Periodontal pathogens and their association with incident all‐cause and Alzheimer’s disease dementia in a large national survey
Bibliographic record
Abstract
Abstract Background Infectious agents including periodontal pathogens have recently appeared as important actors in Alzheimer’s Disease (AD) pathology. We examined associations of serum immunoglobulin G (IgG) against periodontal pathogens and Pd markers with incident all‐cause and AD dementia as well as AD mortality among U.S. middle‐aged and older adults. Method Periodontal disease (Pd) markers [Clinical Attachment Loss (CAL); probing depth and periodontal pathogen immunoglobulin G (IgG)] were investigated in relation to AD and all‐cause dementia incidence and to AD mortality, using data from the third National Health and Nutrition Examination Surveys (NHANES III, 1988‐1994) linked longitudinally with National Death Index and Medicare data through January 1st, 2014, with up to 26y of follow‐up. Sex‐ and age group‐specific multivariable‐adjusted Cox proportional hazards models were conducted. Result Among those ≥65y, AD incidence and mortality were consistently associated with probing depth, two factors and one cluster comprised of IgG titers against Porphyromonas gingivalis (Pg), Prevotella melaninogenica (Pm) and Campylobacter rectus (Cr) among others. Specifically, AD incidence was linked to a composite of Cr and Pg titers (per SD, aHR=1.22; 95% CI, 1.04‐1.43, P=0.012), while AD mortality risk was increased with another composite (per SD, aHR=1.46; 95% CI, 1.09‐1.96, P=0.017) loading highly on Pg, Prevotella intermedia, Prevotella nigrescens, Fusobacterium nucleatum, Cr, Streptococcus intermedius, Capnocylophaga Ochracea and Pm. Conclusion This study provides evidence for an association between periodontal pathogens and AD, which was stronger for older adults. Effectiveness of periodontal pathogen treatment on reducing sequelae of neurodegeneration should be tested in randomized controlled trials.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".