EDUCATION AS A RISK FACTOR OF MILD COGNITIVE IMPAIRMENT—THE ROLE OF THE GUT MICROBIOME
Bibliographic record
Abstract
Abstract Background Social determinants of health relate to an individual’s risk of MCI and dementia. However, pathways from identified modifiable risk factors such as education are not well understood, yet. While previous findings suggest distinct taxonomic signatures of the gut microbiome in dementia and MCI patients, and further education linked to its composition, we sought to test the possibly mediating role of the gut microbiome in the relationship between education and MCI. Method: Gut microbiome composition was ascertained with 16S rRNA gene amplicon sequencing. MCI classification was based on the Montreal Cognitive Assessment. Education in years was grouped (0-10, 10-16, 16+). Mediation analysis was conducted decomposing direct and indirect effects of education on MCI mediated by gut microbiome diversity (Chao1, Inverse Simpson, Shannon) or individual species at Genus level (ldm-med, permanova-med). Differential abundance analysis across education groups was conducted (ANCOM-BC, DESeq2). Result: After exclusion of participants with PD, below age 50, or with missing data, n=256 participants (n=58 with MCI) of the Luxembourg Parkinson’s Study were eligible for analysis (M[SD] Age=64.7[8.3] years). Education (16+ compared to 0-10 years of education) had a natural direct effect of NDE=0.36 (P<.01) on MCI, Chao1 included as mediator. We did not find significant mediation by gut microbiome composition or individual species. Conclusion Our findings indicate direct effects of education not mediated by the gut microbiome. Taxonomic analysis suggests a signature linked to lower risk of dementia in higher educated individuals. Longitudinal research is needed to investigate associations over time.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".