Perspectives and challenges in patient stratification in Alzheimer’s disease

Network dynamics-based subtyping of Alzheimer's disease with microglial genetic risk factors

BACKGROUND

The potential of microglia as a target for Alzheimer's disease (AD) treatment is promising, yet the clinical and pathological diversity within microglia, driven by genetic factors, poses a significant challenge. Subtyping AD is imperative to enable precise and effective treatment strategies. However, existing subtyping methods fail to comprehensively address the intricate complexities of AD pathogenesis, particularly concerning genetic risk factors. To address this gap, we have employed systems biology approaches for AD subtyping and identified potential therapeutic targets.

METHODS

We constructed patient-specific microglial molecular regulatory network models by utilizing existing literature and single-cell RNA sequencing data. The combination of large-scale computer simulations and dynamic network analysis enabled us to subtype AD patients according to their distinct molecular regulatory mechanisms. For each identified subtype, we suggested optimal targets for effective AD treatment.

RESULTS

To investigate heterogeneity in AD and identify potential therapeutic targets, we constructed a microglia molecular regulatory network model. The network model incorporated 20 known risk factors and crucial signaling pathways associated with microglial functionality, such as inflammation, anti-inflammation, phagocytosis, and autophagy. Probabilistic simulations with patient-specific genomic data and subsequent dynamics analysis revealed nine distinct AD subtypes characterized by core feedback mechanisms involving SPI1, CASS4, and MEF2C. Moreover, we identified PICALM, MEF2C, and LAT2 as common therapeutic targets among several subtypes. Furthermore, we clarified the reasons for the previous contradictory experimental results that suggested both the activation and inhibition of AKT or INPP5D could activate AD through dynamic analysis. This highlights the multifaceted nature of microglial network regulation.

CONCLUSIONS

These results offer a means to classify AD patients by their genetic risk factors, clarify inconsistent experimental findings, and advance the development of treatments tailored to individual genotypes for AD.

Identifying Proteomic Prognostic Markers for Alzheimer's Disease with Survival Machine Learning: the Framingham Heart Study

Background

Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is challenging by their high dimensionality and inherent correlations.

Methods

Our study analyzed 1128 plasma proteins, measured by the SOMAscan platform, from 858 participants 55 years and older (mean age 63 years, 52.9% women) of the Framingham Heart Study (FHS) Offspring cohort. We conducted regression analysis and machine learning models, including LASSO-based Cox proportional hazard regression model (LASSO) and generalized boosted regression model (GBM), to identify protein prognostic markers. These markers were used to construct a weighted proteomic composite score, the AD prediction performance of which was assessed using time-dependent area under the curve (AUC). The association between the composite score and memory domain was examined in 339 (of the 858) participants with available memory scores, and in an independent group of 430 participants younger than 55 years (mean age 46, 56.7% women).

Results

Over a mean follow-up of 20 years, 132 (15.4%) participants developed AD. After adjusting for baseline age, sex, education, and APOE ε4+ status, regression models identified 309 proteins ( P ≤ 0.2). After applying machine learning methods, nine of these proteins were selected to develop a composite score. This score improved AD prediction beyond the factors of age, sex, education, and APOE ε4+ status across 15 to 25 years of follow-up, achieving its peak AUC of 0.84 in the LASSO model at the 22-year follow-up. It also showed a consistent negative association with memory scores in 339 participants (beta = -0.061, P = 0.046), 430 independent participants (beta = -0.060, P = 0.018), and the pooled 769 samples (beta = -0.058, P = 0.003).

Conclusion

These findings highlight the utility of proteomic markers in improving AD prediction and emphasize the complex pathology of AD. The composite score may aid early AD detection and efficacy monitoring, warranting further validation in diverse populations.

The ROSMAP project: aging and neurodegenerative diseases through omic sciences

The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort studies, which have been collecting clinicopathological and molecular data since the early 1990s. This extensive dataset includes a wide array of omic data, revealing the complex interactions between molecular levels in neurodegenerative diseases (ND) and aging. Neurodegenerative diseases (ND) are frequently associated with morbidity and cognitive decline in older adults. Omics research, in conjunction with clinical variables, is crucial for advancing our understanding of the diagnosis and treatment of neurodegenerative diseases. This summary reviews the extensive omics research-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and multiomics-conducted through the ROSMAP study. It highlights the significant advancements in understanding the mechanisms underlying neurodegenerative diseases, with a particular focus on Alzheimer's disease.

