A novel multi-omics-based highly accurate prediction of symptoms, comorbid conditions, and possible long-term complications of COVID-19

  1. Debmalya Barh
  2. Sandeep Tiwari
  3. Bruno Silva Andrade
  4. Marianna E Weener
  5. Aristóteles Góes-Neto
  6. Vasco Azevedo
  7. Preetam Ghosh
  8. Kenneth Blum
  9. Nirmal Kumar Ganguly

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Comprehensive clinical pictures, comorbid conditions, and long-term complications of COVID-19 are still unknown. Recently, using a multi-omics-based strategy, we predicted potential drugs for COVID-19 with ∼70% accuracy. Herein, using a novel multi-omics-based bioinformatic approach and three ways of analysis, we identified the symptoms, comorbid conditions, and short-, mid-, and possible long-term complications of COVID-19 with >90% precision including 27 parent, 170 child, and 403 specific conditions. Among the specific conditions, 36 viral, 53 short-term, 62 short-mid-long-term, 194 mid-long-term, and 57 congenital conditions are identified. At a threshold “count of occurrence” of 4, we found that 83–100% (average 92.67%) of enriched conditions are associated with COVID-19. Except for dry cough and loss of taste, all the other COVID-19-associated mild and severe symptoms are enriched. CVDs, and pulmonary, metabolic, musculoskeletal, neuropsychiatric, kidney, liver, and immune system disorders are top comorbid conditions. Specific diseases like myocardial infarction, hypertension, COPD, lung injury, diabetes, cirrhosis, mood disorders, dementia, macular degeneration, chronic kidney disease, lupus, arthritis, etc. along with several other NCDs were found to be top candidates. Interestingly, many cancers and congenital disorders associated with COVID-19 severity are also identified. Arthritis, gliomas, diabetes, psychiatric disorders, and CVDs having a bidirectional relationship with COVID-19 are also identified as top conditions. Based on our accuracy (>90%), the long-term presence of SARS-CoV-2 RNA in human, and our “genetic remittance” assumption, we hypothesize that all the identified top-ranked conditions could be potential long-term consequences in COVID-19 survivors, warranting long-term observational studies.

Article activity feed

  1. Version published to 10.1039/d0mo00189a
  2. ScreenIT

    SciScore for 10.1101/2020.12.08.20245753: (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

    No key resources detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2020.12.08.20245753 on medRxiv