Genome-wide Association Study of Long COVID (preprint)

Infections can lead to persistent or long-term symptoms and diseases such as shingles after varicella zoster, cancers after human papillomavirus, or rheumatic fever after streptococcal infections(1,2). Similarly, infection by SARS-CoV-2 can result in Long COVID, a condition characterized by symptoms of fatigue and pulmonary and cognitive dysfunction(3-5). The biological mechanisms that contribute to the development of Long COVID remain to be clarified. We leveraged the COVID-19 Host Genetics Initiative(6,7) to perform a genome-wide association study for Long COVID including up to 6,450 Long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We identified the first genome-wide significant association for Long COVID at the FOXP4 locus. FOXP4 has been previously associated with COVID-19 severity(6), lung function(8), and cancers(9), suggesting a broader role for lung function in the pathophysiology of Long COVID. While we identify COVID-19 severity as a causal risk factor for Long COVID, the impact of the genetic risk factor located in the FOXP4 locus could not be solely explained by its association to severe COVID-19. Our findings further support the role of pulmonary dysfunction and COVID-19 severity in the development of Long COVID.

Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data.

Background: Since the outset of the COVID-19 pandemic, substantial public attention has focused on the role of seasonality in impacting transmission. Misconceptions have relied on seasonal mediation of respiratory diseases driven solely by environmental variables. However, seasonality is expected to be driven by host social behavior, particularly in highly susceptible populations. A key gap in understanding the role of social behavior in respiratory disease seasonality is our incomplete understanding of the seasonality of indoor human activity. Methods: We leverage a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States. We use an observational mobile app-based location dataset encompassing over 5 million locations nationally. We classify locations as primarily indoor (e.g. stores, offices) or outdoor (e.g. playgrounds, farmers markets), disentangling location-specific visits into indoor and outdoor, to arrive at a fine-scale measure of indoor to outdoor human activity across time and space. Results: We find the proportion of indoor to outdoor activity during a baseline year is seasonal, peaking in winter months. The measure displays a latitudinal gradient with stronger seasonality at northern latitudes and an additional summer peak in southern latitudes. We statistically fit this baseline indoor-outdoor activity measure to inform the incorporation of this complex empirical pattern into infectious disease dynamic models. However, we find that the disruption of the COVID-19 pandemic caused these patterns to shift significantly from baseline and the empirical patterns are necessary to predict spatiotemporal heterogeneity in disease dynamics. Conclusions: Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large scale with a high spatiotemporal resolutio and provides a parsimonious parameterization of seasonal behavior that can be included in infectious disease dynamics models. We provide critical evidence and methods necessary to inform the public health of seasonal and pandemic respiratory pathogens and improve our understanding of the relationship between the physical environment and infection risk in the context of global change. Funding: Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM123007.

Disentangling the rhythms of human activity in the built environment for airborne transmission risk (preprint)

Since the outset of the COVID-19 pandemic, substantial public attention has focused on the role of seasonality in suppressing transmission. Misconceptions have relied on seasonal mediation of respiratory diseases driven solely by environmental variables. However, seasonality is expected to be driven by host social behavior, particularly in highly susceptible populations. A key gap in understanding the role of social behavior in respiratory disease seasonality is our incomplete understanding of the seasonality of indoor human activity. We leverage a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States. We use a mobile app-based location dataset encompassing over 5 million locations nationally. We classify locations as primarily indoor (e.g. stores, offices) or outdoor (e.g. playgrounds, farmers markets), disentangling location-specific visitor counts into indoor and outdoor, to arrive at a fine-scale measure of indoor to outdoor human activity across time and space. We find the proportion of indoor to outdoor activity during a baseline year is seasonal, peaking in winter months. The measure displays a latitudinal gradient with stronger seasonality at northern latitudes and an additional summer peak in southern latitudes. We statistically fit this baseline indoor-outdoor activity measure to inform incorporation of this complex empirical pattern into infectious disease dynamic models. However, we find that the disruption of the COVID-19 pandemic caused these patterns to shift significantly from baseline, and the empirical patterns are necessary to predict spatio-temporal heterogeneity in disease dynamics. Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large-scale with high spatio-temporal resolution, and provides a parsimonious parameterization of seasonal behavior that can be included in infectious disease dynamics models. We provide critical evidence and methods necessary to inform the public health of seasonal and pandemic respiratory pathogens and improve our understanding of the relationship between the physical environment and infection risk in the context of global ecological change.

Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative (preprint)

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,048 severe disease cases and 571,009 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p=5.41x10-7). These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

The COVID-19 Pandemic and Cancer Patients in Germany: Impact on Treatment, Follow-Up Care and Psychological Burden

In response to the ongoing coronavirus disease 2019 (COVID-19) pandemic, governments imposed various measures to decrease the rate of disease spread, and health care policy makers prioritized resource allocation to accommodate COVID-19 patients. We conducted a cross-sectional online survey in Germany (July 2020–June 2021) to assess the frequency of changes to cancer care among cancer patients and to explore the psychological impact of the pandemic writ large. Cancer patients who contacted the Cancer Information Service (Krebsinformationsdienst, KID) of the German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ) via email were invited to complete an online questionnaire, capturing demographics, cancer specifics (e.g., type, disease phase, primary place of treatment, etc.), and any changes to their medical, follow-up, psycho-oncological or nursing care. General level of psychological distress was measured using the Hospital Anxiety and Depression Scale (HADS) along with face-validated items regarding worries and social isolation specific to the pandemic. In total, 13% of 621 patients reported a change to their treatment or care plan. Of those patients with changes, the majority of changes were made to follow-up care after treatment (56%), to monitoring during treatment (29%) and to psychological counseling (20%). Of the overall sample, more than half of patients (55%) reported symptoms of anxiety and 39% reported symptoms of depression. Patients with a change in cancer care were more likely to report symptoms of depression than those with no change (AOR: 2.18;95% CI: 1.26–3.76). Concern about the pandemic affecting the quality of health care was a predictor of both anxiety (AOR: 2.76;95% CI: 1.75–4.35) and depression (AOR: 2.15;95% CI: 1.43–3.23). Results showed that the majority of cancer patients in our study did not experience a change in their cancer care. However, the level of anxiety and psycho-social burden of cancer patients during the pandemic was high throughout the study period. Our findings underscore the need for health care services and policy makers to assess and to attend cancer patients' medical needs, with added emphasis on patients' psychological and social well-being. This applies particularly in situations where the healthcare system is strained and prioritization is necessary.

Systematic review of cases of acute myelitis in individuals with COVID-19.

BACKGROUND AND PURPOSE: An incremental number of cases of acute transverse myelitis (ATM) in individuals with ongoing or recent coronavirus disease 2019 (COVID-19) have been reported. METHODS: A systematic review was performed of cases of ATM described in the context of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by screening both articles published and in preprint. RESULTS: Twenty cases were identified. There was a slight male predominance (60.0%) and the median age was 56 years. Neurological symptoms first manifested after a mean of 10.3 days from the first onset of classical, mostly respiratory symptoms of COVID-19. Overall, COVID-19 severity was relatively mild. Polymerase chain reaction of cerebrospinal fluid for SARS-CoV-2 was negative in all 14 cases examined. Cerebrospinal fluid findings reflected an inflammatory process in most instances (77.8%). Aquaporin-4 and myelin oligodendrocyte protein antibodies in serum (tested in 10 and nine cases, respectively) were negative. On magnetic resonance imaging, the spinal cord lesions spanned a mean of 9.8 vertebral segments, necrotic-hemorrhagic transformation was present in three cases and two individuals had additional acute motor axonal neuropathy. More than half of the patients received a second immunotherapy regimen. Over a limited follow-up period of several weeks, 90% of individuals recovered either partially or near fully. CONCLUSION: Although causality cannot readily be inferred, it is possible that cases of ATM occur para- or post-infectiously in COVID-19. All identified reports are anecdotal and case descriptions are heterogeneous. Whether the condition and the observed radiological characteristics are specific to SARS-CoV-2 infection needs to be clarified.

