Predicting the Role of the Human Gut Microbiome in Constipation Using Machine-Learning Methods: A Meta-Analysis

Study information

Reviewed Marked as Reviewed by Svetlana up on 2025-4-9

study design

meta-analysis

Citation

PMID PubMed identifier for scientific articles.

34683469

DOI Digital object identifier for electronic documents.

https://doi.org/10.3390/microorganisms9102149

URI

Authors

Chen Y, Wu T, Lu W, Yuan W, Pan M, Lee YK, Zhao J, Zhang H, Chen W, Zhu J, Wang H

Journal

Microorganisms

Year

2021

Keywords:

classification model, constipation, feature selection, gut microbiome, machine learning

(1) Background: Constipation is a common condition that affects the health and the quality of life of patients. Recent studies have suggested that the gut microbiome is associated with constipation, but these studies were mainly focused on a single research cohort. Thus, we aimed to construct a classification model based on fecal bacterial and identify the potential gut microbes' biomarkers. (2) Methods: We collected 3056 fecal amplicon sequence data from five research cohorts. The data were subjected to a series of analyses, including alpha- and beta-diversity analyses, phylogenetic profiling analyses, and systematic machine learning to obtain a comprehensive understanding of the association between constipation and the gut microbiome. (3) Results: The alpha diversity of the bacterial community composition was higher in patients with constipation. Beta diversity analysis evidenced significant partitions between the two groups on the base of gut microbiota composition. Further, machine learning based on feature selection was performed to evaluate the utility of the gut microbiome as the potential biomarker for constipation. The Gradient Boosted Regression Trees after chi2 feature selection was the best model, exhibiting a validation performance of 70.7%. (4) Conclusions: We constructed an accurate constipation discriminant model and identified 15 key genera, including Serratia, Dorea, and Aeromonas, as possible biomarkers for constipation.

Experiment 1

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Reviewed Marked as Reviewed by Svetlana up on 2025-4-9

Curated date: 2025/04/01

Curator: Nithya

Revision editor(s): Nithya, Anne-mariesharp, KateRasheed

Subjects

Location of subjects: Australia; China; Poland; United Kingdom; United States of America

Host species Species from which microbiome was sampled. Contact us to have more species added.: Homo sapiens

Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology: Feces Cow dung,Cow pat,Droppings,Dung,Excrement,Excreta,Faeces,Fecal material,Fecal matter,Fewmet,Frass,Guano,Matières fécales@fr,Merde@fr,Ordure,Partie de la merde@fr,Piece of shit,Porción de mierda@es,Portion of dung,Portion of excrement,Portion of faeces,Portion of fecal material,Portion of fecal matter,Portion of feces,Portion of guano,Portion of scat,Portionem cacas,Scat,Spoor,Spraint,Stool,Teil der fäkalien@de,Feces,feces

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: Constipation Constipation,Costiveness,Dyschezia,constipation

Group 0 name Corresponds to the control (unexposed) group for case-control studies: Healthy controls

Group 1 name Corresponds to the case (exposed) group for case-control studies: Constipation patients

Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: Patients clinically diagnosed of constipation by evaluating the stool form and the associated persistent bowel symptoms, such as the Bristol Stool Form Scale and the Rome IV criteria

Group 0 sample size Number of subjects in the control (unexposed) group: 2138

Group 1 sample size Number of subjects in the case (exposed) group: 918

Lab analysis

Sequencing type: 16S

16S variable region One or more hypervariable region(s) of the bacterial 16S gene: Not specified

Statistical Analysis

Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).: relative abundances

Significance threshold p-value or FDR threshold used for differential abundance testing (if any): 0.05

MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?: No

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness: increased

Chao1 Abundance-based estimator of species richness: increased

Richness Number of species: unchanged

Faith Phylogenetic diversity, takes into account phylogenetic distance of all taxa identified in a sample: increased

Signature 1

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Reviewed Marked as Reviewed by Svetlana up on 2025-4-9

Curated date: 2025/04/02

Curator: Nithya

Revision editor(s): Nithya, Anne-mariesharp, KateRasheed

Source: Figure 4D

Description: The balance selected genera that significantly differed between the two groups

Abundance in Group 1: increased abundance in Constipation patients

NCBI	Quality Control	Links
Agathobacter
Alloscardovia
Brachybacterium
Caulobacter
Eremococcus
Parolsenella
Rahnella
uncultured Oscillospiraceae bacterium
Lachnospiraceae

Revision editor(s): Nithya, Anne-mariesharp, KateRasheed

Signature 2

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Reviewed Marked as Reviewed by Svetlana up on 2025-4-9

Curated date: 2025/04/02

Curator: Nithya

Revision editor(s): Nithya, Anne-mariesharp

Source: Figure 4D

Description: The balance selected genera that significantly differed between the two groups

Abundance in Group 1: decreased abundance in Constipation patients

NCBI	Quality Control	Links
Anaerosporobacter
Bacillota
Caproiciproducens
Desulfovibrionaceae
Dorea
Eggerthellaceae
Flavonifractor
Gammaproteobacteria
Puniceicoccaceae
Lachnospiraceae NK3A20 groupLachnospiraceae NK3A20 group

Revision editor(s): Nithya, Anne-mariesharp