6 research outputs found
IBD BioResource: an open-access platform of 25 000 patients to accelerate research in Crohn's and Colitis.
No full textAn alliance of clinicians, academics, research nurses, funders, coordinators, programmers and, most importantly, patients has come together in the UK to deliver a powerful new platform to accelerate Crohn’s and colitis research – the Inflammatory Bowel Disease (IBD) BioResource. As part of the NIHR BioResource for translational research, 25,000 patients in over 90 hospitals UK-wide have signed up since we launched in January 2016 (Fig 1). All have detailed phenotypes databased including Montreal classification1, treatment response history (updated annually), surgical history and comorbidities (IBD BioResource panel descriptive, Clinical data collection sheet and Health and Lifestyle questionnaire). Serum, plasma and DNA samples are banked; and genome-wide genetic profiling undertaken. Participants’ data and samples can be studied, and they themselves surveyed or recalled for resampling or downstream studies (see Fig 2). Critically such studies can be lead by any UK or overseas investigator whether from the worlds of clinical research, pharmacovigilance, science or industry
Thiopurine monotherapy is effective in ulcerative colitis but significantly less so in Crohn’s disease: long-term outcomes for 11 928 patients in the UK inflammatory bowel disease bioresource
No full textObjective Thiopurines are widely used as maintenance therapy in inflammatory bowel disease (IBD) but the evidence base for their use is sparse and their role increasingly questioned. Using the largest series reported to date, we assessed the long-term effectiveness of thiopurines in ulcerative colitis (UC) and Crohn's disease (CD), including their impact on need for surgery. Design Outcomes were assessed in 11 928 patients (4968 UC, 6960 CD) in the UK IBD BioResource initiated on thiopurine monotherapy with the intention of maintaining medically induced remission. Effectiveness was assessed retrospectively using patient-level data and a definition that required avoidance of escalation to biological therapy or surgery while on thiopurines. Analyses included overall effectiveness, time-to-event analysis for treatment escalation and comparison of surgery rates in patients tolerant or intolerant of thiopurines. Results Using 68 132 patient-years of exposure, thiopurine monotherapy appeared effective for the duration of treatment in 2617/4968 (52.7%) patients with UC compared with 2378/6960 (34.2%) patients with CD (p<0.0001). This difference was corroborated in a multivariable analysis: after adjusting for variables including treatment era, thiopurine monotherapy was less effective in CD than UC (OR 0.47, 95% CI 0.43 to 0.51, p<0.0001). Thiopurine intolerance was associated with increased risk of surgery in UC (HR 2.44, p<0.0001); with a more modest impact on need for surgery in CD (HR=1.23, p=0.0015). Conclusion Thiopurine monotherapy is an effective long-term treatment for UC but significantly less effective in CD
Biologic therapy for inflammatory bowel disease: Real-world comparative effectiveness and impact of drug sequencing in 13,222 patients within the UK IBD BioResource
No full textProduction of microalgal external organic matter in a Chlorella-dominated culture: influence of temperature and stress factors
No full text[EN] Although microalgae are recognised to release external organic matter (EOM), little is known about this phenomenon in microalgae cultivation systems, especially on a large scale. A study on the effect of microalgae-stressing factors such as temperature, nutrient limitation and ammonium oxidising bacteria (AOB) competition in EOM production by microalgae was carried out. The results showed non-statistically significant differences in EOM production at constant temperatures of 25, 30 and 35 degrees C. However, when the temperature was raised from 25 to 35 degrees C for 4 h a day, polysaccharide production increased significantly, indicating microalgae stress. Nutrient limitation also seemed to increase EOM production. No significant differences were found in EOM production under lab conditions when the microalgae competed with AOB for ammonium uptake. However, when the EOM concentration was monitored during continuous outdoor operation of a membrane photobioreactor (MPBR) plant, nitrifying bacteria activity was likely to be responsible for the increase in EOM concentration in the culture. Other factors such as high temperatures, ammonium-depletion and low light intensities could also have induced cell deterioration and thus have influenced EOM production in the outdoor MPBR plant. Membrane fouling seemed to depend on the biomass concentration of the culture. However, under the operating conditions tested, the behaviour of fouling rate with respect to the EOM concentration was different depending on the initial membrane state.This research work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO, Projects CTM2014-54980-C2-1-R and CTM2014-54980-C2-2-R) jointly with the European Regional Development Fund (ERDF), both of which are gratefully acknowledged. This was also supported by the Spanish Ministry of Education, Culture and Sport via a pre-doctoral FPU fellowship to author J. Gonzalez-Camejo (FPU14/05082)Gonzalez-Camejo, J.; Paches Giner, MAV.; Marín, A.; Jiménez Benítez, AL.; Seco, A.; Barat, R. (2020). Production of microalgal external organic matter in a Chlorella-dominated culture: influence of
