Role of Magmas in protein transport and human mitochondria biogenesis

Magmas, a conserved mammalian protein essential for eukaryotic development, is overexpressed in prostate carcinomas and cells exposed to granulocyte-macrophage colony-stimulating factor (GM-CSF). Reduced Magmas expression resulted in decreased proliferative rates in cultured cells. However, the cellular function of Magmas is still elusive. In this report, we have showed that human Magmas is an ortholog of Saccharomyces cerevisiae Pam16 having similar functions and is critical for protein translocation across mitochondrial inner membrane. Human Magmas shows a complete growth complementation of Δpam16 yeast cells at all temperatures. On the basis of our analysis, we report that Magmas localizes into mitochondria and is peripherally associated with inner mitochondrial membrane in yeast and humans. Magmas forms a stable subcomplex with J-protein Pam18 or DnaJC19 through its C-terminal region and is tethered to TIM23 complex of yeast and humans. Importantly, amino acid alterations in Magmas leads to reduced stability of the subcomplex with Pam18 that results in temperature sensitivity and in vivo protein translocation defects in yeast cells. These observations highlight the central role of Magmas in protein import and mitochondria biogenesis. In humans, absence of a functional DnaJC19 leads to dilated cardiac myophathic syndrome (DCM), a genetic disorder with characteristic features of cardiac myophathy and neurodegeneration. We propose that the mutations resulting in decreased stability of functional Magmas:DnaJC19 subcomplex at human TIM23 channel leads to impaired protein import and cellular respiration in DCM patients. Together, we propose a model showing how Magmas:DnaJC19 subcomplex is associated with TIM23 complex and thus regulates mitochondrial import process

How does study quality affect the results of a diagnostic meta-analysis?

Background: The use of systematic literature review to inform evidence based practice in diagnostics is rapidly expanding. Although the primary diagnostic literature is extensive, studies are often of low methodological quality or poorly reported. There has been no rigorously evaluated, evidence based tool to assess the methodological quality of diagnostic studies. The primary objective of this study was to determine the extent to which variations in the quality of primary studies impact the results of a diagnostic meta-analysis and whether this differs with diagnostic test type. A secondary objective was to contribute to the evaluation of QUADAS, an evidence-based tool for the assessment of quality in diagnostic accuracy studies. Methods: This study was conducted as part of large systematic review of tests used in the diagnosis and further investigation of urinary tract infection (UTI) in children. All studies included in this review were assessed using QUADAS, an evidence-based tool for the assessment of quality in systematic reviews of diagnostic accuracy studies. The impact of individual components of QUADAS on a summary measure of diagnostic accuracy was investigated using regression analysis. The review divided the diagnosis and further investigation of UTI into the following three clinical stages: diagnosis of UTI, localisation of infection, and further investigation of the UTI. Each stage used different types of diagnostic test, which were considered to involve different quality concerns. Results: Many of the studies included in our review were poorly reported. The proportion of QUADAS items fulfilled was similar for studies in different sections of the review. However, as might be expected, the individual items fulfilled differed between the three clinical stages. Regression analysis found that different items showed a strong association with test performance for the different tests evaluated. These differences were observed both within and between the three clinical stages assessed by the review. The results of regression analyses were also affected by whether or not a weighting (by sample size) was applied. Our analysis was severely limited by the completeness of reporting and the differences between the index tests evaluated and the reference standards used to confirm diagnoses in the primary studies. Few tests were evaluated by sufficient studies to allow meaningful use of meta-analytic pooling and investigation of heterogeneity. This meant that further analysis to investigate heterogeneity could only be undertaken using a subset of studies, and that the findings are open to various interpretations. Conclusion: Further work is needed to investigate the influence of methodological quality on the results of diagnostic meta-analyses. Large data sets of well-reported primary studies are needed to address this question. Without significant improvements in the completeness of reporting of primary studies, progress in this area will be limited

