Current Alzheimer Research - Volume 21, Issue 9, 2024

This study analyzed the current status, hotspots, and development trends of tau protein research in Alzheimer’s disease (AD) and to provide a reference for future research in this field. CiteSpace software was used to scientifically measure and visualize the relevant articles in the field of tau protein in AD brain from the Web of Science Core Collection database from 1991 to 2022.

A total of 568 articles were included, with an exponential growth in the number of articles published from 1991 to 2022, with an average of 17.8 articles per year. The United States is the most productive country in this field, accounting for 46.83% of the total literature. The New York State Institute for Basic Research is the most productive organization, followed by MRC Laboratory Molecular Biology in the UK. The most influential are Kings College London, University of California, San Francisco, and others. Iqbal K is the most productive author.

The most productive journal is the Journal of Biological Chemistry, and the journal with the highest impact factor is Acta Neuropathologica. The journal with the highest cumulative impact factor is Nature. The research hotspots mainly focus on the formation and degradation mechanisms of tau protein paired helical filaments and abnormal phosphorylation, AD neurofibrillary tangles and degenerative changes, and model research, mainly involving tau protein abnormal phosphorylation, phosphorylation sites, dephosphorylation, aggregate helical filaments, neurofibrillary tangles mouse models.

The research frontier trends mainly focus on the study of pathological changes in tau protein, intervention mechanisms, and the development and practice of clinical therapeutic drugs.

Alzheimer's disease (AD) is an alarmingly prevalent worldwide neurological disorder that affects millions of people and has severe effects on cognitive functions. The amyloid hypothesis, which links AD to Aβ (amyloid beta) plaque aggregation, is a well-acknowledged theory. The β-secretase (BACE1) is the main cause of Aβ production, which makes it a possible target for therapy. FDA-approved therapies for AD do exist, but none of them explicitly target BACE1, and their effectiveness is constrained and accompanied by adverse effects.

We determined the essential chemical components of medicinal herbs by conducting a thorough literature research for BACE1. Computational methods like molecular docking, ADMET (Absorption, distribution, metabolism, excretion, toxicity) screening, molecular dynamic simulations, and MMPBSA analysis were performed in order to identify the most promising ligands for β-secretase.

The results suggested that withasomniferol, tinosporide, and curcumin had better binding affinity with BACE1, suggesting their potential as therapeutic candidates against Alzheimer’s disease.

Herbal therapeutics have immense applications in the treatment of chronic diseases like Alzheimer’s disease, and there is an urgent need to assess their efficacy as therapeutics.

Alzheimer's disease (AD) is a progressive neurodegenerative disease with rising prevalence due to the aging global population. Existing methods for diagnosing AD are struggling to detect the condition in its earliest and most treatable stages. One early indicator of AD is a substantial decrease in the brain’s glucose metabolism. Metabolomics can detect disturbances in biofluids, which may be advantageous for early detection of some AD-related changes. The study aims to predict brain hypometabolism in Alzheimer's disease using metabolomics findings and develop a predictive model based on metabolomic data.

The data used in this study were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. We conducted a longitudinal study with three assessment time points to investigate the predictive power of baseline metabolomics for modeling longitudinal fluorodeoxyglucose-positron emission tomography (FDG-PET) trajectory changes in AD patients. A total of 44 participants with AD were included. The Alzheimer's Disease Assessment Scale (ADAS), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) were used for cognitive assessments. A single global brain hypo-metabolism index was used as the outcome variable.

Across models, we observed consistent positive relationships between specific cholesterol esters - CE (20:3) (p = 0.005) and CE (18:3) (p = 0.0039) - and FDG-PET metrics, indicating these baseline metabolites may be valuable indicators of future PET score changes. Selected triglycerides like DG-O (16:0-20:4) also showed time-specific positive associations (p = 0.017).

This research provides new insights into the disruptions in the metabolic network linked to AD pathology. These findings could pave the way for identifying novel biomarkers and potential treatment targets for AD.

^#Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/ how_to_apply/ADNI_Acknowledgement_List.pdf