Cerebral venous thrombosis and severe acute respiratory syndrome coronavirus-2 infection: A systematic review and meta-analysis.

BACKGROUND AND PURPOSE: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection predisposes patients to arterial and venous thrombosis. This study aimed to systematically review the available evidence in the literature for cerebral venous thrombosis (CVT) in association with coronavirus disease-2019 (COVID-19). METHODS: We searched MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases to identify cases of COVID-19-associated CVT. The search period spanned 1 January 2020 to 1 December 2020, and the review protocol (PROSPERO-CRD42020214327) followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Identified studies were evaluated for bias using the Newcastle-Ottawa scale. A proportion meta-analysis was performed to estimate the frequency of CVT among hospitalized COVID-19 patients. RESULTS: We identified 57 cases from 28 reports. Study quality was mostly classified as low. CVT symptoms developed after respiratory disease in 90%, and the mean interval was 13 days. CVT involved multiple sites in 67% of individuals, the deep venous system was affected in 37%, and parenchymal hemorrhage was found in 42%. Predisposing factors for CVT beyond SARS-CoV-2 infection were present in 31%. In-hospital mortality was 40%. Using data from 34,331 patients, the estimated frequency of CVT among patients hospitalized for SARS-CoV-2 infection was 0.08% (95% confidence interval [CI]: 0.01-0.5). In an inpatient setting, CVT accounted for 4.2% of cerebrovascular disorders in individuals with COVID-19 (cohort of 406 patients, 95% CI: 1.47-11.39). CONCLUSIONS: Cerebral venous thrombosis in the context of SARS-CoV-2 infection is a rare, although there seems to be an increased relative risk. High suspicion is necessary, because the diagnosis of this potentially life-threatening condition in COVID-19 patients can be challenging. Evidence is still scarce on the pathophysiology and potential prevention of COVID-19-associated CVT.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Age-dependent impact of the major common genetic risk factor for COVID-19 on severity and mortality.

BackgroundThere is considerable variability in COVID-19 outcomes among younger adults, and some of this variation may be due to genetic predisposition.MethodsWe combined individual level data from 13,888 COVID-19 patients (n = 7185 hospitalized) from 17 cohorts in 9 countries to assess the association of the major common COVID-19 genetic risk factor (chromosome 3 locus tagged by rs10490770) with mortality, COVID-19-related complications, and laboratory values. We next performed metaanalyses using FinnGen and the Columbia University COVID-19 Biobank.ResultsWe found that rs10490770 risk allele carriers experienced an increased risk of all-cause mortality (HR, 1.4; 95% CI, 1.2-1.7). Risk allele carriers had increased odds of several COVID-19 complications: severe respiratory failure (OR, 2.1; 95% CI, 1.6-2.6), venous thromboembolism (OR, 1.7; 95% CI, 1.2-2.4), and hepatic injury (OR, 1.5; 95% CI, 1.2-2.0). Risk allele carriers age 60 years and younger had higher odds of death or severe respiratory failure (OR, 2.7; 95% CI, 1.8-3.9) compared with those of more than 60 years (OR, 1.5; 95% CI, 1.2-1.8; interaction, P = 0.038). Among individuals 60 years and younger who died or experienced severe respiratory failure, 32.3% were risk-variant carriers compared with 13.9% of those not experiencing these outcomes. This risk variant improved the prediction of death or severe respiratory failure similarly to, or better than, most established clinical risk factors.ConclusionsThe major common COVID-19 genetic risk factor is associated with increased risks of morbidity and mortality, which are more pronounced among individuals 60 years or younger. The effect was similar in magnitude and more common than most established clinical risk factors, suggesting potential implications for future clinical risk management.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity. (preprint)

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole exome sequencing data of about 4,000 SARS-CoV-2-positive individuals were used to define an interpretable machine learning model for predicting COVID-19 severity. Firstly, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthly, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

