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

摘要

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 490% 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.