Environmental and intrinsic factors shaping gut microbiota composition and diversity and its relation to metabolic health in children and early adolescents: A population-based study

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Reviewed Marked as Reviewed by Atrayees on 2023-7-3
Citation
PMID PubMed identifier for scientific articles.
DOI Digital object identifier for electronic documents.
Authors
Moran-Ramos S, Lopez-Contreras BE, Villarruel-Vazquez R, Ocampo-Medina E, Macias-Kauffer L, Martinez-Medina JN, Villamil-Ramirez H, León-Mimila P, Del Rio-Navarro BE, Ibarra-Gonzalez I, Vela-Amieva M, Gomez-Perez FJ, Velazquez-Cruz R, Salmeron J, Reyes-Castillo Z, Aguilar-Salinas C, Canizales-Quinteros S
Journal
Gut microbes
Year
2020
Keywords:
adolescents, children, co-abundance groups, diversity, enterotypes, gut microbiota, obesity
BACKGROUND: Gut microbiota, by influencing multiple metabolic processes in the host, is an important determinant of human health and disease. However, gut dysbiosis associated with metabolic complications shows inconsistent patterns. This is likely driven by factors shaping gut microbial composition that have largely been under-evaluated, at a population level, in school-age children, especially from developing countries. RESULTS: Through characterization, by 16S sequencing, of the largest gut microbial population-based school-aged children cohort in Latin America (ORSMEC, N = 926, aged 6-12 y), we identified associations of 14 clinical and environmental covariates (PFDR<0.1), collectively explaining 15.7% of the inter-individual gut microbial variation. Extrinsic factors such as markers of socioeconomic status showed a major influence in the most abundant taxa and in the enterotypes' distribution. Age was positively correlated with higher diversity, but only in normal-weight children (rho = 0.138, P =2 × 10-3). In contrast, this correlation although not significant, was negative in overweight and obese children (rho = -0.125, P = 0.104 and rho = -0.058, P = 0.409, respectively). Finally, co-abundance groups (CAGs) were associated with the presence of metabolic complications. CONCLUSIONS: Our study offers evidence that the presence of overweight and obesity could impair the microbial diversity maturation associated with age. Furthermore, it provides novel results toward a better understanding of gut microbiota in the pediatric population that will ultimately help to develop therapeutic approaches to improve metabolic status.

Experiment 1


Reviewed Marked as Reviewed by Atrayees on 2023-7-3

Curated date: 2022/07/22

Curator: Kaluifeanyi101

Revision editor(s): Kaluifeanyi101, Atrayees

Subjects

Location of subjects
Mexico
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
Socioeconomic status class,Socioeconomic status,socioeconomic status,socioeconomic factors
Group 0 name Corresponds to the control (unexposed) group for case-control studies
lower monthly household income
Group 1 name Corresponds to the case (exposed) group for case-control studies
higher monthly household income
Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group
higher monthly household income
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
V4
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).
relative abundances
Statistical test
PERMANOVA
Significance threshold p-value or FDR threshold used for differential abundance testing (if any)
.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, household income, sex, antibiotic exposure

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness
unchanged
Chao1 Abundance-based estimator of species richness
unchanged

Signature 1

Reviewed Marked as Reviewed by Atrayees on 2023-7-3

Curated date: 2022/07/23

Curator: Kaluifeanyi101

Revision editor(s): Kaluifeanyi101, Atrayees

Source: Table 1, text

Description: Taxa significantly associated with monthly household income.

Abundance in Group 1: increased abundance in higher monthly household income

NCBI Quality ControlLinks
Eggerthella lenta
Bacteroides fragilis
Rikenellaceae
Holdemania
Oscillospira
Amedibacillus dolichus

Revision editor(s): Kaluifeanyi101, Atrayees

Signature 2

Reviewed Marked as Reviewed by Atrayees on 2023-7-3

Curated date: 2022/07/23

Curator: Kaluifeanyi101

Revision editor(s): Kaluifeanyi101, Atrayees

Source: Table 1, text

Description: taxa significantly associated with monthly household income.

Abundance in Group 1: decreased abundance in higher monthly household income

NCBI Quality ControlLinks
Prevotellaceae
Prevotella
Veillonellaceae

Revision editor(s): Kaluifeanyi101, Atrayees