Evidence mapPaperPMID 36798115Full record

ArticleFrontiers in immunology2023

Characterization of METRNβ as a novel biomarker of Coronavirus disease 2019 severity and prognosis.

Xun Gao, Paul Kay-Sheung Chan, Katie Ching-Yau Wong, Rita Wai-Yin Ng, Apple Chung-Man Yeung, Grace Chung-Yan Lui, Lowell Ling, David Shu-Cheong Hui, Danqi Huang, Chun-Kwok Wong

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 37% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. 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

10 authors at 1 institution in 1 country.

Xun GaoCenter of Clinical Laboratory Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
Paul Kay-Sheung ChanDepartment of Microbiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Katie Ching-Yau WongDepartment of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Rita Wai-Yin NgDepartment of Microbiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Apple Chung-Man YeungDepartment of Microbiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Grace Chung-Yan LuiStanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Lowell LingDepartment of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
David Shu-Cheong HuiStanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Danqi HuangDepartment of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Chun-Kwok WongDepartment of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Chinese University of Hong Kong · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Coronavirus disease 2019 (COVID-19) is increasing worldwide, with complications due to frequent viral mutations, an intricate pathophysiology, and variable host immune responses. Biomarkers with predictive and prognostic value are crucial but lacking. Methods: Serum samples from authentic and D614G variant (non-Omicron), and Omicron-SARS-CoV-2 infected patients were collected for METRNβ detection and longitudinal cytokine/chemokine analysis. Correlation analyses were performed to compare the relationships between serum METRNβ levels and cytokines/chemokines, laboratory parameters, and disease severity. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were used to evaluate the predictive value of METRNβ in COVID-19. Results: The serum level of METRNβ was highly elevated in non-Omicron-SARS-CoV-2 infected patients compared to healthy individuals, and the non-survivor displayed higher METRNβ levels than survivors among the critical ones. METRNβ concentration showed positive correlation with viral load in NAPS. ROC curve showed that a baseline METRNβ level of 1886.89 pg/ml distinguished COVID-19 patients from non-infected individuals with an AUC of 0.830. Longitudinal analysis of cytokine/chemokine profiles revealed a positive correlation between METRNβ and pro-inflammatory cytokines such as IL6, and an inverse correlation with soluble CD40L (sCD40L). Higher METRNβ was associated with increased mortality. These findings were validated in a second and third cohort of COVID-19 patients identified in a subsequent wave. Discussion: Our study uncovered the precise role of METRNβ in predicting the severity of COVID-19, thus providing a scientific basis for further evaluation of the role of METRNβ in triage therapeutic strategies.

Indexed as

COVID-19BiomarkersChemokinesCytokinesHumansPrognosisSARS-CoV-2BiomarkersChemokinesCytokinesbiomarkerCOVID-19cytokinesMETRNβprognostic

Identifiers

PMID36798115
PMCPMC9927217
OpenAlexW4318707894

What Socratic holds

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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 Socratic graph.