Impaired glucose metabolism in patients with diabetes, prediabetes, and obesity is associated with severe COVID‐19

  1. Stephen M. Smith
  2. Avinash Boppana
  3. Julie A. Traupman
  4. Enrique Unson
  5. Daniel A. Maddock
  6. Kathy Chao
  7. David P. Dobesh
  8. Adam Brufsky
  9. Ruth I. Connor

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

Identification of risk factors of severe coronavirus disease 2019 (COVID‐19) is critical for improving therapies and understanding severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) pathogenesis. We analyzed 184 patients hospitalized for COVID‐19 in Livingston, New Jersey for clinical characteristics associated with severe disease. The majority of patients with COVID‐19 had diabetes mellitus (DM) (62.0%), Pre‐DM (23.9%) with elevated fasting blood glucose (FBG), or a body mass index >30 with normal hemoglobin A1c (HbA1C) (4.3%). SARS‐CoV‐2 infection was associated with new and persistent hyperglycemia in 29 patients, including several with normal HbA1C levels. Forty‐four patients required intubation, which occurred significantly more often in patients with DM as compared with non‐diabetics. Severe COVID‐19 occurs in the presence of impaired glucose metabolism in patients, including those with DM, preDM, and obesity. COVID‐19 is associated with elevated FBG and several patients presented with new onset DM or in DKA. The association of dysregulated glucose metabolism and severe COVID‐19 suggests that SARS‐CoV‐2 pathogenesis involves a novel interplay with glucose metabolism. Exploration of pathways by which SARS‐CoV‐2 interacts glucose metabolism is critical for understanding disease pathogenesis and developing therapies.

Article activity feed

  1. Version published to 10.1002/jmv.26227
  2. ScreenIT

    SciScore for 10.1101/2020.06.04.20122507: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical approval for the study was granted by the Institutional Review Board of St. Barnabas Medical Center.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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:
    It is important to note that our study has several limitations. Patients were seen at a single clinical site and cared for by one group of clinicians. While it is possible our study population is disproportionately weighted towards patients with poor underlying health, the Covid-19 patients in this study were consecutive referrals to our service over the course of seven weeks in a suburban hospital. It is, therefore, unlikely that a selection bias exists, except for the criteria used by the admitting physicians. Diabetes itself was not considered a criterion for referral. Given the urgency of finding solutions to this present crisis, our findings may assist in prognostication and triage decisions. Our data shed light on the impact of DM, preDM and uncontrolled hyperglycemia in driving severe Covid-19 and will facilitate identification of novel pathogenesis pathways associated with SARS-CoV-2 infection. This, in turn, may lead to new approaches to therapeutic intervention. Our data currently support the use of tight glycemic control in patients with hyperglycemia. Our observations are also in line with the WHO recommendation that corticosteroids not be used for COVID-19 pneumonia. Finally, our findings caution that Covid-19 patients with DM, PreDM or obesity should be monitored closely. Those not infected should be particularly careful to avoid exposure to SARS-CoV-2. This information may be useful in healthcare and other settings to reduce the chances of infection in these hi...

    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.

  3. Version published to 10.1101/2020.06.04.20122507v1 on medRxiv