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Background: Severe COVID-19 and obesity are characterized by higher inflammation. We aimed to examine early inflammatory patterns in people with (Ob) and without (NOb) obesity and COVID-19 and how they relate to COVID-19 disease severity Methods: Ob (BMI >30 Kg/m2) and NOb with COVID-19 matched for age, sex and WHO disease severity provided blood early after diagnosis. Immunoassays measured 57 plasma biomarkers reflecting innate immune and endothelial activation, systemic inflammation, coagulation, metabolism and microbial translocation (Fig 1). Between-group differences were assessed by Mann- Whitney. Associations between subsequent maximal COVID-19 severity (mild vs moderate/severe/critical) and biomarkers were explored by logistic regression adjusted for age, sex, hypertension (HTN) and diabetes (DM). Data are median pg/mL [IQR] or n [%] unless stated Results: Of 100 subjects (50 Ob and 50 Nob) presenting between April 2020 and March 2021, characteristics (Ob vs Nob) included: age 65 [23-91] vs 65 [21-95];female sex 27 (48%) vs 28 (56%);BMI 33.7 [30.0-71.8] vs 23.3 [15.3-25.9];disease severity mild 22 [48%] vs 23 [46%], moderate 15 [30%] vs 13 [26%], severe 6 [12%] vs 7 [14%];HTN 30 (60%) vs 17 (34%);DM 19 [38%] vs 6 [12%];days from symptom onset 7 [2-17] vs 8 [1-15];vaccinated 3 (6%) vs 0 (0%). Compared to NOb, Ob had higher IFN-alpha (1.8 [0.6;11] vs 0.9 [0.1;4.7]), CRP (10 mAU/mL [9.6;10.2] vs 9.7 [7.2;10]), IL-1RA (197 [122;399] vs 138 [88;253]), IL-4 (288 AU/mL [161;424] vs 205 [82;333]), vWF (252 [166;383] vs 163 [96;318]), Zonulin (114 ng/mL [77;131] vs 57 [18;106]), Resistin (956 [569;1153] vs 727 [712;1525]), Leptin (3482 [1513;5738] vs 848 [249;2114]), and lower Adiponectin (1.12 mg/L [0.09;1.5] vs 1.5 [1.18;1.93]), all p< 0.05. In both groups higher, proinflammatory IL-18 and lower levels of antiinflammatory CCL22 and IL-5 were associated with higher odds of disease severity, and lower E-selectin with higher disease severity only in Ob. However, in NOb higher type 3 interferons (IL-28A), macrophage activation (sCD163, CCL3) and vascular inflammation markers (ICAM-1, VCAM-1), along with higher S100B, GM-CSF and leptin were also associated with disease severity, a pattern not observed in Ob (Fig 1) Conclusion(s): Although Ob had higher overall levels of inflammation than NOb, few biomarkers predicted subsequent COVID-19 severity in Ob. These differential inflammatory patterns suggest dysregulated immune responses in Ob with COVID-19. (Figure Presented).
