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

Study information

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

study design

cross-sectional observational, not case-control

Citation

PMID PubMed identifier for scientific articles.

31973685

DOI Digital object identifier for electronic documents.

10.1080/19490976.2020.1712985

URI

https://pubmed.ncbi.nlm.nih.gov/31973685/

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

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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