Specific serum autoantibodies predict the development and progression of Alzheimer's disease with high accuracy

Subjective cognitive decline in conjunction with cerebrospinal fluid anti-ATP1A3 autoantibodies and a low amyloid β 1-42/1-40 ratio: Report and literature review

BACKGROUND

Animal studies reveal the role of the sodium/potassium transporting ATPase α-3 subunit (ATP1A3) in maintaining the resting membrane potential and thus in synaptic information processing and potentially cognitive disorders. However, autoantibodies against AT1A3 have not previously been reported in patients with subjective cognitive decline.

CASE PRESENTATION

We report the case of a 57-year-old female who underwent neuropsychological testing, magnetic resonance imaging (MRI) and 18 F fluorodesoxyglucose positron emission tomography (FDG-PET) imaging, and cerebrospinal fluid (CSF) analysis. Neural autoantibodies were assessed in serum and CSF. We found a normal cognitive profile together with a self-reported cognitive decline, and such consistent with subjective cognitive decline (SCD). Analysis of the cerebrospinal fluid revealed anti-ATP1A3 autoantibodies. ATP1A3 autoantibodies were also detected in serum. Analysis of amyloid pathology markers in the CSF showed a slightly reduced amyloid β1-42/ amyloid β1-40 ratio. In view of the possible paraneoplastic autoantibodies, whole-body FDG-PET was performed, which did not reveal a malignancy-specific lesion. FDG-PET of the brain also showed no hypometabolism. We diagnosed SCD based on CSF-affirmed possible Alzheimer´s pathologic change with ATP1A3 autoantibodies in CSF and serum.

CONCLUSIONS

To our knowledge, this is the first report of CSF and serum ATP1A3 autoantibodies associated with SCD although an incidental finding cannot be fully excluded. In addition, amyloid pathology was detected via CSF biomarkers, suggesting that ATP1A3 autoantibodies are a potentially promising biomarker in SCD with an Alzheimer´s pathologic change if confirmed in large-scale studies.

Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network

The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, interpretability, ethical integration, population variety, and picture standardization must be addressed using deep learning for the functional magnetic resonance imaging (MRI) classification of AD in order to guarantee a trustworthy and practical therapeutic application. In this manuscript Classifying AD using a finite basis physics neural network (CAD-FBPINN) is proposed. Initially, images are collected from AD Neuroimaging Initiative (ADNI) dataset. The images are fed to Pre-processing segment. During the preprocessing phase the reverse lognormal Kalman filter (RLKF) is used to enhance the input images. Then the preprocessed images are given to the feature extraction process. Feature extraction is done by Newton-time-extracting wavelet transform (NTEWT), which is used to extract the statistical features such as the mean, kurtosis, and skewness. Finally the features extracted are given to FBPINNs for Classifying AD such as early mild cognitive impairment (EMCI), AD, mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), normal control (NC), and subjective memory complaints (SMCs). In General, FBPINN does not express adapting optimization strategies to determine optimal factors to ensure correct AD classification. Hence, sea-horse optimization algorithm (SHOA) to optimize FBPINN, which accurately classifies AD. The proposed technique implemented in python and efficacy of the CAD-FBPINN technique is assessed with support of numerous performances like accuracy, precision, Recall, F1-score, specificity and negative predictive value (NPV) is analyzed. Proposed CAD-FBPINN method attain 30.53%, 23.34%, and 32.64% higher accuracy; 20.53%, 25.34%, and 29.64% higher precision; 20.53%, 25.34%, and 29.64% higher NP values analyzed with the existing for Classifying AD Stages through Brain Modifications using FBPINNs Optimized with sea-horse optimizer. Then, the effectiveness of the CAD-FBPINN technique is compared to other methods that are currently in use, such as AD diagnosis and classification using a convolution neural network algorithm (DC-AD-AlexNet), Predicting diagnosis 4 years before Alzheimer's disease incident (PDP-ADI-GCNN), and Using the DC-AD-AlexNet convolution neural network algorithm, diagnose and classify AD.

