Diabetes-related acute metabolic emergencies in COVID-19 patients: a systematic review and meta-analysis

  1. Vasileios P. Papadopoulos
  2. Marios-Vasileios Koutroulos
  3. Dimitra-Georgia Zikoudi
  4. Stefania-Aspasia Bakola
  5. Peny Avramidou
  6. Ntilara Touzlatzi
  7. Dimitrios K. Filippou

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Abstract

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  1. Version published to 10.1007/s13340-021-00502-9
  2. ScreenIT

    SciScore for 10.1101/2021.01.12.21249697: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    BlindingThese three couples were blinded to each other’s decisions. D.F. was responsible to dissolve any disagreement.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Literature search: A systematic literature review was conducted using PubMed/MEDLINE and EMBASE databases from January 01, 2020 until January 09, 2021 to identify all case report series, cross-sectional studies and meta-analyses of case reports written in English and describing mortality rate in DKA, HHS, combined DKA/HHS, and EDKA in COVID-19 patients.
    PubMed/MEDLINE
    suggested: None
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    The Google Scholar and ResearchGate databases were used as an additional pool of published data, dissertations and other unpublished work; an iterative search was performed until no additional publication could be traced.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    ResearchGate
    suggested: (ResearchGate, RRID:SCR_006505)
    Study selection: The review was conducted using a search strategy that included the PubMed search terms [diabetes] AND [ketoacidosis] AND [covid] OR [diabetic] AND [ketoacidosis] AND [covid] OR [euglycemic] AND [diabetic] AND [ketoacidosis] AND [covid] OR [hyperglycaemic] AND [hyperosmolar] AND [state] AND [covid].
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    MedCalc Software Ltd, Ostend, Belgium; https://www.medcalc.org; 2020).
    MedCalc
    suggested: (MedCalc, RRID:SCR_015044)
    All statistical tests were carried out using IBM SPSS Statistics software, version 26.0.0.0, for Windows (IBM Corp ©).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    The major limitation of the present study might be dual: first, the combination of data from different kind of studies, namely two case report series, one case-control study, and one meta-analysis of 41 case reports; second, the very small number of studies included. However, as the topic is totally novel, any study that respects adherence to protocol followed, investigates causes of heterogeneity, and assesses the impact of risk of bias on the evidence synthesis might be valuable [24]. A serious query could focus on the decision to proceed to the meta-analysis despite the considerable amount of heterogeneity. However, several reasons might support our approach: 1) there was little evidence of publication bias (as funnel plot did not decline from asymmetry), there was no evidence of small size studies effect (as Egger’s and Begg’s tests were not statistically significant), 3) there was no considerable qualitative interaction. In conclusion, the present meta-analysis illustrated that COVID-19 related acute metabolic emergencies (DKA, HHS, and EDKA) are characterized by considerable mortality; thus, clinicians should be aware of timely detection and immediate treatment commencing. Future, cumulative evidence are welcome to further enlighten this field.

    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 10.1101/2021.01.12.21249697v1 on medRxiv
  4. Version published to 10.1101/2021.01.10.21249550v2 on medRxiv
  5. ScreenIT

    SciScore for 10.1101/2021.01.10.21249550: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    BlindingThese couples were blinded to each other’s decisions.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Literature search: A systematic literature review was conducted using EMBASE and PubMed/Medline from January 2020 to December 2020 to identify all case reports describing DKA, EDKA, HHS, and DKA/HHS, in patients with confirmed COVID-19 infection through positive RT-PCR for SARS-CoV-2 RNA in nasopharyngeal swab or bronchoalveolar lavage, using the search strategy that included the terms (diabetes AND ketoacidosis AND
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Google Scholar database was used as an additional pool of published data; iterative search was performed until no additional publication could be traced.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    The relevant odds ratios (OR) were used to construct a forest plot for visualization purposes using Revman 5.3 software [30].
    Revman
    suggested: (RevMan, RRID:SCR_003581)

    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 have not detected any correlation of antidiabetic drug category other than SGLT-2i with any special type of metabolic emergency or outcome; this observation conveys the limitations of the small sample size of the present study. GLP-1 RAs reduce circulating inflammatory biomarkers in diabetic and/or obese patients while insulin reduces these biomarkers in critically ill patients. Pioglitazone was also shown to upregulate ACE2 in hepatocytes of rats fed with a high fat diet. Finally, the DPP-4 is the entry receptor of MERSCoV, raising concerns about the impact of DPP-4i during the course of coronavirus infection [82]. Interestingly, we demonstrated that insulin-treated patients presented an increased OR to succumb in contrast with those treated with metformin. Though insulin administration had been associated with poor prognosis by another group of investigators [83] it restores ACE and ACE2 serum levels, thus hypothesizing that it exerts a protective effect at least in patients that are non-insulin-depleted [84]. Therefore, this finding of ours could reflect a confounder effect due to either the type of diabetes, or increased age. T1D patients, who are by definition insulin-dependent, when compared with T2D patients, are more prone to adverse outcome during COVID-19 infection [5]; moreover, unfavorable outcome was observed more often in older patients presenting COVID-19-related DKA [19]. COVID-19 might either induce new onset diabetes or unmask previously undiagnosed T1D o...

    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.

  6. Version published to 10.1101/2021.01.10.21249550v1 on medRxiv