Infectious Disease Burden and the Risk of Alzheimer’s Disease: A Population-Based Study
Bibliographic record
Abstract
Background: Previous studies suggested a link between various infectious pathogens and the development of Alzheimer’s disease (AD), posing the question whether infectious disease could present a novel modifiable risk factor. Objective: To assess whether infectious disease burden due to clinically apparent infections is associated with an increased risk of AD. Methods: We conducted a population-based nested case-control study using the United Kingdom Clinical Practice Research Datalink. We included all dementia-free subjects ≥50 years of age enrolling in the database between January 1988 and December 2017. Each case of AD identified during follow-up was matched with up to 40 controls. Conditional logistic regression estimated adjusted odds ratios (ORs) with 95% confidence intervals (CIs) of AD associated with ≥1 infection diagnosed > 2 years before the index date compared with no infection during the study period. We further stratified by time since first infection and cumulative number of infections. Results: The cohort included overall 4,262,092 individuals (mean age at cohort entry 60.4 years; 52% female). During a median follow-up of 10.5 years, 40,455 cases of AD were matched to 1,610,502 controls. Compared with having no burden of infectious disease, having a burden of infectious disease was associated with an increase in the risk of AD (OR, 1.05; 95% CI, 1.02 to 1.08). The risk increased with longer time since first infection, peaking after 12–30 years (OR, 1.11; 95% CI, 1.05–1.17). The risk did not increase with cumulative number of infections. Conclusion: The overall risk of AD associated with infectious disease burden was small but increased gradually with longer time since first infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".