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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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S44 Table 1Summary of significant medical events, thoracic computed tomography (CT) and pulmonary function tests (PFTs) in ORBCEL-C and placebo groups at 1 year follow upORBCEL-C Placebo Number of patients followed up 20 21 Significant medical events Number of patients with SMEs 6/20 9/21 Total SME events 7 11 Classification Respiratory,thoracic and mediastinal disorders 4 6 Neoplasm - benign, malignant, unspecified 1 0 Infections and infestations 1 1 Cardiac disorders 1 0 Metabolism and nutrition disorders 0 1 Injury, poisoning and procedural complications 0 1 Renal and urinary disorders 0 1 Gastrointestinal disorders 0 1 Thoracic CT Number of CTs available 5 8 Time to CT (Median, IQR) 181 (157–198) 203 (95–233) Evidence of ILD on CT 4 6 PFTs Number of PFTs available 10 8 Time to PFTs (Median, IQR) 184.5 (117.5–292.75) 203.5 (118.25–242.5) FEV1 (Mean, SD) 84.9 (13.6) 80.5 (13.3) FEV1 <80% predicted (n,%) 4/10 (44%) 4/8 (50%) FVC (Mean, SD) 78.4 (13.2) 79.3 (16.5) FVC <80% predicted (n,%) 5/10 (55%) 5/8 (62.5%) FEV1/FVC ratio (Mean, SD, n) 0.88 (0.12) N=8 0.76 (0.05) N=5 FEV1/FVC <0.7 (n,%) 0 (0%) 0 (0%) TLCO (Mean, SD, n) 78.9 (14.8) N=9 61.9 (13.4) N=7 TLCO <80% (n,%) 6/9 (66.7%) 7/7 (100%) ConclusionsOne year follow up supports the safety of ORBCEL-C MSCs in patients with moderate to severe ARDS due to COVID-19. A similar incidence of pulmonary dysfunction is reported in both groups at long term follow up.Please refer to page A?? for declarations of interest related to this .