Health and wellbeing amongst older people research in Northamptonshire

The Ageing Research Centre of the University of Northampton (2014-current), in collaboration with the East Midlands Research into Ageing Network (EMRAN) is pleased to compile this brochure on research activity associated with older people across the county of Northamptonshire. This provides a comprehensive overview of activity that is relevant and of value to practice, identifying research outcomes that have real significance to age-related health and wellbeing. The brochure provides a summary of research activity over the last five years from academic, clinical and professional colleagues and demonstrates cross sector networks of collaboration around the common agenda of aging. Such collaboration will enhance the capacity of research understanding across the county and provide information and support for the needs of older people, their families and carers. The translation of research outcomes into practice is essential if we are to promote wellness, independence and healthy aging within the county and beyond and I would like to thank all contributors for their commitment and hard work in the production of this brochure

A mutation in the melon Vacuolar Protein Sorting 41prevents systemic infection of Cucumber mosaic virus

[EN] In the melon exotic accession PI 161375, the gene cmv1, confers recessive resistance to Cucumber mosaic virus (CMV) strains of subgroup II. cmv1 prevents the systemic infection by restricting the virus to the bundle sheath cells and impeding viral loading to the phloem. Here we report the fine mapping and cloning of cmv1. Screening of an F2 population reduced the cmv1 region to a 132 Kb interval that includes a Vacuolar Protein Sorting 41 gene. CmVPS41 is conserved among plants, animals and yeast and is required for post-Golgi vesicle trafficking towards the vacuole. We have validated CmVPS41 as the gene responsible for the resistance, both by generating CMV susceptible transgenic melon plants, expressing the susceptible allele in the resistant cultivar and by characterizing CmVPS41 TILLING mutants with reduced susceptibility to CMV. Finally, a core collection of 52 melon accessions allowed us to identify a single amino acid substitution (L348R) as the only polymorphism associated with the resistant phenotype. CmVPS41 is the first natural recessive resistance gene found to be involved in viral transport and its cellular function suggests that CMV might use CmVPS41 for its own transport towards the phloem.The TILLING platform is supported by the Program Saclay Plant Sciences (SPS, ANR-10-LABX-40) and the European Research Council (ERC-SEXYPARTH). This work was supported by grants AGL2009-12698-C02-01 and AGL2012-40130-C02-01 from the Spanish Ministry of Science and Innovation, the Spanish Ministry of Econom and Competitiveness, through the "Severo Ochoa Programme for Centres of Excellence in R&D" 2016-2019 (SEV-2015-0533)" and the CERCA Programme/Generalitat de Catalunya.Giner, A.; Pascual, L.; Bourgeois, M.; Gyetvai, G.; Rios, P.; Picó Sirvent, MB.; Troadec, C.... (2017). A mutation in the melon Vacuolar Protein Sorting 41prevents systemic infection of Cucumber mosaic virus. Scientific Reports. 7:1-12. https://doi.org/10.1038/s41598-017-10783-3S1127Anderson, P. K. et al. Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends in Ecology & Evolution 19, 535–544, doi: 10.1016/j.tree.2004.07.021 (2004).Nicaise, V. Crop immunity against viruses: outcomes and future challenges. Frontiers in Plant Science 5, 660, doi: 10.3389/fpls.2014.00660 (2014).Kang, B.