The role of autoantibodies in Alzheimer's disease: Pathogenetic connections or epiphenomena?
Bibliographic record
Abstract
INTRODUCTION: The current evidence supporting the complex, multifaceted etiology for Alzheimer's disease (AD) grows by the day, prompting increased research in non-"amyloid hypothesis"-related pathways. One of these pathways of interest pertains to an autoimmune component in this disease. METHODS: In this review, we briefly discuss current evidence of potential contributions of autoimmunity to AD pathobiology and describe the putative role of autoantibodies detected in patient fluids. We draw attention to the fact that the reported AD-related autoantibodies differ dramatically between published studies, raising doubts about the reliability and robustness of these findings. RESULTS: We hypothesize, and provide indirect evidence, that many of the reported autoantibodies in AD may represent false discoveries. We suggest follow-up validation and confirmatory studies with sufficient power, preferably by employing orthogonal testing techniques. DISCUSSION: Uncovering the putative autoimmune components of AD is important and could pave the way to new concepts for AD pathogenesis, diagnosis, and therapy. HIGHLIGHTS: Although Alzheimer's disease (AD) is not traditionally considered an autoimmune disease, growing evidence suggests immune system dysregulation and autoantibody generation, either in the form of naturally occurring or pathogenic autoantibodies. Numerous studies have discovered autoantibodies in AD, but only a few of them have been found independently and multiple times, including amyloid β (Aβ) and tau autoantibodies. Many of these findings represent false discoveries. Follow-up validation and confirmatory studies with sufficient power are imperative, preferably by employing orthogonal testing techniques. Understanding the immune and autoimmune landscape in AD will assist in future immunotherapy strategies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".