Long-term adherence to IFN beta-1a treatment when using rebismart1device in patients with relapsing-remitting multiple sclerosis

The effectiveness of disease-modifying drugs in the treatment of multiple sclerosis is associated with adherence. RebiSmart® electronic device provides useful information about adherence to the treatment with subcutaneous (sc) interferon (IFN) ß-1a (Rebif®). The aim of the study was to determine long-term adherence to this treatment in patients with relapsing- remitting multiple sclerosis (RRMS). This retrospective multicentre observational study analysed 258 patients with RRMS who were receiving sc IFN ß-1a (Rebif®) treatment by using RebiSmart® until replacement (36 months maximum lifetime) or treatment discontinuation. Adherence was calculated with data (injection dosage, time, and date) automatically recorded by RebiSmart®. Patients in the study had a mean age of 41 years with a female proportion of 68%. Mean EDSS score at start of treatment was 1.8 (95% CI, 1.6-1.9). Overall adherence was 92.6%(95% CI, 90.6-94.5%). A total of 30.2% of patients achieved an adherence rate of 100%, 80.6% at least 90%, and only 13.2% of patients showed a suboptimal adherence (<80%). A total of 59.9% of subjects were relapse-free after treatment initiation. Among 106 subjects (41.1%) who experienced, on average, 1.4 relapses, the majority were mild (40.6%) or moderate (47.2%). Having experienced relapses from the beginning of the treatment was the only variable significantly related to achieving an adherence of at least 80% (OR = 3.06, 1.28-7.31). Results of this study indicate that sc IFN ß-1a administration facilitated by RebiSmart® could lead to high rates of adherence to a prescribed dose regimen over 36 months

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

Estado de salud de las personas ancianas y hospitalización en servicios geriátricos, médicos y quirúrgicos. Estudio poblacional en Toledo

FUNDAMENTO: El estado de salud y algunas características clínicas configuran un grupo de ancianos que necesitan más cuidados, que podrían beneficiarse de asistencia geriátrica especializada, aunque no existe consenso para identificar estos pacientes. El objetivo de este trabajo es describir el perfil de los pacientes mayores de 64 años ingresados en una unidad geriátrica y compararlo con los mayores de esta edad ingresados en los servicios médicos y quirúrgicos. MÉTODO: Durante 18 meses se siguió una cohorte representativa de la población mayor de 64 años del Partido Judicial de Toledo (n=3214), para identificar los ingresos y estancias hospitalarias en los hospitales públicos del área sanitaria. Las variables sobre estado de salud, se recogieron por entrevista personal, y los ingresos y sus características según datos del servicio de admisiones hospitalario. RESULTADOS: Ingresaron 410 personas (12,8%), en geriatría 168 pacientes (30,7%), en servicios médicos 204 (37,3%) y en servicios quirúrgicos 174 (32,0%). En geriatría la edad media fue significativamente mayor (77,4 años), sin diferencias en la estancia media (12,8 días;IC95%:10,6-14,0), fallecieron 44 pacientes (8,1%), ingresados en geriatría 26 (59,1%). En los servicios quirúrgicos ingresaron más mujeres, pacientes más jóvenes y con déficit leve de visión y audición; en geriatría, respecto a servicios médicos, más pacientes mayores de 80 años, viviendo en residencias, sin pareja, dependencia funcional moderada-severa, deterioro cognitivo, depresión, mala calidad de vida y escasos recursos sociales. CONCLUSIONES: No se observaron diferencias en el estado de salud entre los mayores de 64 años ingresados en servicios no quirúrgicos y quirúrgicos. En geriatría, respecto a los otros grupos de servicios, los pacientes tuvieron una edad media más elevada, peor estado de salud, mayor mortalidad y estancia media similar

Derivación y validación de una puntuación de riesgo de ingreso en la Unidad de Cuidados Intensivos para pacientes con COVID-19.

This work aims to identify and validate a risk scale for admission to intensive care units (ICU) in hospitalized patients with coronavirus disease 2019 (COVID-19). We created a derivation rule and a validation rule for ICU admission using data from a national registry of a cohort of patients with confirmed SARS-CoV-2 infection who were admitted between March and August 2020 (n = 16,298). We analyzed the available demographic, clinical, radiological, and laboratory variables recorded at hospital admission. We evaluated the performance of the risk score by estimating the area under the receiver operating characteristic curve (AUROC). Using the β coefficients of the regression model, we developed a score (0 to 100 points) associated with ICU admission. The mean age of the patients was 67 years; 57% were men. A total of 1,420 (8.7%) patients were admitted to the ICU. The variables independently associated with ICU admission were age, dyspnea, Charlson Comorbidity Index score, neutrophil-to-lymphocyte ratio, lactate dehydrogenase levels, and presence of diffuse infiltrates on a chest X-ray. The model showed an AUROC of 0.780 (CI: 0.763-0.797) in the derivation cohort and an AUROC of 0.734 (CI: 0.708-0.761) in the validation cohort. A score of greater than 75 points was associated with a more than 30% probability of ICU admission while a score of less than 50 points reduced the likelihood of ICU admission to 15%. A simple prediction score was a useful tool for forecasting the probability of ICU admission with a high degree of precision

Lipid-specific immunoglobulin M bands in cerebrospinal fluid are associated with a reduced risk of developing progressive multifocal leukoencephalopathy during treatment with natalizumab

[Objective]: Progressive multifocal leukoencephalopathy (PML) is a serious side effect associated with natalizumab treatment in multiple sclerosis (MS). PML risk increases in individuals seropositive for anti–John Cunningham virus (JC) antibodies, with prolonged duration of natalizumab treatment, and with prior exposure to immunosuppressants. We explored whether the presence of lipid‐specific immunoglobulin M oligoclonal bands in cerebrospinal fluid (CSF; IgM bands), a recognized marker of highly inflammatory MS, may identify individuals better able to counteract the potential immunosuppressive effect of natalizumab and hence be associated with a reduced risk of developing PML. [Methods]: We studied 24 MS patients who developed PML and another 343 who did not suffer this opportunistic infection during natalizumab treatment. Patients were recruited at 25 university hospitals. IgM bands were studied by isoelectric focusing and immunodetection. CSF lymphocyte counts were explored in 151 MS patients recruited at Ramon y Cajal Hospital in Madrid, Spain. [Results]: IgM bands were independently associated with decreased PML risk (odds ratio [OR] = 45.9, 95% confidence interval [CI] = 5.9–339.3, p < 0.0001) in patients treated with natalizumab. They were also associated with significantly higher CSF CD4, CD8, and B‐cell numbers. Patients positive for IgM bands and anti‐JC antibodies had similar levels of reduced PML risk to those who were anti‐JC negative (OR = 1.55, 95% CI = 0.09–25.2, p = 1.0). Higher risk was observed in patients positive for anti‐JC antibodies and negative for IgM bands (19% of the total cohort, OR = 59.71, 95% CI = 13.6–262.2). [Interpretation]: The presence of IgM bands reflects a process that may diminish the risk of PML by counteracting the excess of immunosuppression that may occur during natalizumab therapy. Ann Neurol 2015;77:447–457This work was supported by grants; PI12–00239 from FIS; Instituto de Salud Carlos III; SAF 2012‐34670, and RD12/0032/0005 from the Spanish Ministry of Economy and Competitiveness; and BMBF grant KKNMS (Competence Net Multiple Sclerosis; H.T.)

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)