Reply to: ‘Comment on “Microbiota Composition and Metabolism Are Associated With Gut Function in Parkinson’s Disease”’
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,031 | 0,035 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,017 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».