to explain the decisions of the model and identify brain regions related to AD.

RESULTS

The model attained a noteworthy classification accuracy of 98.1% for Alzheimer's disease (AD) using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Upon examining the visualization results, distinct brain regions associated with AD diagnosis were observed across different image modalities. Notably, the left parahippocampal region emerged consistently as a prominent and significant brain area.

CONCLUSIONS

A large number of comparative experiments have been carried out for the model, and the experimental results verify the reliability of the model. In addition, the model adopts a visualization analysis method based on the characteristics of the model, which improves the interpretability of the model. Some disease-related brain regions were found in the visualization results, which provides reliable information for AD clinical research.

Western diet-induced obesity results in brain mitochondrial dysfunction in female Ossabaw swine

Diet-induced obesity is implicated in the development of a variety of neurodegenerative disorders. Concurrently, the loss of mitochondrial Complex I protein or function is emerging as a key phenotype across an array of neurodegenerative disorders. Therefore, the objective of this study was to determine if Western diet (WD) feeding in swine [carbohydrate = 40.8% kCal (17.8% of total calories from high fructose corn syrup), protein = 16.2% kcal, fat = 42.9% kCal, and 2% cholesterol] would result in Complex I syndrome pathology. To characterize the effects of WD-induced obesity on brain mitochondria in swine, high resolution respirometry measurements from isolated brain mitochondria, oxidative phosphorylation Complex expression, and indices of oxidative stress and mitochondrial biogenesis were assessed in female Ossabaw swine fed a WD for 6-months. In line with Complex I syndrome, WD feeding severely reduced State 3 Complex I, State 3 Complex I and II, and uncoupled mitochondrial respiration in the hippocampus and prefrontal cortex (PFC). State 3 Complex I mitochondrial respiration in the PFC inversely correlated with serum total cholesterol. WD feeding also significantly reduced protein expression of oxidative phosphorylation Complexes I-V in the PFC. WD feeding significantly increased markers of antioxidant defense and mitochondrial biogenesis in the hippocampi and PFC. These data suggest WD-induced obesity may contribute to Complex I syndrome pathology by increasing oxidative stress, decreasing oxidative phosphorylation Complex protein expression, and reducing brain mitochondrial respiration. Furthermore, these findings provide mechanistic insight into the clinical link between obesity and mitochondrial Complex I related neurodegenerative disorders.

Harnessing Transfer Learning for Dementia Prediction: Leveraging Sex-Different Mild Cognitive Impairment Prognosis

This paper presents a machine learning-based prediction for dementia, leveraging transfer learning to reuse the knowledge learned from prediction of mild cognitive impairment, a precursor of dementia. We also examine the impacts of temporal aspects of longitudinal data and sex differences. The methodology encompasses key components such as setting the duration window, comparing different modeling strategies, conducting comprehensive evaluations, and examining the sex-specific impacts of simulated scenarios. The findings reveal that cognitive deficits in females, once detected at the mild cognitive impairment stage, tend to deteriorate over time, while males exhibit more diverse decline across various characteristics without highlighting specific ones. However, the underlying reasons for these sex differences remain unknown and warrant further investigation.

The Role of Methionine-Rich Diet in Unhealthy Cerebrovascular and Brain Aging: Mechanisms and Implications for Cognitive Impairment

As aging societies in the western world face a growing prevalence of vascular cognitive impairment and Alzheimer's disease (AD), understanding their underlying causes and associated risk factors becomes increasingly critical. A salient concern in the western dietary context is the high consumption of methionine-rich foods such as red meat. The present review delves into the impact of this methionine-heavy diet and the resultant hyperhomocysteinemia on accelerated cerebrovascular and brain aging, emphasizing their potential roles in cognitive impairment. Through a comprehensive exploration of existing evidence, a link between high methionine intake and hyperhomocysteinemia and oxidative stress, mitochondrial dysfunction, inflammation, and accelerated epigenetic aging is drawn. Moreover, the microvascular determinants of cognitive deterioration, including endothelial dysfunction, reduced cerebral blood flow, microvascular rarefaction, impaired neurovascular coupling, and blood-brain barrier (BBB) disruption, are explored. The mechanisms by which excessive methionine consumption and hyperhomocysteinemia might drive cerebromicrovascular and brain aging processes are elucidated. By presenting an intricate understanding of the relationships among methionine-rich diets, hyperhomocysteinemia, cerebrovascular and brain aging, and cognitive impairment, avenues for future research and potential therapeutic interventions are suggested.

