Dopaminergic Medication Accentuates Fecal Gut Microbiome Changes in Parkinson’s Disease
Bibliographic record
Abstract
Abstract Fecal gut microbiota changes are associated with Parkinson’s disease (PD). However, disease related changes cannot readily be discerned from medication effects, as almost all participants in previous studies were using PD medication, and conclusive longitudinal data related to treatment initiation is lacking. Here, fecal gut microbiota composition was assessed in 62 de novo PD participants who were untreated at baseline and used PD medication at one-year follow-up, by means of 16S-sequencing. In addition, participants were stratified for the type of dopaminergic medication. Overall gut microbiota composition did not differ between baseline and one-year follow-up, but was associated with levodopa dose and levodopa equivalent daily dose (LEDD). Several differentially abundant taxa are in line with previously described changes in PD. These included reduced levels of amplicon sequence variants (ASVs) belonging to Faecalibacterium prausnitzii and Lachnospiraceae in all participants at follow-up, and increased levels of an ASV belonging to Bifidobacterium in dopamine agonist users. The family Bifidobacteriaceae was increased in dopamine agonist users who only used pramipexole. Levodopa dose was inversely related to the abundance of the families Ruminococcaceae and Lachnospiraceae, and the genus Lachnospiraceae ND3007 group . PD medications exert a measurable and dose-dependent effect on gut microbiota composition and accentuate several previously described gut microbiota changes in PD. Detailed knowledge of medication effects should be part of future trial designs of gut microbiome studies in PD and are necessary to interpret previously published data.
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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 | 0.000 |
| 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".