Evidence map›Paper›PMID 30240894›Full record

ReviewClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association2019

Microbiome 101: Studying, Analyzing, and Interpreting Gut Microbiome Data for Clinicians.

Celeste Allaband, Daniel McDonald, Yoshiki Vázquez-Baeza, Jeremiah J Minich, Anupriya Tripathi, David A Brenner, Rohit Loomba, Larry Smarr, William J Sandborn, Bernd Schnabl and 3 more

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
160citing papers in PubMed, 14 pooled it
10.7field-weighted citation impact, top 1% of its field
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

160 citing papers in PubMed, 14 syntheses or guidelines pooled it, 316 citations in OpenAlex.

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  9. Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2022
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  17. Feasibility of an At-Home Adult Stool Specimen Collection Method in Rural Cambodia.International journal of environmental research and public health · 2021
    Trial
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  19. Work-Influenced Circadian Disruption Connected to Disease Risk but Not Microbiomes in a Cohort of Philadelphia Nurses.American journal of human biology : the official journal of the Human Biology Council · 2026
    Article
  20. Review

100 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

13 authors at 3 institutions in 1 country.

Celeste AllabandBiomedical Sciences Graduate Program, University of California San Diego, La Jolla, California.
Daniel McDonaldDepartment of Pediatrics, University of California San Diego, La Jolla, California.
Yoshiki Vázquez-BaezaDepartment of Pediatrics, University of California San Diego, La Jolla, California.
Jeremiah J MinichScripps Institution of Oceanography, University of California San Diego, La Jolla, California.
Anupriya TripathiDivision of Biological Sciences, University of California San Diego, La Jolla, California.
David A BrennerDepartment of Medicine, University of California San Diego, La Jolla, California.
Rohit LoombaDepartment of Medicine, University of California San Diego, La Jolla, California; Center for Microbiome Innovation, University of California San Diego, La Jolla, California.
Larry SmarrCenter for Microbiome Innovation, University of California San Diego, La Jolla, California; Department of Computer Science and Engineering, University of California San Diego, La Jolla, California; California Institute of Telecommunications and Information Technology, University of California San Diego, La Jolla, California.
William J SandbornCenter for Microbiome Innovation, University of California San Diego, La Jolla, California; Division of Gastroenterology, Veterans Administration San Diego Health System, La Jolla, California.
Bernd SchnablDepartment of Medicine, University of California San Diego, La Jolla, California; Center for Microbiome Innovation, University of California San Diego, La Jolla, California; Division of Gastroenterology, Veterans Administration San Diego Health System, La Jolla, California.
Pieter DorresteinDepartment of Pediatrics, University of California San Diego, La Jolla, California; Center for Microbiome Innovation, University of California San Diego, La Jolla, California; Skaggs School of Pharmacy, University of California San Diego, La Jolla, California.
Amir ZarrinparDepartment of Medicine, University of California San Diego, La Jolla, California; Center for Microbiome Innovation, University of California San Diego, La Jolla, California; Division of Gastroenterology, Veterans Administration San Diego Health System, La Jolla, California.
Rob KnightDepartment of Pediatrics, University of California San Diego, La Jolla, California; Center for Microbiome Innovation, University of California San Diego, La Jolla, California; Department of Computer Science and Engineering, University of California San Diego, La Jolla, California; Department of Bioengineering, University of California San Diego, La Jolla, California. Electronic address: robknight@ucsd.edu.
University of California San Diego · USUC San Diego Health System · USScripps Institution of Oceanography · US

Funding

UCSD Research Training Program for VeterinariansT32OD017863 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ERNST, PETER B., SIGURDSON, CHRISTINA · 2014 to 2023
$2.0M
The Relationship of Gut Microbiome and Time Restricted Feeding in MetabolismK08DK102902 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZARRINPAR, AMIR · 2015 to 2019
$789k
NIDDK NIH HHS K08 DK102902NIH HHS T32 OD017863
6 · The paper itself

Abstract

Advances in technical capabilities for reading complex human microbiomes are leading to an explosion of microbiome research, leading in turn to intense interest among clinicians in applying these techniques to their patients. In this review, we discuss the content of the human microbiome, including intersubject and intrasubject variability, considerations of study design including important confounding factors, and different methods in the laboratory and on the computer to read the microbiome and its resulting gene products and metabolites. We highlight several common pitfalls for clinicians, including the expectation that an individual's microbiome will be stable, that diet can induce rapid changes that are large compared with the differences among subjects, that everyone has essentially the same core stool microbiome, and that different laboratory and computational methods will yield essentially the same results. We also highlight the current limitations and future promise of these techniques, with the expectation that an understanding of these considerations will help accelerate the path toward routine clinical application of these techniques developed in research settings.

Indexed as

Gastrointestinal MicrobiomeMicrobiotaComputational BiologyGastrointestinal TractHumansIndividualityMetabolomicsMetagenomicsClinicianDiagnosisGut MicrobiomePrognosisStudy Design

Identifiers

PMID30240894
PMCPMC6391518
OpenAlexW2890526629

What OpenQuestion holds

Textmetadata
LicenceTDM
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.