ABSTRACT
Rationale: Severe coronavirus disease 2019 (COVID-19) is associated with important variations within the immune system and the coagulation cascade. We have developed a robust method for computing ventilation (CT-V) and perfusion (CT-P) from dynamic non-contrast CT scan, which can detect ventilation-perfusion (VQ) mismatch at a voxel level. We hypothesize that COVID-19 patients with mild disease will still have a higher VQ mismatch compared to patients with no respiratory symptoms. Methods: We included 12 random patients with mild symptoms from a prospective study characterizing quantitative lung function in patients with COVID-19 (NCT04320511) and compared their VQ scores to 12 patients with no respiratory symptoms in the NORM dataset (NCT00848406) matched to age, gender and BMI. The CT-P and CT-V methods apply image processing and physical modeling to an Inhale/Exhale CT image pair to generate a quantitative CT-P and CT-V images. We calculated VQ mismatch as the percent of lung voxels with residual fit errors 4 standard deviations apart from a least median of squares quadratic regression model describing CT-P as a function of CT-V.Results: All included COVID cases were hospitalized to regular floors and were breathing at room air, except for 3 patients on supplemental oxygen < 3L/min. The mean CT-V scores were significantly lower in COVID-19 cases compared to controls (0.46 vs. 1.39, 95% CI difference 0.51, 1.33, p 0.001). Likewise, the mean CT-P scores were significantly lower in COVID-19 cases vs. controls (0.14 vs. 0.18, 95% CI difference 0.02, 0.08, p 0.004). However, the median VQ mismatch scores were significantly higher in COVID-19 cases [0.300 (IQR 0.287,0.320) vs. 0.270 (IQR 0.233,0.293), p 0.04], see figure. Conclusion: Patients with COVID-19 have significant derangements in pulmonary physiology, VQ mismatch despite having minimal to no-oxygen requirements. Progression of VQ mismatch from an early stage could be studied to identify patients at risk for mechanical ventilation and mortality. Figure: (Left) Box plots of the VQ scores for each COVID cases vs. controls. (Right) Representative CT-V and CT-P in a patient with COVID-19 pneumonia with corresponding CT scan. Higher function areas appear red and low function areas appear bluer. Top row depicts "Dead space ventilation" with normal appearing CT scan but diminished areas of perfusion in the right lung and preserved ventilation. Bottom row demonstrates area of pneumonia in the left upper lung zone with "Shunt physiology". CT-P shows increased activity with reddish hue, whereas corresponding ventilation is diminished in the same area.
ABSTRACT
RATIONALE: The genes that influence the pathophysiology of COVID-19 have yet to be identified. Association analysis has found genetic loci for COVID-191. We used integrative genomics (IG) to combine gene expression and proteomic information with COVID-19 susceptibility loci in order to identify candidate genes for this disease. METHODS: For these analyses we used the COVID-19 Host Genetics Initiative genome-wide association (GWA) meta-analysis version 4 (COVID-19 positive versus COVID-19 negative), the Lung eQTL study2 (n=1,038), eQTLGen3 study (n=31,784) and the INTERVAL4 study (n=3,301). We conducted two IG methods (Bayesian Colocalization [coloc] and Summary Based Mendelian Randomization) to link gene and protein expression in lung and blood tissues with COVID-19 susceptibility loci. We identified the most consistently colocalized gene and conducted a Mendelian Randomization (MR) to assess the causal association of its protein ('exposure') with COVID-19 susceptibility ('outcomes'). Significant MR was set as P<0.05. RESULTS: The expression of 6 genes in lung and 12 in blood colocalized with COVID-19 susceptibility loci. SMR results demonstrated that the expression levels of 6 genes in lung tissue and 5 in blood were associated with COVID-19. Out of the candidate genes identified, two (ABO and SLC6A20) were within previously identified loci (Figure 1). Based on the SMR we found that the expression of SLC6A20 in lung was associated with a higher risk of COVID-19. Novel discovered associations included ERMP1, FCER1G, and CA11, genes which have been previously linked with respiratory diseases (i.e.: asthma) and host immune responses (i.e.: neutrophil and eosinophil counts). COVID-19 susceptibility also colocalized with plasma protein levels of ABO. Based on MR, ABO demonstrated a significant causal association (P = 2.10 × 10-5) with the risk of COVID-19 with increased levels of this protein in plasma associated with an increased risk of COVID-19. The top variant in the MR test (rs505922) was in complete linkage disequilibrium with the variant responsible for the blood O genotype, conferring reduced risk. CONCLUSIONS: This multi-omics approach led to the discovery of novel genes associated with COVID-19. We found that the ABO protein is a causal risk factor for COVID-19, with blood group O being protective against COVID-19. REFERENCES: 1. Ellinghaus, D. et al. N. Engl. J. Med. (2020). 2. Hao, K. et al. PLoS Genet. (2012). 3. Ṽsa, U. et al. bioRxiv. (2018). 4. Sun, B. B. et al. Nature. (2018) .
ABSTRACT
BACKGROUND: The global push for the use of hydroxychloroquine (HCQ) and chloroquine (CQ) against COVID-19 has resulted in an ongoing discussion about the effectivity and toxicity of these drugs. Recent studies report no effect of (H)CQ on 28-day mortality. We investigated the effect of HCQ and CQ in hospitalized patients on the non-ICU COVID-ward. METHODS: A nationwide, observational cohort study was performed in The Netherlands. Hospitals were given the opportunity to decide independently on the use of three different COVID-19 treatment strategies: HCQ, CQ, or no treatment. We compared the outcomes between these groups. The primary outcomes were 1) death on the COVID-19 ward, and 2) transfer to the intensive care unit (ICU). RESULTS: The analysis included 1064 patients from 14 hospitals: 566 patients received treatment with either HCQ (n = 189) or CQ (n = 377), and 498 patients received no treatment. In a multivariate propensity-matched weighted competing regression analysis, there was no significant effect of (H)CQ on mortality on the COVID ward. However, HCQ was associated with a significantly decreased risk of transfer to the ICU (hazard ratio (HR) = 0.47, 95% CI = 0.27-0.82, p = 0.008) when compared with controls. This effect was not found in the CQ group (HR = 0.80, 95% CI = 0.55-1.15, p = 0.207), and remained significant after competing risk analysis. CONCLUSION: The results of this observational study demonstrate a lack of effect of (H)CQ on non-ICU mortality. However, we show that the use of HCQ - but not CQ - is associated with a 53% reduction in risk of transfer of COVID-19 patients from the regular ward to the ICU. Recent prospective studies have reported on 28-day, all-cause mortality only; therefore, additional prospective data on the early effects of HCQ in preventing transfer to the ICU are still needed.