Effects of hypertension, diabetes and coronary heart disease on COVID-19 diseases severity: a systematic review and meta-analysis

  1. Yingyu Chen
  2. Xiao Gong
  3. Lexun Wang
  4. Jiao Guo

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Abstract

Background

COVID-19 patients with chronic diseases such as hypertension, diabetes and coronary heart diseases is more likely to worsen, but with mixed results for COVID-19 severity. This meta-analysis is to analyze the correlation between hypertension, diabetes, coronary heart disease and COVID-19 disease severity.

Methods

Available data from PubMed, Web of Science, China National Knowledge Infrastructure Database, WanFang Database and VIP Database, were analyzed using a fixed effects model meta-analysis to derive overall odds ratios ( OR ) with 95% CIs . Funnel plots and Begg’s were used to assess publication bias.

Findings

Of 182 articles found following our initial search, we assessed 34 full-text articles, of which 9 articles with 1936 COVID-19 patients met all selection criteria for our meta-analysis. No significant heterogeneity between studies. There were significant correlations between COVID-19 severity and hypertension [ OR =2.3 [95% CI (1.76, 3.00), P <0.01], diabetes [ OR =2.67, 95% CI (1.91, 3.74), P <0.01], coronary heart disease [ OR =2.85 [95% CI (1.68, 4.84), P <0.01]. Most of the studies in the funnel plot are on the upper part and few on the base part, and are roughly symmetrical left and right. Begg’s test: hypertension ( Z =-0.1, P =1.0), diabetes ( Z =0.73, P =0.466), coronary heart disease ( Z =0.38, P =0.707), all found no publication bias.

Interpretation

Hypertension, diabetes, and coronary heart disease can affect the severity of COVID-19. It may be related to the imbalance of angiotensin-converting enzyme 2 (ACE2) and the cytokine storm induced by Glucolipid metabolic disorders (GLMD).

Funding

National Natural Science Foundation of China (No. 81830113, 81530102); Major basic and applied basic research projects of Guangdong Province of China (No. 2019B030302005); National key R & D plan “Research on modernization of traditional Chinese medicine” (No. 2018YFC1704200) and Natural Science Foundation of Guangdong Province (No. 2018A030313391)

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  1. ScreenIT

    SciScore for 10.1101/2020.03.25.20043133: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: (b) It defined the degree of severity of COVID-19 (severe vs. non-severe; or intensive care unit (ICU) vs. non-ICU; or progression and improvement/stabilization) at the time of admission using the American Thoracic Society guidelines for community-acquired pneumonia or Novel coronavirus pneumonia diagnosis and treatment plan (Published by the National Health Committee of the People’s Republic of China, Trial version 4-6).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search strategy and selection criteria: Data sources and searches the PubMed (1970-2020.03.06, English), Web of science (1950-2020.03.06, English), China National Knowledge Infrastructure Database (CNKI, 1979-2020.03.06, Chinese), WanFang Database (1970-2020.03.06, Chinese), and VIP Database (1970-2020.03.06, Chinese) were searched for observational studies of COVID-19.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Search strategies included MeSH, Emtree, Meth terms and free-text, such as “COVID-19”, “SARS Cov-2”, “clinical characteristics”, “clinical features”, and “epidemiological characteristics”.
    MeSH
    suggested: (MeSH, RRID:SCR_004750)
    All publications were managed using EndNote X9 software (EndNoteX9), and trials that were excluded in each step were recorded and the reasons were indicated.
    EndNote
    suggested: (EndNote, RRID:SCR_014001)
    All statistical analyses were conducted with RevMan 5.3 software.
    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:
    Limitations: It should be acknowledged that the limitations of this meta-analysis may exist in the following points: First, we have included a total of 9 studies, a total of 386 severe patients, 1550 non-severe patients, which is limited to improve the accuracy of the results, only large samples and high-quality research can truly and accurately reflect the correlation between hypertension, diabetes, coronary heart disease and the severity of COVID-19. Second, due to the relatively small number of eligible studies, it is not possible to perform subgroup analysis on the type of severity. In addition, most of the literature does not carry out correlation studies on disease severity time and outcome, so corresponding subgroup analysis cannot be performed. This may also affect its accuracy. Third, there are many factors that affect the severity of COVID-19 disease. In addition hypertension, diabetes and coronary heart disease, it is also related to various factors such as age, other diseases, living environment, economic and health conditions, smoking, viral virulence, and compliance. Most of the included literature is unable to control all factors, which may also affect our results. Finally, as with all meta-analysis, most gray literature is rarely published, and the impact of publication bias on the results of this study cannot be completely ruled out.

    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 10.1101/2020.03.25.20043133v1 on medRxiv