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Depression symptom-specific genetic associations in clinically diagnosed and proxy case Alzheimer’s disease
No full textData availability:
All GWAS summary statistics generated in the process of conducting this study have been deposited on Zenodo at https://doi.org/10.5281/zenodo.13828101 (ref. 121). Individual-level data from UK Biobank, GLAD and PROTECT are subject to restrictions. Data are available on reasonable request from UK Biobank (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/contact-us) through application to the NIHR BioResource for GLAD (https://bioresource.nihr.ac.uk/using-our-bioresource/academic-and-clinical-researchers/apply-for-bioresource-data/) and through the PROTECT data team (https://medicine.exeter.ac.uk/clinical-biomedical/research/protect/). The Alzheimer’s disease GWAS summary statistics used in this study are publically available through the GWAS catalog (https://www.ebi.ac.uk/gwas/efotraits/MONDO_0004975). GWAS summary statistics for the Wightman et al. GWAS excluding the UK Biobank are available at https://vu.data.surfsara.nl/index.php/s/LGjeIk6phQ6zw8I. For clinical and broad depression, summary statistics are available through the Psychiatric Genomic Consortium (https://pgc.unc.edu). eQTL summary datasets used in SMR analysis from Lloyd-Jones et al.100 and PsychENCODE101 can be obtained from the website of the Yang laboratory (https://yanglab.westlake.edu.cn/software/smr/#eQTLsummarydata). This study has been pre-registered on the Open Science Framework (https://osf.io/94q35/?view_only=e77f72d4100d47eea7f3ef07dfa9c059).Code availability;
Code for performing these analyses has been deposited on GitHub (https://github.com/lpgilchrist/PHQ-9_AD_genetic_overlap_project). This study made use of the following publicly available analysis software: CAUSE (https://jean997.github.io/cause/index.html); coloc (https://chr1swallace.github.io/coloc/); COLOC-reporter (https://github.com/ThomasPSpargo/COLOC-reporter); FUMA GWAS (https://fuma.ctglab.nl); HDL (https://github.com/zhenin/HDL); LAVA (https://github.com/josefin-werme/LAVA); LDSC (https://github.com/bulik/ldsc); MegaPRS (https://dougspeed.com/megaprs/); METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation); MTAG (https://github.com/JonJala/mtag); MungeSumstats (https://github.com/Al-Murphy/MungeSumstats); REGENIE (https://rgcgithub.github.io/regenie/); SMR (https://yanglab.westlake.edu.cn/software/smr/); susieR (https://stephenslab.github.io/susieR/index.html); TwoSampleMR (https://mrcieu.github.io/TwoSampleMR/).Supplementary information is available online at: https://www.nature.com/articles/s44220-024-00369-0#Sec33 .For the purposes of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any accepted author manuscript version arising from this submission.Depression is a risk factor for the later development of Alzheimer’s disease (AD), but evidence for the genetic relationship is mixed. Assessing depression symptom-specific genetic associations may better clarify this relationship. To address this, we conducted genome-wide meta-analysis (a genome-wide association study, GWAS) of the nine depression symptom items, plus their sum score, on the Patient Health Questionnaire (PHQ-9) (GWAS-equivalent N: 224,535–308,421) using data from UK Biobank, the GLAD study and PROTECT, identifying 37 genomic risk loci. Using six AD GWASs with varying proportions of clinical and proxy (family history) case ascertainment, we identified 20 significant genetic correlations with depression/depression symptoms. However, only one of these was identified with a clinical AD GWAS. Local genetic correlations were detected in 14 regions. No statistical colocalization was identified in these regions. However, the region of the transmembrane protein 106B gene (TMEM106B) showed colocalization between multiple depression phenotypes and both clinical-only and clinical + proxy AD. Mendelian randomization and polygenic risk score analyses did not yield significant results after multiple testing correction in either direction. Our findings do not demonstrate a causal role of depression/depression symptoms on AD and suggest that previous evidence of genetic overlap between depression and AD may be driven by the inclusion of family history-based proxy cases/controls. However, colocalization at TMEM106B warrants further investigation.L.G. is funded by the King’s College London DRIVE-Health Centre for Doctoral Training and the Perron Institute for Neurological and Translational Science. P.P. is