Multimechanism biological profiling of tetrahydro-β-carboline analogues as selective HDAC6 inhibitors for the treatment of Alzheimer's disease

With the intensive research on the pathogenesis of Alzheimer's disease (AD), inhibition of HDAC6 appears to be a potential therapeutic approach for AD. In this paper, a series of tetrahydro-β-carboline derivatives with hydroxamic acid group were fast synthesized. Among all, the most potent 15 selectively inhibited HDAC6 with IC50 of 15.2 nM and markedly increased acetylated alpha-tubulin levels. In cellular assay, 15 showed excellent neurotrophic effect by increasing the expression of GAP43 and Beta-3 tubulin markers. Besides, 15 showed neuroprotective effects in PC12 or SH-SY5Y cells against H2O2 and 6-OHDA injury through activation of Nrf2, catalase and Prx II, and significantly reduced H2O2-induced reactive oxygen species (ROS) production. In vivo, 15 significantly attenuated zebrafish anxiety-like behaviour and memory deficits in a SCOP-induced zebrafish model of AD. To sum up, multifunctional 15 might be a good lead to develop novel tetrahydrocarboline-based agents for the treatment of AD.

Recent advances in Alzheimer's disease: Mechanisms, clinical trials and new drug development strategies

Alzheimer's disease (AD) stands as the predominant form of dementia, presenting significant and escalating global challenges. Its etiology is intricate and diverse, stemming from a combination of factors such as aging, genetics, and environment. Our current understanding of AD pathologies involves various hypotheses, such as the cholinergic, amyloid, tau protein, inflammatory, oxidative stress, metal ion, glutamate excitotoxicity, microbiota-gut-brain axis, and abnormal autophagy. Nonetheless, unraveling the interplay among these pathological aspects and pinpointing the primary initiators of AD require further elucidation and validation. In the past decades, most clinical drugs have been discontinued due to limited effectiveness or adverse effects. Presently, available drugs primarily offer symptomatic relief and often accompanied by undesirable side effects. However, recent approvals of aducanumab (1) and lecanemab (2) by the Food and Drug Administration (FDA) present the potential in disrease-modifying effects. Nevertheless, the long-term efficacy and safety of these drugs need further validation. Consequently, the quest for safer and more effective AD drugs persists as a formidable and pressing task. This review discusses the current understanding of AD pathogenesis, advances in diagnostic biomarkers, the latest updates of clinical trials, and emerging technologies for AD drug development. We highlight recent progress in the discovery of selective inhibitors, dual-target inhibitors, allosteric modulators, covalent inhibitors, proteolysis-targeting chimeras (PROTACs), and protein-protein interaction (PPI) modulators. Our goal is to provide insights into the prospective development and clinical application of novel AD drugs.

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification

INTRODUCTION

Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images.

METHODS

1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC).

RESULTS

Analysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did.

CONCLUSIONS

The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

TMS-derived short afferent inhibition discriminates cognitive status in older adults without dementia

Aging is a complex and diverse biological process characterized by progressive molecular, cellular, and tissue damage, resulting in a loss of physiological integrity and heightened vulnerability to pathology. This biological diversity corresponds with highly variable cognitive trajectories, which are further confounded by genetic and environmental factors that influence the resilience of the aging brain. Given this complexity, there is a need for neurophysiological indicators that not only discern physiologic and pathologic aging but also closely align with cognitive trajectories. Transcranial Magnetic Stimulation (TMS) may have utility in this regard as a non-invasive brain stimulation tool that can characterize features of cortical excitability. Particularly, as a proxy for central cholinergic function, short-afferent inhibition (SAI) dysfunction is robustly associated with cognitive deficits in the latter stages of Alzheimer's Disease and Related Dementia (ADRD). In this study, we evaluated SAI in healthy young adults and older adults who, though absent clinical diagnoses, were algorithmically classified as cognitively normal (CN) or cognitively impaired (CI) according to the Jak/Bondi actuarial criteria. We report that SAI is preserved in the Old-CN cohort relative to the young adults, and SAI is significantly diminished in the Old-CI cohort relative to both young and CN older adults. Additionally, diminished SAI was significantly associated with impaired sustained attention and working memory. As a proxy measure for central cholinergic deficits, we discuss the potential value of SAI for discerning physiological and pathological aging.

