Therapeutic B-cell depletion reverses progression of Alzheimer's disease.
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ABSTRACT:
SUBMITTER: Kim K
PROVIDER: S-EPMC8042032 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The clinical presentation and the pathological processes underlying Alzheimer's disease (AD) can be very heterogeneous in severity, location, and composition including the amount and distribution of AB deposition and spread of neurofibrillary tangles in different brain regions resulting in atypical clinical patterns and the existence of distinct AD variants. Heterogeneity in AD may be related to demographic factors (such as age, sex, educational and socioeconomic level) and genetic factors, which influence underlying pathology, the cognitive and behavioral phenotype, rate of progression, the occurrence of neuropsychiatric features, and the presence of comorbidities (e.g., vascular disease, neuroinflammation). Heterogeneity is also manifest in the individual resilience to the development of neuropathology (brain reserve) and the ability to compensate for its cognitive and functional impact (cognitive and functional reserve). The variability in specific cognitive profiles and types of functional impairment may be associated with different progression rates, and standard measures assessing progression may not be equivalent for individual cognitive and functional profiles. Other factors, which may govern the presence, rate, and type of progression of AD, include the individuals' general medical health, the presence of specific systemic conditions, and lifestyle factors, including physical exercise, cognitive and social stimulation, amount of leisure activities, environmental stressors, such as toxins and pollution, and the effects of medications used to treat medical and behavioral conditions. These factors that affect progression are important to consider while designing a clinical trial to ensure, as far as possible, well-balanced treatment and control groups.
| S-EPMC9130395 | biostudies-literature
Project description:Alzheimer's disease (AD) is the most common age related neurodegenerative disease. Currently, there are no disease modifying drugs, existing therapies only offer short-term symptomatic relief. Two of the pathognomonic indicators of AD are the presence of extracellular protein aggregates consisting primarily of the A? peptide and oxidative stress. Both of these phenomena can potentially be explained by the interactions of A? with metal ions. In addition, metal ions play a pivotal role in synaptic function and their homeostasis is tightly regulated. A breakdown in this metal homeostasis and the generation of toxic A? oligomers are likely to be responsible for the synaptic dysfunction associated with AD. Therefore, approaches that are designed to prevent A? metal interactions, inhibiting the formation of toxic A? species as well as restoring metal homeostasis may have potential as disease modifying strategies for treating AD. This review summarizes the physiological and pathological interactions that metal ions play in synaptic function with particular emphasis placed on interactions with A?. A variety of therapeutic strategies designed to address these pathological processes are also described. The most advanced of these strategies is the so-called 'metal protein attenuating compound' approach, with the lead molecule PBT2 having successfully completed early phase clinical trials. The success of these various strategies suggests that manipulating metal ion interactions offers multiple opportunities to develop disease modifying therapies for AD.
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Project description:BackgroundAlzheimer's disease (AD) is a neurodegenerative impairment mainly recognized by memory loss and cognitive deficits. However, the current therapies against AD are mostly limited to palliative medications, prompting researchers to investigate more efficient therapeutic approaches for AD, such as stem cell therapy. Recent evidence has proposed that extensive neuronal and synaptic loss and altered adult neurogenesis, which is perceived pivotal in terms of plasticity and network maintenance, occurs early in the course of AD, which exacerbates neuronal vulnerability to AD. Thus, regeneration and replenishing the depleted neuronal networks by strengthening the endogenous repair mechanisms or exogenous stem cells and their cargoes is a rational therapeutic approach. Currently, several stem cell-based therapies as well as stem cell products like exosomes, have shown promising results in the early diagnosis of AD.ObjectiveThis review begins with a comparison between AD and normal aging pathophysiology and a discussion on open questions in the field. Next, summarizing the current stem cell-based therapeutic and diagnostic approaches, we declare the advantages and disadvantages of each method. Also, we comprehensively evaluate the human clinical trials of stem cell therapies for AD.MethodologyPeer-reviewed reports were extracted through Embase, PubMed, and Google Scholar until 2021.ResultsWith several ongoing clinical trials, stem cells and their derivatives (e.g., exosomes) are an emerging and encouraging field in diagnosing and treating neurodegenerative diseases. Although stem cell therapies have been successful in animal models, numerous clinical trials in AD patients have yielded unpromising results, which we will further discuss.
| S-EPMC9886816 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2023-09-28 | GSE227223 | GEO
Project description:BACKGROUND:Given the complex and progressive nature of Alzheimer's disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions. METHODS:Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer's Disease Assessment Scale-cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups' distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression. RESULTS:The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels. CONCLUSIONS:Our results, 'learned' from clinical data, indicate the existence of at least three subgroups of Alzheimer's patients, each demonstrating a different trajectory of disease progression. This hypothesis-generating approach has detected distinct AD subgroups that may prove to be discrete endophenotypes linked to specific aetiologies. These findings could enable stratification within a clinical trial or study context, which may help identify new targets for intervention and guide better care.
| S-EPMC6389228 | biostudies-literature
Project description:INTRODUCTION:Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. METHODS:We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated. RESULTS:A model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R2 = 24%) and memory (R2 = 25%) in sporadic AD. Model-based risk-enrichment reduced the sample size required for detecting simulated intervention effects by 50%-75%. DISCUSSION:Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.
| S-EPMC7222030 | biostudies-literature
Project description:Mild cognitive impairment (MCI) is considered as a transition phase between normal aging and Alzheimer's disease (AD). MCI confers an increased risk of developing AD, although the state is heterogeneous with several possible outcomes, including even improvement back to normal cognition. We sought to determine the serum metabolomic profiles associated with progression to and diagnosis of AD in a prospective study. At the baseline assessment, the subjects enrolled in the study were classified into three diagnostic groups: healthy controls (n=46), MCI (n=143) and AD (n=47). Among the MCI subjects, 52 progressed to AD in the follow-up. Comprehensive metabolomics approach was applied to analyze baseline serum samples and to associate the metabolite profiles with the diagnosis at baseline and in the follow-up. At baseline, AD patients were characterized by diminished ether phospholipids, phosphatidylcholines, sphingomyelins and sterols. A molecular signature comprising three metabolites was identified, which was predictive of progression to AD in the follow-up. The major contributor to the predictive model was 2,4-dihydroxybutanoic acid, which was upregulated in AD progressors (P=0.0048), indicating potential involvement of hypoxia in the early AD pathogenesis. This was supported by the pathway analysis of metabolomics data, which identified upregulation of pentose phosphate pathway in patients who later progressed to AD. Together, our findings primarily implicate hypoxia, oxidative stress, as well as membrane lipid remodeling in progression to AD. Establishment of pathogenic relevance of predictive biomarkers such as ours may not only facilitate early diagnosis, but may also help identify new therapeutic avenues.
| S-EPMC3309497 | biostudies-literature
Project description:Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.
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Project description:Neurodegenerative diseases such as Alzheimer's, Huntington's and Parkinson's diseases have multifaceted nature because of the different factors contributing to their progression. The complex nature of neurodegenerative diseases has developed a pressing need to design multitarget-directed ligands to address the complementary pathways involved in these diseases. The major enzyme targets for development of therapeutics for Alzheimer's disease are cholinesterase and β-secretase enzymes. In this review, we discuss recent advances in profiling single target inhibitors based on these enzymes to multitarget-directed ligands as potential therapeutics for this devastating disease. In addition, therapeutics based on iron chelation strategy are discussed as well.
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