Exploring the link between tooth loss, cognitive function, and brain wellness in the context of healthy aging
Bibliographic record
Abstract
Abstract Aims The aim of this study was to evaluate the utility of using MRI‐derived tooth count, an indirect and nonspecific indicator of oral/periodontal health, and brain age gap (BAG), an MRI‐based measure of premature brain aging, in predicting cognition in a population of otherwise healthy adults. Methods This retrospective study utilized data from 329 participants from the University of South Carolina's Aging Brain Cohort Repository. Participants underwent neuropsychological testing including the Montreal Cognitive Assessment (MoCA), completed an oral/periodontal health questionnaire, and submitted to high‐resolution structural MRI imaging. The study compared variability on cognitive scores (MoCA) accounted for by MRI‐derived BAG, MRI‐derived total tooth count, and self‐reported oral/periodontal health. Results We report a significant positive correlation between the total number of teeth and MoCA total scores after controlling for age, sex, and race, indicating a robust relationship between tooth count and cognition, r (208) = .233, p < .001. In a subsample of participants identified as being at risk for MCI (MoCA <= 25, N = 36) inclusion of MRI‐based tooth count resulted in an R 2 change of .192 ( H 0 = 0.138 → H 1 = 0.330), F (1,31) = 8.86, p = .006. Notably, inclusion of BAG, a valid and reliable measure of overall brain health, did not significantly improve prediction of MoCA scores in similar linear regression models. Conclusions Our data support the idea that inclusion of MRI‐based total tooth count may enhance the ability to predict clinically meaningful differences in cognitive abilities in healthy adults. This study contributes to the growing body of evidence linking oral/periodontal health with cognitive function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".