An In Silico Study Based on QSAR and Molecular Docking and Molecular Dynamics Simulation for the Discovery of Novel Potent Inhibitor against AChE

Acetylcholinesterase (AChE) is one of the main drug targets for treating Alzheimer's disease. This current study relies on multiple molecular modeling approaches to develop new potent inhibitors of AChE. We explored a 2D QSAR study using the statistical method of multiple linear regression based on a set of substituted 5-phenyl-1,3,4-oxadiazole and N-benzylpiperidine analogs, which were recently synthesized and proved their inhibitory activities against acetylcholinesterase (AChE). The molecular descriptors, polar surface area, dipole moment, and molecular weight are the key structural properties governing AChE inhibition activity. The MLR model was selected based on its statistical parameters: R2 = 0.701, R2test = 0.76, Q2CV = 0.638, and RMSE = 0.336, demonstrating its predictive reliability. Randomization tests, VIF tests, and applicability domain tests were adopted to verify the model's robustness. As a result, 11 new molecules were designed with higher anti-Alzheimer's activities than the model molecule. We demonstrated their improved pharmacokinetic properties through an in silico ADMET study. A molecular docking study was conducted to explore their AChE inhibition mechanisms and binding affinities in the active site. The binding scores of compounds M1, M2, and M6 were (-12.6 kcal/mol), (-13 kcal/mol), and (-12.4 kcal/mol), respectively, which are higher than the standard inhibitor Donepezil with a binding score of (-10.8 kcal/mol). Molecular dynamics simulations over 100 ns were used to validate the molecular docking results, indicating that compounds M1 and M2 remain stable in the active site, confirming their potential as promising anti-AChE inhibitors.

The Olfactory Trail of Neurodegenerative Diseases

Alterations in olfactory functions are proposed as possible early biomarkers of neurodegenerative diseases. Parkinson's and Alzheimer's diseases manifest olfactory dysfunction as a symptom, which is worth mentioning. The alterations do not occur in all patients, but they can serve to rule out neurodegenerative pathologies that are not associated with small deficits. Several prevalent neurodegenerative conditions, including impaired smell, arise in the early stages of Parkinson's and Alzheimer's diseases, presenting an attractive prospect as a snitch for early diagnosis. This review covers the current knowledge on the link between olfactory deficits and Parkinson's and Alzheimer's diseases. The review also covers the emergence of olfactory receptors as actors in the pathophysiology of these diseases. Olfactory receptors are not exclusively expressed in olfactory sensory neurons. Olfactory receptors are widespread in the human body; they are expressed, among others, in the testicles, lungs, intestines, kidneys, skin, heart, and blood cells. Although information on these ectopically expressed olfactory receptors is limited, they appear to be involved in cell recognition, migration, proliferation, wound healing, apoptosis, and exocytosis. Regarding expression in non-chemosensory regions of the central nervous system (CNS), future research should address the role, in both the glia and neurons, of olfactory receptors. Here, we review the limited but relevant information on the altered expression of olfactory receptor genes in Parkinson's and Alzheimer's diseases. By unraveling how olfactory receptor activation is involved in neurodegeneration and identifying links between olfactory structures and neuronal death, valuable information could be gained for early diagnosis and intervention strategies in neurodegenerative diseases.

Exploring Heparan Sulfate Proteoglycans as Mediators of Human Mesenchymal Stem Cell Neurogenesis

