β-amyloid and tau drive early Alzheimer's disease decline while glucose hypometabolism drives late decline

Synbiotic therapy with Clostridium sporogenes and xylan promotes gut-derived indole-3-propionic acid and improves cognitive impairments in an Alzheimer's disease mouse model

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized primarily by cognitive impairment. Recent investigations have highlighted the potential of nutritional interventions that target the gut-brain axis, such as probiotics and prebiotics, in forestalling the onset of AD. In this study, whole-genome sequencing was employed to identify xylan as the optimal carbon source for the tryptophan metabolism regulating probiotic Clostridium sporogenes (C. sporogenes). Subsequent in vivo studies demonstrated that administration of a synbiotic formulation comprising C. sporogenes (1 × 1010 CFU per day) and xylan (1%, w/w) over a duration of 30 days markedly enhanced cognitive performance and spatial memory faculties in the 5xFAD transgenic AD mouse model. The synbiotic treatment significantly reduced amyloid-β (Aβ) accumulation in the cortex and hippocampus of the brain. Importantly, synbiotic therapy substantially restored the synaptic ultrastructure in AD mice and suppressed neuroinflammatory responses. Moreover, the intervention escalated levels of the microbial metabolite indole-3-propionic acid (IPA) and augmented the relative prevalence of IPA-synthesizing bacteria, Lachnospira and Clostridium, while reducing the dominant bacteria in AD, such as Aquabacterium, Corynebacterium, and Romboutsia. Notably, synbiotic treatment also prevented the disruption of gut barrier integrity. Correlation analysis indicated a strong positive association between gut microbiota-generated IPA levels and behavioral changes. In conclusion, this study demonstrates that synbiotic supplementation significantly improves cognitive and intellectual deficits in 5xFAD mice, which could be partly attributed to enhanced IPA production by gut microbiota. These findings provide a theoretical basis for considering synbiotic therapy as a novel microbiota-targeted approach for the treatment of metabolic and neurodegenerative diseases.

Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection

Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) and Shaply Additive exPlanation (SHAP) frameworks have grown as popular interpretive tools for ML and DL models. This article provides a systematic review of the application of LIME and SHAP in interpreting the detection of Alzheimer's disease (AD). Adhering to PRISMA and Kitchenham's guidelines, we identified 23 relevant articles and investigated these frameworks' prospective capabilities, benefits, and challenges in depth. The results emphasise XAI's crucial role in strengthening the trustworthiness of AI-based AD predictions. This review aims to provide fundamental capabilities of LIME and SHAP XAI frameworks in enhancing fidelity within clinical decision support systems for AD prognosis.

Hepatopancreatic metabolic disorders and their implications in the development of Alzheimer's disease and vascular dementia

Dementia has been faced with significant public health challenges and economic burdens that urges the need to develop safe and effective interventions. In recent years, an increasing number of studies have focused on the relationship between dementia and liver and pancreatic metabolic disorders that result in diseases such as diabetes, obesity, hypertension and dyslipidemia. Previous reports have shown that there is a plausible correlation between pathologies caused by hepatopancreatic dysfunctions and dementia. Glucose, insulin and IGF-1 metabolized in the liver and pancreas probably have an important influence on the pathophysiology of the most common dementias: Alzheimer's and vascular dementia. This current review highlights recent studies aimed at identifying convergent mechanisms, such as insulin resistance and other diseases, linked to altered hepatic and pancreatic metabolism, which are capable of causing brain changes that ultimately lead to dementia.

Lower GLUT1 and unchanged MCT1 in Alzheimer's disease cerebrovasculature

The brain is a highly demanding organ, utilizing mainly glucose but also ketone bodies as sources of energy. Glucose transporter-1 (GLUT1) and monocarboxylates transporter-1 (MCT1) respectively transport glucose and ketone bodies across the blood-brain barrier. While reduced glucose uptake by the brain is one of the earliest signs of Alzheimer's disease (AD), no change in the uptake of ketone bodies has been evidenced yet. To probe for changes in GLUT1 and MCT1, we performed Western immunoblotting in microvessel extracts from the parietal cortex of 60 participants of the Religious Orders Study. Participants clinically diagnosed with AD had lower cerebrovascular levels of GLUT1, whereas MCT1 remained unchanged. GLUT1 reduction was associated with lower cognitive scores. No such association was found for MCT1. GLUT1 was inversely correlated with neuritic plaques and cerebrovascular β-secretase-derived fragment levels. No other significant associations were found between both transporters, markers of Aβ and tau pathologies, sex, age at death or apolipoprotein-ε4 genotype. These results suggest that, while a deficit of GLUT1 may underlie the reduced transport of glucose to the brain in AD, no such impairment occurs for MCT1. This study thus supports the exploration of ketone bodies as an alternative energy source for the aging brain.

Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection

Black-box deep learning (DL) models trained for the early detection of Alzheimer's Disease (AD) often lack systematic model interpretation. This work computes the activated brain regions during DL and compares those with classical Machine Learning (ML) explanations. The architectures used for DL were 3D DenseNets, EfficientNets, and Squeeze-and-Excitation (SE) networks. The classical models include Random Forests (RFs), Support Vector Machines (SVMs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), Decision Trees (DTs), and Logistic Regression (LR). For explanations, SHapley Additive exPlanations (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (GradCAM), GradCAM++ and permutation-based feature importance were implemented. During interpretation, correlated features were consolidated into aspects. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The validation includes internal and external validation on the Australian Imaging and Lifestyle flagship study of Ageing (AIBL) and the Open Access Series of Imaging Studies (OASIS). DL and ML models reached similar classification performances. Regarding the brain regions, both types focus on different regions. The ML models focus on the inferior and middle temporal gyri, and the hippocampus, and amygdala regions previously associated with AD. The DL models focus on a wider range of regions including the optical chiasm, the entorhinal cortices, the left and right vessels, and the 4th ventricle which were partially associated with AD. One explanation for the differences is the input features (textures vs. volumes). Both types show reasonable similarity to a ground truth Voxel-Based Morphometry (VBM) analysis. Slightly higher similarities were measured for ML models.

Early-Onset Hearing Loss in Mouse Models of Alzheimer's Disease and Increased DNA Damage in the Cochlea

There is considerable interest in whether sensory deficiency is associated with the development of Alzheimer's disease (AD). Notably, the relationship between hearing impairment and AD is of high relevance but still poorly understood. In this study, we found early-onset hearing loss in two AD mouse models, 3xTgAD and 3xTgAD/Polβ+/-. The 3xTgAD/Polβ+/- mouse is DNA repair deficient and has more humanized AD features than the 3xTgAD. Both AD mouse models showed increased auditory brainstem response (ABR) thresholds between 16 and 32 kHz at 4 weeks of age, much earlier than any AD cognitive and behavioral changes. The ABR thresholds were significantly higher in 3xTgAD/Polβ+/- mice than in 3xTgAD mice at 16 kHz, and distortion product otoacoustic emission signals were reduced, indicating that DNA damage may be a factor underlying early hearing impairment in AD. Poly ADP-ribosylation and protein expression levels of DNA damage markers increased significantly in the cochlea of the AD mice but not in the adjacent auditory cortex. Phosphoglycerate mutase 2 levels and the number of synaptic ribbons in the presynaptic zones of inner hair cells were decreased in the cochlea of the AD mice. Furthermore, the activity of sirtuin 3 was downregulated in the cochlea of these mice, indicative of impaired mitochondrial function. Taken together, these findings provide new insights into potential mechanisms for hearing dysfunction in AD and suggest that DNA damage in the cochlea might contribute to the development of early hearing loss in AD.

A case for seeking sex-specific treatments in Alzheimer's disease

There is no satisfactory explanation for the sex-related differences in the incidence of many diseases and this is also true of Alzheimer's disease (AD), where females have a higher lifetime risk of developing the disease and make up about two thirds of the AD patient population. The importance of understanding the cause(s) that account for this disproportionate distribution cannot be overestimated, and is likely to be a significant factor in the search for therapeutic strategies that will combat the disease and, furthermore, potentially point to a sex-targeted approach to treatment. This review considers the literature in the context of what is known about the impact of sex on processes targeted by drugs that are in clinical trial for AD, and existing knowledge on differing responses of males and females to these drugs. Current knowledge strongly supports the view that trials should make assessing sex-related difference in responses a priority with a focus on exploring the sex-stratified treatments.

