Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Obesidad y menopausia Obesity and menopause

La menopausia es una de las etapas críticas en la vida de la mujer en la que se favorece la ganancia de peso y el desarrollo o agravamiento de la obesidad. Es en ésta época cuando se encuentra la prevalencia de obesidad más elevada. Las causas de éste problema son múltiples, unas se relacionan claramente con el hipoestrogenismo y otras dependen de la edad, condicionando un aumento de la ingesta y una disminución del gasto energético. Esta ganancia ponderal se asocia a consecuencias adversas para la salud, que se agravan por los cambios de distribución grasa que se observan durante la menopausia. El aumento de la grasa visceral facilita el desarrollo de insulinorresistencia y sus consecuencias clínicas como las alteraciones del metabolismo de los hidratos de carbono y la diabetes tipo 2, la hipertensión arterial y la dislipemia con el consiguiente aumento de riesgo cardiovascular, entre otras complicaciones.Menopause is one of the critical periods of a woman's life during which weight gain and onset or worsening of obesity are favoured. It is at this period when obesity prevalence is the highest. There are several causes for this disorder, ones clearly related with hypo-oestrogenism and others depend on age favouring increased food intake and decreased energy waste. This weight gain is related to adverse health effects that get worse due to changes in fat distribution observed during menopause. The increase in visceral fat favours the development of insulin resistance and its clinical consequences such as carbohydrate metabolism impairments and type 2 diabetes, arterial hypertension, and dyslipidaemia, leading to increased cardiovascular risk, among other complications

Homocistinuria; curso clínico y tratamiento dietético: a propósito de dos casos Clinical management of homocystinuria: case report and review of the literature

La homocistinuria es un error congénito del metabolismo de la metionina que conduce al acúmulo de metionina y de su principal metabolito, homocisteína, en plasma, orina y tejidos. El acúmulo de homocisteína posee toxicidad sobre los sistemas óseo (osteoporosis), ocular (luxación del cristalino), nervioso (convulsiones, alteraciones psiquiátricas) y vascular (accidentes cerebrovasculares, enfermedad cardiovascular). Presentamos 2 casos de homocistinuria en 2 pacientes hermanos y, a continuación, revisamos las estrategias terapéuticas disponibles.Homocystinuria is a congenital disorder of methyonine metabolism that leads to increased plasmatic, urinary and tissue deposits of methyonine and its main metabolite: homocysteine. Homocysteine deposits are toxic for the skeletal system (osteoporosis), the eyes (lens dislocation), central nervous system (seizures, psychiatric disorders) and also induce vascular damage (stroke and other cardiovascular events). This article reports two patients with homocystinuria in two siblings, followed by a concise review on the therapeutic strategies available for this disorder

Guía clínica para el diagnóstico y seguimiento de la distrofia miotónica tipo 1, DM1o enfermedad de Steinert

Antecedentes y objetivos: La enfermedad de Steinert o distrofia miotónica tipo 1 (DM1), (OMIM 160900) es la miopatía más prevalente en el adulto. Es una enfermedad multisistémica con alteración de prácticamente todos los órganos y tejidos y una variabilidad fenotípica muy amplia, lo que implica que deba ser atendida por diferentes especialistas que dominen las alteraciones más importantes. En los últimos años se ha avanzado de manera exponencial en el conocimiento de la enfermedad y en su manejo. El objetivo de la guía es establecer recomendaciones para el diagnóstico, el pronóstico, el seguimiento y el tratamiento de las diferentes alteraciones de la DM1. Material y métodos: Esta guía de consenso se ha realizado de manera multidisciplinar. Se ha contado con neurólogos, neumólogos, cardiólogos, endocrinólogos, neuropediatras y genetistas que han realizado una revisión sistemática de la literatura. Recomendaciones: Se recomienda realizar un diagnóstico genético con cuantificación precisa de tripletes CTG. Los pacientes con DM1 deben seguir control cardiológico y neumológico de por vida. Antes de cualquier cirugía con anestesia general debe realizarse una evaluación respiratoria. Debe monitorizarse la presencia de síntomas de disfagia periódicamente. Debe ofrecerse consejo genético a los pacientes con DM1 y a sus familiares. Conclusión: La DM1 es una enfermedad multisistémica que requiere un seguimiento en unidades especializadas multidisciplinares

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

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, 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).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)