Evidence map›Paper›PMID 32451742›Full record

ReviewMetabolomics : Official journal of the Metabolomic Society2020

The key points in the pre-analytical procedures of blood and urine samples in metabolomics studies.

Hai Bi, Zhengyang Guo, Xiao Jia, Huiying Liu, Lulin Ma, Lixiang Xue

Abstract readReview
PubMed Publisher
In one paragraph

Review in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
3.8field-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

36 citing papers in PubMed, 73 citations in OpenAlex.

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  11. Metabolic insights into HIV/TB co-infection: an untargeted urinary metabolomics approach.Metabolomics : Official journal of the Metabolomic Society · 2024
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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

6 authors at 2 institutions in 1 country.

Hai BiDepartment of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, People's Republic of China.
Zhengyang GuoMedical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China.
Xiao JiaMedical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China.
Huiying LiuDepartment of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Beijing, People's Republic of China.
Lulin MaDepartment of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, People's Republic of China. malulin@medmail.com.cn.
Lixiang XueMedical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China. lixiangxue@hsc.pku.edu.cn.
Peking University Third Hospital · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMetabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results. AIM OF REVIEW: In this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing. KEY SCIENTIFIC CONCEPTS OF REVIEW: Clinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze-thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.

Indexed as

BloodBlood Chemical AnalysisBody FluidsChromatography, LiquidHumansMetabolomeMetabolomicsPlasmaReproducibility of ResultsSerumSpecimen HandlingUrinalysisUrineMetabolomicsPlasmaPreprocessingSerumUrine

Identifiers

PMID32451742
OpenAlexW3030218399

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.