A mediation analysis of the role of total free fatty acids on pertinence of gut microbiota composition and cognitive function in late life depression
Bibliographic record
Abstract
BACKGROUND: Extensive evidence demonstrates correlations among gut microbiota, lipid metabolism and cognitive function. However, there is still a lack of researches in the field of late-life depression (LLD). This research targeted at investigating the relationship among gut microbiota, lipid metabolism indexes, such as total free fatty acids (FFAs), and cognitive functions in LLD. METHODS: Twenty-nine LLD patients from the Cognitive Outcome Cohort Study of Depression in Elderly were included. Cognitive functions were estimated through the Chinese version of Montreal Cognitive Assessment (MoCA). Blood samples were collected to evaluate serum lipid metabolism parameters. Fecal samples were evaluated for gut microbiota determination via 16S rRNA sequencing. Spearman correlation, linear regression and mediation analysis were utilized to explore relationship among gut microbiota, lipid metabolism and cognitive function in LLD patients. RESULTS: Spearman correlation analysis revealed significant correlations among Akkermansia abundance, total Free Fatty Acids (FFAs) and MoCA scores (P < 0.05). Multiple regression indicated Akkermansia and total FFAs significantly predicted MoCA scores (P < 0.05). Mediation analysis demonstrated that the correlation between decreased Akkermansia relative abundance and cognitive decline in LLD patients was partially mediated by total FFAs (Bootstrap 95%CI: 0.023-0.557), accounting for 43.0% of the relative effect. CONCLUSION: These findings suggested a significant relationship between cognitive functions in LLD and Akkermansia, as well as total FFAs. Total FFAs partially mediated the relationship between Akkermansia and cognitive functions. These results contributed to understanding the gut microbial-host lipid metabolism axis in the cognitive function of LLD.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".