Alzheimer's disease diagnostic accuracy by fluid and neuroimaging ATN framework

OBJECTIVES

The ATN's different modalities (fluids and neuroimaging) for each of the Aβ (A), tau (T), and neurodegeneration (N) elements are used for the biological diagnosis of Alzheimer's disease (AD). We aim to identify which ATN category achieves the highest potential for diagnosis and predictive accuracy of longitudinal cognitive decline.

METHODS

Based on the availability of plasma ATN biomarkers (plasma-derived Aβ42/40 , p-tau181, NFL, respectively), CSF ATN biomarkers (CSF-derived Aβ42 /Aβ40 , p-tau181, NFL), and neuroimaging ATN biomarkers (18F-florbetapir (FBP) amyloid-PET, 18F-flortaucipir (FTP) tau-PET, and fluorodeoxyglucose (FDG)-PET), a total of 2340 participants were selected from ADNI.

RESULTS

Our data analysis indicates that the area under curves (AUCs) of CSF-A, neuroimaging-T, and neuroimaging-N were ranked the top three ATN candidates for accurate diagnosis of AD. Moreover, neuroimaging ATN biomarkers display the best predictive ability for longitudinal cognitive decline among the three categories. To note, neuroimaging-T correlates well with cognitive performances in a negative correlation manner. Meanwhile, participants in the "N" element positive group, especially the CSF-N positive group, experience the fastest cognitive decline compared with other groups defined by ATN biomarkers. In addition, the voxel-wise analysis showed that CSF-A related to tau accumulation and FDG-PET indexes more strongly in subjects with MCI stage. According to our analysis of the data, the best three ATN candidates for a precise diagnosis of AD are CSF-A, neuroimaging-T, and neuroimaging-N.

CONCLUSIONS

Collectively, our findings suggest that plasma, CSF, and neuroimaging biomarkers differ considerably within the ATN framework; the most accurate target biomarkers for diagnosing AD were the CSF-A, neuroimaging-T, and neuroimaging-N within each ATN modality. Moreover, neuroimaging-T and CSF-N both show excellent ability in the prediction of cognitive decline in two different dimensions.

Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization

Background

Longitudinal magnetic resonance imaging (MRI) has been proposed for tracking the progression of Alzheimer's disease (AD) through the assessment of brain atrophy.

Objective

Detection of brain atrophy patterns in patients with AD as the longitudinal disease tracker.

Methods

We used a refined version of orthonormal projective non-negative matrix factorization (OPNMF) to identify six distinct spatial components of voxel-wise volume loss in the brains of 83 subjects with AD from the ADNI3 cohort relative to healthy young controls from the ABIDE study. We extracted non-negative coefficients representing subject-specific quantitative measures of regional atrophy. Coefficients of brain atrophy were compared to subjects with mild cognitive impairment and controls, to investigate the cross-sectional and longitudinal associations between AD biomarkers and regional atrophy severity in different groups. We further validated our results in an independent dataset from ADNI2.

Results

The six non-overlapping atrophy components represent symmetric gray matter volume loss primarily in frontal, temporal, parietal and cerebellar regions. Atrophy in these regions was highly correlated with cognition both cross-sectionally and longitudinally, with medial temporal atrophy showing the strongest correlations. Subjects with elevated CSF levels of TAU and PTAU and lower baseline CSF Aβ42 values, demonstrated a tendency toward a more rapid increase of atrophy.

Conclusions

The present study has applied a transferable method to characterize the imaging changes associated with AD through six spatially distinct atrophy components and correlated these atrophy patterns with cognitive changes and CSF biomarkers cross-sectionally and longitudinally, which may help us better understand the underlying pathology of AD.

High-Fat Diets in Animal Models of Alzheimer's Disease: How Can Eating Too Much Fat Increase Alzheimer's Disease Risk?

High dietary intake of saturated fatty acids is a suspected risk factor for neurodegenerative diseases, including Alzheimer's disease (AD). To decipher the causal link behind these associations, high-fat diets (HFD) have been repeatedly investigated in animal models. Preclinical studies allow full control over dietary composition, avoiding ethical concerns in clinical trials. The goal of the present article is to provide a narrative review of reports on HFD in animal models of AD. Eligibility criteria included mouse models of AD fed a HFD defined as > 35% of fat/weight and western diets containing > 1% cholesterol or > 15% sugar. MEDLINE and Embase databases were searched from 1946 to August 2022, and 32 preclinical studies were included in the review. HFD-induced obesity and metabolic disturbances such as insulin resistance and glucose intolerance have been replicated in most studies, but with methodological variability. Most studies have found an aggravating effect of HFD on brain Aβ pathology, whereas tau pathology has been much less studied, and results are more equivocal. While most reports show HFD-induced impairment on cognitive behavior, confounding factors may blur their interpretation. In summary, despite conflicting results, exposing rodents to diets highly enriched in saturated fat induces not only metabolic defects, but also cognitive impairment often accompanied by aggravated neuropathological markers, most notably Aβ burden. Although there are important variations between methods, particularly the lack of diet characterization, these studies collectively suggest that excessive intake of saturated fat should be avoided in order to lower the incidence of AD.

FTIR Spectroscopy and Blood-Derived Extracellular Vesicles Duo in Alzheimer's Disease

Background

Alzheimer's disease (AD) diagnosis is difficult, and new accurate tools based on peripheral biofluids are urgently needed. Extracellular vesicles (EVs) emerged as a valuable source of biomarker profiles for AD, since their cargo is disease-specific and these can be easily isolated from easily accessible biofluids, as blood. Fourier Transform Infrared (FTIR) spectroscopy can be employed to analyze EVs and obtain the spectroscopic profiles from different regions of the spectra, simultaneously characterizing carbohydrates, nucleic acids, proteins, and lipids.

Objective

The aim of this study was to identify blood-derived EVs (bdEVs) spectroscopic signatures with AD discriminatory potential.

Methods

Herein, FTIR spectra of bdEVs from two biofluids (serum and plasma) and distinct sets of Controls and AD cases were acquired, and EVs' spectra analyzed.

Results

Analysis of bdEVs second derivative peaks area revealed differences between Controls and AD cases in distinct spectra regions, assigned to carbohydrates and nucleic acids, amides, and lipids.

Conclusions

EVs' spectroscopic profiles presented AD discriminatory value, supporting the use of bdEVs combined with FTIR as a screening or complementary tool for AD diagnosis.

Exercise Intervention for Alzheimer's Disease: Unraveling Neurobiological Mechanisms and Assessing Effects

Alzheimer's disease (AD) is a progressive neurodegenerative disease and a major cause of age-related dementia, characterized by cognitive dysfunction and memory impairment. The underlying causes include the accumulation of beta-amyloid protein (Aβ) in the brain, abnormal phosphorylation, and aggregation of tau protein within nerve cells, as well as neuronal damage and death. Currently, there is no cure for AD with drug therapy. Non-pharmacological interventions such as exercise have been widely used to treat AD, but the specific molecular and biological mechanisms are not well understood. In this narrative review, we integrate the biology of AD and summarize the knowledge of the molecular, neural, and physiological mechanisms underlying exercise-induced improvements in AD progression. We discuss various exercise interventions used in AD and show that exercise directly or indirectly affects the brain by regulating crosstalk mechanisms between peripheral organs and the brain, including "bone-brain crosstalk", "muscle-brain crosstalk", and "gut-brain crosstalk". We also summarize the potential role of artificial intelligence and neuroimaging technologies in exercise interventions for AD. We emphasize that moderate-intensity, regular, long-term exercise may improve the progression of Alzheimer's disease through various molecular and biological pathways, with multimodal exercise providing greater benefits. Through in-depth exploration of the molecular and biological mechanisms and effects of exercise interventions in improving AD progression, this review aims to contribute to the existing knowledge base and provide insights into new therapeutic strategies for managing AD.

Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning

BACKGROUND

The pathological process of Alzheimer's disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established.

METHODS

The main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods.

RESULTS

Overall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity.

CONCLUSIONS

Our study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.

Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis

INTRODUCTION

This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).

METHODS

This analysis consisted of 150 normal controls (CN), 257 MCI, and 205  AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.

RESULTS

Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.

DISCUSSION

Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.

Prebiotic inulin enhances gut microbial metabolism and anti-inflammation in apolipoprotein E4 mice with sex-specific implications

Gut dysbiosis has been identified as a crucial factor of Alzheimer's disease (AD) development for apolipoprotein E4 (APOE4) carriers. Inulin has shown the potential to mitigate dysbiosis. However, it remains unclear whether the dietary response varies depending on sex. In the study, we fed 4-month-old APOE4 mice with inulin for 16 weeks and performed shotgun metagenomic sequencing to determine changes in microbiome diversity, taxonomy, and functional gene pathways. We also formed the same experiments with APOE3 mice to identify whether there are APOE-genotype dependent responses to inulin. We found that APOE4 female mice fed with inulin had restored alpha diversity, significantly reduced Escherichia coli and inflammation-associated pathway responses. However, compared with APOE4 male mice, they had less metabolic responses, including the levels of short-chain fatty acids-producing bacteria and the associated kinases, especially those related to acetate and Erysipelotrichaceae. These diet- and sex- effects were less pronounced in the APOE3 mice, indicating that different APOE variants also play a significant role. The findings provide insights into the higher susceptibility of APOE4 females to AD, potentially due to inefficient energy production, and imply the importance of considering precision nutrition for mitigating dysbiosis and AD risk in the future.

Treatment of Alzheimer's Disease: Beyond Symptomatic Therapies

In an ever-increasing aged world, Alzheimer's disease (AD) represents the first cause of dementia and one of the first chronic diseases in elderly people. With 55 million people affected, the WHO considers AD to be a disease with public priority. Unfortunately, there are no final cures for this pathology. Treatment strategies are aimed to mitigate symptoms, i.e., acetylcholinesterase inhibitors (AChEI) and the N-Methyl-D-aspartate (NMDA) antagonist Memantine. At present, the best approaches for managing the disease seem to combine pharmacological and non-pharmacological therapies to stimulate cognitive reserve. Over the last twenty years, a number of drugs have been discovered acting on the well-established biological hallmarks of AD, deposition of β-amyloid aggregates and accumulation of hyperphosphorylated tau protein in cells. Although previous efforts disappointed expectations, a new era in treating AD has been working its way recently. The Food and Drug Administration (FDA) gave conditional approval of the first disease-modifying therapy (DMT) for the treatment of AD, aducanumab, a monoclonal antibody (mAb) designed against Aβ plaques and oligomers in 2021, and in January 2023, the FDA granted accelerated approval for a second monoclonal antibody, Lecanemab. This review describes ongoing clinical trials with DMTs and non-pharmacological therapies. We will also present a future scenario based on new biomarkers that can detect AD in preclinical or prodromal stages, identify people at risk of developing AD, and allow an early and curative treatment.

Gut microbiome association with brain imaging markers, APOE genotype, calcium and vegetable intakes, and obesity in healthy aging adults

Introduction

Advanced age is a significant factor in changes to brain physiology and cognitive functions. Recent research has highlighted the critical role of the gut microbiome in modulating brain functions during aging, which can be influenced by various factors such as apolipoprotein E (APOE) genetic variance, body mass index (BMI), diabetes, and dietary intake. However, the associations between the gut microbiome and these factors, as well as brain structural, vascular, and metabolic imaging markers, have not been well explored.

Methods

We recruited 30 community dwelling older adults between age 55-85 in Kentucky. We collected the medical history from the electronic health record as well as the Dietary Screener Questionnaire. We performed APOE genotyping with an oral swab, gut microbiome analysis using metagenomics sequencing, and brain structural, vascular, and metabolic imaging using MRI.

Results

Individuals with APOE e2 and APOE e4 genotypes had distinct microbiota composition, and higher level of pro-inflammatory microbiota were associated higher BMI and diabetes. In contrast, calcium- and vegetable-rich diets were associated with microbiota that produced short chain fatty acids leading to an anti-inflammatory state. We also found that important gut microbial butyrate producers were correlated with the volume of the thalamus and corpus callosum, which are regions of the brain responsible for relaying and processing information. Additionally, putative proinflammatory species were negatively correlated with GABA production, an inhibitory neurotransmitter. Furthermore, we observed that the relative abundance of bacteria from the family Eggerthellaceae, equol producers, was correlated with white matter integrity in tracts connecting the brain regions related to language, memory, and learning.

Discussion

These findings highlight the importance of gut microbiome association with brain health in aging population and could have important implications aimed at optimizing healthy brain aging through precision prebiotic, probiotic or dietary interventions.

Role of NFE2L1 in the Regulation of Proteostasis: Implications for Aging and Neurodegenerative Diseases

A hallmark of aging and neurodegenerative diseases is a disruption of proteome homeostasis ("proteostasis") that is caused to a considerable extent by a decrease in the efficiency of protein degradation systems. The ubiquitin proteasome system (UPS) is the major cellular pathway involved in the clearance of small, short-lived proteins, including amyloidogenic proteins that form aggregates in neurodegenerative diseases. Age-dependent decreases in proteasome subunit expression coupled with the inhibition of proteasome function by aggregated UPS substrates result in a feedforward loop that accelerates disease progression. Nuclear factor erythroid 2- like 1 (NFE2L1) is a transcription factor primarily responsible for the proteasome inhibitor-induced "bounce-back effect" regulating the expression of proteasome subunits. NFE2L1 is localized to the endoplasmic reticulum (ER), where it is rapidly degraded under basal conditions by the ER-associated degradation (ERAD) pathway. Under conditions leading to proteasome impairment, NFE2L1 is cleaved and transported to the nucleus, where it binds to antioxidant response elements (AREs) in the promoter region of proteasome subunit genes, thereby stimulating their transcription. In this review, we summarize the role of UPS impairment in aging and neurodegenerative disease etiology and consider the potential benefit of enhancing NFE2L1 function as a strategy to upregulate proteasome function and alleviate pathology in neurodegenerative diseases.

Self-Supervised Learning Application on COVID-19 Chest X-ray Image Classification Using Masked AutoEncoder

The COVID-19 pandemic has underscored the urgent need for rapid and accurate diagnosis facilitated by artificial intelligence (AI), particularly in computer-aided diagnosis using medical imaging. However, this context presents two notable challenges: high diagnostic accuracy demand and limited availability of medical data for training AI models. To address these issues, we proposed the implementation of a Masked AutoEncoder (MAE), an innovative self-supervised learning approach, for classifying 2D Chest X-ray images. Our approach involved performing imaging reconstruction using a Vision Transformer (ViT) model as the feature encoder, paired with a custom-defined decoder. Additionally, we fine-tuned the pretrained ViT encoder using a labeled medical dataset, serving as the backbone. To evaluate our approach, we conducted a comparative analysis of three distinct training methods: training from scratch, transfer learning, and MAE-based training, all employing COVID-19 chest X-ray images. The results demonstrate that MAE-based training produces superior performance, achieving an accuracy of 0.985 and an AUC of 0.9957. We explored the mask ratio influence on MAE and found ratio = 0.4 shows the best performance. Furthermore, we illustrate that MAE exhibits remarkable efficiency when applied to labeled data, delivering comparable performance to utilizing only 30% of the original training dataset. Overall, our findings highlight the significant performance enhancement achieved by using MAE, particularly when working with limited datasets. This approach holds profound implications for future disease diagnosis, especially in scenarios where imaging information is scarce.

