Pediatric obesity is associated with an altered gut microbiota and discordant shifts in Firmicutes populations

Study information

Reviewed Marked as Reviewed by Shaimaa Elsafoury on 2021/02/09

study design

case-control

Citation

PMID PubMed identifier for scientific articles.

27450202

DOI Digital object identifier for electronic documents.

10.1111/1462-2920.13463

URI

Authors

Riva A, Borgo F, Lassandro C, Verduci E, Morace G, Borghi E, Berry D

Journal

Environmental microbiology

Year

2017

An altered gut microbiota has been linked to obesity in adulthood, although little is known about childhood obesity. The aim of this study was to characterize the composition of the gut microbiota in obese (n = 42) and normal-weight (n = 36) children aged 6 to 16. Using 16S rRNA gene-targeted sequencing, we evaluated taxa with differential abundance according to age- and sex-normalized body mass index (BMI z-score). Obesity was associated with an altered gut microbiota characterized by elevated levels of Firmicutes and depleted levels of Bacteroidetes. Correlation network analysis revealed that the gut microbiota of obese children also had increased correlation density and clustering of operational taxonomic units (OTUs). Members of the Bacteroidetes were generally better predictors of BMI z-score and obesity than Firmicutes, which was likely due to discordant responses of Firmicutes OTUs. In accordance with these observations, the main metabolites produced by gut bacteria, short chain fatty acids (SCFAs), were higher in obese children, suggesting elevated substrate utilisation. Multiple taxa were correlated with SCFA levels, reinforcing the tight link between the microbiota, SCFAs and obesity. Our results suggest that gut microbiota dysbiosis and elevated fermentation activity may be involved in the etiology of childhood obesity.

Experiment 1

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Reviewed Marked as Reviewed by Shaimaa Elsafoury on 2021/02/09

Curated date: 2021/01/10

Curator: WikiWorks

Revision editor(s): WikiWorks, Victoria

Subjects

Location of subjects: Italy

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: Obesity Adiposis,Adiposity,Obese,Obese (finding),obesity,Obesity (disorder),Obesity [Ambiguous],obesity disease,obesity disorder,Obesity NOS,Obesity, unspecified,Overweight and obesity,Obesity

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

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

Group 0 sample size Number of subjects in the control (unexposed) group: 36

Group 1 sample size Number of subjects in the case (exposed) group: 42

Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any): 6 months

Lab analysis

Sequencing type: 16S

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

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Illumina

Statistical Analysis

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

Statistical test: Linear Regression

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)?: Yes

Confounders controlled for Confounding factors that have been accounted for by stratification or model adjustment: age, sex

Alpha Diversity

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

Chao1 Abundance-based estimator of species richness: unchanged

Inverse Simpson Modification of Simpsons index D as 1/D to obtain high values in datasets of high diversity and vice versa: unchanged

Richness Number of species: unchanged

Signature 1

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Reviewed Marked as Reviewed by Shaimaa Elsafoury on 2021/02/09

Curated date: 2021/01/10

Curator: Marianthi Thomatos

Revision editor(s): WikiWorks

Source: Table 1 & Supplementary Table 5

Description: Differential abundance in pediatric obesity versus normal weight controls

Abundance in Group 1: increased abundance in pediatric obese

NCBI	Quality Control	Links
Bacillota
Clostridia
Eubacteriales
Oscillospiraceae
Faecalibacterium prausnitzii

Revision editor(s): WikiWorks

Signature 2

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Your review change was submitted

Reviewed Marked as Reviewed by Shaimaa Elsafoury on 2021/02/09

Curated date: 2021/01/10

Curator: Marianthi Thomatos

Revision editor(s): WikiWorks

Source: Table 1 & Supplementary Table 5

Description: Differential abundance in pediatric obesity versus normal weight controls

Abundance in Group 1: decreased abundance in pediatric obese

NCBI	Quality Control	Links
Bacteroidota
Bacteroidia
Bacteroidales
Bacteroidaceae
Bacteroides
Phocaeicola vulgatus
Bacteroides stercoris

Revision editor(s): WikiWorks