Longitudinally persisting KCNA2-autoantibodies in mild amnestic dementia with Alzheimer´s pathology - Report and literature review

BACKGROUND

Neural autoantibodies are being increasingly detected in conjunction with neurodegenerative dementias such as Alzheimer's disease dementia (AD), yet their significance is not well clarified. In this case report, we report the previously unreported long-lasting persistence of potassium voltage-gated channel subfamily A member 2 (KCNA2) antibodies in biomarker-supported AD.

METHODS

We report on a 77-year-old, male patient evaluated in our outpatient memory clinic of the Department of Psychiatry and Psychotherapy, University Medical Center Göttingen. Neuropsychological test results and autoantibody testing in serum over a period of 4-5 years is provided.

REPORT

Our patient exhibited mild dementia syndrome and was diagnosed with AD on the basis of a prototypical biomarker profile (reduced Aβ42/40 ratio and elevated p-tau181 protein in cerebrospinal fluid). Within a 5-year follow-up with regular visits to our memory clinic, we observed a nearly stable neuropsychological profile of mild, amnestic variant dementia that did not noticeably progress. KCNA2 autoantibodies were also detectable in serum over 4 years with increasing titers over time. Combined anti-dementia therapy with donepezil, multimodal therapy including non-pharmacological cognitive therapy, and immunotherapy with intravenous methylprednisolone was carried out as an individual treatment approach.

CONCLUSIONS

KCNA2 autoantibody persistence in biomarker-supported AD does not necessarily trigger a poor outcome in the long-term, as cognitive impairment did not progress subsequently. At the same time, mild immunotherapy did not result in less immunoreactivity in conjunction with the detection of KCNA2 autoantibodies. This detection of KCNA2 autoantibodies in AD could provide indices of a potentially benign long-term AD course that should be further evaluated in studies.

Alzheimer's Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery

Alzheimer's disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements have shifted the focus from the traditionally dominant amyloid hypothesis toward a multifactorial understanding of the disease. Emerging evidence suggests that while amyloid-beta (Aβ) accumulation is central to AD, it may not be the primary driver but rather part of a broader pathogenic process. Novel hypotheses have been proposed, including the role of tau protein abnormalities, mitochondrial dysfunction, and chronic neuroinflammation. Additionally, the gut-brain axis and epigenetic modifications have gained attention as potential contributors to AD progression. The limitations of existing therapies underscore the need for innovative strategies. This study explores the integration of machine learning (ML) in drug discovery to accelerate the identification of novel targets and drug candidates. ML offers the ability to navigate AD's complexity, enabling rapid analysis of extensive datasets and optimizing clinical trial design. The synergy between these themes presents a promising future for more effective AD treatments.

Identification of Drug-Targetable Genes for Eczema and Dermatitis Using Integrated Genomic and Proteomic Approaches

Background: Eczema and dermatitis are common inflammatory skin conditions with significant morbidity. Identifying drug-targetable genes can facilitate the development of effective treatments. Methods: This study analyzed data obtained by meta-analysis of 2 genome-wide association studies on eczema/dermatitis (57,311 cases and 896,779 controls, European ancestry). We identified drug-targetable genes from the Drug-Gene Interaction Database and Finan et al's findings. Cis-expression quantitative trait loci (eQTL) data from human blood and skin tissues were used for Mendelian randomization (MR) analysis. Bayesian colocalization, proteomic MR, and meta-analysis validated the causal relationships. Finally, protein-protein interactions (PPIs) and correlation analysis of potential drug targets and cytokines were performed. Results: We identified 2532 drug-targetable genes; 3378 Single Nucleotide Polymorphism (SNPs) were associated with 1531 genes in blood cis-eQTLs, 664 SNPs with 667 genes in sun-exposed skin eQTLs, and 572 SNPs with 574 genes in nonsun-exposed skin eQTLs. Five genes (SLC22A5, NOTCH4, AGER, HLA-DRB5, and EHMT2) showed causal relationships with eczema/dermatitis across multiple datasets. Single-variable and multi-variable Mendelian randomization (SMR) and multi-SNP SMR analysis identified 8 genes (PIK3R4, DHODH, CXCR2, Interleukin (IL)18, LGALS9, RPS6KB2, SLC22A5, and AGER) across all tissues. Functional Summary Information for Variants in the Online Network (FUSION) analysis confirmed associations for SLC22A5 and AGER. Bayesian colocalization indicated AGER (PPH4: 0.95) as a shared causal variant. Proteomic MR and meta-analysis showed that increased AGER protein levels were associated with a lower risk of eczema or dermatitis (odds ratio: 0.995, 95% confidence interval: 0.997-0.993, P = 0.0002). A PPI network revealed interactions of AGER with NOTCH4 and multiple cytokines, whereas SLC22A5 showed no cytokine interactions. Conclusions: This study identified potential drug-targetable genes, with AGER showing strong potential as a target for reducing eczema/dermatitis risk. These findings provide a basis for developing targeted therapies.

