ASSOCIATION BETWEEN GUT MICROBIOTA, DIET, AND COGNITION IN ELDERLY PATIENTS WITH MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Abstract Objectives To compare the gut microbiota in individuals with mild cognitive impairment (MCI) and normal controls (NCs) and explore the association between dietary intake and various cognitive domains across identified enterotypes, providing a basis for dietary interventions based on the Microbiota-Gut-Brain Axis. Methods We analyzed fecal samples and 16S ribosomal RNA sequences for microbiota among 100 participants (NCs: 50, MCI: 50). Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), and Auditory Verbal Learning Test (AVLT). A semi-quantitative food frequency questionnaire was used to assess participants’ dietary intake, and differences in dietary habits and cognitive function among distinct enterotypes were analyzed. Results Participants with MCI exhibited lower bacterial richness. Linear Discriminant Analysis of Effect Sizes (LEfSe-LDA) revealed different major microbial genera between the groups, and a random forest model based on dominant bacterial genera could distinguish between MCI and NCs (AUC=0.743, 95%CI:0.646-0.840). Cluster analysis identified two enterotypes: Escherichia-Shigella (enterotype E) and Bacteroides (enterotype B). When grouped by enterotypes, similar dietary intake was associated with different cognitive domains: notably, egg intake positively correlated with MOCA and AVLT scores for enterotype E, but not for enterotype B. Conclusions: Significant differences exist in the gut microbiota of individuals with MCI compared to those with normal cognition, with dominant genera showing good predictive performance. The impact of different enterotypes on the relationship between dietary intake and cognitive function should be considered when making dietary recommendations in clinical practice.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".