Latin American consensus on hypertension in patients with diabetes type 2 and metabolic syndrome

The present document has been prepared by a group of experts, members of cardiology, endocrinology and diabetes societies of Latin American countries, to serve as a guide to physicians taking care of patients with diabetes, hypertension and comorbidities or complications of both conditions. Although the concept of 'metabolic syndrome' is currently disputed, the higher prevalence in Latin America of that cluster of metabolic alterations has suggested that 'metabolic syndrome' is a useful nosographic entity in the context of Latin American medicine. Therefore, in the present document, particular attention is paid to this syndrome in order to alert physicians on a particularly high-risk population, usually underestimated and undertreated. These recommendations result from presentations and debates by discussion panels during a 2-day conference held in Bucaramanga, in October 2012, and all the participants have approved the final conclusions. The authors acknowledge that the publication and diffusion of guidelines do not suffice to achieve the recommended changes in diagnostic or therapeutic strategies, and plan suitable interventions overcoming knowledge, attitude and behavioural barriers, preventing both physicians and patients from effectively adhering to guideline recommendations.Fil: López Jaramillo, Patricio. Universidad Francisco de Paula Santander; ColombiaFil: Sánchez, Ramiro A.. Fundación Favaloro; ArgentinaFil: Diaz, Margarita. Clínica Platinum; UruguayFil: Cobos, Leonardo. Colegio Panamericano del Endotelio; ChileFil: Bryce, Alfonso. Clínica del Golf; PerúFil: Parra Carrillo, Jose Z.. Universidad de Guadalajara; MéxicoFil: Lizcano, Fernando. Universidad de la Sabana; ColombiaFil: Lanas, Fernando. Universidad de la Frontera; ChileFil: Sinay, Isaac. Instituto Cardiovascular de Buenos Aires; ArgentinaFil: Sierra, Iván D.. Asociación Latinoamericana de Diabetes; ColombiaFil: Peñaherrera, Ernesto. Hospital Luis Vernaza; EcuadorFil: Bendersky, Mario. Universidad Nacional de Córdoba; ArgentinaFil: Schmid, Helena. Universidade Federal do Rio Grande do Sul; BrasilFil: Botero, Rodrigo. Centro Médico; ColombiaFil: Urina, Ezequiel. Sociedad Colombiana de Cardiología; ColombiaFil: Lara, Joffre. Sociedad Ecuatoriana de Aterosclerosis; EcuadorFil: Foss, Milton C.. Universidade de Sao Paulo; BrasilFil: Márquez, Gustavo. Federation Diabetológica Colombiana; ColombiaFil: Harrap, Stephen. The University of Melbourne; AustraliaFil: Ramírez, Agustín José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación Favaloro; ArgentinaFil: Zanchetti, Alberto. Istituto Auxologico Italiano; Itali

Task Force of the Latin American Society of Hypertension

ABI, ankle-brachial index; ABPM, ambulatory blood pressure monitoring; ACCORD, Action to Control Cardiovascular Risk in Diabetes; ACE-I, angiotensin-converting-enzyme-inhibitors; ARB, AT1 blockers; BP, blood pressure; CARMELA, Cardiovascular Risk Factor Multiple Evaluation in Latin America; CARMEN, Community Actions for Multifactorial Reduction of Non- Communicable Diseases; CCB, calcium channel blocker; CCM, Wagner’s Chronic Care Model; CDC, Chronic Disease Center; CTA, computed tomography angiography; CV, cardiovascular; DALY, disability-adjusted life year; DPP- 4, dipeptidyl-peptidase-4; GLP-1, glucagon-like peptide 1; HBPM, home blood pressure monitoring; HOPE, Heart Outcomes Prevention Evaluation; HOT, Hypertension Optimal Treatment; HT, hypertension; LA, Latin America; LASH, Latin American Society of Hypertension; MRA, magnetic resonance angiography; NCD, noncommunicable disease; OSAS, obstructive apnea–hypopnea syndrome; PAD, peripheral artery disease; PAHO, Pan American Health Organization; RAAS, renin–angiotensin–aldosterone system; RISS, Redes Integradas de Servicios de Salud; SGLUT2, sodium–glucose cotransporter-2; SPRINT, SBP Intervention Trial; UKPDS, United Kingdom Prospective Diabetes Study; VIDA, Veracruz Initiative for Diabetes Awarenes

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)

