Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury (preprint)

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.

An Inner-Inter City Graph Neural Network for Predicting the Course of COVID-19 Cases (preprint)

Highly infectious diseases, for instance, the recent novel corona virus pandemic (COVID-19) have impacted millions of lives in the globe. From its early stage, it's clear that its transmission is closely related to the crowd mobility. However, research on the predictive power of city level mobility is lacking. To fill this gap, in this paper, we propose the Inner-Inter city Learner (IIL), a method based on the high correlation between human interaction and the new cases, to conduct short and midterm predictions of the COVID-19 cases at a city level. In term of design, IIL is composed of two main components: an inter city transmission learner and an inner city propagation pattern learner. In the first module, we specially generate graphs based on the mobility between cities. Using graph neural networks fed with the city level mobility graphs, the model learns how the transmission pattern in a city is impacted by its neighborhood cities. And in the second part, capitalizing in the highly contagious nature of the virus, we use the human interactions within the cities to capture the invariant features that directly correlate with the spread. In addition, to account for the limited inner mobility data, we employed the model agnostic meta learning to transfer the features common to all cities. We conduct various experiments and compare our methods with the state-of-art baselines. The results show the superiority of our method in various forecast horizons.

ChineseCVD: first-in-world, web-based, Chinese-specific Cardiovascular Risk Calculator incorporating long COVID, COVID-19 vaccination, SGLT2i and PCSK9i treatment effects (preprint)

BackgroundWeb-based risk prediction tools for cardiovascular diseases are crucial for providing rapid risk estimates for busy clinicians, but there is none available specifically for Chinese subjects. This study developed ChineseCVD, first-in-world web-based Chinese-specific Cardiovascular Risk Calculator incorporating long COVID, COVID-19 vaccination, SGLT2i and PCSK9i treatment effects. MethodsAdult patients attending government-funded family medicine clinics in Hong Kong between 1st January 2000 and 31st December 2003 were included. The primary outcome was major adverse cardiovascular events (MACE) defined as a composite of myocardial infarction, heart failure, transient ischaemic attacks/ischaemic strokes, and cardiovascular mortality. ResultsA total of 155,066 patients were used as the derivation cohort. Over a median follow-up of 16.1 (11.6-17.8) years, 31,061 (20.44%) had MACE. Cox regression identified male gender, age, comorbidities, cardiovascular medications, systolic and diastolic blood pressure, and laboratory test results (neutrophil-lymphocyte ratio, creatinine, ALP, AST, ALT, HbA1c, fasting glucose, triglyceride, LDL and HDL) as significant predictors of the above outcomes. ChineseCVD further incorporates the impact of smoking status, COVID-19 infection, number of COVID-19 vaccination doses, and modifier effects of newest medication classes of PCSK9i and SGLT2i. The calculator enables clinicians to demonstrate to patients how risks vary with different medications. ConclusionsThe ChineseCVD risk calculator enables rapid web-based risk assessment for adverse cardiovascular outcomes, thereby facilitating clinical decision-making at the bedside or in the clinic.

Mobile Phone-Based Population Flow Data for the COVID-19 Outbreak in Mainland China

Background Human migration is one of the driving forces for amplifying localized infectious disease outbreaks into widespread epidemics. During the outbreak of COVID-19 in China, the travels of the population from Wuhan have furthered the spread of the virus as the period coincided with the world's largest population movement to celebrate the Chinese New Year. Methods We have collected and made public an anonymous and aggregated mobility dataset extracted from mobile phones at the national level, describing the outflows of population travel from Wuhan. We evaluated the correlation between population movements and the virus spread by the dates when the number of diagnosed cases was documented. Results From Jan 1 to Jan 22 of 2020, a total of 20.2 million movements of at-risk population occurred from Wuhan to other regions in China. A large proportion of these movements occurred within Hubei province (84.5%), and a substantial increase of travels was observed even before the beginning of the official Chinese Spring Festival Travel. The outbound flows from Wuhan before the lockdown were found strongly correlated with the number of diagnosed cases in the destination cities (log-transformed). Conclusions The regions with the highest volume of receiving at-risk populations were identified. The movements of the at-risk population were strongly associated with the virus spread. These results together with province-by-province reports have been provided to governmental authorities to aid policy decisions at both the state and provincial levels. We believe that the effort in making this data available is extremely important for COVID-19 modelling and prediction.

