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Clinically distinct COVID-19 cases share notably similar immune response progression: A follow-up analysis.

ABSTRACT: Inflammatory responses to the novel coronavirus SARS-CoV-2, which causes COVID-19, range from asymptomatic to severe. Here we present a follow-up analysis of a longitudinal study characterizing COVID-19 immune responses from a father and son with distinctly different clinical courses. The father required a lengthy hospital stay for severe symptoms, whereas his son had mild symptoms and no fever yet tested positive for SARS-CoV-2 for 29 days. Father and son, as well as another unrelated COVID-19 patient, displayed a robust increase of SERPING1, the transcript encoding C1 esterase inhibitor (C1-INH). We further bolstered this finding by incorporating a serum proteomics dataset and found that serum C1-INH was consistently increased in COVID-19 patients. C1-INH is a central regulator of the contact and complement systems, potentially linking COVID-19 to complement hyperactivation, fibrin clot formation, and immune depression. Furthermore, despite distinct clinical cases, significant parallels were observed in transcripts involved in interferon and B cell signaling. As symptoms were resolving, widespread decreases were seen in immune-related transcripts to levels below those of healthy controls. Our study provides insight into the immune responses of likely millions of people with extremely mild symptoms who may not be aware of their infection with SARS-CoV-2 and implies a potential for long-lasting consequences that could contribute to reinfection risk.

SUBMITTER: Hausburg MA

PROVIDER: S-EPMC7788102 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:RationaleDNA-based microbiological studies are moving beyond studying healthy human microbiota to investigate diverse infectious diseases, including chronic respiratory infections, such as those in the airways of people with cystic fibrosis (CF) and non-CF bronchiectasis. The species identified in the respiratory secretion microbiota from such patients can be classified into those that are common and abundant among similar subjects (core) versus those that are infrequent and rare (satellite). This categorization provides a vital foundation for investigating disease pathogenesis and improving therapy. However, whether the core microbiota of people with different respiratory diseases, which are traditionally associated with specific culturable pathogens, are unique or shared with other chronic infections of the lower airways is not well studied. Little is also known about how these chronic infection microbiota change from childhood to adulthood.ObjectivesWe sought to compare the core microbiota in respiratory specimens from children and adults with different chronic lung infections.MethodsWe used bacterial 16S rRNA gene pyrosequencing, phylogenetic analysis, and ecological statistical tools to compare the core microbiota in respiratory samples from three cohorts of symptomatic children with clinically distinct airway diseases (protracted bacterial bronchitis, bronchiectasis, CF), and from four healthy children. We then compared the core pediatric respiratory microbiota with those in samples from adults with bronchiectasis and CF.Measurements and main resultsAll three pediatric disease cohorts shared strikingly similar core respiratory microbiota that differed from adult CF and bronchiectasis microbiota. The most common species in pediatric disease cohort samples were also detected in those from healthy children. The adult CF and bronchiectasis microbiota also differed from each other, suggesting common early infection airway microbiota that diverge by adulthood. The shared core pediatric microbiota included both traditional pathogens and many species not routinely identified by standard culture.ConclusionsOur results indicate that these clinically distinct chronic airway infections share common early core microbiota, which are likely shaped by natural aspiration and impaired clearance of the same airway microbes, but that disease-specific characteristics select for divergent microbiota by adulthood. Longitudinal and interventional studies will be required to define the relationships between microbiota, treatments, and disease progression.

Project description:Barriers to medication adherence may differ from barriers in other domains of adherence. In this study, we assessed the association between pre-kidney transplantation (KT) factors with nonadherent behaviors in 3 different domains post-KT.MethodsWe conducted a prospective cohort study with patient interviews at initial KT evaluation (baseline-nonadherence predictors in sociodemographic, condition-related, health system, and patient-related psychosocial factors) and at ≈6 mo post-KT (adherence outcomes: medications, healthcare follow-up, and lifestyle behavior). All patients who underwent KT at our institution and had ≈6-mo follow-up interview were included in the study. We assessed nonadherence in 3 different domains using continuous composite measures derived from the Health Habit Survey. We built multiple linear and logistic regression models, adjusting for baseline characteristics, to predict adherence outcomes.ResultsWe included 173 participants. Black race (mean difference in adherence score: -0.72; 95% confidence interval [CI], -1.12 to -0.32) and higher income (mean difference: -0.34; 95% CI, -0.67 to -0.02) predicted lower medication adherence. Experience of racial discrimination predicted lower adherence (odds ratio, 0.31; 95% CI, 0.12-0.76) and having internal locus of control predicted better adherence (odds ratio, 1.46; 95% CI, 1.06-2.03) to healthcare follow-up. In the lifestyle domain, higher education (mean difference: 0.75; 95% CI, 0.21-1.29) and lower body mass index (mean difference: -0.08; 95% CI, -0.13 to -0.03) predicted better adherence to dietary recommendations, but no risk factors predicted exercise adherence.ConclusionsDifferent nonadherence behaviors may stem from different motivation and risk factors (eg, clinic nonattendance due to experiencing racial discrimination). Thus adherence intervention should be individualized to target at-risk population (eg, bias reduction training for medical staff to improve patient adherence to clinic visit).

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Clinically distinct COVID-19 cases share notably similar immune response progression: A follow-up analysis.

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