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Background: Better understanding of host inflammatory changes that precede development of severe COVID-19 could improve delivery of available antiviral and immunomodulatory therapies, and provide insights for the development of new therapies. Method(s): In plasma from individuals with COVID-19, sampled <=10 days from symptom onset from the All-Ireland Infectious Diseases Cohort study, we measured 61 biomarkers, including markers of innate immune and T cell activation, coagulation, tissue repair, lung injury, and immune regulation. We used principal component analysis (PCA) and k-means clustering to derive biomarker clusters, and univariate and multivariate ordinal logistic regression to explore association between cluster membership and maximal disease severity, adjusting for risk factors for severe COVID-19, including age, sex, ethnicity, BMI, hypertension and diabetes. Result(s): From March 2020-April 2021, we included 312 individuals, (median (IQR) age 62 (48-77) years, 7 (4-9) days from symptom onset, 54% male) in the analysis. PCA and clustering derived 4 clusters. Compared to cluster 1, clusters 2-4 were significantly older and of higher BMI but there were no significant differences in sex or ethnicity. Cluster 1 had low levels of inflammation, cluster 2 had higher levels of markers of tissue repair and endothelial activation (EGF, VEGF, PDGF, TGFalpha, serpin E1 and p-selectin). Cluster 3 and 4 were both characterised by higher overall inflammation, but compared to cluster 4, cluster 3 had downregulation of growth factors, markers of endothelial activation, and immune regulation (IL10, PDL1), but higher alveolar epithelial injury markers (RAGE, ST2). In univariate analysis, compared to cluster 1, cluster 3 had the highest odds of severe disease (OR (95% CI) 9.02 (4.62-18.31), followed by cluster 4: 5.59 (2.75-11.72) then cluster 2: 4.5 (2.38-8.81), all p < 0.05). Cluster 3 remained most strongly associated with severe disease in fully adjusted analyses;cluster 3: OR(95% CI) 5.99 (2.69-13.35), cluster 2: 3.14 (1.54-6.42), cluster 4: 3.13 (1.36-7.19), all p< 0.05). Conclusion(s): Distinct early inflammatory profiles predicted maximal disease severity independent of known risk factors for severe COVID-19. A cluster characterised by downregulation of growth factor and endothelial markers and early evidence of alveolar injury was associated with highest risk of developing severe COVID19. Whether this reflects a dysregulated inflammatory response that could improve targeted treatment requires further study. Heatmap of biomarker derived clusters and forest plot of association between clusters and disease severity. A: Heatmap demonstrating differences in biomarkers between clusters B: Forest plot demonstrating odds ratio of specific clusters for progressing to moderate or severe disease (reference Cluster 1), calculated using ordinal logistic regression. Odds ratio (95% CI) presented as unadjusted and fully adjusted (for age, sex, ethnicity, BMI, hypertension, diabetes, immunosuppression, smoking and baseline anticoagulant use). Maximal disease severity graded per the WHO severity scale.
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Introduction. Children over 4 years old spend an average 1 hour and 28 minutes per day, in France, in front of a television. The Covid pandemic increased this consumption. The noxious effects of screens are well known by the international scientific community, mainly on weight, sleep, learning and behaviour. These effects appear as of one hour of everyday use. Objectives. To study the knowledge about screens effects on children health of 0 to 9 years old children's parents consulting their general practitioner (GP). Method. An observational, descriptive, cross-sectional study, by anonymous self-survey, was led with 60 GP randomly chosen on Reunion island. Results were interpreted with a 13 points score. Results. Two hundred seventy-five surveys were included from February to march 2021. The average score was 10/13. Fifty-two percent respondents ignored the French 3 years old threshold. Thirty three percent thought that screens taught their children to speak and 9 % that it improved school results. Twenty-nine percent ignored the effects of background TV. Twenty-eight percent ignored the link between screens and obesity. Twenty-three percent considered screens as a good way to calm children. Ten percent were not convinced to be examples for their children. Fifty-three percent thought it was their GP's role to inform them about screens health issues. The total score was link to parents study level, presence of rules about screens at home, having already heard about this topic, duration of use of parents and children. Conclusion. The health effects of screens were well known by the population. The impact on weight, sleep and background TV were still ignored by some parents. GPs were the best discussion partner on this numerical prevention.
