Evidence map›Paper›PMID 41990855›Full record

ArticleVirus research2026

Prognostic prediction in COVID-19 patients: a comprehensive analysis of gut microbiota and hematological biomarkers.

Ying Liu, Chuwen Wang, Danying Yan, Yingying Zhang, Dandan Shi, Yuping Zhou, Xianwang Ye, Guoqing Qian

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Article in Virus research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ying LiuHealth Science Center, Ningbo University, Ningbo, Zhejiang Province, China; Department of Respiratory and Critical Care Medicine, Jiangshan People's Hospital, Quzhou, Zhejiang Province, China.
Chuwen WangDepartment of Infectious Diseases, The First Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China.
Danying YanDepartment of Infectious Diseases, The First Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China.
Yingying ZhangHealth Science Center, Ningbo University, Ningbo, Zhejiang Province, China.
Dandan ShiHealth Science Center, Ningbo University, Ningbo, Zhejiang Province, China.
Yuping ZhouDepartment of Traditional Chinese Medicine, The First Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China. Electronic address: nbuzhouyuping@126.com.
Xianwang YeDepartment of Radiology, The First Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China. Electronic address: 25636061@qq.com.
Guoqing QianHealth Science Center, Ningbo University, Ningbo, Zhejiang Province, China; Department of Infectious Diseases, The First Affiliated Hospital, Ningbo University, Ningbo, Zhejiang Province, China. Electronic address: bill.qian@outlook.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates gut microbiota dynamics in COVID-19 patients and evaluates the prognostic potential of clinical hematological indicators. We collected 293 fecal samples from 225 confirmed COVID-19 cases (January-March 2023). After excluding samples with missing information, those from patients receiving Traditional Chinese Medicine, and those failing quality control, 169 samples from 169 unique patients were retained for 16S rRNA sequencing to assess gut microbiota composition and function in relation to disease severity, clinical scores (CURB65, qSOFA, PSI), and patient demographics. Results revealed significant changes in the microbiota, with critical cases showing reduced alpha diversity (Chao1, P < 0.01) and distinct beta diversity patterns compared to moderate/severe groups. Taxonomic profiling revealed significant microbiota changes associated with COVID-19 severity. Specifically, moderate cases exhibited an enriched abundance of Lachnospiraceae, whereas critical patients showed increased Streptococcaceae levels. Notably, Bacteroides abundance was significantly reduced in high-risk patients. Functional annotation further revealed that these taxonomic shifts were accompanied by distinct metabolic pathway disruptions, which correlated with disease severity progression. Concurrently, hematological markers (hematocrit, total bilirubin, albumin, sodium, hemoglobin) strongly correlated with disease progression. Our findings demonstrate that severe COVID-19 is associated with gut dysbiosis, which may exacerbate disease pathogenesis, and highlight the utility of blood biomarkers in predicting clinical outcomes, providing new insights for risk stratification and therapeutic strategies.

Indexed as

COVID-19Gastrointestinal MicrobiomeAdultAgedBacteriaBiomarkersDysbiosisFecesFemaleHumansMaleMiddle AgedPrognosisRNA, Ribosomal, 16SSARS-CoV-2Severity of Illness IndexBiomarkersRNA, Ribosomal, 16SBiomarkerCOVID-19DysbiosisMicrobiotaSARS-CoV

Identifiers

PMID41990855
PMCPMC13126023

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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.