Evidence map›Paper›PMID 29695307›Full record

ArticleMicrobiome2018

Use of dietary indices to control for diet in human gut microbiota studies.

Ruth C E Bowyer, Matthew A Jackson, Tess Pallister, Jane Skinner, Tim D Spector, Ailsa A Welch, Claire J Steves

Abstract read
In one paragraph

Article in Microbiome, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

78 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  19. Long-term follow-up study on the impact of a gut microbiota-supportive diet on heart failure and mortality outcomes.International journal of cardiology. Cardiovascular risk and prevention · 2025
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18 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ruth C E BowyerThe Department of Twin Research, Kings College London, 3-4th Floor South Wing Block D, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.
Matthew A JacksonThe Department of Twin Research, Kings College London, 3-4th Floor South Wing Block D, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.
Tess PallisterThe Department of Twin Research, Kings College London, 3-4th Floor South Wing Block D, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.
Jane SkinnerNorwich Medical School 2.02, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.
Tim D SpectorThe Department of Twin Research, Kings College London, 3-4th Floor South Wing Block D, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.
Ailsa A WelchNorwich Medical School 2.02, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.
Claire J StevesThe Department of Twin Research, Kings College London, 3-4th Floor South Wing Block D, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK. claire.j.steves@kcl.ac.uk.

Funding

REACH US PATH FOR WOMEN (PROMOTING ACCESS TO HEALTH FOR PACIFIC ISLANDER )U58DP001006 · DP · ORANGE CTY ASIAN/PACIFIC ISL CMTY ALLNCE · PI FOO, MARY ANNE · 2007 to 2011
$4.2M
Department of HealthMedical Research Council MR/N01183X/1Medical Research Council MR/N030125/1Wellcome TrustWorld Health Organization 001
6 · The paper itself

Abstract

backgroundEnvironmental factors have a large influence on the composition of the human gut microbiota. One of the most influential and well-studied is host diet. To assess and interpret the impact of non-dietary factors on the gut microbiota, we endeavoured to determine the most appropriate method to summarise community variation attributable to dietary effects. Dietary habits are multidimensional with internal correlations. This complexity can be simplified by using dietary indices that quantify dietary variance in a single measure and offer a means of controlling for diet in microbiota studies. However, to date, the applicability of different dietary indices to gut microbiota studies has not been assessed. Here, we use food frequency questionnaire (FFQ) data from members of the TwinsUK cohort to create three different dietary measures applicable in western-diet populations: The Healthy Eating Index (HEI), the Mediterranean Diet Score (MDS) and the Healthy Food Diversity index (HFD-Index). We validate and compare these three indices to determine which best summarises dietary influences on gut microbiota composition.

resultsAll three indices were independently validated using established measures of health, and all were significantly associated with microbiota measures; the HEI had the highest t values in models of alpha diversity measures, and had the highest number of associations with microbial taxa. Beta diversity analyses showed the HEI explained the greatest variance of microbiota composition. In paired tests between twins discordant for dietary index score, the HEI was associated with the greatest variation of taxa and twin dissimilarity.

conclusionsWe find that the HEI explains the most variance in, and has the strongest association with, gut microbiota composition in a western (UK) population, suggesting that it may be the best summary measure to capture gut microbiota variance attributable to habitual diet in comparable populations.

Indexed as

DietGastrointestinal MicrobiomeBiodiversityDiet, HealthyFeeding BehaviorHumansReproducibility of ResultsDietary covariateDietary IndexFFQFood frequency questionnaireHealthy Eating IndexHealthy Food Diversity IndexHEIHFD-IndexHuman microbiotaMDSMediterranean Dietary ScoreMicrobiomeMicrobiota

Identifiers

PMID29695307
PMCPMC5918560

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.