-C., Yeam, I. & Jahn, M. Genetics of plant virus resistance. Annual Review of Phytopathology 43, 581–621, doi: 10.1146/annurev.phyto.43.011205.141140 (2005).Fraser, R. S. S. The genetics of plant-virus interactions: implications for plant breeding. Euphytica 63, 175–185, doi: 10.1007/bf00023922 (1992).Truniger, V. & Aranda, M. Recessive resistance to plant viruses. Advances in virus research 119–159 (2009).Sanfaçon, H. Plant translationfactors and virus resistance. Viruses 7, 3392–3419, doi: 10.3390/v7072778 (2015).Yang, P. et al. PROTEIN DISULFIDE ISOMERASE LIKE 5-1 is a susceptibility factor to plant viruses. Proceedings of the National Academy of Sciences of the United States of America 111, 2104–2109, doi: 10.1073/pnas.1320362111 (2014).Ouibrahim, L. et al. Cloning of the Arabidopsis rwm1 gene for resistance to Watermelon mosaic virus points to a new function for natural virus resistance genes. The Plant Journal 79, 705–716, doi: 10.1111/tpj.12586 (2014).Rubio, M., Nicolaï, M., Caranta, C. & Palloix, A. Allele mining in the pepper gene pool provided new complementation effects between pvr2-eIF4E and pvr6-eIF(iso)4E alleles for resistance to pepper veinal mottle virus. Journal of General Virology 90, 2808–2814, doi: 10.1099/vir.0.013151-0 (2009).Ibiza, V. P., Cañizares, J. & Nuez, F. EcoTILLING in Capsicum species: searching for new virus resistances. BMC Genomics 11, 631–631, doi: 10.1186/1471-2164-11-631 (2010).Chandrasekaran, J. et al. Development of broad virus resistance in non-transgenic cucumber using CRISPR/Cas9 technology. Molecular Plant Pathology. doi: 10.1111/mpp.12375 (2016).Rodríguez-Hernández, A. et al. Melon RNA interference (RNAi) lines silenced for Cm-eIF4E show broad virus resistance. Molecular plant pathology 13, 755–763, doi: 10.1111/j.1364-3703.2012.00785.x (2012).Edwardson, J. R. & Christie, R. G. In CRC Handbook of Viruses Infecting Legumes (eds J.R. Edwardson & R.G. Christie) 293–319 (CRC Press, 1991).Roossinck, M. J. Cucumber mosaic virus, a model for RNA virus evolution. Molecular Plant Pathology 2, 59–63 (2001).Caranta, C. et al. QTLs involved in the restriction of Cucumber mosaic virus (CMV) long-distance movement in pepper. Theor Appl Genet 104, 586–591 (2002).Valkonen, J. P. & Watanabe, K. N. Autonomous cell death, temperature sensitivity and the genetic control associated with resistance to Cucumber mosaic virus (CMV) in diploid potatoes (Solanum spp). Theor Appl Genet 99, 996–1005 (1999).Ohnishi, S. et al. Multigenic system controlling viral systemic infection determined by the interactions between Cucumber mosaic virus genes and quantitative trait loci of soybean cultivars. Phytopathology 101, 575–582, doi: 10.1094/phyto-06-10-0154 (2011).Kobori, T., Satoshi, T. & Osaki, T. Movement of Cucumber mosaic virus is restricted at the interface between mesophyll and phloem pathway in Cucumis figarei. Journal of General Plant Pathology 66, 159–166, doi: 10.1007/pl00012939 (2000).Argyris, J. M., Pujol, M., Martín-Hernández, A. M. & Garcia-Mas, J. Combined use of genetic and genomics resources to understand virus resistance and fruit quality traits in melon. Physiologia Plantarum 155, 4–11, doi: 10.1111/ppl.12323 (2015).Diaz, J. A. et al. Potential sources of resistance for melon to nonpersistently aphid-borne viruses. Plant Disease 87, 960–964 (2003).Karchi, Z., Cohen, S. & Govers, A. Inheritance of resistance to Cucumber Mosaic virus in melons. Phytopathology 65, 479–481 (1975).Risser, G., Pitrat, M. & Rode, J. C. Etude de la résistance du melon (Cucumis melo L.) au virus de la mosaïque du concombre. Ann Amél Plant 27, 509–522 (1977).Dogimont, C. et al. Identification of QTLs contributing to resistance to different strains of Cucumber mosaic cucumovirus in melon. Acta Hortic 510, 391–398 (2000).Essafi, A. et al. Dissection of the oligogenic resistance to Cucumber mosaic virus in the melon accession PI 161375. Theoretical and Applied Genetics 118, 275–284 (2009).Guiu-Aragonés, C., Díaz-Pendón, J. A. & Martín-Hernández, A. M. Four sequence positions of the movement protein of Cucumber mosaic virus determine the virulence against cmv1-mediated resistance in melon. Molecular Plant Pathology 16, 675–684, doi: 10.1111/mpp.12225 (2015).Guiu-Aragonés, C. et al. The complex resistance to Cucumber mosaic cucumovirus (CMV) in the melon accession PI 161375 is governed by one gene and at least two quantitative trait loci. Molecular Breeding 34, 351–362, doi: 10.1007/s11032-014-0038-y (2014).Guiu-Aragonés, C. et al. cmv1 is a gate for Cucumber mosaic virus transport from bundle sheath cells to phloem in melon. Mol. Plant Pathology 17, 973–984 (2016).Sanseverino, W. et al. Transposon Insertions, Structural Variations, and SNPs Contribute to the Evolution of the Melon Genome. Molecular Biology and Evolution 32, 2760–2774, doi: 10.1093/molbev/msv152 (2015).Pols, M. S., ten Brink, C., Gosavi, P., Oorschot, V. & Klumperman, J. The HOPS Proteins hVps41 and hVps39 Are Required for Homotypic and Heterotypic Late Endosome Fusion. Traffic 14, 219–232, doi: 10.1111/tra.12027 (2013).Asensio, C. S. et al. Self-assembly of VPS41 promotes sorting required for biogenesis of the regulated secretory pathway. Developmental cell 27, 425–437, doi: 10.1016/j.devcel.2013.10.007 (2013).Kolesnikova, L. et al. Vacuolar protein sorting pathway contributes to the release of Marburg virus. Journal of Virology8 3, 2327–2337, doi: 10.1128/JVI.02184-08 (2009).Nuñez-Palenius, H. G. et al. Melon Fruits: Genetic Diversity, Physiology, and Biotechnology Features. Critical Reviews in Biotechnology 28, 13–55, doi: 10.1080/07388550801891111 (2008).Castelblanque, L. et al. Improving the genetic transformation efficiency of Cucumis melo subsp. melo “Piel de Sapo” via Agrobacterium. Procedings of the IXth UECARPIA Meeting on Genetics and Breeding of Cucurbitaceae, 21-24th May, Avignon,, 627–631 (2008).Dahmani-Mardas, F. et al. Engineering melon plants with improved fruit shelf life using the TILLING approach. PLoS ONE 5, doi: 10.1371/journal.pone.0015776 (2010).Esteras, C. et al. SNP genotyping in melons: genetic variation, population structure, and linkage disequilibrium. Theoretical and Applied Genetics 126, 1285–1303, doi: 10.1007/s00122-013-2053-5 (2013).Leida, C. et al. Variability of candidate genes, genetic structure and association with sugar accumulation and climacteric behavior in a broad germplasm collection of melon (Cucumis melo L.). BMC Genetics 16, 28, doi: 10.1186/s12863-015-0183-2 (2015).Balderhaar, H. Jk & Ungermann, C. CORVET and HOPS tethering complexes – coordinators of endosome and lysosome fusion. Journal of Cell Science 126, 1307–1316, doi: 10.1242/jcs.107805 (2013).Darsow, T., Katzmann, D. J., Cowles, C. R. & Emr, S. D. Vps41p Function in the Alkaline Phosphatase Pathway Requires Homo-oligomerization and Interaction with AP-3 through Two Distinct Domains. Molecular Biology of the Cell 12, 37–51 (2001).Ostrowicz, C. W. et al. Defined subunit arrangement and Rab interactions are required for functionality of the HOPS tethering complex. Traffic 11, 1334–1346, doi: 10.1111/j.1600-0854.2010.01097.x (2010).Solinger, J. A. & Spang, A. Tethering complexes in the endocytic pathway: CORVET and HOPS. FEBS Journal 280, 2743–2757, doi: 10.1111/febs.12151 (2013).Rehling, P., Dawson, T., Katzmann, D. J. & Emr, S. D. Formation of AP-3 transport intermediates requires Vps41 function. Nature Cell Biology 1, 346–353 (1999).Niihama, M., Takemoto, N., Hashiguchi, Y., Tasaka, M. & Morita, M. T. ZIP genes encode proteininvolved inmembrane trafficking of the TGN–PVC/Vacuoles. Plant and Cell Physiology 50, 2057–2068, doi: 10.1093/pcp/pcp137 (2009).Wang, A. & Krishnaswamy, S. Eukaryotic translation initiation factor 4E-mediated recessive resistance to plant viruses and its utility in crop improvement. Molecular Plant Pathology 13, 795–803, doi: 10.1111/j.1364-3703.2012.00791.x (2012).Ruffel, S. et al. A natural recessive resistance gene against potato virus Y in pepper corresponds to the eukaryotic initiation factor 4E (eIF4E). The Plant Journal 32, 1067–1075, doi: 10.1046/j.1365-313X.2002.01499.x (2002).Morosky, S., Lennemann, N. J. & Coyne, C. B. BPIFB6 regulatessecretory pathway trafficking and Enterovirus replication. Journal of Virology 90, 5098–5107, doi: 10.1128/jvi.00170-16 (2016).Serra-Soriano, M., Pallás, V. & Navarro, J. A. A model for transport of a viral membrane protein through the early secretory pathway: minimal sequence and endoplasmic reticulum lateral mobility requirements. The Plant Journal 77, 863–879, doi: 10.1111/tpj.12435 (2014).Vale-Costa, S. & Amorim, M. J. Recycling Endosomes and Viral Infection. Viruses 8, 64, doi: 10.3390/v8030064 (2016).Carette, J. E. et al. Ebola virus entry requires the cholesterol transporter