Evidence map›Paper›PMID 31220183›Full record

ArticlePloS one2019

Assessment of reproducibility and biological variability of fasting and postprandial plasma metabolite concentrations using 1H NMR spectroscopy.

Ruifang Li-Gao, David A Hughes, Saskia le Cessie, Renée de Mutsert, Martin den Heijer, Frits R Rosendaal, Ko Willems van Dijk, Nicholas J Timpson, Dennis O Mook-Kanamori

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–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

26 citing papers in PubMed.

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  19. Maternal Body Mass Index, Early-Pregnancy Metabolite Profile, and Birthweight.The Journal of clinical endocrinology and metabolism · 2022
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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

9 authors.

Ruifang Li-GaoDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.ORCID 0000-0003-0650-1351
David A HughesMRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom.
Saskia le CessieDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
Renée de MutsertDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
Martin den HeijerDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
Frits R RosendaalDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
Ko Willems van DijkDepartment of Internal Medicine, division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands.ORCID 0000-0002-2172-7394
Nicholas J TimpsonMRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom.
Dennis O Mook-KanamoriDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.

Funding

Cancer Research UK C18281/A19169Department of Health BRC-1215-20011Medical Research Council MC_UU_12013/3Wellcome Trust 102215/2/13/2Wellcome Trust 202802/Z/16/Z
6 · The paper itself

Abstract

introductionIt is crucial to understand the factors that introduce variability before applying metabolomics to clinical and biomarker research.

objectivesWe quantified technical and biological variability of both fasting and postprandial metabolite concentrations measured using 1H NMR spectroscopy in plasma samples.

methodsIn the Netherlands Epidemiology of Obesity study (n = 6,671), 148 metabolite concentrations (101 metabolites belonging to lipoprotein subclasses) were measured under fasting and postprandial states (150 minutes after a mixed liquid meal). Technical variability was evaluated among 265 fasting and 851 postprandial samples, with the identical blood plasma sample being measured twice by the same laboratory protocol. Biological reproducibility was assessed by measuring 165 individuals twice across time for evaluation of short- (<6 months) and long-term (>3 years) biological variability. Intra-class coefficients (ICCs) were used to assess variability. The ICCs of the fasting metabolites were compared with the postprandial metabolites using two-sided paired Wilcoxon test separately for short- and long-term measurements.

resultsBoth fasting and postprandial metabolite concentrations showed high technical reproducibility using 1H NMR spectroscopy (median ICC = 0.99). Postprandial metabolite concentrations revealed slightly higher ICC scores than fasting ones in short-term repeat measures (median ICC in postprandial and fasting metabolite concentrations 0.72 versus 0.67, Wilcoxon p-value = 8.0×10-14). Variability did not increase further in a long-term repeat measure, with median ICC in postprandial of 0.64 and in fasting metabolite concentrations 0.66.

conclusionTechnical reproducibility is excellent. Biological reproducibility of postprandial metabolite concentrations showed a less or equal variability than fasting metabolite concentrations over time.

Indexed as

MetabolomeAnalysis of VarianceBiological Variation, PopulationBiomarkersBlood Chemical AnalysisFastingFemaleHumansMagnetic Resonance SpectroscopyMaleMetabolomicsMiddle AgedPostprandial PeriodReproducibility of ResultsBiomarkers

Identifiers

PMID31220183
PMCPMC6586348

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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.