MétaCan
Menu
Back to cohort
Record W3087037569 · doi:10.1002/mds.28208

Reply to: ‘Comment on “Microbiota Composition and Metabolism Are Associated With Gut Function in Parkinson’s Disease”’

2020· letter· en· W3087037569 on OpenAlexafffund
Mihai Cîrstea, Adam C. Yu, Ella Golz, Kristen Sundvick, Daniel Kliger, Nina Radisavljevic, Liam H. Foulger, Melissa Mackenzie, Tao Huan, B. Brett Finlay, Silke Appel‐Cresswell

Bibliographic record

VenueMovement Disorders · 2020
Typeletter
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCanadian Institutes of Health ResearchParkinson Canada
KeywordsParkinson's diseaseGut floraFunction (biology)DiseaseGut–brain axisMedicineNeuroscienceBiologyInternal medicineImmunologyEvolutionary biology

Abstract

fetched live from OpenAlex

We thank Dr. Zhang for his interest in our study1 and for the valuable feedback.2 Dr. Zhang correctly remarks that individuals in the same household typically share similar diets, highlighting our decision to enroll patient spouses as controls whenever possible. Control selection for case-control studies involves inherent trade-offs between comparability and efficiency,3 and we accordingly made substantial effort to control for potential confounders beyond simply selection strategy. This includes rigorous analyses of associations between diet and all variables of interest (see pp. 11–19 of the R-Markdown published as a supplementary file with our original article1). Dr. Zhang raises the interesting point that there may be relevant dietary variation in participants without a study-matched spouse, which is masked in the overall cohort when the 43 spousal pairs are included. Importantly, only 2 results presented in the article involved direct patient–control comparisons, where spousal–subgroup analysis might be relevant: (1) microbiota differential abundance (primarily performed to show validity with previous studies and largely presented as supplemental data) and (2) differences in microbial metabolite concentrations. To address Dr. Zhang’s point, we have repeated our dietary analyses in the subgroup of participants without study spouses (n = 154 patients with Parkinson’s disease and n = 60 controls). Confirming our observations in the full cohort, we found no significant differences between patients and controls in the consumption of any dietary items in this subgroup (all false discovery rate (FDR)-adjusted P > 0.78, see Methods section in our article1), and no significant correlations between dietary items and microbial genera (FDR-adjusted P > 0.41) or microbial metabolites (FDR-adjusted P > 0.86). Visualizing dietary intake by principal component analysis reveals no separation by group (Fig. 1A), with permutational multivariate analysis of variance (PERMANOVA) test confirming no significant difference in participant distribution by Parkinson’s disease × spouse group status (P = 0.53, 99,999 permutations). We have also repeated our primary analyses involving direct patient–control comparisons by spousal subgroup. Reassuringly, the results are highly consistent across groups (Fig. 1B,C), with diminished statistical power attributed to smaller sample size, especially in the spousal subgroup. Notably, as only 125/300 participants had metabolomics data and only 86/300 participants were spouses, the resulting overlap of complete study couples with metabolomics data was only n = 26 (ie, 13 pairs), and our study was not powered to detect statistically significant differences in groups this small (Fig. 1C). As noted previously, we made a substantial effort to demonstrate that diet was not confounding these relationships. Cohort studies by their nature involve sampling a subset of a population and inferring broader generalizability. Interestingly, many of the microbiota differences we observe in patients with Parkinson’s disease, including increased Akkermansia and Bifidobacterium and decreased Faecalibacterium and Lachnospiraceae, are repeatedly observed in other cohorts across multiple continents4, 5 despite significant geographical and dietary differences. We believe this supports the notion that consistent microbiota alterations—and by extension, the novel metabolomic and gastrointestinal function results reported in our study—are widely generalizable to the broader population with Parkinson’s disease, recognizing, as always, that further studies are needed. (1) Research Project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript: A. First Draft, B. Review and Critique. M.S.C.: 1B, 1C, 2A, 2B, 2C, 3A, 3B A.C.Y.: 1B, 1C, 3B E.G.: 1B, 1C, 3B K.S.: 1A, 1B, 1C D.K.: 1B, 1C L.H.F.: 1B, 1C M.M.: 1B, 1C N.R.: 1C, 3B T.H.: 2C, 3B B.B.F.: 1A, 1B, 1C, 2A, 2C, 3B S.A.C.: 1A, 1B, 1C, 2A, 2C, 3B

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.220
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations9
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueMovement DisordersSame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207