Niemann-Pick C1. Nature 477, 340–343, doi: 10.1038/nature10348 (2011).Barajas, D., Martín, I. Fd. C., Pogany, J., Risco, C. & Nagy, P. D. Noncanonical role for the host Vps4 AAA + ATPase ESCRT protein in the formation of Tomato bushy stunt virus replicase. PLoS Pathog 10, e1004087, doi: 10.1371/journal.ppat.1004087 (2014).Richardson, L. G. L. et al. A unique N-Tterminal sequence in the Carnation Italian ringspot virus p36 replicase-associated protein interacts with the host cell ESCRT-I component Vps23. Journal of Virology 88, 6329–6344, doi: 10.1128/JVI.03840-13 (2014).Jiang, J., Patarroyo, C., Garcia Cabanillas, D., Zheng, H. & Laliberté, J.-F. The vesicle-forming 6K2 protein of Turnip mosaic virus interacts with the COPII coatomer Sec. 24a for viral systemic infection. Journal of Virology 89, 6695–6710, doi: 10.1128/jvi.00503-15 (2015).Genovés, A., Navarro, J. A. & Pallás, V. Theintra- and intercellular movement of Melon necrotic spot virus (MNSV) depends on an active secretory pathway. Molecular Plant-Microbe Interactions 23, 263–272, doi: 10.1094/MPMI-23-3-0263 (2010).Amano, M. et al. High-resolution mapping of zym, a recessive gene for Zucchini yellow mosaic virus resistance in cucumber. Theoretical and Applied Genetics 126, 2983–2993, doi: 10.1007/s00122-013-2187-5 (2013).Lewis, J. D. & Lazarowitz, S. G. Arabidopsis synaptotagmin SYTA regulates endocytosis and virus movement protein cell-to-cell transport. Proceedings of the National Academy of Sciences of the United States of America 107, 2491–2496, doi: 10.1073/pnas.0909080107 (2010).Rizzo, T. & Palukaitis, P. Construction of full-length cDNA clones of Cucumber mosaic virus RNAs 1, 2 and 3: Generation of infectious RNA transcripts. Molecular and General Genetics 122, 249–256 (1990).Zhang, L., Handa, K. & Palukaitis, P. Mapping local and systemic symptom determinants of Cucumber mosaic cucumovirus in tobacco. The Journal of General Virology 75, 3185–3191 (1994).Fukino, N., Sakata, Y., Kunihisa, M. & S., M. Characterisation of novel simple sequence repeat (SSR) markers for melon (Cucumis melo L.) and their use for genotype identification. Journal of Horticultural Science & Biotechnology 82, 330–334 (2007).Garcia-Mas, J. et al. The genome of melon (Cucumis melo L.). Proceedings of the National Academy of Sciences 109, 11872–11877, doi: 10.1073/pnas.1205415109 (2012).Untergasser, A. et al. Primer3—new capabilities and interfaces. Nucleic Acids Research 40, e115–e115, doi: 10.1093/nar/gks596 (2012).Gonzalo, M. J. et al. Simple-sequence repeat markers used in merging linkage maps of melon (Cucumis melo L.). Theor Appl Genet 110, 802–811 (2005).Doyle, J. J. & Doyle, J. L. Isolation of plant DNA from fresh tissue. Focus1 2, 13–15 (1990).Argyris, J. M. et al. Use of targeted SNP selection for an improved anchoring of the melon (Cucumis melo L.) scaffold genome assembly. BMC Genomics 16, 4, doi: 10.1186/s12864-014-1196-3 (2015).Proost, S. et al. PLAZA 3.0: an access point for plant comparative genomics. Nucleic Acids Research 43, D974–D981, doi: 10.1093/nar/gku986 (2015).Choi, Y., Sims, G. E., Murphy, S., Miller, J. R. & Chan, A. P. Predicting the Functional Effect of Amino Acid Substitutions and Indels. PLoS ONE 7, e46688, doi: 10.1371/journal.pone.0046688 (2012).Li, B. et al. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics 25, 2744–2750, doi: 10.1093/bioinformatics/btp528 (2009).Earley, K. W. et al. Gateway-compatible vectors for plant functional genomics and proteomics. The Plant Journal 45, 616–629, doi: 10.1111/j.1365-313X.2005.02617.x (2006).Gonzalez-Ibeas, D. et al. MELOGEN: an EST database for melon functional genomics. BMC Genomics 8, 306 (2007).Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Research 29, 2002–2007 (2001).Dalmais, M. et al. UTILLdb, a Pisum sativum in silico forward and reverse genetics tool. Genome Biology 9, R43–R43, doi: 10.1186/gb-2008-9-2-r43 (2008).Saladié, M. et al. Comparative transcriptional profiling analysis of developing melon (Cucumis melo L.) fruit from climacteric and non-climacteric varieties. BMC Genomics 16, 440, doi: 10.1186/s12864-015-1649-3 (2015)