Sex and gender differences in Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis: a narrative review

Neurodegenerative diseases (NDs), including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), exhibit high phenotypic variability and they are very common in the general population. These diseases are associated with poor prognosis and a significant burden on patients and their caregivers. Although increasing evidence suggests that biological sex is an important factor for the development and phenotypical expression of some NDs, the role of sex and gender in the diagnosis and prognosis of NDs has been poorly explored. Current knowledge relating to sex- and gender-related differences in the epidemiology, clinical features, biomarkers, and treatment of AD, PD, and ALS will be summarized in this narrative review. The cumulative evidence hitherto collected suggests that sex and gender are factors to be considered in explaining the heterogeneity of these NDs. Clarifying the role of sex and gender in AD, PD, and ALS is a key topic in precision medicine, which will facilitate sex-specific prevention and treatment strategies to be implemented in the near future.

Genetic and Epigenetic Sexual Dimorphism of Brain Cells during Aging

In recent years, much of the attention paid to theoretical and applied biomedicine, as well as neurobiology, has been drawn to various aspects of sexual dimorphism due to the differences that male and female brain cells demonstrate during aging: (a) a dimorphic pattern of response to therapy for neurodegenerative disorders, (b) different age of onset and different degrees of the prevalence of such disorders, and (c) differences in their symptomatic manifestations in men and women. The purpose of this review is to outline the genetic and epigenetic differences in brain cells during aging in males and females. As a result, we hereby show that the presence of brain aging patterns in males and females is due to a complex of factors associated with the effects of sex chromosomes, which subsequently entails a change in signal cascades in somatic cells.

Associations between Sedentary Duration and Cognitive Function in Older Adults: A Longitudinal Study with 2-Year Follow-Up

OBJECTIVES

This study aimed to investigate the association between different forms of sedentary behavior and cognitive function in Chinese community-dwelling older adults.

DESIGN

A longitudinal study with a 2-year follow-up.

SETTING AND PARTICIPANTS

Data from 5356 participants at baseline and 956 participants at the follow-up of the Anhui Healthy Longevity Survey (AHLS) were analysed.

MEASUREMENTS

Cognitive function was evaluated by the Mini-Mental State Examination (MMSE), and mild cognitive impairment (MCI) was classified according to education-specific criteria. Self-report questionnaires were used to assess the sedentary behavior of the participants.

RESULTS

The participants who reported longer screen-watching sedentary duration had higher MMSE scores (1-2 hours: β=0.758, 95% CI: 0.450, 1.066; > 2 hours: β=1.240, 95% CI: 0.917, 1.562) and lower likelihoods of MCI (1-2 hours: OR= 0.787, 95% CI: 0.677, 0.914; >2 hours: OR=0.617, 95% CI: 0.524, 0.726). The participants who had played cards (or mahjong) sedentary had higher MMSE scores (β= 1.132, 95% CI: 0.788, 1.476) and lower likelihoods of MCI (OR=0.572, 95% CI: 0.476, 0.687). However, the participants who reported longer other forms of sedentary duration had lower MMSE scores (1-2 hours: β=-0.409, 95% CI: -0.735, -0.082; > 2 hours: β=-1.391, 95% CI: -1.696, -1.087) and higher likelihoods of MCI (1-2 hours: OR=1.271, 95% CI: 1.081, 1.496; > 2 hours: OR=1.632, 95% CI: 1.409, 1.889). No significant association was detected between sedentary duration and MCI incidence.

CONCLUSION

Variations in the impact of diverse sedentary behaviors on the cognitive function were detected in Chinese older adults. However, such associations were cross-sectional and longitudinal associations were not found in the current study.

Biological and Clinical Implications of Sex-Specific Differences in Alzheimer's Disease

Mounting evidence indicates that the female sex is a risk factor for Alzheimer's disease (AD), the most common cause of dementia worldwide. Decades of research suggest that sex-specific differences in genetics, environmental factors, hormones, comorbidities, and brain structure and function may contribute to AD development. However, although significant progress has been made in uncovering specific genetic factors and biological pathways, the precise mechanisms underlying sex-biased differences are not fully characterized. Here, we review several lines of evidence, including epidemiological, clinical, and molecular studies addressing sex differences in AD. In addition, we discuss the challenges and future directions in advancing personalized treatments for AD.