Acute administration of ketone beta-hydroxybutyrate downregulates 7T proton magnetic resonance spectroscopy-derived levels of anterior and posterior cingulate GABA and glutamate in healthy adults

Glucose metabolism is impaired in brain aging and several neurological conditions. Beneficial effects of ketones have been reported in the context of protecting the aging brain, however, their neurophysiological effect is still largely uncharacterized, hurdling their development as a valid therapeutic option. In this report, we investigate the neurochemical effect of the acute administration of a ketone d-beta-hydroxybutyrate (D-βHB) monoester in fasting healthy participants with ultrahigh-field proton magnetic resonance spectroscopy (MRS). In two within-subject metabolic intervention experiments, 7 T MRS data were obtained in fasting healthy participants (1) in the anterior cingulate cortex pre- and post-administration of D-βHB (N = 16), and (2) in the posterior cingulate cortex pre- and post-administration of D-βHB compared to active control glucose (N = 26). Effect of age and blood levels of D-βHB and glucose were used to further explore the effect of D-βHB and glucose on MRS metabolites. Results show that levels of GABA and Glu were significantly reduced in the anterior and posterior cortices after administration of D-βHB. Importantly, the effect was specific to D-βHB and not observed after administration of glucose. The magnitude of the effect on GABA and Glu was significantly predicted by older age and by elevation of blood levels of D-βHB. Together, our results show that administration of ketones acutely impacts main inhibitory and excitatory transmitters in the whole fasting cortex, compared to normal energy substrate glucose. Critically, such effects have an increased magnitude in older age, suggesting an increased sensitivity to ketones with brain aging.

Betanin mitigates scopolamine-induced cognitive impairment by restoring cholinergic function, boosting brain antioxidative status, and increasing BDNF level in the zebrafish model

Betalains obtained from Beta vulgaris (family Caryophyllales) are regularly consumed as part of the regular diet with medicinal benefits due to antioxidant and anti-inflammatory properties. The objective of this article was to evaluate betanin's neuroprotective properties in a scopolamine-induced zebrafish paradigm. Betanin (BET) (50, 100, and 200 mg/L), and donepezil (10 mg/L) were delivered to zebrafish in a treatment tank once a day for 8 days, while memory impairment was produced by scopolamine (100 µM), which was given 60 min before behavioral assessments. The treatment dosages were determined based on acute toxicity studies. The existence of betacyanin and betaxanthins of BET was tested using liquid chromatography-mass spectrometry (LC-MS). The Y-maze task was used to examine the novelty and spatial memory, while the novel tank diving test was used to assess anxiety-like behavior (NTT). The activities of acetylcholinesterase (AChE) and the oxidative stress sensitivity in zebrafish brains were examined. Also, brain-derived neurotrophic factor (BDNF) level is quantified by an ELISA kit. Scopolamine-induced rises in AChE activity, memory loss, anxiety, and brain oxidant capacity were all reduced by BET. These results suggest that BET (50 and 100 mg/L) has a therapeutic ability to treat brain oxidative stress and cognitive deficits in amnesic zebrafish.

Tau as a biomarker of cognitive impairment and neuropsychiatric symptom in Alzheimer's disease

The A/T/N research framework has been proposed for the diagnosis and prognosis of Alzheimer's disease (AD). However, the spatial distribution of ATN biomarkers and their relationship with cognitive impairment and neuropsychiatric symptoms (NPS) need further clarification in patients with AD. We scanned 83 AD patients and 38 cognitively normal controls who independently completed the mini-mental state examination and Neuropsychiatric Inventory scales. Tau, Aβ, and hypometabolism spatial patterns were characterized using Statistical Parametric Mapping together with [18F]flortaucipir, [18F]florbetapir, and [18F]FDG positron emission tomography. Piecewise linear regression, two-sample t-tests, and support vector machine algorithms were used to explore the relationship between tau, Aβ, and hypometabolism and cognition, NPS, and AD diagnosis. The results showed that regions with tau deposition are region-specific and mainly occurred in inferior temporal lobes in AD, which extensively overlaps with the hypometabolic regions. While the deposition regions of Aβ were unique and the regions affected by hypometabolism were widely distributed. Unlike Aβ, tau and hypometabolism build up monotonically with increasing cognitive impairment in the late stages of AD. In addition, NPS in AD were associated with tau deposition closely, followed by hypometabolism, but not with Aβ. Finally, hypometabolism and tau had higher accuracy in differentiating the AD patients from controls (accuracy = 0.88, accuracy = 0.85) than Aβ (accuracy = 0.81), and the combined three were the highest (accuracy = 0.95). These findings suggest tau pathology is superior over Aβ and glucose metabolism to identify cognitive impairment and NPS. Its results support tau accumulation can be used as a biomarker of clinical impairment in AD.

Efficient Training on Alzheimer's Disease Diagnosis with Learnable Weighted Pooling for 3D PET Brain Image Classification

Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze Alzheimer's disease (AD) brain images for a better understanding of the disease progress or predicting the conversion from cognitively impaired (CU) or mild cognitive impairment status. It is well-known that training 3D-CNN is computationally expensive and with the potential of overfitting due to the small sample size available in the medical imaging field. Here we proposed a novel 3D-2D approach by converting a 3D brain image to a 2D fused image using a Learnable Weighted Pooling (LWP) method to improve efficient training and maintain comparable model performance. By the 3D-to-2D conversion, the proposed model can easily forward the fused 2D image through a pre-trained 2D model while achieving better performance over different 3D and 2D baselines. In the implementation, we chose to use ResNet34 for feature extraction as it outperformed other 2D CNN backbones. We further showed that the weights of the slices are location-dependent and the model performance relies on the 3D-to-2D fusion view, with the best outcomes from the coronal view. With the new approach, we were able to reduce 75% of the training time and increase the accuracy to 0.88, compared with conventional 3D CNNs, for classifying amyloid-beta PET imaging from the AD patients from the CU participants using the publicly available Alzheimer's Disease Neuroimaging Initiative dataset. The novel 3D-2D model may have profound implications for timely AD diagnosis in clinical settings in the future.

Potential mechanisms to modify impaired glucose metabolism in neurodegenerative disorders

Neurodegeneration refers to the selective and progressive loss-of-function and atrophy of neurons, and is present in disorders such as Alzheimer's, Huntington's, and Parkinson's disease. Although each disease presents with a unique pattern of neurodegeneration, and subsequent disease phenotype, increasing evidence implicates alterations in energy usage as a shared and core feature in the onset and progression of these disorders. Indeed, disturbances in energy metabolism may contribute to the vulnerability of neurons to apoptosis. In this review we will outline these disturbances in glucose metabolism, and how fatty acids are able to compensate for this impairment in energy production in neurodegenerative disorders. We will also highlight underlying mechanisms that could contribute to these alterations in energy metabolism. A greater understanding of these metabolism-neurodegeneration processes could lead to improved treatment options for neurodegenerative disease patients.

Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer's Disease

BACKGROUND

The complex and not yet fully understood etiology of Alzheimer's disease (AD) shows important proteopathic signs which are unlikely to be linked to a single protein. However, protein subsets from deep proteomic datasets can be useful in stratifying patient risk, identifying stage dependent disease markers, and suggesting possible disease mechanisms.

OBJECTIVE

The objective was to identify protein subsets that best classify subjects into control, asymptomatic Alzheimer's disease (AsymAD), and AD.

METHODS

Data comprised 6 cohorts; 620 subjects; 3,334 proteins. Brain tissue-derived predictive protein subsets for classifying AD, AsymAD, or control were identified and validated with label-free quantification and machine learning.

RESULTS

A 29-protein subset accurately classified AD (AUC = 0.94). However, an 88-protein subset best predicted AsymAD (AUC = 0.92) or Control (AUC = 0.92) from AD (AUC = 0.98). AD versus Control: APP, DHX15, NRXN1, PBXIP1, RABEP1, STOM, and VGF. AD versus AsymAD: ALDH1A1, BDH2, C4A, FABP7, GABBR2, GNAI3, PBXIP1, and PRKAR1B. AsymAD versus Control: APP, C4A, DMXL1, EXOC2, PITPNB, RABEP1, and VGF. Additional predictors: DNAJA3, PTBP2, SLC30A9, VAT1L, CROCC, PNP, SNCB, ENPP6, HAPLN2, PSMD4, and CMAS.

