Effectiveness of statins as prevention in people with gout: a population-based cohort study

Background: Cardiovascular guidelines do not give firm recommendations on statin therapy in patients with gout because evidence is lacking. Aim: To analyze the effectiveness of statin therapy in primary prevention of coronary heart disease (CHD), ischemic stroke (IS), and all-cause mortality in a population with gout. Methods: A retrospective cohort study (July 2006 to December 2017) based on Information System for the Development of Research in Primary Care (SIDIAPQ), a research-quality database of electronic medical records, included primary care patients (aged 35-85 years) without previous cardiovascular disease (CVD). Participants were categorized as nonusers or new users of statins (defined as receiving statins for the first time during the study period). Index date was first statin invoicing for new users and randomly assigned to nonusers. The groups were compared for the incidence of CHD, IS, and all-cause mortality, using Cox proportional hazards modeling adjusted for propensity score. Results: Between July 2006 and December 2008, 8018 individuals were included; 736 (9.1%) were new users of statins. Median follow-up was 9.8 years. Crude incidence of CHD was 8.16 (95% confidence interval [CI]: 6.25-10.65) and 6.56 (95% CI: 5.85-7.36) events per 1000 person-years in new users and nonusers, respectively. Hazard ratios were 0.84 (95% CI: 0.60-1.19) for CHD, 0.68 (0.44-1.05) for IS, and 0.87 (0.67-1.12) for all-cause mortality. Hazard for diabetes was 1.27 (0.99-1.63). Conclusions: Statin therapy was not associated with a clinically significant decrease in CHD. Despite higher risk of CVD in gout populations compared to general population, patients with gout from a primary prevention population with a low-to-intermediate incidence of CHD should be evaluated according to their cardiovascular risk assessment, lifestyle recommendations, and preferences, in line with recent European League Against Rheumatism recommendations

Excess of all-cause mortality after a fracture in type 2 diabetic patients: a population-based cohort study

Summary Post-fracture mortality in type 2 diabetes mellitus (T2DM) patients has been poorly studied. We report an absolute and relative excess all-cause mortality following a fracture in these patients compared to non-diabetic patients. Introduction: T2DM and osteoporotic fractures are independently associated with a reduced lifespan, but it is unknown if T2DM confers an excess post-fracture mortality compared to non-diabetic fracture patients. We report post-fracture all-cause mortality according to T2DM status. Methods: This is a population-based cohort study using data from the SIDIAP database. All ≥50 years old T2DM patients registered in SIDIAP in 2006–2013 and two diabetes-free controls matched on age, gender, and primary care center were selected. Study outcome was all-cause mortality following incident fractures. Participants were followed from date of any fracture (AF), hip fracture (HF), and clinical vertebral fracture (VF) until the earliest of death or censoring. Cox regression was used to calculate mortality according to T2DM status after adjustment for age, gender, body mass index, smoking, alcohol intake, and previous ischemic heart and cerebrovascular disease. Results: We identified 166,106 T2DM patients and 332,212 non-diabetic, of which 11,066 and 21,564, respectively, sustained a fracture and were then included. Post-fracture mortality rates (1000 person-years) were (in T2DM vs non-diabetics) 62.7 vs 49.5 after AF, 130.7 vs 112.7 after HF, and 54.9 vs 46.2 after VF. Adjusted HR (95% CI) for post-AF, post-HF, and post-VF mortality was 1.30 (1.23–1.37), 1.28 (1.20–1.38), and 1.20 (1.06–1.35), respectively, for T2DM compared to non-diabetics. Conclusions: T2DM patients have a 30% increased post-fracture mortality compared to non-diabetics and a remarkable excess in absolute mortality risk. More research is needed on the causes underlying such excess risk, and on the effectiveness of measures to reduce post-fracture morbi-mortality in T2DM subjects

Clinical and genetic characteristics of late-onset Huntington's disease

Background: The frequency of late-onset Huntington's disease (>59 years) is assumed to be low and the clinical course milder. However, previous literature on late-onset disease is scarce and inconclusive. Objective: Our aim is to study clinical characteristics of late-onset compared to common-onset HD patients in a large cohort of HD patients from the Registry database. Methods: Participants with late- and common-onset (30\u201350 years)were compared for first clinical symptoms, disease progression, CAG repeat size and family history. Participants with a missing CAG repeat size, a repeat size of 6435 or a UHDRS motor score of 645 were excluded. Results: Of 6007 eligible participants, 687 had late-onset (11.4%) and 3216 (53.5%) common-onset HD. Late-onset (n = 577) had significantly more gait and balance problems as first symptom compared to common-onset (n = 2408) (P <.001). Overall motor and cognitive performance (P <.001) were worse, however only disease motor progression was slower (coefficient, 120.58; SE 0.16; P <.001) compared to the common-onset group. Repeat size was significantly lower in the late-onset (n = 40.8; SD 1.6) compared to common-onset (n = 44.4; SD 2.8) (P <.001). Fewer late-onset patients (n = 451) had a positive family history compared to common-onset (n = 2940) (P <.001). Conclusions: Late-onset patients present more frequently with gait and balance problems as first symptom, and disease progression is not milder compared to common-onset HD patients apart from motor progression. The family history is likely to be negative, which might make diagnosing HD more difficult in this population. However, the balance and gait problems might be helpful in diagnosing HD in elderly patients

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)