Alzheimer's disease (AD) and traumatic brain injury (TBI) are major public health issues worldwide, with over 38 million people living with AD and approximately 48 million people (27-69 million) experiencing TBI annually. Neurodegenerative conditions are characterised by the accumulation of neurotoxic amyloid beta (Aβ) and microtubule-associated protein Tau (Tau) with current treatments focused on managing symptoms rather than addressing the underlying cause. Heparan sulfate proteoglycans (HSPGs) are a diverse family of macromolecules that interact with various proteins and ligands and promote neurogenesis, a process where new neural cells are formed from stem cells. The syndecan (SDC) and glypican (GPC) HSPGs have been implicated in AD pathogenesis, acting as drivers of disease, as well as potential therapeutic targets. Human mesenchymal stem cells (hMSCs) provide an attractive therapeutic option for studying and potentially treating neurodegenerative diseases due to their relative ease of isolation and subsequent extensive in vitro expansive potential. Understanding how HSPGs regulate protein aggregation, a key feature of neurodegenerative disorders, is essential to unravelling the underlying disease processes of AD and TBI, as well as any link between these two neurological disorders. Further research may validate HSPG, specifically SDCs or GPCs, use as neurodegenerative disease targets, either via driving hMSC stem cell therapy or direct targeting.

Impact of mechanical ventilation on clinical outcomes in ICU-admitted Alzheimer's disease patients: a retrospective cohort study

Background

Alzheimer's disease (AD) is increasingly recognized as a pressing global public health issue, demanding urgent development of scientific AD management strategies. In recent years, the proportion of AD patients in Intensive Care Units (ICU) has been on the rise. Simultaneously, the use of mechanical ventilation (MV) is becoming more prevalent among this specific patient group. Considering the pathophysiological characteristics of AD, the application of MV in AD patients may lead to different outcomes. However, due to insufficient research data, the significant impact of MV on the prognosis of AD patients in the ICU remains unclear. Therefore, we conducted this study to comprehensively evaluate the potential influence of MV on the survival rate of AD patients in the ICU.

Methods

We obtained data from the MIMIC-IV database for patients diagnosed with AD. Using propensity score matching (PSM), we paired patients who received MV treatment with those who did not receive treatment. Next, we conducted Cox regression analysis to evaluate the association between MV and in-hospital mortality, 7-day mortality, 28-day mortality, 90-day mortality, 4-year mortality, length of hospital stay, and ICU stay.

Results

The data analysis involved a cohort of 641 AD patients spanning from 2008 to 2019, inclusive. Following a 1:2 propensity score matching (PSM) procedure, 300 patients were successfully paired, comprising 123 individuals who underwent MV treatment and 177 who did not. MV demonstrated an association with an elevated risk of in-hospital mortality (HR 5.782; 95% CI 2.981-11.216; p < 0.001), 7-day mortality (HR 6.353; 95% CI 3.014-13.392; p < 0.001), 28-day mortality (HR 3.210; 95% CI 1.977-5.210; p < 0.001), 90-day mortality (HR 2.334; 95% CI 1.537-3.544; p < 0.001), and 4-year mortality (HR 1.861; 95% CI 1.370-2.527; p < 0.001). Furthermore, it was associated with a prolonged length of ICU stay [3.6(2.2,5.8) vs. 2.2(1.6,3.7); p = 0.001]. In the subgroup analysis, we further confirmed the robustness of the results obtained from the overall population. Additionally, we observed a significant interaction (p-interaction <0.05) between age, admission type, aspirin use, statin use, and the use of MV.

Conclusion

In patients with AD who are receiving treatment in the ICU, the use of MV has been linked to higher short-term, medium-term, and long-term mortality rates, as well as prolong ICU stays. Therefore, it is crucial to break away from conventional thinking and meticulously consider both the medical condition and personal preferences of these vulnerable patients. Personalized treatment decisions, comprehensive communication between healthcare providers and patients, formulation of comprehensive treatment plans, and a focus on collaboration between the ICU and community organizations become imperative.

Neuropathogenesis-on-chips for neurodegenerative diseases

Developing diagnostics and treatments for neurodegenerative diseases (NDs) is challenging due to multifactorial pathogenesis that progresses gradually. Advanced in vitro systems that recapitulate patient-like pathophysiology are emerging as alternatives to conventional animal-based models. In this review, we explore the interconnected pathogenic features of different types of ND, discuss the general strategy to modelling NDs using a microfluidic chip, and introduce the organoid-on-a-chip as the next advanced relevant model. Lastly, we overview how these models are being applied in academic and industrial drug development. The integration of microfluidic chips, stem cells, and biotechnological devices promises to provide valuable insights for biomedical research and developing diagnostic and therapeutic solutions for NDs.