CONCLUSION

Biomarkers were dynamically separable across disease stages. Predictive proteins were significantly enriched to sugar metabolism.

Inulin supplementation prior to mild traumatic brain injury mitigates gut dysbiosis, and brain vascular and white matter deficits in mice

Introduction

Mild traumatic brain injury (mTBI) has been shown to negatively alter bacterial diversity and composition within the gut, known as dysbiosis, in rodents and humans. These changes cause secondary consequences systemically through decreased bacterial metabolites such as short chain fatty acids (SCFAs) which play a role in inflammation and metabolism. The goal of the study was to identify if giving prebiotic inulin prior to closed head injury (CHI) could mitigate gut dysbiosis, increase SCFAs, and improve recovery outcomes, including protecting cerebral blood flow (CBF) and white matter integrity (WMI) in young mice.

Methods

We fed mice at 2 months of age with either inulin or control diet (with cellulose as fiber source) for two months before the CHI and continued till the end of the study. We analyzed gut microbiome composition and diversity, determined SCFAs levels, and measured CBF and WMI using MRI. We compared the results with Naïve and Sham-injury mice at 24 hours, 1.5 months, and 3-4 months post-injury.

Results

We found that both CHI and Sham mice had time-dependent changes in gut composition and diversity after surgery. Inulin significantly reduced the abundance of pathobiont bacteria, such as E. coli, Desulfovibrio spp and Pseudomonas aeruginosa, in Sham and CHI mice compared to mice fed with control diet. On the other hand, inulin increased SCFAs-producing bacteria, such as Bifidobacterium spp and Lactobacillus spp, increased levels of SCFAs, including butyrate and propionate, and significantly altered beta diversity as early as 24 hours post-injury, which lasted up to 3-4 months post-injury. The mitigation of dysbiosis is associated with protection of WMI in fimbria, internal and external capsule, and CBF in the right hippocampus of CHI mice, suggesting protection of memory and cognitive functions.

Discussion

The results indicate that giving inulin prior to CHI could promote recovery outcome through gut microbiome modulation. As inulin, microbiome analysis, and MRI are readily to be used in humans, the findings from the study may pave a way for a cost-effective, accessible intervention for those at risk of sustaining a head injury, such as military personnel or athletes in contact sports.

Synthetic Mono-Carbonyl Curcumin Analogues Attenuate Oxidative Stress in Mouse Models

Alzheimer’s disease is the commonest form of dementia associated with short-term memory loss and impaired cognition and, worldwide, it is a growing health issue. A number of therapeutic strategies have been studied to design and develop an effective anti-Alzheimer drug. Curcumin has a wide spectrum of biological properties. In this regard, the antioxidant potentials of mono-carbonyl curcumin analogues (h1−h5) were investigated using in vitro antioxidant assays and hippocampal-based in vivo mouse models such as light−dark box, hole board, and Y-maze tests. In the in vitro assay, mono-carbonyl curcumin analogues h2 and h3 with methoxy and chloro-substituents, respectively, showed promising 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2, 2′-azinobis-3-ethylbenzothiazo-line-6-sulfonate (ABTS) free radical scavenging activities. In the in vivo studies, scopolamine administration significantly (p < 0.001) induced oxidative stress and memory impairment in mice, in comparison to the normal control group. The pretreatment with mono-carbonyl curcumin analogues, specifically h2 and h3, significantly decreased (123.71 ± 15.23 s (p < 0.001), n = 8; 156.53 ± 14.13 s (p < 0.001), n = 8) the duration of time spent in the light chamber and significantly enhanced (253.95 ± 19.05 s (p < 0.001), n = 8, and 239.57 ± 9.98 s (p < 0.001), n = 8) the time spent in the dark compartment in the light−dark box arena. The numbers of hole pokings were significantly (p < 0.001, n = 8) enhanced in the hole board test and substantially increased the percent spontaneous alternation performance (SAP %) in the Y-maze mouse models in comparison to the stress control group. In the biomarker analysis, the significant reduction in the lipid peroxidation (MDA) level and enhanced catalase (CAT), superoxide dismutase (SOD), and glutathione (GSH) activities in the brain hippocampus reveal their antioxidant and memory enhancing potentials. However, further research is needed to find out the appropriate mechanism of reducing oxidative stress in pathological models.

The Mitochondrial Enzyme 17βHSD10 Modulates Ischemic and Amyloid-β-Induced Stress in Primary Mouse Astrocytes

Severe brain metabolic dysfunction and amyloid-β accumulation are key hallmarks of Alzheimer's disease (AD). While astrocytes contribute to both pathologic mechanisms, the role of their mitochondria, which is essential for signaling and maintenance of these processes, has been largely understudied. The current work provides the first direct evidence that the mitochondrial metabolic switch 17β-hydroxysteroid dehydrogenase type 10 (17βHSD10) is expressed and active in murine astrocytes from different brain regions. While it is known that this protein is overexpressed in the brains of AD patients, we found that 17βHSD10 is also upregulated in astrocytes exposed to amyloidogenic and ischemic stress. Importantly, such catalytic overexpression of 17βHSD10 inhibits mitochondrial respiration during increased energy demand. This observation contrasts with what has been found in neuronal and cancer model systems, which suggests astrocyte-specific mechanisms mediated by the protein. Furthermore, the catalytic upregulation of the enzyme exacerbates astrocytic damage, reactive oxygen species (ROS) generation and mitochondrial network alterations during amyloidogenic stress. On the other hand, 17βHSD10 inhibition through AG18051 counters most of these effects. In conclusion, our data represents novel insights into the role of astrocytic mitochondria in metabolic and amyloidogenic stress with implications of 17βHSD10 in multiple neurodegenerative mechanisms.

Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer's disease continuum

Background

Large-scale neuronal network breakdown underlies memory impairment in Alzheimer's disease (AD). However, the differential trajectories of the relationships between network organisation and memory across pathology and cognitive stages in AD remain elusive. We determined whether and how the influences of individual-level structural and metabolic covariance network integrity on memory varied with amyloid pathology across clinical stages without assuming a constant relationship.

Methods

Seven hundred and eight participants from the Alzheimer's Disease Neuroimaging Initiative were studied. Individual-level structural and metabolic covariance scores in higher-level cognitive and hippocampal networks were derived from magnetic resonance imaging and [18F] fluorodeoxyglucose positron emission tomography using seed-based partial least square analyses. The non-linear associations between network scores and memory across cognitive stages in each pathology group were examined using sparse varying coefficient modelling.

Results

We showed that the associations of memory with structural and metabolic networks in the hippocampal and default mode regions exhibited pathology-dependent differential trajectories across cognitive stages using sparse varying coefficient modelling. In amyloid pathology group, there was an early influence of hippocampal structural network deterioration on memory impairment in the preclinical stage, and a biphasic influence of the angular gyrus-seeded default mode metabolic network on memory in both preclinical and dementia stages. In non-amyloid pathology groups, in contrast, the trajectory of the hippocampus-memory association was opposite and weaker overall, while no metabolism covariance networks were related to memory. Key findings were replicated in a larger cohort of 1280 participants.

Conclusions

Our findings highlight potential windows of early intervention targeting network breakdown at the preclinical AD stage.

Funding

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). We also acknowledge the funding support from the Duke NUS/Khoo Bridge Funding Award (KBrFA/2019-0020) and NMRC Open Fund Large Collaborative Grant (OFLCG09May0035).

The changes in cognitive function following bariatric surgery considering the function of gut microbiome

Background

There is a correlation between gut microbiota and cognitive function. The mechanisms and pathways explain why the incidence of Alzheimer's disease in subjects undergoing bariatric surgery is lower than in other people with obesity.

Methods

In this review article, we aim to discuss the association of obesity, cognitive impairment, and physiological changes after bariatric surgery.

Results

Bariatric surgery has a series of physiological benefits which may lead to an improvement in cognitive functions in individuals who are prone to later developing Alzheimer's disease. Also, taxonomical change in the gut microbiome profile provides a healthy condition for living with better levels of cognition without neuropathological damages in older ages.