Comparisons of the rate of acute myocardial infarction between COVID-19 patients and individuals received COVID-19 vaccines: a population-based study (preprint)

Background: Both Coronavirus Disease-2019 (COVID-19) infection and COVID-19 vaccination have been associated with the development of acute myocardial infarction (AMI). This study compared the rates of AMI after COVID-19 infection and among the COVID-19 vaccinated populations in Hong Kong. Methods: This was a population-based cohort study from Hong Kong, China. Patients with positive real time- polymerase chain reaction (RT-PCR) test for COVID-19 between January 1st, 2020 and June 30th, 2021 were included. The data of the vaccinated and unvaccinated population was obtained from the "Reference Data of Adverse Events in Public Hospitals" published by the local government. The individuals who were vaccinated with COVID-19 vaccination prior the observed period (December 6th, 2021 to January 2nd, 2022) in Hong Kong were also included. The vaccination data of other countries were obtained by searching PubMed using the terms ["COVID-19 vaccine" AND "Myocardial infarction"] from its inception to February 1st, 2022. The main exposures were COVID-19 test positivity or previous COVID-19 vaccination. The primary outcome was the development of AMI within 28 days observed period. Results: This study included 11441 COVID-19 patients, of whom 25 suffered from AMI within 28 days of exposure (rate per million: 2185; 95% confidence interval [CI]: 1481-3224). The rates of AMI were much higher than those who were not vaccinated by the COVID-19 vaccine before December 6th, 2021 (rate per million: 162; 95% CI: 147-162) with a rate ratio of 13.5 (95% CI: 9.01-20.2). Meanwhile, the rate of AMI was lower amongst the vaccinated population (rate per million: 47; 95% CI: 41.3-53.5) than COVID-19 infection with a rate ratio of 0.02 (0.01, 0.03). Regarding post-vaccination AMI, COVID-19 infection was associated with a significantly higher rate of AMI than post-COVID-19 vaccination AMI in other countries. Conclusions: COVID-19 infection was associated with a higher rate of AMI than the vaccinated general population, and those immediately after COVID-19 vaccination.

Identified human breastmilk compositions effectively inhibit SARS-CoV-2 and variants infection and replication

The global pandemic of COVID-19 caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection confers great threat to the public health. Human breastmilk is a complex with nutritional composition to nourish infants and protect them from different kinds of infectious diseases including COVID-19. Here, we identified lactoferrin (LF), mucin1 (MUC1) and α-lactalbumin (α-LA) from human breastmilk inhibit SARS-CoV-2 infection using a SARS-CoV-2 pseudovirus system and transcription and replication-competent SARS-CoV-2 virus-like-particles (trVLP). Additionally, LF and MUC1 inhibited multiple steps including viral attachment, entry and post-entry replication, while α-LA inhibited viral attachment and entry. Importantly, LF, MUC1 and α-LA possessed potent antiviral activities towards variants such as B.1.1.7 (alpha), B.1.351 (beta), P.1 (gamma) and B.1.617.1 (kappa). Taken together, our study provides evidence that human breastmilk components (LF, MUC1 and α-LA) are promising antiviral and potential therapeutic candidates warranting further development or treating COVID-19. Graphical

Comparisons of the risk of myopericarditis between COVID-19 patients and individuals receiving COVID-19 vaccines: a population-based study (preprint)

Background Both COVID-19 infection and COVID-19 vaccines have been associated with the development of myopericarditis. The objective of this study is to 1) analyze the rates of myopericarditis after COVID-19 infection and COVID-19 vaccination in Hong Kong and 2) compare to the background rates, and 3) compare the rates of myopericarditis after COVID-19 vaccination to those reported in other countries. Methods This was a population-based cohort study from Hong Kong, China. Patients with positive RT-PCR test for COVID-19 between 1 st January 2020 and 30 th June 2021 or individuals who received COVID-19 vaccination until 31 st August were included. The main exposures were COVID-19 positivity or COVID-19 vaccination. The primary outcome was myopericarditis. Results This study included 11441 COVID-19 patients from Hong Kong, of whom four suffered from myopericarditis (rate per million: 350; 95% confidence interval [CI]: 140-900). The rate was higher than the pre-COVID-19 background rate in 2020 (rate per million: 61, 95% CI: 55-67) with a rate ratio of 5.73 (95% CI: 2.23-14.73. Compared to background rates, the rate of myopericarditis among vaccinated subjects in Hong Kong was substantially lower (rate per million: 8.6; 95% CI: 6.4-11.6) with a rate ratio of 0.14 (95% CI: 0.10-0.19). The rates of myocarditis after vaccination in Hong Kong are comparable to those vaccinated in the United States, Israel, and the United Kingdom. Conclusions COVID-19 infection is associated with a higher rate of myopericarditis whereas COVID-19 vaccination is associated with a lower rate of myopericarditis compared to the background.

