Genome-wide association study in Alzheimer's disease: a bibliometric and visualization analysis

Dynamics of Cognitive Impairment in MCI Patients over a Three-Year Period: The Informative Role of Blood Biomarkers, Neuroimaging, and Genetic Factors

Given the high growth rates of cognitive decline among the elderly population and the lack of effective etiological treatments, early diagnosis of cognitive impairment progression is an imperative task for modern science and medicine. It is of particular interest to identify predictors of an unfavorable subsequent course of cognitive disorders, specifically, rapid progression. Our study assessed the informative role of various risk factors on the dynamics of cognitive impairment among mild cognitive impairment (MCI) patients. The study included patients with MCI (N = 338) who underwent neuropsychological assessment, magnetic resonance imaging (MRI) examination, blood sampling for general and biochemical analysis, APOE genotyping, and polygenic risk score (PRS) evaluation. The APOE ε4/ε4 genotype was found to be associated with a diminished overall cognitive scores initial assessment and negative cognitive dynamics. No associations were found between cognitive changes and the PRS. The progression of cognitive impairment was associated with the width of the third ventricle and hematological parameters, specifically, hematocrit and erythrocyte levels. The absence of significant associations between the dynamics of cognitive decline and PRS over three years can be attributed to the provided suitable medical care for the prevention of cognitive impairment. Adding other risk factors and their inclusion in panels assessing the risk of progression of cognitive impairment should be considered.

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

Top 100 most-cited articles on tau protein: a bibliometric analysis and evidence mapping

Background

Tau, a microtubule-associated protein extensively distributed within the central nervous system (CNS), exhibits close associations with various neurodegenerative disorders. Here, we aimed to conduct a qualitative and quantitative bibliometric study of the top 100 most-cited publications on tau protein and reveal the current research hotspots and future perspectives.

Methods

The relevant literature was retrieved from the Web of Science Core Collection. CiteSpace (v6.2.R4) and VOSviewer (1.6.19) were adopted for bibliometric analysis with statistical and visual analysis.

Results

Citations per article ranged from 615 to 3,123, with a median number of 765.5 times. "Neuroscience" emerged as the most extensively researched subject in this field. The USA has emerged as the leading country, with a publication record (n = 65), total citations (n = 66,543), strong centrality (0.29), and extensive international collaborations. Harvard University (n = 11) and the University of California, San Francisco (n = 11) were the top two institutions in terms of publications. Neuron dominated with 13 articles in the 37 high-quality journals. M. Goedert from the MRC Laboratory of Molecular Biology was the most productive (n = 9) and top co-cited (n = 179) author. The most frequently studied keywords were Alzheimer's disease (n = 38). Future research is anticipated to intensify its focus on the pathogenesis of various tau-related diseases, emphasizing the phosphorylation and structural alterations of tau protein, particularly in Alzheimer's disease.

Conclusion

The pathogenesis of various tau-related diseases, including the phosphorylation and structural alterations of the tau protein, will be the primary focus of future research, with particular emphasis on Alzheimer's disease as a central area of investigation.