Association of dietary fibre intake and gut microbiota in adults

Study information

Needs review

study design

case-control

Citation

PMID PubMed identifier for scientific articles.

30355393

DOI Digital object identifier for electronic documents.

10.1017/S0007114518002465

URI Uniform resource identifier for web resources.

Authors

Lin D, Peters BA, Friedlander C, Freiman HJ, Goedert JJ, Sinha R, Miller G, Bernstein MA, Hayes RB, Ahn J

Journal

The British journal of nutrition

Year

2018

Increasing evidence indicates that gut microbiota may influence colorectal cancer risk. Diet, particularly fibre intake, may modify gut microbiota composition, which may affect cancer risk. We investigated the relationship between dietary fibre intake and gut microbiota in adults. Using 16S rRNA gene sequencing, we assessed gut microbiota in faecal samples from 151 adults in two independent study populations: National Cancer Institute (NCI), n 75, and New York University (NYU), n 76. We calculated energy-adjusted fibre intake based on FFQ. For each study population with adjustment for age, sex, race, BMI and smoking, we evaluated the relationship between fibre intake and gut microbiota community composition and taxon abundance. Total fibre intake was significantly associated with overall microbial community composition in NYU (P=0·008) but not in NCI (P=0·81). In a meta-analysis of both study populations, higher fibre intake tended to be associated with genera of class Clostridia, including higher abundance of SMB53 (fold change (FC)=1·04, P=0·04), Lachnospira (FC=1·03, P=0·05) and Faecalibacterium (FC=1·03, P=0·06), and lower abundance of Actinomyces (FC=0·95, P=0·002), Odoribacter (FC=0·95, P=0·03) and Oscillospira (FC=0·96, P=0·06). A species-level meta-analysis showed that higher fibre intake was marginally associated with greater abundance of Faecalibacterium prausnitzii (FC=1·03, P=0·07) and lower abundance of Eubacterium dolichum (FC=0·96, P=0·04) and Bacteroides uniformis (FC=0·97, P=0·05). Thus, dietary fibre intake may impact gut microbiota composition, particularly class Clostridia, and may favour putatively beneficial bacteria such as F. prausnitzii. These findings warrant further understanding of diet-microbiota relationships for future development of colorectal cancer prevention strategies.

Experiment 1

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Curated date: 2021/01/10

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Subjects

Location of subjects: United States of America

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: diet Dietary,Diets,diet

Group 0 name Corresponds to the control (unexposed) group for case-control studies: high fiber (Q3 and Q4)
Group 1 name Corresponds to the case (exposed) group for case-control studies: low fiber (Q1 and Q2)
Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: lower quartiles (Q1 and Q2) of fiber intake vs control (Q3 and Q4)
Group 0 sample size Number of subjects in the control (unexposed) group: 38
Group 1 sample size Number of subjects in the case (exposed) group: 37
Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any): no "long term" antibiotics use

Lab analysis

Sequencing type: 16S

16S variable region One or more hypervariable region(s) of the bacterial 16S gene: V3-V4

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Roche454

Statistical Analysis

Statistical test: DESeq2

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, body height, body weight, race, smoking behavior, body mass index

Alpha Diversity

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

Signature 1

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Curated date: 2021/01/10

Curator: Lora Kasselman

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Source: Table 2.

Description: Table 2. Meta-analysis of the association between total fibre intake and genera and species in National Cancer Institute (NCI) and New York University (NYU) study populations - NCI data

Abundance in Group 1: increased abundance in low fiber (Q1 and Q2)

NCBI	Links
Actinomyces

Revision editor(s): WikiWorks