Predicting clinical progression trajectories of early Alzheimer's disease patients

BACKGROUND

Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring.

METHODS

Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures.

RESULTS

The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%.

DISCUSSION

Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.

Morus Alba Fruit Extract and its Fractions Ameliorate Streptozotocin Induced Cognitive Deficit in Mice via Modulating Oxidative and Cholinergic Systems

Increased oxidative stress and acetylcholinesterase (AChE) activity are key pathological characters contributing to the memory disorders. Thus, drugs targeting both oxidative stress and AChE are being explored for the management of cognitive dysfunction. Morus alba fruits (commonly consumed for its high nutritious value) are known to have antioxidant and AChE inhibitory effects. However, the role of Morus alba fruits in the management of memory disorders has not reported yet. This investigation was conducted to assess the antioxidant and AChE inhibitory potential of Morus alba fruit extracts in-vitro and to identify the components responsible for such effects. Further, the obtained bioactive component was studied for possible memory improvement effects against streptozotocin (STZ) induced dementia. To isolate the bioactive component in-vitro DPPH and AChE assays guided fractionation was performed. Memory functions in mice were determined using Morris Water Maze test while brain biochemical parameters were measured to understand the mechanism of action. In-vitro assays revealed strong AChE and DPPH inhibitory potential of methanol extract (ME), therefore, it was further fractionated. Among various fractions obtained, ethyl-acetate fraction (EAF) was found to possess marked AChE and DPPH inhibitory activities. On subsequent fractionation of EAF, bioactivity of obtained sub-fractions was found to be inferior to EAF. Further, both ME and EAF improved STZ (intracerebroventricular) induced cognitive dysfunction in animals by restoring endogenous antioxidant status (superoxide dismutase and reduced glutathione) and reducing thiobarbituric acid reactive species and nitric oxide levels along with brain AChE and myeloperoxidase activity. TLC densitometric studies showed appreciable levels of phenolic acids and quercetin in both EAF and ME. It can be concluded that Morus alba fruit extract has the ability to modulate cholinergic and oxidative system due to presence of phenolic and flavonoid compounds and hence, could aid in the management of memory disorders.

Cognitive Screening for Mild Cognitive Impairment: Clinician Perspectives on Current Practices and Future Directions

 This study surveyed 51 specialist clinicians for their views on existing cognitive screening tests for mild cognitive impairment and their opinions about a hypothetical remote screener driven by artificial intelligence (AI). Responses revealed significant concerns regarding the sensitivity, specificity, and time taken to administer current tests, along with a general willingness to consider adopting telephone-based screening driven by AI. Findings highlight the need to design screeners that address the challenges of recognizing the earliest stages of cognitive decline and that prioritize not only accuracy but also stakeholder input.

Impact of insulin resistance on mild cognitive impairment in type 2 diabetes mellitus patients with non-alcoholic fatty liver disease

AIMS

Insulin resistance (IR) is a pivotal factor in the pathogenesis of type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD). Nevertheless, the impact of IR on cognitive dysfunction in T2DM patients with NAFLD remains inadequately understood. We aim to investigate the effect of IR on mild cognitive impairment (MCI) in T2DM individuals with NAFLD.

MATERIALS AND METHODS

143 T2DM individuals were categorized into Non-MCI and MCI groups, as well as Non-NAFLD and NAFLD groups. Clinical parameters and cognitive preference test outcomes were compared. Correlation and regression analyses were executed to explore the interconnections between IR and cognitive details across all T2DM patients, as well as within the subgroup of individuals with NAFLD.

RESULTS

In comparison to the Non-MCI group, the MCI group displayed elevated HOMA-IR levels. Similarly, the NAFLD group exhibited higher HOMA-IR levels compared to the Non-NAFLD group. Additionally, a higher prevalence of MCI was observed in the NAFLD group as opposed to the Non-NAFLD group. Notably, HOMA-IR levels were correlated with Verbal Fluency Test (VFT) and Trail Making Test-B (TMTB) scores, both related to executive functions. Elevated HOMA-IR emerged as a risk factor for MCI in the all patients. Intriguingly, increased HOMA-IR not only correlated with TMTB scores but also demonstrated an influence on TMTA scores, reflecting information processing speed function in patients with NAFLD.