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Background. Which components of the immune response to SARS-CoV-2 vaccination best protect against subsequent infection remains unclear. We explored SARS-CoV-2 specific antibody and B-cell responses post 3rd dose vaccine and their relationship to subsequent SARS-CoV-2 infection. Methods. In a multicentre prospective cohort, adult subjects provided samples before and 14 days (d14) post 3rd dose vaccine with Pfizer-BioNTech 162b2. At 18-22 weeks post vaccine, subjects self-reported SARS-CoV-2 infection (confirmed by PCR or antigen test). We used electrochemiluminescence assays to quantify antibodies to SARS-CoV-2 spike subunit 1 (S1), subunit 2 (S2) and receptor-binding domain (RBD) in plasma (reported inWHOIU/mL). In a subset of subjects, we assessed SARS-CoV-2 specific differentiated B-cell (plasma cell) and memory B-cell responses from peripheral blood mononuclear cells. Unstimulated plasma cells, and memory B cells stimulated with R848 and IL2, were seeded on plates coated with RBD or full Spike antigen and antigen-specific responses measured by ELISpot (Mabtech ELISpot, Sweden). We compared between group differences by Wilcoxon signed rank or Mann-Whitney tests. Data are median [IQR] unless specified. Results. Of 133 subjects (age 43 [32-50], 81.2% female (table 1), 77 subjects in the B-cell subgroup (table 2)), 47 (35.3%) reported SARS-CoV-2 infection post 3rd vaccine. Antibody titres, plasma cell and memory B-cell responses all increased significantly at d14 post 3rd vaccine (Table 1 & 2, all P< 0.001). Although d14 antibody titres did not differ in those with and without subsequent infection (table 1), those reporting subsequent infection had significantly lower d14 RBD-specific plasma cells and a lower proportion of RBD-specific memory B-cells (Figure 1a-b, both P< 0.05). Similar results were observed with full-spike-specific memory B-cell responses (Figure 1d). The differences persisted when the non-infected group was restricted only to those reporting a confirmed close contact (n=26). Conclusion. Infection following 3rd dose vaccine is associated with lower d14 circulating and memory B cell responses, but not antibody titres, suggesting B-cell responses better predict protection against subsequent SARS-CoV-2 infection.
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Background. A wide array of assays to detect the serologic response to SARS-CoV-2 have been developed since the emergence of the pandemic. The majority of these are either qualitative or semi-quantitative, detect antibodies against one antigenic target, and are not adaptable to new antigens. Methods. We developed a new, multiplex immunoassay to detect antibodies against the receptor binding domain, S1 and S2 spike subunits and nucleocapsid (N) antigens of SARS-CoV-2 (the CEPHR SARS-CoV-2 Serology Assay). This assay uses electrochemiluminescence technology which allows for a broad dynamic range, and a linker format which allows for the addition of new antigenic targets. We tested this assay on a series of biobanked samples and validated its performance against the Abbott SARS-CoV-2 IgG and Abbott SARS-CoV-2 IgG II assays, and the MesoScale Diagnostics V-PLEX SARS-CoV-2 Panel 2 Kit. Results. Participant demographics are shown in Table 1. The mean (standard deviation (SD)) intra-assay (within plate) coefficient of variation (CV) of 80 plasma samples run on the same plate was 3.9% (2.9) for N, 3.8% (6.2) for RBD, 3.8% (5.9) for S1 and 3.9% (5.3) for S2. The mean (SD) inter-assay CV derived from 5 samples run across 3 days by two different operators was 11% (6.5) for N, 13% (5.7) for RBD, 14% (8.9) for S1 and 13% (5.1) for S2. In the convalescent group (n=193), overall sensitivity for each assay was;RBD 82% (95% CI 76-87), S1 86% (81-91%), S2 88% (83 - 92%) and N 72% (64 - 78%). Sensitivity improved when analysis included only individuals who were sampled more than 14 days from onset of symptoms (n=166), RBD 87% (81 - 95%), S1 91% (85 - 95%), S2 91% (85 - 95%) but not for the N-target (73% (66-80%)). In vaccinated individuals (n = 58), 100% (94-100%) had both detectable RBD and S1 antibodies. Overall specificity was 96% (87-99%) for RBD, 90% (78-97%) for S1, 94% (84-99%) for S2 and 90% (78-97%) for N. There was excellent correlation between the Abbott IgG II and both CEPHR anti-RBD IgG (rho 0.91) and CEPHR anti-S1 IgG (rho 0.9, both p < 0.001, Figure 1.) and the V-PLEX full spike and both CEPHR RBD IgG (rho 0.83) and S1 IgG (rho 0.82, both p < 0.001, Figure 4). Conclusion. The CEPHR SARS-CoV-2 Serology Assay is a robust, customisable, multiplex serologic assay for the detection of several different IgG specific to SARS-CoV-2.