Conclusion

It can be concluded that there is a possible correlation between cognitive dysfunction and increased risk of cognitive dysfunction in people with a BMI higher than 40 kg/m2. Bariatric surgery may increase neurotransmitters and improve the gut bacteria, leading to a significant reduction in the risk of Alzheimer's disease.

Contribution of Alzheimer's biomarkers and risk factors to cognitive impairment and decline across the Alzheimer's disease continuum

INTRODUCTION

Amyloid beta (Aβ), tau, and neurodegeneration jointly with the Alzheimer's disease (AD) risk factors affect the severity of clinical symptoms and disease progression.

METHODS

Within 248 Aβ-positive elderly with and without cognitive impairment and dementia, partial least squares structural equation pathway modeling was used to assess the direct and indirect effects of imaging biomarkers (global Aβ-positron emission tomography [PET] uptake, regional tau-PET uptake, and regional magnetic resonance imaging-based atrophy) and risk-factors (age, sex, education, apolipoprotein E [APOE], and white-matter lesions) on cross-sectional cognitive impairment and longitudinal cognitive decline.

RESULTS

Sixteen percent of variance in cross-sectional cognitive impairment was accounted for by Aβ, 46% to 47% by tau, and 25% to 29% by atrophy, although 53% to 58% of total variance in cognitive impairment was explained by incorporating mediated and direct effects of AD risk factors. The Aβ-tau-atrophy pathway accounted for 50% to 56% of variance in longitudinal cognitive decline while Aβ, tau, and atrophy independently explained 16%, 46% to 47%, and 25% to 29% of the variance, respectively.

DISCUSSION

These findings emphasize that treatments that remove Aβ and completely stop downstream effects on tau and neurodegeneration would only be partially effective in slowing of cognitive decline or reversing cognitive impairment.

Clinicopathologic Factors Associated With Reversion to Normal Cognition in Patients With Mild Cognitive Impairment

BACKGROUND AND OBJECTIVES

To identify clinicopathologic factors contributing to mild cognitive impairment (MCI) reversion to normal cognition.

METHODS

We analyzed 3 longitudinal cohorts in this study: the Mayo Clinic Study of Aging (MCSA), the Religious Orders Study and Memory and Aging Project (ROSMAP), and the National Alzheimer's Coordinating Center (NACC). Demographic characteristics and clinical outcomes were compared between patients with MCI with or without an experience of reversion to normal cognition (referred to as reverters and nonreverters, respectively). We also compared longitudinal changes in cortical thickness, glucose metabolism, and amyloid and tau load in a subcohort of reverters and nonreverters in MCSA with MRI or PET imaging information from multiple visits.

RESULTS

We identified 164 (56.4%) individuals in MCSA, 508 (66.8%) individuals in ROSMAP, and 280 (34.1%) individuals in NACC who experienced MCI reversion to normal cognition. Cox proportional hazards regression models showed that MCI reverters had an increased chance of being cognitively normal at the last visit in MCSA (HR 3.31, 95% CI 2.14-5.12), ROSMAP (HR 3.72, 95% CI 2.50-5.56), and NACC (HR 9.29, 95% CI 6.45-13.40) and a reduced risk of progression to dementia (HR 0.12, 95% CI 0.05-0.29 in MCSA; HR 0.41, 95% CI 0.32-0.53 in ROSMAP; and HR 0.29, 95% CI 0.21-0.40 in NACC). Compared with MCI nonreverters, reverters had better-preserved cortical thickness (β = 0.082, p <0.001) and glucose metabolism (β = 0.119, p = 0.001) and lower levels of amyloid, albeit statistically nonsignificant (β = -0.172, p = 0.090). However, no difference in tau load was found between reverters and nonreverters (β = 0.073, p = 0.24).

DISCUSSION

MCI reversion to normal cognition is likely attributed to better-preserved cortical structure and glucose metabolism.

Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning

Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48-95 years) with Alzheimer's disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer's disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer's disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer's disease continuum group (n = 718; consisting of amyloid-positive Alzheimer's disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer's disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer's disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56-0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer's disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer's disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer's disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer's disease.

Dissociation of tau pathology and neuronal hypometabolism within the ATN framework of Alzheimer's disease

Alzheimer's disease (AD) is defined by amyloid (A) and tau (T) pathologies, with T better correlated to neurodegeneration (N). However, T and N have complex regional relationships in part related to non-AD factors that influence N. With machine learning, we assessed heterogeneity in 18F-flortaucipir vs. 18F-fluorodeoxyglucose positron emission tomography as markers of T and neuronal hypometabolism (NM) in 289 symptomatic patients from the Alzheimer's Disease Neuroimaging Initiative. We identified six T/NM clusters with differing limbic and cortical patterns. The canonical group was defined as the T/NM pattern with lowest regression residuals. Groups resilient to T had less hypometabolism than expected relative to T and displayed better cognition than the canonical group. Groups susceptible to T had more hypometabolism than expected given T and exhibited worse cognitive decline, with imaging and clinical measures concordant with non-AD copathologies. Together, T/NM mismatch reveals distinct imaging signatures with pathobiological and prognostic implications for AD.

The Relationship of Plasma Transthyretin Level with Global or Regional Amyloid Beta Burden in Subjects with Amnestic Mild Cognitive Impairment: Cross-Sectional Amyloid PET Study

Background

To investigate the relationships of plasma transthyretin levels with amyloid beta deposition and medial temporal atrophy in amnestic mild cognitive impairment.

Methods

This is a cross-sectional study of association of subjects with amnestic mild cognitive impairment. Plasma transthyretin levels, brain magnetic resonance imaging, and 18F-florbetaben positron emission tomography were simultaneously measured in subjects with amnestic mild cognitive impairment.

Results

Plasma transthyretin levels were positively associated with amyloid beta deposition in global (r = 0.394, P = .009), frontal cortex (r = 0.316, P = .039), parietal cortex (r = 0.346, P = .023), temporal cortex (r = 0.372, P = .014), occipital cortex (r = 0.310, P = .043), right posterior cingulate (r = 0.350, P = .021), left precuneus (r = 0.314, P = .040), and right precuneus (r = 0.398, P = .008). No association between plasma transthyretin level and medial temporal sub-regional atrophies was found.

Conclusions

Our findings of positive association of plasma transthyretin levels with global and regional amyloid beta burden suggest upregulation of transthyretin level as a reactive response to amyloid beta deposition during the early stages of the Alzheimer's disease process.

Correlated functional connectivity and glucose metabolism in brain white matter revealed by simultaneous MRI/positron emission tomography

PURPOSE

There has been converging evidence of reliable detections of blood oxygenation level dependent (BOLD) signals evoked by neural stimulation and in a resting state in white matter (WM), within which few studies examined the relationship between BOLD functional signals and tissue metabolism. The purpose of the present study was to explore whether such relationship exists using combined functional MRI and positron emission tomography (PET) measurements of glucose uptake.

METHODS

Functional and metabolic imaging data from 25 right-handed healthy human adults (aged 18-23 years, 18 females) were analyzed. Measures, including average resting state functional connectivity (FC) with respect to 82 Brodmann areas, fractional amplitude of low-frequency fluctuations (FALFF), and average fluorodeoxyglucose (FDG) uptake by PET, were computed for 48 predefined WM bundles. Pearson correlations across the bundles and 25 subjects studied were calculated among these measures. Linear mixed effects models were used to estimate the variance explainable by a predictor variable in the absence of inter-subject variations.

RESULTS

Analysis of six separate imaging intervals found that average FC the bundles was significantly correlated with local FDG uptake (r = 0.25, p < 0.001), and the FC also covaried significantly with FALFF (r = 0.41, p < 0.001). When random effects from inter-subject variations were controlled, these correlations appeared to be medium to strong (r = 0.41 for FC vs. FDG uptake, and r = 0.65 for FALFF vs. FC).

CONCLUSION

This study indicates that BOLD signals in WM are directly related to variations in metabolic demand and engagement with cortical processing and suggests they should be incorporated into more complete models of brain function.