CONCLUSION

IR emerges as a contributory factor to cognitive dysfunction in T2DM patients. Furthermore, it appears to underlie impaired executive function and information processing speed function in T2DM individuals with NAFLD.

Exosomal miR-let-7c-5p is involved in the cognitive function of type 2 diabetes mellitus patients by interleukin 10: A cross-sectional study

BACKGROUND

Interleukin (IL)-10 plays a notable role in the inflammatory-associated mild cognitive impairment (MCI). We aimed to investigate whether IL-10 and its upstream factors exert an impact on MCI in type 2 diabetes mellitus (T2DM) patients.

METHODS

A total of 117 T2DM patients were recruited and divided into Control group and MCI group based on the presence or absence of MCI. Clinical parameters were collected. The Montreal Cognitive Assessment (MoCA) was conducted for global cognitive function. Digit Span Test (DST), Verbal Fluency Test (VFT), and Trail Making Test-B (TMTB) were used to evaluate the executive functions of the diabetic patients. Trail Making Test-A (TMTA) was performed to examine the information processing speed function. Patients' scene memory was examined by Logical Memory Test (LMT). After the baseline data were compared, correlation and regression analyses were performed to explore the relationship among IL-10, miR-let-7c-5p and cognitive function.

RESULTS

Compared to 80 patients in the control group, 37 patients in the MCI group exhibited lower IL-10 in plasma and higher miR-let-7c-5p levels in exosomes from plasma. The IL-10 level was negatively associated with MoCA. Likewise, miR-let-7c-5p levels were negatively correlated with IL-10 levels and MoCA. Elevated miR-let-7c-5p levels and decreased IL-10 levels are risk factors for MCI in T2DM patients. Increased miR-let-7c-5p and downregulated IL-10 may influence VFT and TMTB, respectively, associated with executive function.

CONCLUSIONS

We demonstrated that IL-10 is correlated to the executive function of T2DM patients. Decreased IL-10 may result from the regulation of miR-let-7c-5p in exosomes.

Persistence of neuropsychiatric symptoms and dementia prognostication: A comparison of three operational case definitions of mild behavioral impairment

INTRODUCTION

We compared three operational case definitions of mild behavioral impairment (MBI) in the context of MBI prevalence estimates and dementia risk modeling.

METHODS

Participants were dementia-free older adults (n = 13701) from the National Alzheimer's Coordinating Center. Operational case definitions of MBI were generated based on neuropsychiatric symptoms at one (OV), two-consecutive (TCV), or more than two-thirds (TTV) of dementia-free study visits. Definitions were compared in prevalence and in Cox regressions using MBI to predict incident dementia.

RESULTS

OV MBI was the most prevalent (54.4%), followed by TCV (32.3%) and TTV (26.7%) MBI. However, OV MBI had the lowest rate of incident dementia (hazard ratio [HR] = 2.54, 95% confidence interval [CI]: 2.33-2.78) and generated poorer model metrics than TCV MBI (HR = 4.06, 95% CI: 3.74-4.40) and TTV MBI (HR = 5.77, 95% CI: 5.32-6.26).

DISCUSSION

Case ascertainment with longer timeframe MBI operational case definitions may more accurately define groups at risk of dementia in datasets lacking tools designed to detect MBI.Highlights: Mild behavioral impairment (MBI) can identify older adults at risk of dementia.Neuropsychiatric symptom (NPS) assessment tools can be proxy measures for MBI.Hazard for dementia was highest for MBI defined by NPS presence at more than two-thirds of visits.

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease

BACKGROUND

Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD.

METHODS

We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset.

RESULTS

Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset.

CONCLUSIONS

The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.

