Evidence map›Paper›PMID 34335566›Full record

ArticleFrontiers in immunology2021

Comprehensive Profiling of Inflammatory Factors Revealed That Growth Differentiation Factor-15 Is an Indicator of Disease Severity in COVID-19 Patients.

Xiangyun Teng, Jiaqi Zhang, Yaling Shi, Yuntao Liu, Yanqing Yang, Jinyong He, Shuhong Luo, Yile Huang, Yanxia Liu, Dongdong Liu and 5 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
4.8field-weighted citation impact, top 4% 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

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 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 3 institutions in 2 countries.

Xiangyun TengDepartment of Laboratory Medicine, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China.
Jiaqi ZhangDepartment of Laboratory Medicine, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China.
Yaling ShiDepartment of Laboratory Medicine, Guangzhou Eighth People's Hospital, Guangzhou, China.
Yuntao LiuEmergency Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Yanqing YangResearch and Development Department, RayBiotech, Inc., Guangzhou, China.
Jinyong HeDepartment of Laboratory Medicine, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China.
Shuhong LuoResearch and Development Department, RayBiotech, Inc., Guangzhou, China.
Yile HuangDepartment of Laboratory Medicine, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China.
Yanxia LiuDepartment of Laboratory Medicine, Guangzhou Eighth People's Hospital, Guangzhou, China.
Dongdong LiuDepartment of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Yizhe LiDepartment of Laboratory Medicine, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China.
Shuangzhe ZhangResearch and Development Department, RayBiotech, Inc., Guangzhou, China.
Ruo-Pan HuangResearch and Development Department, RayBiotech, Inc., Guangzhou, China.
Dawei WangDepartment of Pulmonary and Critical Care Medicine, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China.
Jianhua XuDepartment of Laboratory Medicine, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China.
Guangzhou University of Chinese Medicine · CNRayBiotech (China) · CNGuangzhou Eighth People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To systematically explore potential biomarkers which can predict disease severity in COVID-19 patients and prevent the occurrence or development of severe COVID-19, the levels of 440 factors were analyzed in patients categorized according to COVID-19 disease severity; including asymptomatic, mild, moderate, severe, convalescent and healthy control groups. Factor candidates were validated by ELISA and functional relevance was uncovered by bioinformatics analysis. To identify potential biomarkers of occurrence or development of COVID-19, patient sera from three different severity groups (moderate, severe, and critical) at three time points (admission, remission, and discharge) and the expression levels of candidate biomarkers were measured. Eleven differential factors associated with disease severity were pinpointed from 440 factors across 111 patients of differing disease severity. The dynamic changes of GDF15 reflect the progression of the disease, while the other differential factors include TRAIL R1, IGFBP-1, IGFBP-4, VCAM-1, sFRP-3, FABP2, Transferrin, GDF15, IL-1F7, IL-5Rα, and CD200. Elevation of white blood cell count, neutrophil count, neutrophil-lymphocyte ratio (NLR), Alanine aminotransferase and Aspartate aminotransferase, low lymphocyte and eosinophil counts in the severe group were associated with the severity of COVID-19. GDF15 levels were observed to be associated with the severity of COVID-19 and the dynamic change of GDF15 levels was closely associated with the COVID-19 disease progression. Therefore, GDF15 might serve as an indicator of disease severity in COVID-19 patients.

Indexed as

AdultAgedBiomarkersComputational BiologyCOVID-19FemaleGrowth Differentiation Factor 15HumansInflammation MediatorsMaleMiddle AgedRetrospective StudiesSARS-CoV-2Severity of Illness IndexYoung AdultBiomarkersGrowth Differentiation Factor 15Inflammation MediatorsbiomarkerCOVID-19disease severityGDF15inflammatory factors

Identifiers

PMID34335566
PMCPMC8320433
OpenAlexW3186672405

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.