Evidence map›Paper›PMID 38630120›Full record

ArticleInfectious diseases & immunity2021

Systematic Discovery and Pathway Analyses of Metabolic Disturbance in COVID-19.

Bo-Wen Li, Xing Fan, Wen-Jing Cao, He Tian, Si-Yu Wang, Ji-Yuan Zhang, Sin Man Lam, Jin-Wen Song, Chao Zhang, Shao-Hua Zhang and 8 more

Open access · diamondAbstract read
In one paragraph

Article in Infectious diseases & immunity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 41% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
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

18 authors at 5 institutions in 1 country.

Bo-Wen LiLipidALL Technologies Company Limited, Changzhou, Jiangsu 213022, China.
Xing FanDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Wen-Jing CaoDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
He TianState Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
Si-Yu WangDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Ji-Yuan ZhangDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Sin Man LamLipidALL Technologies Company Limited, Changzhou, Jiangsu 213022, China.
Jin-Wen SongDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Chao ZhangDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Shao-Hua ZhangState Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
Zhe XuDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Ruo-Nan XuDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Jun-Liang FuDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Lei HuangDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Tian-Jun JiangDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Ming ShiDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Fu-Sheng WangDepartment of Infectious Diseases, Fifth Medical Center of Chinese PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing 100039, China.
Guang-Hou ShuiState Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
Chinese PLA General Hospital · CNChinese Academy of Sciences · CNBengbu Medical College · CNInstitute of Genetics and Developmental Biology · CNLipidALL Technologies (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The ongoing global coronavirus disease 2019 (COVID-19) pandemic is posing a serious public health threat to nations worldwide. Understanding the pathogenesis of the disease and host immune responses will facilitate the discovery of therapeutic targets and better management of infected patients. Metabolomics technology can provide an unbiased tool to explore metabolic perturbation. Methods: Twenty-six healthy controls and 50 COVID-19 patients with mild, moderate, and severe symptoms in the Fifth Medical Center of PLA General Hospital from January 22 to February 16, 2020 were recruited into the study. Fasting blood samples were collected and subject to metabolomics analysis by liquid chromatography-mass spectrometry. Metabolite abundance was measured by peak area and was log-transformed before statistical analysis. The principal component analysis, different expression analysis, and metabolic pathway analysis were performed using R package. Co-regulated metabolites and their associations with clinical indices were identified by the weighted correlation network analysis and Spearman correlation coefficients. The potential metabolite biomarkers were analyzed using a random forest model. Results: We uncovered over 100 metabolites that were associated with COVID-19 disease and many of them correlated with disease severity. Sets of highly correlated metabolites were identified and their correlations with clinical indices were presented. Further analyses linked the differential metabolites with biochemical reactions, metabolic pathways, and biomedical MeSH terms, offering contextual insights into disease pathogenesis and host responses. Finally, a panel of metabolites was discovered to be able to discriminate COVID-19 patients from healthy controls, and also another list for mild against more severe cases. Our findings showed that in COVID-19 patients, citrate cycle, sphingosine 1-phosphate in sphingolipid metabolism, and steroid hormone biosynthesis were downregulated, while purine metabolism and tryptophan metabolism were disturbed. Conclusion: This study discovered key metabolites as well as their related biological and medical concepts pertaining to COVID-19 pathogenesis and host immune response, which will facilitate the selection of potential biomarkers for prognosis and discovery of therapeutic targets.

Indexed as

COVID-19Functional metabolitesHost immune responseMetabolomicsPathway analysis

Identifiers

PMID38630120
PMCPMC8291038
OpenAlexW3189004762

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.