Are infections the missing elements in predicting Alzheimer’s disease?
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) begins many years before its clinical expression. Therefore, an important goal is accurate long‐term prediction, especially for prevention trials and when evaluating the impacts of early intervention. Yet, current efforts still fall short, which is indicative of missing information in the modelling. Recently, the Epstein‐Barr virus has been shown to trigger the development of multiple sclerosis, years after infection. Similarly, some studies have reported odds ratios for AD that were almost as high as age for some chronic infections, such as those caused by either the herpes simplex virus or cytomegalovirus. We aim to review the literature on prediction models to examine whether infection load has been sufficiently studied as an early biomarker of AD. Method We used the same strategy employed in previous reviews by Stephen et al. (2010), Tang et al. (2015), and Hou et al. (2018) to perform a systematic review of the literature published between January 2018 to May 2021 for the prediction of AD in cognitively intact individuals from the general population. We examined the type of model and input data as well as the performance of the prediction models in the selected articles with a particular focus on infection load. Result Our initial search yielded 18,915 articles. Artificial intelligence was found to be the most widely used technique for prediction model computation. Most models included chronological age, and long‐term prediction accuracy was mainly determined by this parameter. Of note, models with a measure of amyloid beta misfolding were highly predictive of the progression to AD. Only one model included data exploring the contribution of infection. Conclusion Long‐term prediction models for AD in the general population of cognitively intact older adults were mostly driven by age. Despite the growing literature associating specific infectious agents with the pathophysiological development of AD, only one model used infection load as a parameter to predict AD. The potential of serological infection status as a parameter to improve prediction models in the general population must therefore be further explored.
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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 | 0.019 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".