Relationship between method of anastomosis and anastomotic failure after right hemicolectomy and ileo-caecal resection: an international snapshot audit

Aim The anastomosis technique used following right-sided colonic resection is widely variable and may affect patient outcome. This study aimed to assess the association between leak and anastomosis technique (stapled vs handsewn). Method This was a prospective, multicentre, international audit including patients undergoing elective or emergency right hemicolectomy or ileo-caecal resection operations over a 2-month period in early 2015. The primary outcome measure was the presence of anastomotic leak within 30 days of surgery, determined using a prespecified definition. Mixed effects logistic regression models were used to assess the association between leak and anastomosis method, adjusting for patient, disease and operative cofactors, with centre included as a random-effect variable. Results This study included 3208 patients, of whom 78.4\% (n = 2515) underwent surgery for malignancy and 11.7\% (n = 375) underwent surgery for Crohn's disease. An anastomosis was performed in 94.8\% (n = 3041) of patients, which was handsewn in 38.9\% (n = 1183) and stapled in 61.1\% (n = 1858). Patients undergoing hand-sewn anastomosis were more likely to be emergency admissions (20.5\% handsewn vs 12.9\% stapled) and to undergo open surgery (54.7\% handsewn vs 36.6\% stapled). The overall anastomotic leak rate was 8.1\% (245/3041), which was similar following handsewn (7.4\%) and stapled (8.5\%) techniques (P = 0.3). After adjustment for cofactors, the odds of a leak were higher for stapled anastomosis (adjusted OR = 1.43; 95\% CI: 1.04-1.95; P = 0.03). Conclusion Despite being used in lower-risk patients, stapled anastomosis was associated with an increased anastomotic leak rate in this observational study. Further research is needed to define patient groups in whom a stapled anastomosis is safe

Measurements of the Total and Differential Higgs Boson Production Cross Sections Combining the H??????? and H???ZZ*???4??? Decay Channels at s\sqrt{s}=8??????TeV with the ATLAS Detector

Measurements of the total and differential cross sections of Higgs boson production are performed using 20.3~fb$^{-1}$ of $pp$ collisions produced by the Large Hadron Collider at a center-of-mass energy of $\sqrt{s} = 8$ TeV and recorded by the ATLAS detector. Cross sections are obtained from measured $H \rightarrow \gamma \gamma$ and $H \rightarrow ZZ ^{*}\rightarrow 4\ell$ event yields, which are combined accounting for detector efficiencies, fiducial acceptances and branching fractions. Differential cross sections are reported as a function of Higgs boson transverse momentum, Higgs boson rapidity, number of jets in the event, and transverse momentum of the leading jet. The total production cross section is determined to be $\sigma_{pp \to H} = 33.0 \pm 5.3 \, ({\rm stat}) \pm 1.6 \, ({\rm sys}) \mathrm{pb}$. The measurements are compared to state-of-the-art predictions.Measurements of the total and differential cross sections of Higgs boson production are performed using 20.3  fb-1 of pp collisions produced by the Large Hadron Collider at a center-of-mass energy of s=8  TeV and recorded by the ATLAS detector. Cross sections are obtained from measured H→γγ and H→ZZ*→4ℓ event yields, which are combined accounting for detector efficiencies, fiducial acceptances, and branching fractions. Differential cross sections are reported as a function of Higgs boson transverse momentum, Higgs boson rapidity, number of jets in the event, and transverse momentum of the leading jet. The total production cross section is determined to be σpp→H=33.0±5.3 (stat)±1.6 (syst)  pb. The measurements are compared to state-of-the-art predictions.Measurements of the total and differential cross sections of Higgs boson production are performed using 20.3 fb$^{-1}$ of $pp$ collisions produced by the Large Hadron Collider at a center-of-mass energy of $\sqrt{s} = 8$ TeV and recorded by the ATLAS detector. Cross sections are obtained from measured $H \rightarrow \gamma \gamma$ and $H \rightarrow ZZ ^{*}\rightarrow 4\ell$ event yields, which are combined accounting for detector efficiencies, fiducial acceptances and branching fractions. Differential cross sections are reported as a function of Higgs boson transverse momentum, Higgs boson rapidity, number of jets in the event, and transverse momentum of the leading jet. The total production cross section is determined to be $\sigma_{pp \to H} = 33.0 \pm 5.3 \, ({\rm stat}) \pm 1.6 \, ({\rm sys}) \mathrm{pb}$. The measurements are compared to state-of-the-art predictions