Higher body mass index is an important risk factor in COVID-19 patients: a systematic review and meta-analysis

  1. Vivek Singh Malik
  2. Khaiwal Ravindra
  3. Savita Verma Attri
  4. Sanjay Kumar Bhadada
  5. Meenu Singh

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Abstract

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  1. Version published to 10.1007/s11356-020-10132-4
  2. ScreenIT

    SciScore for 10.1101/2020.05.11.20098806: (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
    Boolean operators: (BMI) AND (COVID-19), (Obesity) AND (COVID-19), ((Underweight) AND (COVID-19)) OR (CASRS-Cov-2), (BMI) OR (Body Mass Index) AND (COVID-19) were used for PubMed database and for Google Scholar, MEDLINE, EMBASE, Scopus, Medrixv and BioRixv using appropriate keywords (e.g., “Obesity”, “BMI”, “Body Mass Index”, “2019-nCov”, “COVID-19, “novel coronavirus”, “coronavirus disease”).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Data analysis was undertaken using Microsoft Excel (45).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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:
    (53) Limitations: Though there are limitation in the current systematic review and meta-analysis. Population, continuous variable, clinical condition, and statistical methods have the potency to differ and may cause heterogeneity among studies included for the meta-analysis. Further, the study reviewed only the risk of BMI (>25 kg/m2 and <25 kg/m2) of COVID-19 subjects and their severity. Study Importance: This is the first study with a large number of studies, linking BMI a critical risk factor for COVID-19 infection and severity. Hence, while treating, COVID-19 patients with higher BMI should be given special medical consideration as they also have other comorbidities. Implications of our study: This is the first meta-analysis to give an account of the higher BMI and COVID-19 infection. Conclusion: The BMI play aa significant role in COVID-19 infection and severity in all ages, especially elderly a population. COVID-19 patients with higher BMI should be reviewed by a panel for the risk factors. Further, there should be a procedure for increased vigilance, testing a priority, and therapy for patients with obesity and COVID-19 disease whose illness has entered 7-10 days, having age >50 yrs, and elevated CRP levels. The severity of COVID-19 has to be found a significant burden on intensive care resources in hospitals worldwide and specifically in lower-and-middle income countries due to lack of health finance and resources. Hence, patients with higher BMI with other comorbidit...

    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 10.1101/2020.05.11.20098806v1 on medRxiv