Effect of altered gene expression in lipid metabolism on cognitive improvement in patients with Alzheimer’s dementia following fecal microbiota transplantation: a preliminary study
Bibliographic record
Abstract
Background:The brain–gut axis has emerged as a potential target in neurodegenerative diseases, including dementia, as individuals with dementia exhibit distinct gut microbiota compositions. Fecal microbiota transplantation (FMT), the transfer of fecal solution from a healthy donor to a patient, has shown promise in restoring homeostasis and cognitive enhancement.Objective:This study aimed to explore the effects of FMT on specific cognitive performance measures in Alzheimer’s dementia (AD) patients and investigate the relationship between cognition and the gut microbiota by evaluating changes in gene expression following FMT.Methods:Five AD patients underwent FMT, and their cognitive function [Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB)] was assessed before and after FMT. The patients’ fecal samples were analyzed with 16S rRNA to compare the composition of their gut microbiota. We also assessed modifications in the serum mRNA expression of patients’ genes related to lipid metabolism using serum RNA sequencing and quantitative real-time polymerase chain reaction.Results:Significant improvements in cognitive function, as measured by the MMSE (pre- and post-FMT was 13.00 and 18.00) and MoCA were seen. The MoCA scores at 3 months post-FMT (21.0) were the highest (12.0). The CDR-SOB scores at pre- and post-FMT were 10.00 and 5.50, respectively. Analysis of the gut microbiome composition revealed changes via 16S rRNA sequencing with an increase in Bacteroidaceae and a decrease in Enterococcaceae. Gene expression analysis identified alterations in lipid metabolism-related genes after FMT.Conclusion:These findings suggest a link between alterations in the gut microbiome, gene expression related to lipid metabolism, and cognitive function. The study highlights the importance of gut microbiota in cognitive function and provides insights into potential biomarkers for cognitive decline progression. FMT could complement existing therapies and show potential as a therapeutic intervention to mitigate cognitive decline in AD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".