Amyloid-β42 stimulated hippocampal lactate release is coupled to glutamate uptake

Since brain glucose hypometabolism is a feature of Alzheimer’s disease (AD) progression, lactate utilization as an energy source may become critical to maintaining central bioenergetics. We have previously shown that soluble amyloid-β (Aβ)₄₂ stimulates glutamate release through the α7 nicotinic acetylcholine receptor (α7nAChR) and hippocampal glutamate levels are elevated in the APP/PS1 mouse model of AD. Accordingly, we hypothesized that increased glutamate clearance contributes to elevated extracellular lactate levels through activation of the astrocyte neuron lactate shuttle (ANLS). We utilized an enzyme-based microelectrode array (MEA) selective for measuring basal and phasic extracellular hippocampal lactate in male and female C57BL/6J mice. Although basal lactate was similar, transient lactate release varied across hippocampal subregions with the CA1 > CA3 > dentate for both sexes. Local application of Aβ₄₂ stimulated lactate release throughout the hippocampus of male mice, but was localized to the CA1 of female mice. Coapplication with a nonselective glutamate or lactate transport inhibitor blocked these responses. Expression levels of SLC16A1, lactate dehydrogenase (LDH) A, and B were elevated in female mice which may indicate compensatory mechanisms to upregulate lactate production, transport, and utilization. Enhancement of the ANLS by Aβ₄₂-stimulated glutamate release during AD progression may contribute to bioenergetic dysfunction in AD.

Inulin Supplementation Mitigates Gut Dysbiosis and Brain Impairment Induced by Mild Traumatic Brain Injury during Chronic Phase

Mild traumatic brain injury (mTBI) has been shown to acutely alter the gut microbiome diversity and composition, known as dysbiosis, which can further exacerbate metabolic and vascular changes in the brain in both humans and rodents. However, it remains unknown how mTBI affects the gut microbiome in the chronic phase recovery (past one week post injury). It is also unknown if injury recovery can be improved by mitigating dysbiosis. The goal of the study is to fill the knowledge gap. First, we aim to understand how mTBI alters the gut microbiome through the chronic period of recovery (3 months post injury). In addition, as the gut microbiome can be modulated by diet, we also investigated if prebiotic inulin, a fermentable fiber that promotes growth of beneficial bacteria and metabolites, would mitigate dysbiosis, improve systemic metabolism, and protect brain structural and vascular integrity when administered after 3 months post closed head injury (CHI). We found that CHI given to male mice at 4 months of age induced gut dysbiosis which peaked at 1.5 months post injury, reduced cerebral blood flow (CBF) and altered brain white matter integrity. Interestingly, we also found that Sham mice had transient dysbiosis, which peaked 24 hours after injury and then normalized. After 8 weeks of inulin feeding, CHI mice had increased abundance of beneficial/anti-inflammatory bacteria, reduced abundance of pathogenic bacteria, enriched levels of short-chain fatty acids, and restored CBF in both hippocampi and left thalamus, compared to the CHI-control fed and Sham groups. Using machine learning, we further identified top bacterial species that separate Sham and CHI mice with and without the diet. Our results indicate that there is an injury- and time-dependent dysbiosis between CHI and Sham mice; inulin is effective to mitigate dysbiosis and improve brain injury recovery in the CHI mice. As there are currently no effective treatments for mTBI, the study may have profound implications for developing therapeutics or preventive interventions in the future.

Amyloid-PET Levels in the Precuneus and Posterior Cingulate Cortices Are Associated with Executive Function Scores in Preclinical Alzheimer's Disease Prior to Overt Global Amyloid Positivity

BACKGROUND

Global amyloid-β (Aβ) deposition in the brain can be quantified by Aβ-PET scans to support or refute a diagnosis of preclinical Alzheimer's disease (pAD). Yet, Aβ-PET scans enable quantitative evaluation of regional Aβ elevations in pAD, potentially allowing even earlier detection of pAD, long before global positivity is achieved. It remains unclear as to whether such regional changes are clinically meaningful.

OBJECTIVE

Test the hypothesis that early focal regional amyloid deposition in the brain is associated with cognitive performance in specific cognitive domain scores in pAD.

METHODS

Global and regional standardized uptake value ratios (SUVr) from 18F-florbetapir PET/CT scanning were determined using the Siemens Syngo.via® Neurology software package across a sample of 99 clinically normal participants with Montreal Cognitive Assessment (MoCA) scores≥23. Relationships between regional SUVr and cognitive test scores were analyzed using linear regression models adjusted for age, sex, and education. Participants were divided into two groups based on SUVr in the posterior cingulate and precuneus gyri (SUVR≥1.17). Between group differences in cognitive test scores were analyzed using ANCOVA models.

RESULTS

Executive function performance was associated with increased regional SUVr in the precuneus and posterior cingulate regions only (p < 0.05). There were no significant associations between memory and Aβ-PET SUVr in any regions of the brain.

CONCLUSION

These data demonstrate that increased Aβ deposition in the precuneus and posterior cingulate (the earliest brain regions affected with Aβ pathology) is associated with changes in executive function that may precede memory decline in pAD.

DNA Methylation in Genetic and Sporadic Forms of Neurodegeneration: Lessons from Alzheimer's, Related Tauopathies and Genetic Tauopathies

Genetic and sporadic forms of tauopathies, the most prevalent of which is Alzheimer's Disease, are a scourge of the aging society, and in the case of genetic forms, can also affect children and young adults. All tauopathies share ectopic expression, mislocalization, or aggregation of the microtubule associated protein TAU, encoded by the MAPT gene. As TAU is a neuronal protein widely expressed in the CNS, the overwhelming majority of tauopathies are neurological disorders. They are characterized by cognitive dysfunction often leading to dementia, and are frequently accompanied by movement abnormalities such as parkinsonism. Tauopathies can lead to severe neurological deficits and premature death. For some tauopathies there is a clear genetic cause and/or an epigenetic contribution. However, for several others the disease etiology is unclear, with few tauopathies being environmentally triggered. Here, we review current knowledge of tauopathies listing known genetic and important sporadic forms of these disease. Further, we discuss how DNA methylation as a major epigenetic mechanism emerges to be involved in the disease pathophysiology of Alzheimer's, and related genetic and non-genetic tauopathies. Finally, we debate the application of epigenetic signatures in peripheral blood samples as diagnostic tools and usages of epigenetic therapy strategies for these diseases.

Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer's disease

BACKGROUND

In Alzheimer's disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology.

METHODS

A cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts.

RESULTS

A β pathology status was determined based on the A β₄₂/A β₄₀ ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R² score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F₁ score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R² score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R² score of 0.228.

CONCLUSION

Our analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.

Metabolism navigates neural cell fate in development, aging and neurodegeneration

An uninterrupted energy supply is critical for the optimal functioning of all our organs, and in this regard the human brain is particularly energy dependent. The study of energy metabolic pathways is a major focus within neuroscience research, which is supported by genetic defects in the oxidative phosphorylation mechanism often contributing towards neurodevelopmental disorders and changes in glucose metabolism presenting as a hallmark feature in age-dependent neurodegenerative disorders. However, as recent studies have illuminated roles of cellular metabolism that span far beyond mere energetics, it would be valuable to first comprehend the physiological involvement of metabolic pathways in neural cell fate and function, and to subsequently reconstruct their impact on diseases of the brain. In this Review, we first discuss recent evidence that implies metabolism as a master regulator of cell identity during neural development. Additionally, we examine the cell type-dependent metabolic states present in the adult brain. As metabolic states have been studied extensively as crucial regulators of malignant transformation in cancer, we reveal how knowledge gained from the field of cancer has aided our understanding in how metabolism likewise controls neural fate determination and stability by directly wiring into the cellular epigenetic landscape. We further summarize research pertaining to the interplay between metabolic alterations and neurodevelopmental and psychiatric disorders, and expose how an improved understanding of metabolic cell fate control might assist in the development of new concepts to combat age-dependent neurodegenerative diseases, particularly Alzheimer's disease.

