Autoimmune aspects of Alzheimer's disease as exemplified by the diversity of autoantibodies found in patient serum and CSF
Bibliographic record
Abstract
BACKGROUND: The current evidence supporting the complex and multifaceted etiology of Alzheimer's disease (AD) grows by the day, and it has prompted increased research in non-"amyloid hypothesis"-related pathways. One of these pathways of interest pertains to an autoimmune component in this disease. METHOD: We systematically compiled published literature supporting the view of autoimmunity in AD between 1988 and 2024, sourced from PubMed. This review critically discusses the current evidence of potential contributors to autoimmunity to AD pathobiology and describes the putative role of autoantibodies detected in patient biofluid. Special consideration was given to evaluating whether the reported autoantibodies represent true or false discoveries and the integrity of the methodological techniques. RESULT: The majority of reported putative AD-related autoantibodies differ dramatically between published studies, raising doubts about the reliability and robustness of these findings. Particularly, autoantibodies may be found in a percentage of patients from the same cohort, but the same autoantibodies are undetectable in cohorts of different patients. CONCLUSION: Despite consistent identification of some autoantibodies in AD, their potential causal role in disease initiation and progression has not been experimentally demonstrated. We suggest follow-up confirmatory and validation studies with sufficient power, preferably by employing orthogonal testing techniques. Uncovering the putative autoimmune components of AD is important and could pave the way to new concepts for AD pathogenesis, diagnosis and therapy.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".