Electrochemical Immunosensors Developed for Amyloid-Beta and Tau Proteins, Leading Biomarkers of Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurological disease and a serious cause of dementia, which constitutes a threat to human health. The clinical evidence has found that extracellular amyloid-beta peptides (Aβ), phosphorylated tau (p-tau), and intracellular tau proteins, which are derived from the amyloid precursor protein (APP), are the leading biomarkers for accurate and early diagnosis of AD due to their central role in disease pathology, their correlation with disease progression, their diagnostic value, and their implications for therapeutic interventions. Their detection and monitoring contribute significantly to understanding AD and advancing clinical care. Available diagnostic techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are mainly used to validate AD diagnosis. However, these methods are expensive, yield results that are difficult to interpret, and have common side effects such as headaches, nausea, and vomiting. Therefore, researchers have focused on developing cost-effective, portable, and point-of-care alternative diagnostic devices to detect specific biomarkers in cerebrospinal fluid (CSF) and other biofluids. In this review, we summarized the recent progress in developing electrochemical immunosensors for detecting AD biomarkers (Aβ and p-tau protein) and their subtypes (AβO, Aβ_(1-40), Aβ_(1-42), t-tau, cleaved-tau (c-tau), p-tau₁₈₁, p-tau₂₃₁, p-tau₃₈₁, and p-tau₄₄₁). We also evaluated the key characteristics and electrochemical performance of developed immunosensing platforms, including signal interfaces, nanomaterials or other signal amplifiers, biofunctionalization methods, and even primary electrochemical sensing performances (i.e., sensitivity, linear detection range, the limit of detection (LOD), and clinical application).

Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system

BACKGROUND

Neuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system.

METHODS

We analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets.

RESULTS

MiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons.

CONCLUSIONS

Despite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.

Highly selective butyrylcholinesterase inhibitors related to Amaryllidaceae alkaloids - Design, synthesis, and biological evaluation

Butyrylcholinesterase (BChE) is one of the most frequently implicated enzymes in the advanced stage of Alzheimer's disease (AD). As part of our endeavors to develop new drug candidates for AD, we have focused on natural template structures, namely the Amaryllidaceae alkaloids carltonine A and B endowed with high BChE selectivity. Herein, we report the design, synthesis, and in vitro evaluation of 57 novel highly selective human BChE (hBChE) inhibitors. Most synthesized compounds showed hBChE inhibition potency ranging from micromolar to low nanomolar scale. Compounds that revealed BChE inhibition below 100 nM were selected for detailed biological investigation. The CNS-targeted profile of the presented compounds was confirmed theoretically by calculating the BBB score algorithm, these data were corroborated by determining the permeability in vitro using PAMPA-assay for the most active derivatives. The study highlighted compounds 87 (hBChE IC₅₀ = 3.8 ± 0.2 nM) and 88 (hBChE IC₅₀ = 5.7 ± 1.5 nM) as the top-ranked BChE inhibitors. Compounds revealed negligible cytotoxicity for the human neuroblastoma (SH-SY5Y) and hepatocellular carcinoma (HepG2) cell lines compared to BChE inhibitory potential. A crystallographic study was performed to inspect the binding mode of compound 87, revealing essential interactions between 87 and hBChE active site. In addition, multidimensional QSAR analyses were applied to determine the relationship between chemical structures and biological activity in a dataset of designed agents. Compound 87 is a promising lead compound with potential implications for treating the late stages of AD.

In-depth investigation of the mechanisms of Schisandra chinensis polysaccharide mitigating Alzheimer's disease rat via gut microbiota and feces metabolomics

Schisandra chinensis (S. chinensis) is an herbal medicine used for the treatment of Alzheimer's disease (AD). The purified polysaccharide fraction, namely SCP2, was previously isolated from S. chinensis crude polysaccharide (SCP) and its structure and in vitro activity were investigated. However, the in vivo activity of SCP2 and its potential mechanism for the treatment of AD have yet to be determined. This study used a combination of microbiomics and metabolomics to comprehensively explore the microbiota and metabolic changes in AD rats under SCP2 intervention, with the aim of elucidating the potential mechanisms of SCP2 in the treatment of AD. SCP2 showed significant therapeutic effects in AD rats, as evidenced by improved learning and memory capacity, reduced neuroinflammation, and restoration of the integrity of the intestinal barrier. Fecal metabolomic and microbiomic analyses revealed that SCP2 significantly modulated 19 endogenous metabolites and reversed gut microbiota disorders in AD rats. Moreover, SCP2 significantly increased the content of short-chain fatty acid (SCFAs) in the AD rats. Correlation analysis showed a significant correlation between gut microbes, metabolites and the content of SCFAs. Collectively, these findings will provide the basis for further development of SCP2.

