Evidence map›Paper›PMID 32374853›Full record

Observational studyCardiovascular research2021

Quantifying technical confounders in microbiome studies.

Theda U P Bartolomaeus, Till Birkner, Hendrik Bartolomaeus, Ulrike Löber, Ellen G Avery, Anja Mähler, Daniela Weber, Bastian Kochlik, András Balogh, Nicola Wilck and 4 more

Abstract readComparative StudyMulticenter StudyObservational Study
PubMed Publisher
In one paragraph

Observational study in Cardiovascular research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 4 pooled it
3.5field-weighted citation impact, top 6% 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

46 citing papers in PubMed, 4 syntheses or guidelines pooled it, 79 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 2 institutions in 1 country.

Theda U P BartolomaeusExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Till BirknerExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Hendrik BartolomaeusExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Ulrike LöberExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Ellen G AveryExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Anja MählerExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Daniela WeberDepartment of Molecular Toxicology, German Institute of Human Nutrition (DIfE), Potsdam Rehbrücke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany.
Bastian KochlikDepartment of Molecular Toxicology, German Institute of Human Nutrition (DIfE), Potsdam Rehbrücke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany.
András BaloghExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Nicola WilckExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Michael BoschmannExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Dominik N MüllerExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Lajos MarkóExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Sofia K ForslundExperimental and Clinical Research Center, a Cooperation of Charité-Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany.
Max Delbrück Center · DEGerman Institute of Human Nutrition · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsRecent technical developments have allowed the study of the human microbiome to accelerate at an unprecedented pace. Methodological differences may have considerable impact on the results obtained. Thus, we investigated how different storage, isolation, and DNA extraction methods can influence the characterization of the intestinal microbiome, compared to the impact of true biological signals such as intraindividual variability, nutrition, health, and demographics. METHODS AND

resultsAn observative cohort study in 27 healthy subjects was performed. Participants were instructed to collect stool samples twice spaced by a week, using six different methods (naive and Zymo DNA/RNA Shield on dry ice, OMNIgene GUT, RNALater, 95% ethanol, Zymo DNA/RNA Shield at room temperature). DNA extraction from all samples was performed comparatively using QIAamp Power Fecal and ZymoBIOMICS DNA Kits. 16S rRNA sequencing of the gut microbiota as well as qPCRs were performed on the isolated DNA. Metrics included alpha diversity as well as multivariate and univariate comparisons of samples, controlling for covariate patterns computationally. Interindividual differences explained 7.4% of overall microbiome variability, whereas the choice of DNA extraction method explained a further 5.7%. At phylum level, the tested kits differed in their recovery of Gram-positive bacteria, which is reflected in a significantly skewed enterotype distribution.

conclusionDNA extraction methods had the highest impact on observed microbiome variability, and were comparable to interindividual differences, thus may spuriously mimic the microbiome signatures of various health and nutrition factors. Conversely, collection methods had a relatively small influence on microbiome composition. The present study provides necessary insight into the technical variables which can lead to divergent results from seemingly similar study designs. We anticipate that these results will contribute to future efforts towards standardization of microbiome quantification procedures in clinical research.

Indexed as

Gastrointestinal MicrobiomeSpecimen HandlingAdultBacteriaDNA, BacterialFecesFemaleGermanyHealthy VolunteersHumansIntestinesMaleMiddle AgedPolymerase Chain ReactionRibotypingRNA, Ribosomal, 16SDNA, BacterialRNA, Ribosomal, 16S16S sequencingConfoundersDNA extractionMicrobiomeMicrobiota

Identifiers

PMID32374853
OpenAlexW3022162007

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.