The Risk of Developing Alzheimer’s Disease and Parkinson’s Disease in Patients with Inflammatory Bowel Disease: A Meta-Analysis
Bibliographic record
Abstract
Recently, a growing body of research has linked gut microbiota dysbiosis to central nervous system diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), and has suggested that AD and PD pathology may take its origin from chronic inflammation in the gastrointestinal tract. Thus, this study aimed to elucidate whether inflammatory bowel disease (IBD) is associated with a higher risk of developing AD and PD as compared to the non-IBD population by conducting a meta-analysis. A thorough search of Pubmed and Embase databases was performed to identify all relevant articles. The quality of included studies was assessed using the Newcastle-Ottawa Scale. The odds ratios (ORs) with 95% confidence intervals (CIs) were analyzed using a fixed-effect model. To assess publication bias and heterogeneity among the studies, Egger’s test and L’Abbé plots were used, respectively. A total of eight eligible studies were included in this meta-analysis. No significant heterogeneity or significant publication bias was detected. The risk of developing AD in IBD patients was higher than in non-IBD patients (OR = 0.37; 95% CI = 0.14−1.00; p = 0.05), and there was a relationship between the occurrence of AD and Crohn’s disease or ulcerative colitis (OR = 0.11; 95% CI = 0.04−0.30; p < 0.0001, OR = 0.14; 95% CI = 0.04−0.49; p = 0.0024, respectively). The risk of developing both of the most common neurodegenerative diseases, AD and PD, was also significantly higher in patients diagnosed with Crohn’s disease or ulcerative colitis (OR = 0.21; 95% CI = 0.09−0.49; p = 0.0003, OR = 0.25; 95% CI = 0.13−0.51; p = 0.0001, respectively). This meta-analysis revealed a higher risk of AD and PD among CD and UC patients compared to the general population. It may suggest a key role for the gut microbiota in the pathogenesis of not only Crohn’s disease and ulcerative colitis but also AD and PD. The identification of this potential risk may provide earlier preventive measures to be implemented to reduce comorbidity and mortality rate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".