Contrasting Metabolic Insufficiency in Aging and Dementia

Metabolic insufficiency and neuronal dysfunction occur in normal aging but is exaggerated in dementia and Alzheimer's disease (AD). Metabolic insufficiency includes factors important for both substrate supply and utilization in the brain. Metabolic insufficiency occurs through a number of serial mechanisms, particularly changes in cerebrovascular supply through blood vessel abnormalities (ie, small and large vessel vasculopathy, stroke), alterations in neurovascular coupling providing dynamic blood flow supply in relation to neuronal demand, abnormalities in blood brain barrier including decreased glucose and amino acid transport, altered glymphatic flow in terms of substrate supply across the extracellular space to cells and drainage into CSF of metabolites, impaired transport into cells, and abnormal intracellular metabolism with more reliance on glycolysis and less on mitochondrial function. Recent studies have confirmed abnormal neurovascular coupling in a mouse model of AD in response to metabolic challenges, but the supply chain from the vascular system into neurons is disrupted much earlier in dementia than in equivalently aged individuals, contributing to the progressive neuronal degeneration and cognitive dysfunction associated with dementia. We discuss several metabolic treatment approaches, but these depend on characterizing patients as to who would benefit the most. Surrogate biomarkers of metabolism are being developed to include dynamic estimates of neuronal demand, sufficiency of neurovascular coupling, and glymphatic flow to supplement traditional static measurements. These surrogate biomarkers could be used to gauge efficacy of metabolic treatments in slowing down or modifying dementia time course.

Polyphenol-Peptide Interactions in Mitigation of Alzheimer's Disease: Role of Biosurface-Induced Aggregation

Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder, responsible for nearly two-thirds of all dementia cases. In this review, we report the potential AD treatment strategies focusing on natural polyphenol molecules (green chemistry) and more specifically on the inhibition of polyphenol-induced amyloid aggregation/disaggregation pathways: in bulk and on biosurfaces. We discuss how these pathways can potentially alter the structure at the early stages of AD, hence delaying the aggregation of amyloid-β (Aβ) and tau. We also discuss multidisciplinary approaches, combining experimental and modelling methods, that can better characterize the biochemical and biophysical interactions between proteins and phenolic ligands. In addition to the surface-induced aggregation, which can occur on surfaces where protein can interact with other proteins and polyphenols, we suggest a new concept referred as "confinement stability". Here, on the contrary, the adsorption of Aβ and tau on biosurfaces other than Aβ- and tau-fibrils, e.g., red blood cells, can lead to confinement stability that minimizes the aggregation of Aβ and tau. Overall, these mechanisms may participate directly or indirectly in mitigating neurodegenerative diseases, by preventing protein self-association, slowing down the aggregation processes, and delaying the progression of AD.

Mitochondrial dysfunction in neurodegenerative diseases: A focus on iPSC-derived neuronal models

Progressive neuronal loss is a hallmark of many neurodegenerative diseases, including Alzheimer's and Parkinson's disease. These pathologies exhibit clear signs of inflammation, mitochondrial dysfunction, calcium deregulation, and accumulation of aggregated or misfolded proteins. Over the last decades, a tremendous research effort has contributed to define some of the pathological mechanisms underlying neurodegenerative processes in these complex brain neurodegenerative disorders. To better understand molecular mechanisms responsible for neurodegenerative processes and find potential interventions and pharmacological treatments, it is important to have robust in vitro and pre-clinical animal models that can recapitulate both the early biological events undermining the maintenance of the nervous system and early pathological events. In this regard, it would be informative to determine how different inherited pathogenic mutations can compromise mitochondrial function, calcium signaling, and neuronal survival. Since post-mortem analyses cannot provide relevant information about the disease progression, it is crucial to develop model systems that enable the investigation of early molecular changes, which may be relevant as targets for novel therapeutic options. Thus, the use of human induced pluripotent stem cells (iPSCs) represents an exceptional complementary tool for the investigation of degenerative processes. In this review, we will focus on two neurodegenerative diseases, Alzheimer's and Parkinson's disease. We will provide examples of iPSC-derived neuronal models and how they have been used to study calcium and mitochondrial alterations during neurodegeneration.

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.

Therapeutic implications of hypothalamic-pituitaryadrenal-axis modulation in Alzheimer's disease: A narrative review of pharmacological and lifestyle interventions

With disease-modifying treatments for Alzheimer's disease (AD) still elusive, the search for alternative intervention strategies has intensified. Growing evidence suggests that dysfunction in hypothalamic-pituitaryadrenal-axis (HPAA) activity may contribute to the development of AD pathology. The HPAA, may therefore offer a novel target for therapeutic action. This review summarises and critically evaluates animal and human studies investigating the effects of pharmacological and non-pharmacological intervention on HPAA modulation alongside cognitive performance. The interventions discussed include glucocorticoid receptor antagonists and 11β-hydroxysteroid dehydrogenase inhibitors as well as lifestyle treatments such as physical activity, diet, sleep and contemplative practices. Pharmacological HPAA modulators improve pathology and cognitive deficit in animal AD models, but human pharmacological trials are yet to provide definitive support for such benefits. Lifestyle interventions may offer promising strategies for HPAA modification and cognitive health, but several methodological caveats across these studies were identified. Directions for future research in AD studies are proposed.

Hub gene identification and prognostic model construction for isocitrate dehydrogenase mutation in glioma

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We identified ten hub genes which were driving IDH status in GBM and LGG.

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We constructed a prognostic model for IDH-mutant patients.

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Our findings have important clinical implications for accurate treatment in glioma.

Our study attempted to identify hub genes related to isocitrate dehydrogenase (IDH) mutation in glioma and develop a prognostic model for IDH-mutant glioma patients. In a first step, ten hub genes significantly associated with the IDH status were identified by weighted gene coexpression analysis (WGCNA). The functional enrichment analysis demonstrated that the most enriched terms of these hub genes were cadherin binding and glutathione metabolism. Three of these hub genes were significantly linked with the survival of glioma patients. 328 samples of IDH-mutant glioma were separated into two datasets: a training set (N = 228) and a test set (N = 100). Based on the training set, we identified two IDH-mutant subtypes with significantly different pathological features by using consensus clustering. A 31 gene-signature was identified by the least absolute shrinkage and selection operator (LASSO) algorithm and used for establishing a differential prognostic model for IDH-mutant patients. In addition, the test set was employed for validating the prognostic model, and the model was proven to be of high value in classifying prognostic information of samples. The functional annotation revealed that the genes related to the model were mainly enriched in nuclear division, DNA replication, and cell cycle. Collectively, this study provided novel insights into the molecular mechanism of IDH mutation in glioma, and constructed a prognostic model which can be effective for predicting prognosis of glioma patients with IDH-mutation, which might promote the development of IDH target agents in glioma therapies and contribute to accurate prognostication and management in IDH-mutant glioma patients.

Thymus vulgaris Essential Oil Protects Zebrafish against Cognitive Dysfunction by Regulating Cholinergic and Antioxidants Systems

Thymus vulgaris L. is an aromatic herb used for medicinal purposes such as antimicrobial, spasmolytic, antioxidant, anti-inflammatory, antinociceptive, antitumor, and may have beneficial effects in the treatment of Alzheimer’s disease. The present study aimed to investigate whether Thymus vulgaris L. essential oil enhances cognitive function via the action on cholinergic neurons using scopolamine (Sco)-induced zebrafish (Danio rerio) model of memory impairments. Thymus vulgaris L. essential oil (TEO, 25, 150, and 300 µL/L) was administered by immersion to zebrafish once daily for 13 days, whereas memory impairment was induced by Sco (100 μM), a muscarinic receptor antagonist, delivered 30 min before behavioral tests. Spatial memory was assessed using the Y-maze test and novel object recognition test (NOR). Anxiety and depression were measured in the novel tank diving test (NTT). Gas Chromatograph-Mass Spectrometry (GC-MS) analysis was used to study the phytochemical composition of TEO. Acetylcholinesterase (AChE) activity and oxidative stress response in the brain of zebrafish were determined. TEO ameliorated Sco-induced increasing of AChE activity, amnesia, anxiety, and reduced the brain antioxidant capacity. These results suggest that TEO may have preventive and/or therapeutic potentials in the management of memory deficits and brain oxidative stress in zebrafish with amnesia.