Shared Neurological and Cognitive Factors in COVID-19 and Alzheimer’s Disease: A Literature Review
Bibliographic record
Abstract
Introduction: The neuropathological etiology of COVID-19 suggests infection may increase the risk of neurodegenerative disorders, most notably Alzheimer’s disease (AD). Despite some overlap between COVID-19 and AD, the current research on this relationship is developing and evidence has been mixed. This review aims to assess the literature on how the neurological and cognitive sequalae associated with COVID-19 infection affect risk for AD and its associated symptoms, and vice versa. Methods: Articles were found by searching through the PubMed database with the terms (sensory OR cognitive OR neurological OR neuroimaging) AND (Alzheimer’s disease OR Alzheimer’s OR dementia) AND (COVID-19 OR SARS-CoV-2 OR COVID). Search inclusion criteria required the papers be written in English, be published in 2020 or later, and pertain to COVID-19 and/or AD specifically. This process was supplemented by manual searching. The articles used for this review include meta-analyses, literature reviews, and prospective and retrospective empirical studies. Results: Various well-known AD risk factors and outcomes may be observed in COVID-19 patients, and vice versa. These include amyloid precursor protein (APP) buildup, tau hyperphosphorylation, the apolipoprotein E (APOE) e4e4 genotype, angiotensin converting enzyme 2 (ACE2) gene expression, cholinergic functioning, delirium, cognitive decline, and deleterious effects on brain structure and function. Discussion: COVID-19 may increase AD risk and development by increasing protein build-up and damaging AD-related brain regions, which may underlie sensory and cognitive deficits. Furthermore, COVID-19 and AD may interact in positive feedback loops to worsen the development of both. These interactions may be mediated by demyelination, inflammation, APOE e4e4 genotype, and ACE2 expression. Clinical implications for those with COVID-19 and/or AD include the possibility of treatments aimed at cholinergic functioning, as well as high flow oxygen therapy. Conclusion: COVID-19 may increase the risk for and development of AD. AD, in turn, may also increase risk for COVID-19 infection, acting in a positive feedback loop. Future directives include further research on tau pathology, delirium, and amyloid precursor protein processing. Additionally, studies could benefit from telemedicine. Lastly, assessment of AD risk due to COVID-19 could integrate delirium and subjective reports of “brain fog” as measures for underlying risk factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".