Association of BMI and Obesity with Composite poor outcome in COVID-19 adult patients: A Systematic Review and Meta-Analysis

  1. Arto Yuwono Soeroto
  2. Nanny Natalia Soetedjo
  3. Aga Purwiga
  4. Prayudi Santoso
  5. Iceu Dimas Kulsum
  6. Hendarsyah Suryadinata
  7. Ferdy Ferdian

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Aim

This study aimed to evaluate the association between obesity and composite poor outcome in coronavirus disease 2019 (COVID-19) patients.

Methods

We conducted a systematic literature search from PubMed and Embase database. We included all original research articles in COVID-19 adult patients and obesity based on classification of Body Mass Index (BMI) and composite poor outcome which consist of mortality, morbidity, admission of Intensive Care Unit (ICU), mechanical ventilation, Acute Respiratory Distress Syndrome (ARDS), and severe COVID-19.

Results

Nine studies were included in meta-analysis with 6 studies presented BMI as continuous outcome and 3 studies presented BMI as dichotomous outcome (obese and non-obese). Most studies were conducted in China (55.5%) with remaining studies from French, Germany, and United States (US). COVID-19 patients with composite poor outcome had higher BMI with mean difference 0.55 kg/m 2 (95% CI 0.07–1.03, P=0.02). BMI ≥30 (obese) was associated with composite poor outcome with odds ratio 1.89 (95% CI 1.06–3.34, P=0.03). Multivariate meta-regression analysis by including three moderators: age, hypertension, and Diabetes Mellitus type 2 (DM type 2) showed the association between obesity and composite poor outcome was affected by age with regression coefficient =-0.06 and P=0.02. Subgroup analysis was not performed due to the limited number of studies for several outcomes.

Conclusion

Obesity is a risk factor of composite poor outcome of COVID-19. On the other hand, COVID-19 patients with composite poor outcome have higher BMI. BMI is an important routine procedure that should be assessed in the management of COVID-19 patients and special attention should be given to patients with obesity.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.06.28.20142240: (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
    2.2 Literature Search: We conducted a systematic literature search from PubMed and Embase database.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    In this case, we use approximation formula from Wan et al. for sample size<50 and Cochrane for sample size>50.7, 8 Effect size for BMI as dichotomous (<30 kg/m2 and ≥ 30 kg/m2) outcome were calculated using Mantel-Haenszel formula with random effect model if heterogeneity >75%, otherwise fixed model is preferred and reported as odds ratio (OR).
    Cochrane
    suggested: (Cochrane Library, RRID:SCR_013000)

    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 10.1101/2020.06.28.20142240v1 on medRxiv