Evidence map›Paper›PMID 36557207›Full record

ArticleMetabolites2022

A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies.

Zhan Shi, Haohui Li, Wei Zhang, Youxiang Chen, Chunyan Zeng, Xiuhua Kang, Xinping Xu, Zhenkun Xia, Bei Qing, Yunchang Yuan and 5 more

Abstract read
In one paragraph

Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
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.

Zhan ShiMetanotitia Inc., No 59. Gaoxin South 9th Road, Yuehai Street, Nanshan District, Shenzhen 518056, China.
Haohui LiMetanotitia Inc., No 59. Gaoxin South 9th Road, Yuehai Street, Nanshan District, Shenzhen 518056, China.
Wei ZhangThe First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Nanchang 330209, China.
Youxiang ChenThe First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Nanchang 330209, China.
Chunyan ZengThe First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Nanchang 330209, China.
Xiuhua KangThe First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Nanchang 330209, China.
Xinping XuThe First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Nanchang 330209, China.ORCID 0000-0002-9568-4171
Zhenkun XiaThe Second Xiangya Hospital of Central South University, Furong District, Changsha 410011, China.
Bei QingThe Second Xiangya Hospital of Central South University, Furong District, Changsha 410011, China.ORCID 0000-0003-4740-2923
Yunchang YuanThe Second Xiangya Hospital of Central South University, Furong District, Changsha 410011, China.
Guodong SongThe Second Hospital of Tianjin Medical University, No 23. Pingjiang Road, Hexi District, Tianjin 300211, China.
Camila CaldanaMax Planck Institute of Molecular Plant Physiology, Potsdam Science Park, Am Muehlenberg 1, 14476 Potsdam, Germany.
Junyuan HuMetanotitia Inc., No 59. Gaoxin South 9th Road, Yuehai Street, Nanshan District, Shenzhen 518056, China.
Lothar WillmitzerMax Planck Institute of Molecular Plant Physiology, Potsdam Science Park, Am Muehlenberg 1, 14476 Potsdam, Germany.
Yan LiMetanotitia Inc., No 59. Gaoxin South 9th Road, Yuehai Street, Nanshan District, Shenzhen 518056, China.

Funding

the National Science Foundation of China 82160553
6 · The paper itself

Abstract

As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed

Indexed as

clinical cohortdata modelingdata normalizationGC-MSLC-MSmetabolomicsquality control

Identifiers

PMID36557207
PMCPMC9782571

What OpenQuestion holds

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LicenceCC BY
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.