The Binary Model of Chronic Diseases Applied to COVID-19 - Elkoshi, Zeev (Frontiers in Immunology, 2021)
A binary model for the classification of chronic diseases has formerly been proposed. The model classifies chronic diseases as "high Treg" or "low Treg" diseases according to the extent of regulatory T cells (Treg) activity (frequency or function) observed. The present paper applies this model to SARS-CoV-2 infection. The model correctly predicts the efficacy or inefficacy of several immune-modulating drugs in the treatment of severe COVID-19 disease. It also correctly predicts the class of pathogens mostly associated with SARS-CoV-2 infection. The clinical implications: (a) any search for new immune-modulating drugs for the treatment of COVID-19 should exclude candidates that do not induce "high Treg" immune reaction or these that do not spare CD8+ T cells; (b) immune-modulating drugs which are effective against SARS-CoV-2, may not be effective against any variant of the virus that does not induce "low Treg" reaction; (c) any immune-modulating drug which is effective in treating COVID-19 will also alleviate most co-infections; (d) severe COVID-19 patients should avoid contact with carriers of "low Treg" pathogens.