Evidence map›Paper›PMID 26283357›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2015

Temporal and spatial variation of the human microbiota during pregnancy.

Daniel B DiGiulio, Benjamin J Callahan, Paul J McMurdie, Elizabeth K Costello, Deirdre J Lyell, Anna Robaczewska, Christine L Sun, Daniela S A Goltsman, Ronald J Wong, Gary Shaw and 3 more

Open access · bronzeAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 650 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
650citing papers in PubMed, 4 pooled it
50.5field-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

650 citing papers in PubMed, 4 syntheses or guidelines pooled it, 1,182 citations in OpenAlex.

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590 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 1 institution in 1 country.

Daniel B DiGiulioMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304;
Benjamin J CallahanMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Statistics, Stanford University, Stanford, CA 94305;
Paul J McMurdieMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Statistics, Stanford University, Stanford, CA 94305;
Elizabeth K CostelloMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305;
Deirdre J LyellMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305;
Anna RobaczewskaMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304;
Christine L SunMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305;
Daniela S A GoltsmanMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305;
Ronald J WongMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305.
Gary ShawMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305.
David K StevensonMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305.
Susan P HolmesMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Statistics, Stanford University, Stanford, CA 94305;ORCID http://orcid.org/0000-0002-2208-8168
David A RelmanMarch of Dimes Prematurity Research Center, Stanford University School of Medicine, Stanford, CA 94305; Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305; relman@stanford.edu.
March of Dimes · US

Funding

Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
Heterogeneous in situ data: Kernals, Distances and TreesR01GM086884 · NIGMS · STANFORD UNIVERSITY · PI HOLMES, SUSAN P · 2008 to 2011
$895k
NCATS NIH HHS UL1 TR001085NIGMS NIH HHS R01 GM086884
6 · The paper itself

Abstract

Despite the critical role of the human microbiota in health, our understanding of microbiota compositional dynamics during and after pregnancy is incomplete. We conducted a case-control study of 49 pregnant women, 15 of whom delivered preterm. From 40 of these women, we analyzed bacterial taxonomic composition of 3,767 specimens collected prospectively and weekly during gestation and monthly after delivery from the vagina, distal gut, saliva, and tooth/gum. Linear mixed-effects modeling, medoid-based clustering, and Markov chain modeling were used to analyze community temporal trends, community structure, and vaginal community state transitions. Microbiota community taxonomic composition and diversity remained remarkably stable at all four body sites during pregnancy (P > 0.05 for trends over time). Prevalence of a Lactobacillus-poor vaginal community state type (CST 4) was inversely correlated with gestational age at delivery (P = 0.0039). Risk for preterm birth was more pronounced for subjects with CST 4 accompanied by elevated Gardnerella or Ureaplasma abundances. This finding was validated with a set of 246 vaginal specimens from nine women (four of whom delivered preterm). Most women experienced a postdelivery disturbance in the vaginal community characterized by a decrease in Lactobacillus species and an increase in diverse anaerobes such as Peptoniphilus, Prevotella, and Anaerococcus species. This disturbance was unrelated to gestational age at delivery and persisted for up to 1 y. These findings have important implications for predicting premature labor, a major global health problem, and for understanding the potential impact of a persistent, altered postpartum microbiota on maternal health, including outcomes of pregnancies following short interpregnancy intervals.

Indexed as

MicrobiotaFemaleHumansIntestinesPeriodontiumPregnancySalivaVagina16S rRNA genemicrobiomepregnancypremature laborpreterm birth

Identifiers

PMID26283357
PMCPMC4568272
OpenAlexW1930591173

What OpenQuestion holds

Textmetadata
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.