Association of Obesity with COVID-19 Severity and Mortality: A Systemic Review and Meta-Regression

  1. Romil Singh
  2. Sawai Singh Rathore
  3. Hira Khan
  4. Smruti Karale
  5. Abhishek Bhurwal
  6. Aysun Tekin
  7. Nirpeksh Jain
  8. Ishita Mehra
  9. Sohini Anand
  10. Sanjana Reddy
  11. Guneet Singh Sidhu
  12. Anastasios Panagopoulos
  13. Vishwanath Pattan
  14. Rahul Kashyap
  15. Vikas Bansal

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Abstract

Objective

To estimate the association of obesity with severity (defined as use of invasive mechanical ventilation or intensive care unit admission) and all-cause mortality in coronavirus disease 2019 (COVID-19) patients.

Patients and Methods

A systematic search was conducted from inception of COVID-19 pandemic through January 31st, 2021 for full-length articles focusing on the association of increased BMI/ Obesity and outcome in COVID-19 patients with help of various databases including Medline (PubMed), Embase, Science Web, and Cochrane Central Controlled Trials Registry. Preprint servers such as BioRxiv, MedRxiv, ChemRxiv, and SSRN were also scanned. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were used for study selection and data extraction. The severity in hospitalized COVID-19 patients, such as requirement of invasive mechanical ventilation and intensive care unit admission with high BMI/ Obesity was the chief outcome. While all-cause mortality in COVID-19 hospitalized patients with high BMI/ Obesity was the secondary outcome.

Results

A total of 576,784 patients from 100 studies were included in this meta-analysis. Being obese was associated with increased risk of severe disease (RR=1.46, 95% CI 1.34-1.60, p<0.001, I 2 = 92 %). Similarly, high mortality was observed in obese patients with COVID-19 disease (RR=1.12, 95% CI 1.06-1.19, p<0.001, I 2 = 88%). In a multivariate meta-regression on severity outcome, the covariate of female gender, pulmonary disease, diabetes, older age, cardiovascular diseases, and hypertension was found to be significant and explained R 2 = 50% of the between-study heterogeneity for severity. Similarly, for mortality outcome, covariate of female gender, proportion of pulmonary disease, diabetes, hypertension, and cardiovascular diseases were significant, these covariates collectively explained R 2 =53% of the between-study variability for mortality.

Conclusions

Our findings suggest that obesity is significantly associated with increased severity and higher mortality among COVID-19 patients. Therefore, the inclusion of obesity or its surrogate body mass index in prognostic scores and streamlining the management strategy and treatment guidelines to account for the impact of obesity in patient care management is recommended.

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  1. ScreenIT

    SciScore for 10.1101/2021.05.08.21256845: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variableFrom each study, various details including first author name, study type, hospitalized total covid-19 positive patients, the definition of COVID-19 severity, definition of obesity, total obese & non-obese COVID-19 positive patients, patients with high severity and mortality, median age, gender (female sex proportion), hypertension proportion, pulmonary disease proportion, cardiovascular disease proportion, diabetes proportion, dyslipidemia proportion, liver disease proportion were mentioned in a tabulated format in excel sheet.
    RandomizationWe also scanned the clinicaltrials.gov registry for completed, as well as in-progress randomized controlled trials (RCTs).
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The search strategy consisted of keywords “SARS-CoV-2”, “COVID-19”, “CORONAVIRUS”, “OBESITY”, “BMI”, “OVERWEIGHT” across the COVID-19 database which included articles from Medline (PubMed)
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    ), Embase, Science Web, and Cochrane Central Controlled Trials Registry.
    Cochrane Central Controlled Trials
    suggested: None
    Other literature sources such as the BioRxiv (preprints), MedRxiv (preprints), ChemRxiv (preprints), and SSRN (preprints) were searched as well.
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    All outcomes were analyzed using the Mantel-Haenszel method for dichotomous data to estimate pooled risk ratio (RR) utilizing the Review Manager (RevMan)-Version 5.4, The Cochrane Collaboration, 2020.
    Cochrane Collaboration
    suggested: None

    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:
    However, our study is also subject to few limitations. We included five studies from preprint databases71, 76, 83, 96, 101 that may not be comparable to peer-reviewed articles in terms of their quality of methodology. However, in view of the time-sensitive nature of this pandemic, benefit of early dissemination of critical information and its inclusion in various analyses outweighs the risk from minor methodological flaws. Second factor was the heterogeneity in the studies in terms of the study design and methodology, patient sample and treatment received. There was a lack of uniformity in the type of outcomes evaluated for severity and their definitions in different studies. For the same reason, it was not possible to deduce the effect of obesity on the individual outcomes-ICU admission and mechanical ventilation. Third limitation is that the analysis was done with hospitalized patients only; hence we cannot generalize our results for patients seen in the outpatient clinic or treated at home. Analyzing outpatient data as well may help us to get the complete picture of the impact of obesity on the overall COVID-19 outcomes. Fourth limitation is that our analysis did not compare the outcomes with respect to visceral obesity and only BMI was used. However, it was beyond the scope of this analysis because of the lack of those details in most of the included studies. We suggest that prospective studies should obtain and report this information about their sample population. Lastl...

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.05.08.21256845v1 on medRxiv