Abstract P505: Midlife Cognitive Function is Associated With Gut Microbial Species and Metabolic Pathways: Coronary Artery Risk Development in Young Adults (CARDIA) Study
Bibliographic record
Abstract
Background: The gut microbiota may play a role in cognitive function and decline. Animal models support mechanisms involving short-chain fatty acid production, amino acid metabolism, and purine metabolism. There is need to replicate these findings in population-based human studies. Methods: Data were from 570 participants who attended the Yr. 30 follow-up exam (2015-16, ages 48-60 y, 45%:55% Black:White race, 45%:55% M:F sex), provided a fecal sample, and completed a cognitive battery of 6 assessments: Montreal Cognitive Assessment (MoCA), Digit Symbol Substitution Test (DSST), Rey-Auditory Verbal Learning Test (RAVLT), Stroop, and Letter and Category Fluency. A global cognition score was derived from the 6 assessments using principal components analysis. Species and metabolic pathways were assigned to whole-metagenomics sequence data using standard reference databases. Multivariable-adjusted linear regression was used to test associations between distinct microbial features and measures of cognition, controlling for false discovery rate (FDR). Results: Sequence data mapped to 106 species and 312 metabolic pathways. In multivariable-adjusted regression analysis, adjusted for socio-demographics, health behaviors, and BMI, 5 species and 5 metabolic pathways were significantly associated with measures of cognitive function, FDR < 0.20 (Figure) . These species/pathways are involved in purine and pyrimidine metabolism and short-chain fatty acid production. Conclusion: Identified pathways play a role in neurodevelopment and in the gut-brain axis of physiologic communications. Our findings suggest long-term relationships between gut microbiota composition and cognitive function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".