Evidence map›Paper›PMID 25823017›Full record

ArticlePloS one2015

Pre-analytical sample quality: metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples.

Gabriele Anton, Rory Wilson, Zhong-Hao Yu, Cornelia Prehn, Sven Zukunft, Jerzy Adamski, Margit Heier, Christa Meisinger, Werner Römisch-Margl, Rui Wang-Sattler and 5 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed
6.0field-weighted citation impact, top 3% 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

56 citing papers in PubMed, 112 citations in OpenAlex.

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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

15 authors at 5 institutions in 3 countries.

Gabriele AntonInstitute of Epidemiology II, Helmholtz Center Munich, Munich, Germany.
Rory WilsonInstitute of Epidemiology II, Helmholtz Center Munich, Munich, Germany.
Zhong-Hao YuInstitute of Epidemiology II, Helmholtz Center Munich, Munich, Germany.
Cornelia PrehnInstitute of Experimental Genetics, Helmholtz Center Munich, Munich, Germany.
Sven ZukunftInstitute of Experimental Genetics, Helmholtz Center Munich, Munich, Germany.
Jerzy AdamskiInstitute of Experimental Genetics, Helmholtz Center Munich, Munich, Germany.
Margit HeierInstitute of Epidemiology II, Helmholtz Center Munich, Munich, Germany; Central Hospital of Augsburg, KORA Myocardial Infarction Registry, Augsburg, Germany.
Christa MeisingerInstitute of Epidemiology II, Helmholtz Center Munich, Munich, Germany; Central Hospital of Augsburg, KORA Myocardial Infarction Registry, Augsburg, Germany.
Werner Römisch-MarglInstitute for Bioinformatics and Systems Biology, Helmholtz Center Munich, Munich, Germany.
Rui Wang-SattlerInstitute of Epidemiology II, Helmholtz Center Munich, Munich, Germany.
Kristian HveemDepartment of Public Health and General Practice, Norwegian University of Science and Technology, Levanger, Norway.
Bruce WolfenbuttelDepartment of Endocrinology, University Medical Center Groningen, Groningen, The Netherlands.
Annette PetersInstitute of Epidemiology II, Helmholtz Center Munich, Munich, Germany.
Gabi KastenmüllerInstitute for Bioinformatics and Systems Biology, Helmholtz Center Munich, Munich, Germany.
Melanie WaldenbergerInstitute of Epidemiology II, Helmholtz Center Munich, Munich, Germany.
Helmholtz Zentrum München · DEInstitute of Bioinformatics and Systems Biology · DEUniversity Hospital Augsburg · DENorwegian University of Science and Technology · NOUniversity Medical Center Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in the "omics" field bring about the need for a high number of good quality samples. Many omics studies take advantage of biobanked samples to meet this need. Most of the laboratory errors occur in the pre-analytical phase. Therefore evidence-based standard operating procedures for the pre-analytical phase as well as markers to distinguish between 'good' and 'bad' quality samples taking into account the desired downstream analysis are urgently needed. We studied concentration changes of metabolites in serum samples due to pre-storage handling conditions as well as due to repeated freeze-thaw cycles. We collected fasting serum samples and subjected aliquots to up to four freeze-thaw cycles and to pre-storage handling delays of 12, 24 and 36 hours at room temperature (RT) and on wet and dry ice. For each treated aliquot, we quantified 127 metabolites through a targeted metabolomics approach. We found a clear signature of degradation in samples kept at RT. Storage on wet ice led to less pronounced concentration changes. 24 metabolites showed significant concentration changes at RT. In 22 of these, changes were already visible after only 12 hours of storage delay. Especially pronounced were increases in lysophosphatidylcholines and decreases in phosphatidylcholines. We showed that the ratio between the concentrations of these molecule classes could serve as a measure to distinguish between 'good' and 'bad' quality samples in our study. In contrast, we found quite stable metabolite concentrations during up to four freeze-thaw cycles. We concluded that pre-analytical RT handling of serum samples should be strictly avoided and serum samples should always be handled on wet ice or in cooling devices after centrifugation. Moreover, serum samples should be frozen at or below -80°C as soon as possible after centrifugation.

Indexed as

AdultBiomarkersBlood Chemical AnalysisBlood Specimen CollectionFreezingHigh-Throughput Screening AssaysHumansMaleMetabolomicsPrincipal Component AnalysisTemperatureTime FactorsYoung AdultBiomarkers

Identifiers

PMID25823017
PMCPMC4379062
OpenAlexW2016019757

What OpenQuestion holds

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Registered trials

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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.