Water composed of reduced hydrogen bonds activated by localized surface plasmon resonance effectively enhances anti-viral and anti-oxidative activities of melatonin

Melatonin is a lipophilic antioxidant generally dissolved in organic solvent before delivery. However, the presence of organics may severely depress the functional effects of melatonin. By rendering deionized water (DIW) flow through gold nano-particles under localized surface plasmon resonant illumination, we developed plasmon-activated water (PAW) which successfully increases the solubility of melatonin to 150.325%. Melatonin dissolved in PAW also exhibits stronger anti-viral and anti-oxidative activities than that dissolved in DIW in which the percentage of dengue virus infected human hepatocellular carcinoma cells is remarkably decreased (14.7% vs. 20.6%) whilst the clearance rate of hydroxyl radical is significantly enhanced (11.9% vs. 6.69%), respectively. Moreover, in vivo approaches further show that following chronic sleep deprivation, the level of oxidative stress, hepatic bioenergetics, anti-oxidative enzyme activity, and metabolic function are all significantly improved in rats received melatonin prepared in PAW than that in DIW. As the bio-activity of melatonin depends largely on its solubility, utilizing PAW as a non-organic solvent will not only enhance the anti-viral and anti-oxidative function of melatonin, but also offer great potential for clinical use of melatonin as a therapeutic strategy to depress virus infection and counteract oxidative damage in a more natural, more economic and more efficient way.

Renin-angiotensin system blockers and mortality in COVID-19: a territory-wide study from Hong Kong (preprint)

Aims Renin–angiotensin system blockers such as angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may increase the risk of adverse outcomes in COVID-19. In this study, the relationships between ACEI/ARB use and COVID-19 related mortality were examined. Methods Consecutive patients diagnosed with COVID-19 by RT-PCR at the Hong Kong Hospital Authority between 1 st January and 28 th July 2020 were included. Results This study included 2774 patients. The mortality rate of the COVID-19 positive group was 1.5% (n=42). Those who died had a higher median age (82.3[76.5-89.5] vs. 42.9[28.2-59.5] years old; P<0.0001), more likely to have baseline comorbidities of cardiovascular disease, diabetes mellitus, hypertension, and chronic kidney disease (P<0.0001). They were more frequently prescribed ACEI/ARBs at baseline, and steroids, lopinavir/ritonavir, ribavirin and hydroxychloroquine during admission (P<0.0001). They also had a higher white cell count, higher neutrophil count, lower platelet count, prolonged prothrombin time and activated partial thromboplastin time, higher D-dimer, troponin, lactate dehydrogenase, creatinine, alanine transaminase, aspartate transaminase and alkaline phosphatase (P<0.0001). Multivariate Cox regression showed that age, cardiovascular disease, renal disease, diabetes mellitus, the use of ACEIs/ARBs and diuretics, and various laboratory tests remained significant predictors of mortality. Conclusions We report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. Patients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk. Key Points We report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. Patients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk.

Identifying Sequential Complication and Mortality Patterns in Diabetes Mellitus: Comparisons of Machine Learning Methodologies (preprint)

Background: Diabetes mellitus-related complications adversely affect the quality of life. Better risk-stratified care through mining of sequential complication patterns is needed to enable early detection and prevention. Methods: Univariable and multivariate logistic regression was used to identify significant variables that can predict mortality. A sequence analysis method termed Prefixspan was applied to identify the most common couple, triple, quadruple, quintuple and sextuple sequential complication patterns in the directed comorbidity pathology network. A knowledge enhanced CPT+ (KCPT+) sequence prediction model is developed to predict the next possible outcome along the progression trajectories of diabetes-related complications. Findings: A total of 14,144 diabetic patients (51% males) were included. Acute myocardial infarction (AMI) without known ischaemic heart disease (IHD) (odds ratio [OR]: 2.8, 95% CI: [2.3, 3.4]), peripheral vascular disease (OR: 2.3, 95% CI: [1.9, 2.8]), dementia (OR: 2.1, 95% CI: [1.8, 2.4]), and IHD with AMI (OR: 2.4, 95% CI: [2.1, 2.6]) are the most important multivariate predictors of mortality. KCPT+ shows high accuracy in predicting mortality (F1 score 0.90, ACU 0.88), osteoporosis (F1 score 0.86, AUC 0.82), ophthalmological complications (F1 score 0.82, AUC 0.82), IHD with AMI (F1 score 0.81, AUC 0.85) and neurological complications (F1 score 0.81, AUC 0.83) with a particular prior complication sequence. Interpretation: Sequence analysis identifies the most common pattern characteristics of disease-related complications efficiently. The proposed sequence prediction model is accurate and enables clinicians to diagnose the next complication earlier, provide better risk-stratified care, and devise efficient treatment strategies for diabetes mellitus patients.

