GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

Gravedad de COVID-19 atribuible a obesidad según IMC y CUN-BAE Severity of COVID-19 attributable to obesity according to BMI and CUN-BAE

Introducción: La obesidad es considerada un factor de riesgo en casos graves de la COVID-19, habiendo sido analizada mediante el índice de masa corporal (IMC), estimador que no correlaciona adecuadamente con el porcentaje de grasa corporal (GC). El objetivo de este estudio ha sido analizar la fracción atribuible poblacional a la GC en formas graves de COVID-19 atendiendo al IMC y al CUN-BAE. Material y métodos: Estudio multicéntrico observacional de prevalencia. Se recogió información sociodemográfica, antecedentes personales, IMC y CUN-BAE, de casos positivos SARS-CoV-2, de las provincias de León y La Rioja. Mediante modelos de regresión logística se calcularon odds ratio con sus respectivos intervalos de confianza del 95% ajustando por edad y antecedentes personales, así como la fracción atribuible poblacional a la GC. Resultados: Participaron 785 pacientes, 123 (15,7%) fueron graves. Se detectaron como factores de riesgo la edad, la obesidad (tanto por IMC como por CUN-BAE) y los antecedentes personales. Un 51,6% de casos graves podrían ser atribuidos a un exceso de IMC y un 61,4% a exceso GC estimada según CUN-BAE, observándose una mayor infraestimación del riesgo en mujeres. Conclusiones: El exceso de GC, es un factor de riesgo para formas graves de la COVID-19 junto con la edad avanzada y la presencia de enfermedades cardiovasculares, respiratorias crónicas u oncohematológicas. El IMC infraestima el riesgo, especialmente en mujeres, siendo el CUN-BAE el predictor seleccionado por su mejor estimación del porcentaje de GC.

Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients.

BACKGROUND: Machine learning modeling can provide valuable support in different areas of mental health, because it enables to make rapid predictions and therefore support the decision making, based on valuable data. However, few studies have applied this method to predict symptoms' worsening, based on sociodemographic, contextual, and clinical data. Thus, we applied machine learning techniques to identify predictors of symptomatologic changes in a Spanish cohort of OCD patients during the initial phase of the COVID-19 pandemic. METHODS: 127 OCD patients were assessed using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and a structured clinical interview during the COVID-19 pandemic. Machine learning models for classification (LDA and SVM) and regression (linear regression and SVR) were constructed to predict each symptom based on patient's sociodemographic, clinical and contextual information. RESULTS: A Y-BOCS score prediction model was generated with 100% reliability at a score threshold of ± 6. Reliability of 100% was reached for obsessions and/or compulsions related to COVID-19. Symptoms of anxiety and depression were predicted with less reliability (correlation R of 0.58 and 0.68, respectively). The suicidal thoughts are predicted with a sensitivity of 79% and specificity of 88%. The best results are achieved by SVM and SVR. CONCLUSION: Our findings reveal that sociodemographic and clinical data can be used to predict changes in OCD symptomatology. Machine learning may be valuable tool for helping clinicians to rapidly identify patients at higher risk and therefore provide optimized care, especially in future pandemics. However, further validation of these models is required to ensure greater reliability of the algorithms for clinical implementation to specific objectives of interest.