Comorbidities associated with regional variations in COVID-19 mortality revealed by population-level analysis

  1. Hongxing Yang
  2. Fei Zhong

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), has developed into a global health crisis. Understanding the risk factors for poor outcomes of COVID-19 is thus important for successful management and control of the pandemic. However, the progress and severity of the epidemic across different regions show great differentiations. We hypothesized the origination of these differences are based on location-dependent variations in underlying population-wide health factors. Disease prevalence or incidence data of states and counties of the United States were collected for a group of chronic diseases, including hypertension, diabetes, obesity, stroke, coronary heart disease, heart failure, physical inactivation, and common cancers (e.g., lung, colorectal, stomach, kidney and renal). Correlation and regression analysis identified the prevalence of heart failure as a significant positive factor for region-level COVID-19 mortality. Similarly, the incidence of gastric cancer and thyroid cancer were also identified as significant factors contributing to regional variation in COVID-19 mortality. To explore the implications of these results, we re-analyzed the RNA-seq data for stomach adenocarcinoma (STAD) and colon carcinoma (COAD) from The Cancer Genome Atlas (TCGA) project. We found that expression of genes in the immune response pathways were more severely disturbed in STAD than in COAD, implicating higher probability for STAD patients or individuals with precancerous chronic stomach diseases to develop cytokine storm once infected with COVID-19. Taken together, we conclude that location variations in particular chronic diseases and cancers contribute significantly to the regional variations in COVID-19 mortality.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.07.27.20158105: (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
    12 RNA-seq data and analysis: RNA-seq read counts and normalized expression (i.e., fragments per kilobase of nucleotides of transcripts per million of reads (FPKM)) data for stomach adenocarcinoma (STAD) patients and colon adenocarcinoma (COAD) patients were downloaded the website for The Cancer Genome Atlas (TCGA) via Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/).
    https://portal.gdc.cancer.gov/
    suggested: (Genomic Data Commons Data Portal (GDC Data Portal, RRID:SCR_014514)
    Gene differential expression analysis was performed using methods within the generalized linear regression scheme implemented in the R package edgeR (version 3.30.3).
    edgeR
    suggested: (edgeR, RRID:SCR_012802)

    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 our study is the limited public health, demographic, as well as epidemic data, in both depth and breadth. More systematic analysis using public health and epidemiological data from more countries and regions, and prevalence/incidence data for more diseases, is needed to validate and expand our findings. Despite intensive studies and numerous reports about the risks, mechanisms, intervention adaptation for comorbidities (especially heart diseases) in terms of clinical strategy for constraining damages caused by COVID-19, relatively fewer attention and efforts have been paid on the risks of patients with cancers and subclinical conditions related the development of cancers. Our study suggests more attention to be paid on excess COVID-19 mortality caused by cancer-related health conditions.

    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. ScreenIT

    SciScore for 10.1101/2020.07.27.20158105: (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 variableSurprisingly, we found a negative association between male-to-female population ratio and COVID-19 mortality (Pearson’s r=-0.47, p=5.5e-04).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    12 RNA-seq data and analysis RNA-seq read counts and normalized expression (i.e., fragments per kilobase of nucleotides of transcript per million of reads (FPKM)) data for stomach adenocarcinoma (STAD) patients and colon adenocarcinoma (COAD) patients were downloaded the website for The Cancer Genome Atlas (TCGA) via Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/).
    https://portal.gdc.cancer.gov/
    suggested: (Genomic Data Commons Data Portal (GDC Data Portal), SCR_014514)
    Gene differential expression analysis was performed using methods within the generalized linear regression scheme implemented in the R package edgeR (version 3.30.3).
    edgeR
    suggested: (edgeR, SCR_012802)
    We found that, immune responses related pathways were broadly more highly up-regulated o less down-regulated in STAD than in COAD, implicating a systematically more active immune activities in STAD compared to COAD (Table 4).
    STAD
    suggested: None
    These findings suggested immune responses were more significantly altered in STAD than in COAD, implying higher severity regarding to inflammation and/or malignancy, consistent with much poorer survival rates of stomach cancer relative to colon cancer (All stages combined 5-year survival rates for stomach cancer: 32%, that for colon cancer: 67%; data source: https://www.cancer.org/, website of the American Cancer Society).
    https://www.cancer.org/
    suggested: (American Cancer Society, SCR_005756)

    Data from additional tools added to each annotation on a weekly basis.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.07.27.20158105v1 on medRxiv