New susceptibility loci for severe COVID-19 by detailed GWAS analysis in European populations (preprint)

Due to the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), deepening the host genetic contribution to severe COVID-19 may further improve our understanding about underlying disease mechanisms. Here, we describe an extended GWAS meta-analysis of 3,260 COVID-19 patients with respiratory failure and 12,483 population controls from Italy, Spain, Norway and Germany, as well as hypothesis-driven targeted analysis of the human leukocyte antigen (HLA) region and chromosome Y haplotypes. We include detailed stratified analyses based on age, sex and disease severity. In addition to already established risk loci, our data identify and replicate two genome-wide significant loci at 17q21.31 and 19q13.33 associated with severe COVID-19 with respiratory failure. These associations implicate a highly pleiotropic ~0.9-Mb 17q21.31 inversion polymorphism, which affects lung function and immune and blood cell counts, and the NAPSA gene, involved in lung surfactant protein production, in COVID-19 pathogenesis.

LAMP-Seq enables sensitive, multiplexed COVID-19 diagnostics using molecular barcoding.

Frequent testing of large population groups combined with contact tracing and isolation measures will be crucial for containing Coronavirus Disease 2019 outbreaks. Here we present LAMP-Seq, a modified, highly scalable reverse transcription loop-mediated isothermal amplification (RT-LAMP) method. Unpurified biosamples are barcoded and amplified in a single heat step, and pooled products are analyzed en masse by sequencing. Using commercial reagents, LAMP-Seq has a limit of detection of ~2.2 molecules per µl at 95% confidence and near-perfect specificity for severe acute respiratory syndrome coronavirus 2 given its sequence readout. Clinical validation of an open-source protocol with 676 swab samples, 98 of which were deemed positive by standard RT-qPCR, demonstrated 100% sensitivity in individuals with cycle threshold values of up to 33 and a specificity of 99.7%, at a very low material cost. With a time-to-result of fewer than 24 h, low cost and little new infrastructure requirement, LAMP-Seq can be readily deployed for frequent testing as part of an integrated public health surveillance program.

Mindfulness-based interventions: an overall review.

INTRODUCTION: This is an overall review on mindfulness-based interventions (MBIs). SOURCES OF DATA: We identified studies in PubMed, EMBASE, CINAHL, PsycINFO, AMED, Web of Science and Google Scholar using keywords including 'mindfulness', 'meditation', and 'review', 'meta-analysis' or their variations. AREAS OF AGREEMENT: MBIs are effective for improving many biopsychosocial conditions, including depression, anxiety, stress, insomnia, addiction, psychosis, pain, hypertension, weight control, cancer-related symptoms and prosocial behaviours. It is found to be beneficial in the healthcare settings, in schools and workplace but further research is warranted to look into its efficacy on different problems. MBIs are relatively safe, but ethical aspects should be considered. Mechanisms are suggested in both empirical and neurophysiological findings. Cost-effectiveness is found in treating some health conditions. AREAS OF CONTROVERSY: Inconclusive or only preliminary evidence on the effects of MBIs on PTSD, ADHD, ASD, eating disorders, loneliness and physical symptoms of cardiovascular diseases, diabetes, and respiratory conditions. Furthermore, some beneficial effects are not confirmed in subgroup populations. Cost-effectiveness is yet to confirm for many health conditions and populations. GROWING POINTS: Many mindfulness systematic reviews and meta-analyses indicate low quality of included studies, hence high-quality studies with adequate sample size and longer follow-up period are needed. AREAS TIMELY FOR DEVELOPING RESEARCH: More research is needed on online mindfulness trainings and interventions to improve biopsychosocial health during the COVID-19 pandemic; Deeper understanding of the mechanisms of MBIs integrating both empirical and neurophysiological findings; Long-term compliance and effects of MBIs; and development of mindfulness plus (mindfulness+) or personalized mindfulness programs to elevate the effectiveness for different purposes.