A Single Peroxisomal Targeting Signal Mediates Matrix Protein Import in Diatoms

Peroxisomes are single membrane bound compartments. They are thought to be present in almost all eukaryotic cells, although the bulk of our knowledge about peroxisomes has been generated from only a handful of model organisms. Peroxisomal matrix proteins are synthesized cytosolically and posttranslationally imported into the peroxisomal matrix. The import is generally thought to be mediated by two different targeting signals. These are respectively recognized by the two import receptor proteins Pex5 and Pex7, which facilitate transport across the peroxisomal membrane. Here, we show the first in vivo localization studies of peroxisomes in a representative organism of the ecologically relevant group of diatoms using fluorescence and transmission electron microscopy. By expression of various homologous and heterologous fusion proteins we demonstrate that targeting of Phaeodactylum tricornutum peroxisomal matrix proteins is mediated only by PTS1 targeting signals, also for proteins that are in other systems imported via a PTS2 mode of action. Additional in silico analyses suggest this surprising finding may also apply to further diatoms. Our data suggest that loss of the PTS2 peroxisomal import signal is not reserved to Caenorhabditis elegans as a single exception, but has also occurred in evolutionary divergent organisms. Obviously, targeting switching from PTS2 to PTS1 across different major eukaryotic groups might have occurred for different reasons. Thus, our findings question the widespread assumption that import of peroxisomal matrix proteins is generally mediated by two different targeting signals. Our results implicate that there apparently must have been an event causing the loss of one targeting signal even in the group of diatoms. Different possibilities are discussed that indicate multiple reasons for the detected targeting switching from PTS2 to PTS1

Interferon-driven brain phenotype in a mouse model of RNaseT2 deficient leukoencephalopathy

Solid state NMR/Biophysical Organic Chemistr

Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)

Improved risk stratification of patients with atrial fibrillation: an integrated GARFIELD-AF tool for the prediction of mortality, stroke and bleed in patients with and without anticoagulation.

