Evidence map›Paper›PMID 35645785›Full record

ArticleFrontiers in aging neuroscience2022

Nutritional Intake and Gut Microbiome Composition Predict Parkinson's Disease.

Michal Lubomski, Xiangnan Xu, Andrew J Holmes, Samuel Muller, Jean Y H Yang, Ryan L Davis, Carolyn M Sue

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
2.6field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Review
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Michal LubomskiDepartment of Neurology, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia.
Xiangnan XuSchool of Mathematics and Statistics, Sydney Precision Bioinformatics, University of Sydney, Sydney, NSW, Australia.
Andrew J HolmesCharles Perkins Centre, University of Sydney, Sydney, NSW, Australia.
Samuel MullerSchool of Mathematics and Statistics, Sydney Precision Bioinformatics, University of Sydney, Sydney, NSW, Australia.
Jean Y H YangSchool of Mathematics and Statistics, Sydney Precision Bioinformatics, University of Sydney, Sydney, NSW, Australia.
Ryan L DavisDepartment of Neurogenetics, Faculty of Medicine and Health, Kolling Institute, University of Sydney and Northern Sydney Local Health District, St Leonards, NSW, Australia.
Carolyn M SueDepartment of Neurology, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW, Australia.
University of Sydney · AUNorthern Sydney Local Health District · AURoyal North Shore Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Models to predict Parkinson's disease (PD) incorporating alterations of gut microbiome (GM) composition have been reported with varying success. Objective: To assess the utility of GM compositional changes combined with macronutrient intake to develop a predictive model of PD. Methods: We performed a cross-sectional analysis of the GM and nutritional intake in 103 PD patients and 81 household controls (HCs). GM composition was determined by 16S amplicon sequencing of the V3-V4 region of bacterial ribosomal DNA isolated from stool. To determine multivariate disease-discriminant associations, we developed two models using Random Forest and support-vector machine (SVM) methodologies. Results: Using updated taxonomic reference, we identified significant compositional differences in the GM profiles of PD patients in association with a variety of clinical PD characteristics. Six genera were overrepresented and eight underrepresented in PD patients relative to HCs, with the largest difference being overrepresentation of Conclusion: The notable differences in GM diversity and composition when combined with clinical measures and nutritional data enabled the development of a predictive model to identify PD. These findings support the combination of GM and nutritional data as a potentially useful biomarker of PD to improve diagnosis and guide clinical management.

Indexed as

biomarkerdysbiosisgastrointestinal microbiomegut microbiotamedicationParkinson’s diseaseprediction model

Identifiers

PMID35645785
PMCPMC9131011
OpenAlexW4280535654

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.