Coagulation factors and COVID-19 severity: Mendelian randomization analyses and supporting evidence (preprint)

BackgroundThe evolving pandemic of COVID-19 is arousing alarm to public health. According to epidemiological and observational studies, coagulopathy was frequently seen in severe COVID-19 patients, yet the causality from specific coagulation factors to COVID-19 severity and the underlying mechanism remain elusive. MethodsFirst, we leveraged Mendelian randomization (MR) analyses to assess causal relationship between 12 coagulation factors and severe COVID-19 illness based on two genome-wide association study (GWAS) results of COVID-19 severity. Second, we curated clinical evidence supporting causal associations between COVID-19 severity and particular coagulation factors which showed significant results in MR analyses. Third, we validated our results in an independent cohort from UK Biobank (UKBB) using polygenic risk score (PRS) analysis and logistic regression model. For all MR analyses, GWAS summary-level data were used to ascertain genetic effects on exposures against disease risk. ResultsWe revealed that genetic predisposition to the antigen levels of von Willebrand factor (VWF) and the activity levels of its cleaving protease ADAMTS13 were causally associated with COVID-19 severity, wherein elevated VWF antigen level (P = 0.005, odds ratio (OR) = 1.35, 95% confidence interval (CI): 1.09-1.68 in the Severe COVID-19 GWAS Group cohort; P = 0.039, OR = 1.21, 95% CI: 1.01-1.46 in the COVID-19 Host Genetics Initiative cohort) and lowered ADAMTS13 activity (P = 0.025, OR = 0.69, 95% CI: 0.50-0.96 in the Severe COVID-19 GWAS Group cohort) lead to increased risk of severe COVID-19 illness. No significant causal association of tPA, PAI-1, D-dimer, FVII, PT, FVIII, FXI, aPTT, FX or ETP with COVID-19 severity was observed. In addition, as an independent factor, VWF PRS explains a 31% higher risk of severe COVID-19 illness in the UKBB cohort (P = 0.047, OR per SD increase = 1.31, 95% CI: 1.00-1.71). In combination with age, sex, BMI and several pre-existing disease statues, our model can predict severity risks with an AUC of 0.70. ConclusionTogether with the supporting evidence of recent retrospective cohort studies and independent validation based on UKBB data, our results suggest that the associations between coagulation factors VWF/ADAMTS13 and COVID-19 severity are essentially causal, which illuminates one of possible mechanisms underlying COVID-19 severity. This study also highlights the importance of dynamically monitoring the plasma levels of VWF/ADAMTS13 after SARS-CoV-2 infection, and facilitates the development of treatment strategy for controlling COVID-19 severity and associated thrombotic complication.

Development of a predictive risk model for severe COVID-19 disease using population-based administrative data (preprint)

Background: Recent studies have reported numerous significant predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk score for prompt risk stratification. The objective is to develop a simple risk score for severe COVID-19 disease using territory-wide healthcare data based on simple clinical and laboratory variables. Methods: Consecutive patients admitted to Hong Kong public hospitals between 1st January and 22nd August 2020 diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8th September 2020. Results: COVID-19 testing was performed in 237493 patients and 4445 patients (median age 44.8 years old, 95% CI: [28.9, 60.8]); 50% male) were tested positive. Of these, 212 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, hypertension, stroke, diabetes mellitus, ischemic heart disease/heart failure, respiratory disease, renal disease, increases in neutrophil count, monocyte count, sodium, potassium, urea, alanine transaminase, alkaline phosphatase, high sensitive troponin-I, prothrombin time, activated partial thromboplastin time, D-dimer and C-reactive protein, as well as decreases in lymphocyte count, base excess and bicarbonate levels. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. Conclusions: A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong (preprint)

Background: The coronavirus disease 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission. Methods: Consecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission. Results: This study included 1043 patients (median age 35 (IQR: 32-37; 54% male). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age, male sex, prior coronary artery disease, respiratory diseases, diabetes, hypertension and chronic kidney disease, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male, 2) older age, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression. Conclusions: A machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.

Distributed Link Removal Strategy for Networked Meta-Population Epidemics and its Application to the Control of the COVID-19 Pandemic (preprint)

In this paper, we investigate the distributed link removal strategy for networked meta-population epidemics. In particular, a deterministic networked susceptible-infected-recovered (SIR) model is considered to describe the epidemic evolving process. In order to curb the spread of epidemics, we present the spectrum-based optimization problem involving the Perron-Frobenius eigenvalue of the matrix constructed by the network topology and transition rates. A modified distributed link removal strategy is developed such that it can be applied to the SIR model with heterogeneous transition rates on weighted digraphs. The proposed approach is implemented to control the COVID-19 pandemic by using the reported infected and recovered data in each state of Germany. The numerical experiment shows that the infected percentage can be significantly reduced by using the distributed link removal strategy.