The Associations between Diet and Socioeconomic Disparities and the Intestinal Microbiome in Preadolescence

Study information

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study design

cross-sectional observational, not case-control

Citation

PMID PubMed identifier for scientific articles.

34444813

DOI Digital object identifier for electronic documents.

10.3390/nu13082645

URI Uniform resource identifier for web resources.

https://pubmed.ncbi.nlm.nih.gov/34444813/

Authors

Lapidot Y, Reshef L, Goldsmith R, Na'amnih W, Kassem E, Ornoy A, Gophna U, Muhsen K

Journal

Nutrients

Year

2021

The intestinal microbiome continues to shift and develop throughout youth and could play a pivotal role in health and wellbeing throughout adulthood. Environmental and interpersonal determinants are strong mediators of the intestinal microbiome during the rapid growth period of preadolescence. We aim to delineate associations between the gut microbiome composition, body mass index (BMI), dietary intake and socioeconomic status (SES) in a cohort of ethnically homogenous preadolescents. This cohort included 139 Arab children aged 10-12 years, from varying socioeconomic strata. Dietary intake was assessed using the 24-h recall method. The intestinal microbiome was analyzed using 16S rRNA gene amplicon sequencing. Microbial composition was associated with SES, showing an overrepresentation of Prevotella and Eubacterium in children with lower SES. Higher BMI was associated with lower microbial diversity and altered taxonomic composition, including higher levels of Collinsella, especially among participants from lower SES. Intake of polyunsaturated fatty acids was the strongest predictor of bacterial alterations, including an independent association with Lachnobacterium and Lactobacillus. This study demonstrates that the intestinal microbiome in preadolescents is associated with socioeconomic determinants, BMI and dietary intake, specifically with higher consumption of polyunsaturated fatty acids. Thus, tailored interventions during these crucial years have the potential to improve health disparities throughout the lifespan.

Experiment 1

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Curated date: 2022/07/21

Curator: Kaluifeanyi101

Revision editor(s): Kaluifeanyi101

Subjects

Location of subjects: Israel

Host species Species from which microbiome was sampled (if applicable): 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

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: socioeconomic statussocioeconomic status

Group 0 name Corresponds to the control (unexposed) group for case-control studies: High SES
Group 1 name Corresponds to the case (exposed) group for case-control studies: Low SES
Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: SES score below median score of 6.4 based on (a) residential SES rank; (b) the number of paternal schooling years; and (c) household crowding index.
Group 0 sample size Number of subjects in the control (unexposed) group: 70
Group 1 sample size Number of subjects in the case (exposed) group: 69
Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any): NA

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

Statistical test: Linear Regression; 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, body mass index, diet, sex

Alpha Diversity

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

Signature 1

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Curated date: 2022/07/21

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Source: Figure 2D;

Description: Figure 2D. A heatmap of the multivariable model describing the top 50 associations between the independent variables and bacterial features. Positive associations are colored in red, while inverse associations are colored in blue. The color gradient represents the strength of the association (the effect size), with darker colors representing the stronger associations. The effect size was calculated by the following formula: (−log(qval)*SIGN (coeff)).

Abundance in Group 1: increased abundance in Low SES

NCBI	Links
Catenibacterium
Bulleidia
Mogibacterium
Prevotella
Coriobacteriaceae
Eubacteriales
Eubacterium
Dorea

Revision editor(s): Kaluifeanyi101

Signature 2

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Curated date: 2022/07/21

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Source: Figure 2D

Description: Figure 2D. A heatmap of the multivariable model describing the top 50 associations between the independent variables and bacterial features. Positive associations are colored in red, while inverse associations are colored in blue. The color gradient represents the strength of the association (the effect size), with darker colors representing the stronger associations. The effect size was calculated by the following formula: (−log(qval)*SIGN (coeff)).

Abundance in Group 1: decreased abundance in Low SES

NCBI	Links
Delftia ⚠
Rickenellaceae
Barnesiellaceae
Alistipes
Bacteroides
Clostridium
Erysipelotrichaceae
Parabacteroides
Odoribacter
Ruminococcus
Adlercreutzia

Revision editor(s): Kaluifeanyi101