Preexisting comorbidities shape the immune response associated with severe COVID-19 - 04/08/22
Abstract |
Background |
Comorbidities are risk factors for development of severe coronavirus disease 2019 (COVID-19). However, the extent to which an underlying comorbidity influences the immune response to severe acute respiratory syndrome coronavirus 2 remains unknown.
Objective |
Our aim was to investigate the complex interrelations of comorbidities, the immune response, and patient outcome in COVID-19.
Methods |
We used high-throughput, high-dimensional, single-cell mapping of peripheral blood leukocytes and algorithm-guided analysis.
Results |
We discovered characteristic immune signatures associated not only with severe COVID-19 but also with the underlying medical condition. Different factors of the metabolic syndrome (obesity, hypertension, and diabetes) affected distinct immune populations, thereby additively increasing the immunodysregulatory effect when present in a single patient. Patients with disorders affecting the lung or heart, together with factors of metabolic syndrome, were clustered together, whereas immune disorder and chronic kidney disease displayed a distinct immune profile in COVID-19. In particular, severe acute respiratory syndrome coronavirus 2–infected patients with preexisting chronic kidney disease were characterized by the highest number of altered immune signatures of both lymphoid and myeloid immune branches. This overall major immune dysregulation could be the underlying mechanism for the estimated odds ratio of 16.3 for development of severe COVID-19 in this burdened cohort.
Conclusion |
The combinatorial systematic analysis of the immune signatures, comorbidities, and outcomes of patients with COVID-19 has provided the mechanistic immunologic underpinnings of comorbidity-driven patient risk and uncovered comorbidity-driven immune signatures.
Il testo completo di questo articolo è disponibile in PDF.Graphical abstract |
Key words : COVID-19, SARS-CoV-2, immune response, spectral flow cytometry, comorbidity, metabolic syndrome, chronic kidney disease, heart disease, immune disorder, algorithm-guided analysis
Abbreviations used : ACE2, CKD, CM, COVID-19, DC, EM, HD, ID, LAD, NK, OR, PCA, SARS-CoV-2, TEMRA, T2DM, WHO
Mappa
Supported by the Swiss National Science Foundation, Switzerland (grants 733 310030_170320, 310030_188450, and CRSII5_183478 to B.B, 31CA30_195883 [to S.K., M.C., M.B., and B.B.]), The LOOP Zurich, the Vontobel Foundation (to B.B.), the European Union’s Horizon 2020 Research and Innovation Program (grant 847782 [to B.B. and A.R.]), and the European Research Council (grant 882424 [to B.B.]), the Agence National de la Recherche and Region Pays de la Loire - Flash COVID-19:COVARDS project (to A.R.), the INSPIRE regional initiative (to R.L.), and the French Ministry of Health with the participation of the Groupement Interrégional de Recherche Clinique et d’Innovation Sud-Ouest Outre-Mer (PHRCI 2020 IMMUNOMARK-COV [to R.L. and G.M.-B.]). S. Kreutmair is the recipient of a postdoctoral fellowship of the Deutsche Forschungsgemeinschaft. N.G.N. is the recipient of a University Research Priority Program postdoctoral fellowship. F.I. received a PhD fellowship from the Studienstiftung des deutschen Volkes. |
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Disclosure of potential conflict of interest: The authors declare that they have no relevant conflicts of interest. |
Vol 150 - N° 2
P. 312-324 - Agosto 2022 Ritorno al numeroBenvenuto su EM|consulte, il riferimento dei professionisti della salute.