COVID ‐19: Impact of obesity and diabetes on disease severity

  1. Salman Al‐Sabah
  2. Mohannad Al‐Haddad
  3. Sarah Al‐Youha
  4. Mohammad Jamal
  5. Sulaiman Almazeedi

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Abstract

The coronavirus disease 2019 (COVID‐19) pandemic is straining the healthcare system, particularly for patients with severe outcomes requiring admittance to the intensive care unit (ICU). This study investigated the potential associations of obesity and diabetes with COVID‐19 severe outcomes, assessed as ICU admittance. Medical history, demographic and patient characteristics of a retrospective cohort (1158 patients) hospitalized with COVID‐19 were analysed at a single centre in Kuwait. Univariate and multivariate analyses were performed to explore the associations between different variables and ICU admittance. Of 1158 hospitalized patients, 271 had diabetes, 236 had hypertension and 104 required admittance into the ICU. From patients with available measurements, 157 had body mass index (BMI) ≥25 kg/m 2 . Univariate analysis showed that overweight, obesity class I and morbid obesity were associated with ICU admittance. Patients with diabetes were more likely to be admitted to the ICU. Two models for multivariate regression analysis assessed either BMI or diabetes on ICU outcomes. In the BMI model, class I and morbid obesities were associated with ICU admittance. In the diabetes model, diabetes was associated with increased ICU admittance, whereas hypertension had a protective effect on ICU admittance. In our cohort, overweight, obesity and diabetes in patients with COVID‐19 were associated with ICU admittance, increasing the risk of poor outcomes.

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  1. Version published to 10.1111/cob.12414
  2. ScreenIT

    SciScore for 10.1101/2020.05.24.20111724: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analyses were performed with R software (R Project for Statistical Computing, Vienna, Austria; R Core Team, 2019).
    R Project for Statistical
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)

    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:
    We acknowledge that our study had some limitations. Given its retrospective nature, the unavailability of data as a result of omission or inadequate recording was a major limitation of this study. Another limitation of this study was the relatively higher ratio of male to female patients, which may limit the generalisability of the results to the population. The relatively small size of our sample population was another limitation. Thus, larger, multicentre studies are needed to confirm our findings and provide more robust scientific evidence. Nevertheless, our findings indicate that more patients with obesity and diabetes are likely to be admitted to the ICU as the pandemic continues. Hence, patients with COVID-19 with underlying obesity or diabetes must be categorised as a highrisk group.

    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 10.1101/2020.05.24.20111724 on medRxiv