Cholesterol Metabolism—Impacts on SARS-CoV-2 Infection Prognosis

  1. Yumeng Peng
  2. Luming Wan
  3. Chen Fan
  4. Pingping Zhang
  5. Xiaolin Wang
  6. Jin Sun
  7. Yanhong Zhang
  8. Qiulin Yan
  9. Jing Gong
  10. Huan Yang
  11. Xiaopan Yang
  12. Huilong Li
  13. Yufei Wang
  14. Yulong Zong
  15. Feng Yin
  16. Xiaoli Yang
  17. Hui Zhong
  18. Yuan Cao
  19. Congwen Wei

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Abstract

In this study, we specifically addressed the connection between the SARS-CoV-2 virus with host cholesterol metabolism. Plasma lipid profile was measured in 861 COVID-19 patients classified as mild (n=215), moderate (n=364), severe (n=217) or critical (n=65) and 1108 age- and sex-matched healthy individuals. We showed that the levels of both TG and HDL-C were significantly lower in patients with severe disease than in patients with moderate or mild disease. After successful treatment, cholesterol metabolism was reestablished in patients with SARS-CoV-2 infection. The serum concentrations of TC and HDL-C can be used as indicators of disease severity and prognosis in COVID-19 patients.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.16.20068528: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Clinical data sources: This study was approved by the Ethics Committee of the TaiKang Tongji Hospital ([2020] TKTJLL-003).
    Consent: As we collected the data from electronic medical files without contacting with the patients directly or analyzing the serum, written informed consent was waived.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical methods: In this study, GraphPad 6.0 software was used for statistical calculations and data plotting.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    All statistical analyses were performed using SPSS version 20 (SPSS, Chicago, IL, USA).
    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2020.04.16.20068528 on medRxiv