OBJECTIVES: To provide an accurate, web-based tool for stratifying patients with atrial fibrillation to facilitate decisions on the potential benefits/risks of anticoagulation, based on mortality, stroke and bleeding risks. DESIGN: The new tool was developed, using stepwise regression, for all and then applied to lower risk patients. C-statistics were compared with CHA2DS2-VASc using 30-fold cross-validation to control for overfitting. External validation was undertaken in an independent dataset, Outcome Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF). PARTICIPANTS: Data from 39 898 patients enrolled in the prospective GARFIELD-AF registry provided the basis for deriving and validating an integrated risk tool to predict stroke risk, mortality and bleeding risk. RESULTS: The discriminatory value of the GARFIELD-AF risk model was superior to CHA2DS2-VASc for patients with or without anticoagulation. C-statistics (95% CI) for all-cause mortality, ischaemic stroke/systemic embolism and haemorrhagic stroke/major bleeding (treated patients) were: 0.77 (0.76 to 0.78), 0.69 (0.67 to 0.71) and 0.66 (0.62 to 0.69), respectively, for the GARFIELD-AF risk models, and 0.66 (0.64-0.67), 0.64 (0.61-0.66) and 0.64 (0.61-0.68), respectively, for CHA2DS2-VASc (or HAS-BLED for bleeding). In very low to low risk patients (CHA2DS2-VASc 0 or 1 (men) and 1 or 2 (women)), the CHA2DS2-VASc and HAS-BLED (for bleeding) scores offered weak discriminatory value for mortality, stroke/systemic embolism and major bleeding. C-statistics for the GARFIELD-AF risk tool were 0.69 (0.64 to 0.75), 0.65 (0.56 to 0.73) and 0.60 (0.47 to 0.73) for each end point, respectively, versus 0.50 (0.45 to 0.55), 0.59 (0.50 to 0.67) and 0.55 (0.53 to 0.56) for CHA2DS2-VASc (or HAS-BLED for bleeding). Upon validation in the ORBIT-AF population, C-statistics showed that the GARFIELD-AF risk tool was effective for predicting 1-year all-cause mortality using the full and simplified model for all-cause mortality: C-statistics 0.75 (0.73 to 0.77) and 0.75 (0.73 to 0.77), respectively, and for predicting for any stroke or systemic embolism over 1 year, C-statistics 0.68 (0.62 to 0.74). CONCLUSIONS: Performance of the GARFIELD-AF risk tool was superior to CHA2DS2-VASc in predicting stroke and mortality and superior to HAS-BLED for bleeding, overall and in lower risk patients. The GARFIELD-AF tool has the potential for incorporation in routine electronic systems, and for the first time, permits simultaneous evaluation of ischaemic stroke, mortality and bleeding risks. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier for GARFIELD-AF (NCT01090362) and for ORBIT-AF (NCT01165710)

Two-year outcomes of patients with newly diagnosed atrial fibrillation: results from GARFIELD-AF.

AIMS: The relationship between outcomes and time after diagnosis for patients with non-valvular atrial fibrillation (NVAF) is poorly defined, especially beyond the first year. METHODS AND RESULTS: GARFIELD-AF is an ongoing, global observational study of adults with newly diagnosed NVAF. Two-year outcomes of 17 162 patients prospectively enrolled in GARFIELD-AF were analysed in light of baseline characteristics, risk profiles for stroke/systemic embolism (SE), and antithrombotic therapy. The mean (standard deviation) age was 69.8 (11.4) years, 43.8% were women, and the mean CHA2DS2-VASc score was 3.3 (1.6); 60.8% of patients were prescribed anticoagulant therapy with/without antiplatelet (AP) therapy, 27.4% AP monotherapy, and 11.8% no antithrombotic therapy. At 2-year follow-up, all-cause mortality, stroke/SE, and major bleeding had occurred at a rate (95% confidence interval) of 3.83 (3.62; 4.05), 1.25 (1.13; 1.38), and 0.70 (0.62; 0.81) per 100 person-years, respectively. Rates for all three major events were highest during the first 4 months. Congestive heart failure, acute coronary syndromes, sudden/unwitnessed death, malignancy, respiratory failure, and infection/sepsis accounted for 65% of all known causes of death and strokes for <10%. Anticoagulant treatment was associated with a 35% lower risk of death. CONCLUSION: The most frequent of the three major outcome measures was death, whose most common causes are not known to be significantly influenced by anticoagulation. This suggests that a more comprehensive approach to the management of NVAF may be needed to improve outcome. This could include, in addition to anticoagulation, interventions targeting modifiable, cause-specific risk factors for death. CLINICAL TRIAL REGISTRATION: http://www.clinicaltrials.gov. Unique identifier: NCT01090362