Evidence map›Paper›PMID 32941953›Full record

ArticleGene2021

Serum CCL17 level becomes a predictive marker to distinguish between mild/moderate and severe/critical disease in patients with COVID-19.

Masaya Sugiyama, Noriko Kinoshita, Satoshi Ide, Hidetoshi Nomoto, Takato Nakamoto, Sho Saito, Masahiro Ishikane, Satoshi Kutsuna, Kayoko Hayakawa, Masao Hashimoto and 14 more

Abstract read
In one paragraph

Article in Gene, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 2 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

58 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  16. [Research advances in genetic polymorphisms in Kawasaki disease].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

24 authors.

Masaya SugiyamaGenome Medical Sciences Project, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba 272-8516, Japan. Electronic address: msugiyama@hosp.ncgm.go.jp.
Noriko KinoshitaDisease Control and Prevention Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Satoshi IdeDisease Control and Prevention Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Hidetoshi NomotoDisease Control and Prevention Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Takato NakamotoDisease Control and Prevention Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Sho SaitoDisease Control and Prevention Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Masahiro IshikaneDisease Control and Prevention Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Satoshi KutsunaDisease Control and Prevention Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Kayoko HayakawaDisease Control and Prevention Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Masao HashimotoDepartment of Respiratory Medicine, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Manabu SuzukiDepartment of Respiratory Medicine, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Shinyu IzumiDepartment of Respiratory Medicine, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Masayuki HojoDepartment of Respiratory Medicine, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Kiyoto TsuchiyaAIDS Clinical Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Hiroyuki GatanagaAIDS Clinical Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Jin TakasakiDepartment of Respiratory Medicine, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Masahide UsamiDepartment of Child and Adolescent Psychiatry, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba 272-8516, Japan.
Toshikazu KanoDepartment of Rheumatism and Collagen Diseases, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba 272-8516, Japan.
Hidekatsu YanaiDepartment of Internal Medicine, Kohnodai Hospital, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba 272-8516, Japan.
Nao NishidaGenome Medical Sciences Project, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba 272-8516, Japan.
Tatsuya KantoThe Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1, Kohnodai, Ichikawa, Chiba, 272-8516, Japan.
Haruhito SugiyamaDepartment of Respiratory Medicine, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan.
Norio OhmagariDisease Control and Prevention Center, National Center for Global Health and Medicine Hospital, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan. Electronic address: nohmagari@hosp.ncgm.go.jp.
Masashi MizokamiGenome Medical Sciences Project, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba 272-8516, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19, a novel coronavirus-related illness, has spread worldwide. Patients with apparently mild/moderate symptoms can suddenly develop severe pneumonia. Therefore, almost all COVID-19 patients require hospitalization, which can reduce limited medical resources in addition to overwhelming medical facilities. To identify predictive markers for the development of severe pneumonia, a comprehensive analysis of serum chemokines and cytokines was conducted using serial serum samples from COVID-19 patients. The expression profiles were analyzed along the time axis. Serum samples of common diseases were enrolled from a BioBank to confirm the usefulness of predictive markers. Five factors, IFN-λ3, IL-6, IP-10, CXCL9, and CCL17, were identified as predicting the onset of severe/critical symptoms. The factors were classified into two categories. Category A included IFN-λ3, IL-6, IP-10, and CXCL9, and their values surged and decreased rapidly before the onset of severe pneumonia. Category B included CCL17, which provided complete separation between the mild/moderate and the severe/critical groups at an early phase of SARS-CoV-2 infection. The five markers provided a high predictive value (area under the receiver operating characteristic curve (AUROC): 0.9-1.0, p < 0.001). Low expression of CCL17 was specifically observed in pre-severe COVID-19 patients compared with other common diseases, and the predictive ability of CCL17 was confirmed in validation samples of COVID-19. The factors identified could be promising prognostic markers to distinguish between mild/moderate and severe/critical patients, enabling triage at an early phase of infection, thus avoiding overwhelming medical facilities.

Indexed as

BetacoronavirusBiomarkersChemokine CCL17Coronavirus InfectionsCOVID-19CytokinesHospitalizationHumansPandemicsPneumonia, ViralSARS-CoV-2Severity of Illness IndexBiomarkersCCL17 protein, humanChemokine CCL17CytokinesCCL17COVID-19CXCL9IFN-λ3IL-6IP-10Predictive marker

Identifiers

PMID32941953
PMCPMC7489253

What OpenQuestion holds

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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.