Metabolic disturbances and inflammatory dysfunction predict severity of coronavirus disease 2019 (COVID-19): a retrospective study

  1. Shuke Nie
  2. Xueqing Zhao
  3. Kang Zhao
  4. Zhaohui Zhang
  5. Zhentao Zhang
  6. Zhan Zhang

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Abstract

Background

The coronavirus disease 2019 (COVID-19) is spreading worldwide with 16,558 deaths till date. Serum albumin, high-density lipoprotein (HDL-C), and C-reactive protein have been known to be associated with the severity and mortality of community-acquired pneumonia. However, the characteristics and role of metabolic and inflammatory indicators in COVID-19 is unclear.

Methods

We included 97 hospitalized patients with laboratory-confirmed COVID-19. Epidemiological, clinical, and laboratory indices; radiological features; and treatment were analysed. The differences in the clinical and laboratory parameters between mild and severe COVID-19 patients and the role of these indicators in severity prediction of COVID-19 were investigated.

Results

All were Wuhan residents with contact with confirmed COVID-19 cases. The median age was 39 years (IQR: 30–59). The most common presenting symptoms were fever (58.8%), cough (55.7%), and fatigue (33%). Other features were lymphopenia, impaired fasting glucose, hypoproteinaemia, hypoalbuminemia, low high-density lipoproteinemia. Decrease in lymphocyte count, serum total protein, serum albumin, high-density lipoprotein cholesterol (HDL-C), ApoA1, CD3 + T%, and CD8 + T% were found to be valuable in predicting the transition of COVID-19 from mild to severe illness. Chest computed tomography (CT) images showed that the absorption of bilateral lung lesions synchronized with the recovery of metabolic and inflammatory indicators.

Conclusions

Hypoproteinaemia, hypoalbuminemia, low high-density lipoproteinemia, and decreased ApoA1, CD3 + T%, and CD8 + T% could predict severity of COVID-19. Lymphocyte count, total serum protein, and HDL-C may be potentially useful for the evaluation of COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.24.20042283: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: Study participants and design: This study was approved by the institutional ethics board of Renmin Hospital of Wuhan University.
    Consent: Requirement for written informed consent was waived by the ethics board of Renmin Hospital of Wuhan University (WDRY2020-K100).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Hotmap of correlation analysis was performed by Graphpad Prism 8.
    Graphpad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    All statistical analyses were performed using SPSS 26 software.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    As a retrospective study, this study has some notable limitations. Firstly, due to the requirements of the government to treat COVID-19 patients according to disease grades, our institute only treated mild and severe patients, while critically ill patients were transferred to other designated hospitals. This study was performed in a single centre and only included patients with mild and severe types of COVID-19. Secondly, since the data generation was clinically driven and not systematic, we did not include other markers that have been associated with the outcomes of viral infectious diseases, such as D-dimer. Several patients did not receive procalcitonin and sputum pathogenic microbe detection tests due to overwhelmed medical resources.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.03.24.20042283v1 on medRxiv