Seed amplification assay of nasal swab extracts for accurate and non-invasive molecular diagnosis of neurodegenerative diseases

Nasal swabs are non-invasive testing methods for detecting diseases by collecting samples from the nasal cavity or nasopharynx. Dysosmia is regarded as an early sign of coronavirus disease 2019 (COVID-19), and nasal swabs are the gold standard for the detection. By nasal swabs, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acids can be cyclically amplified and detected using real-time reverse transcriptase-polymerase chain reaction after sampling. Similarly, olfactory dysfunction precedes the onset of typical clinical manifestations by several years in prion diseases and other neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. In neurodegenerative diseases, nasal swab tests are currently being explored using seed amplification assay (SAA) of pathogenic misfolded proteins, such as prion, α-synuclein, and tau. These misfolded proteins can serve as templates for the conformational change of other copies from the native form into the same misfolded form in a prion-like manner. SAA for misfolded prion-like proteins from nasal swab extracts has been developed, conceptually analogous to PCR, showing high sensitivity and specificity for molecular diagnosis of degenerative diseases even in the prodromal stage. Cyclic amplification assay of nasal swab extracts is an attractive and feasible method for accurate and non-invasive detection of trace amount of pathogenic substances for screening and diagnosis of neurodegenerative disease.

Discovery of indole-piperazine derivatives as selective histone deacetylase 6 inhibitors with neurite outgrowth-promoting activities and neuroprotective activities

Novel indole-piperazine derivatives with a hydroxamic acid moiety were designed and synthesized as selective histone deacetylase 6 (HDAC6) inhibitors. In enzymatic assays, all compounds exhibited nanomolar IC₅₀ values. N-hydroxy-4-((4-(7-methyl-1H-indole-3-carbonyl)piperazin-1-yl)methyl)benzamide, 9c, was the most potent HDAC6 inhibitor (IC₅₀, 13.6 nM). In vitro, 9c induced neurite outgrowth of PC12 cells without producing toxic effects, better than Tubastatin A (Tub A). Additionally, 9c demonstrated blatant neuroprotective activity in PC12 cells against H₂O₂-induced oxidative damage. In western blot assay, 9c could increase the acetylation of α-tubulin in a dose-dependent manner.

Molecular Mechanisms of Neuroinflammation in Aging and Alzheimer's Disease Progression

Aging is the most prominent risk factor for late-onset Alzheimer's disease. Aging associates with a chronic inflammatory state both in the periphery and in the central nervous system, the evidence thereof and the mechanisms leading to chronic neuroinflammation being discussed. Nonetheless, neuroinflammation is significantly enhanced by the accumulation of amyloid beta and accelerates the progression of Alzheimer's disease through various pathways discussed in the present review. Decades of clinical trials targeting the 2 abnormal proteins in Alzheimer's disease, amyloid beta and tau, led to many failures. As such, targeting neuroinflammation via different strategies could prove a valuable therapeutic strategy, although much research is still needed to identify the appropriate time window. Active research focusing on identifying early biomarkers could help translating these novel strategies from bench to bedside.

Possible Application of Melatonin in Long COVID

Clinical sequelae and symptoms for a considerable number of COVID-19 patients can linger for months beyond the acute stage of SARS-CoV-2 infection, "long COVID". Among the long-term consequences of SARS-CoV-2 infection, cognitive issues (especially memory loss or "brain fog"), chronic fatigue, myalgia, and muscular weakness resembling myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are of importance. Melatonin may be particularly effective at reducing the signs and symptoms of SARS-CoV-2 infection due to its functions as an antioxidant, anti-inflammatory, and immuno-modulatory agent. Melatonin is also a chronobiotic medication effective in treating delirium and restoring the circadian imbalance seen in COVID patients in the intensive care unit. Additionally, as a cytoprotector, melatonin aids in the prevention of several COVID-19 comorbidities, including diabetes, metabolic syndrome, and ischemic and non-ischemic cardiovascular diseases. This narrative review discusses the application of melatonin as a neuroprotective agent to control cognitive deterioration ("brain fog") and pain in the ME/CFS syndrome-like documented in long COVID. Further studies on the therapeutic use of melatonin in the neurological sequelae of SARS-CoV-2 infection are warranted.