The early diagnosis of Alzheimer's disease: Blood-based panel biomarker discovery by proteomics and metabolomics

Diagnosis and prediction of Alzheimer's disease (AD) are increasingly pressing in the early stage of the disease because the biomarker-targeted therapies may be most effective. Diagnosis of AD largely depends on the clinical symptoms of AD. Currently, cerebrospinal fluid biomarkers and neuroimaging techniques are considered for clinical detection and diagnosis. However, these clinical diagnosis results could provide indications of the middle and/or late stages of AD rather than the early stage, and another limitation is the complexity attached to limited access, cost, and perceived invasiveness. Therefore, the prediction of AD still poses immense challenges, and the development of novel biomarkers is needed for early diagnosis and urgent intervention before the onset of obvious phenotypes of AD. Blood-based biomarkers may enable earlier diagnose and aid detection and prognosis for AD because various substances in the blood are vulnerable to AD pathophysiology. The application of a systematic biological paradigm based on high-throughput techniques has demonstrated accurate alterations of molecular levels during AD onset processes, such as protein levels and metabolite levels, which may facilitate the identification of AD at an early stage. Notably, proteomics and metabolomics have been used to identify candidate biomarkers in blood for AD diagnosis. This review summarizes data on potential blood-based biomarkers identified by proteomics and metabolomics that are closest to clinical implementation and discusses the current challenges and the future work of blood-based candidates to achieve the aim of early screening for AD. We also provide an overview of early diagnosis, drug target discovery and even promising therapeutic approaches for AD.

Serum Exosomal miRNA-125b and miRNA-451a are Potential Diagnostic Biomarker for Alzheimer's Diseases

Aim

To explore the diagnostic value of serum-derived exosomal miRNAs and predict the roles of their target genes in Alzheimer's disease (AD) based on the expression of miRNAs in AD patients.

Methods

We determined the relative concentration of exosomal miRNAs by High-throughput Second-generation Sequencing and real-time quantitative real-time PCR.

Results

71 AD patients and 71 ND subjects were collected. The study demonstrated that hsa-miR-125b-1-3p, hsa-miR-193a-5p, hsa-miR-378a-3p, hsa-miR-378i and hsa-miR-451a are differentially expressed in the serum-derived exosomes of AD patients compared with healthy subjects. According to ROC analysis, hsa-miR-125b-1-3p has an AUC of 0.765 in the AD group compared to the healthy group with a sensitivity and specificity of 82.1-67.7%, respectively. Enrichment analysis of its target genes showed that they were related to neuroactive ligand-receptor interactions, the PI3K-Akt signaling pathway, the Hippo signaling pathway and nervous system-related pathways. And, hsa-miR-451a had an AUC of 0.728 that differentiated the AD group from the healthy group with a sensitivity and specificity of 67.9% and 72.6%, respectively. Enrichment analysis of its target genes showed a relationship with cytokine-cytokine receptor interactions and the PI3K-Akt signaling pathway.

Conclusion

The dysregulation of serum exosomal microRNAs in patients with AD may promote the diagnosis of AD. The target genes of miRNAs may be involved in the occurrence and development of AD through various pathways.