funded by Alzheimer’s Research UK. S.K. is funded by MSWA and the Perron Institute. H.L.D. acknowledges funding from the Economic and Social Research Council (ESRC). D.M.H. is supported by a Sir Henry Wellcome Postdoctoral Fellowship (ref. 213674/Z/18/Z). B.N.A. acknowledges funding from an NIHR pre-doctoral fellowship (NIHR301067). A.I. is funded by the Motor Neurone Disease Association (MNDA), MND Scotland, Darby Rimmer MND Foundation, Rosetrees Trust, Alzheimer’s Research UK, Spastic Paraplegia Foundation, LifeArc and The NIHR Maudsley Biomedical Research Centre. T.P.S. acknowledges funding from the MNDA. This Article represents independent research that was part funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This research was conducted using the UK Biobank Resource under application no. 18177. We thank the UK Biobank Team for collecting the data and making it available. We also thank the UK Biobank participants. We thank the GLAD Study volunteers for their participation, and gratefully acknowledge the NIHR BioResource centers, NHS Trusts and staff for their contribution. We thank the National Institute for Health Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. This study presents independent research funded by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. Further information can be found at https://www.maudsleybrc.nihr.ac.uk/facilities/bioresource/. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the HSC R&D Division, King’s College London or the Department of Health and Social Care. The PROTECT study was funded/supported by the National Institute of Health and Care Research Exeter Biomedical Research Centre. PROTECT genetic data were funded in part by the University of Exeter through the MRC Proximity to Discovery: Industry Engagement Fund (External Collaboration, Innovation and Entrepreneurism: Translational Medicine in Exeter 2 (EXCITEME2) ref. MC_PC_17189). Genotyping was performed at deCODE Genetics.
As data used in this study was obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the ADNI investigators contributed to the conception of the sample and acquisition of the data, but were not participants in other parts of this study, such as conceptualization, data analysis or writing. A full acknowledgment list of ADNI investigators is available at https://adni.loni.usc.edu/wp-content/uploads/2024/07/ADNI-Acknowledgement-List_July2024.pdf. Details on ADNI data access can be found at https://adni.loni.usc.edu/data-samples/adni-data/#AccessData. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health grant U01 AG024904) and DOD ADNI (Department of Defense award no. W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai, Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research are providing funds to support ADNI clinical sites in Canada. Private-sector contributions are facilitated by the Foundation for the National Institutes of Health (https://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Similarly, data were obtained from the GERAD1 Consortium, and GERAD1 investigators contributed to the conception of the cohort and acquisition of the data, but did not participate in the conceptualization, analysis or writing of this study. A full list of GERAD1 collaborators can be found in Supplementary Section 3. For GERAD1 data, Cardiff University was supported by the Wellcome Trust, MRC, ARUK and the Welsh Assembly Government. Cambridge University and King’s College London acknowledge support from the MRC. ARUK supported sample collections at the South West Dementia Bank and the Universities of Nottingham, Manchester and Belfast. The Belfast group acknowledges support from the Alzheimer’s Society, Ulster Garden Villages, Northern Ireland R&D Office and the Royal College of Physicians/Dunhill Medical Trust. The MRC and Mercer’s Institute for Research on Ageing supported the Trinity College group. The South West Dementia Brain Bank acknowledges support from Bristol Research into Alzheimer’s and Care of the Elderly. The Charles Wolfson Charitable Trust supported the OPTIMA group. Washington University was funded by National Institutes of Health (NIH) grants, the Barnes Jewish Foundation and the Charles and Joanne Knight Alzheimer’s Research Initiative. Patient recruitment for the MRC Prion Unit/University College London(UCL) Department of Neurodegenerative Disease collection was supported by the UCL Hospitals/UCL Biomedical Centre and NIHR Queen Square Dementia Biomedical Research Unit. LASER-AD was funded by Lundbeck SA. The Bonn group was supported by the German Federal Ministry of Education and Research, Competence Network Dementia and Competence Network Degenerative Dementia, and Alfried Krupp von Bohlen und Halbach-Stiftung. The Genetic and Environmental Risk for Alzheimer’s Disease (GERAD1) Consortium also used samples ascertained by the National Institute of Mental Health Alzheimer’s Disease Genetics Initiative. The i-Select chip was funded by the French National Foundation on Alzheimer’s disease and related disorders. The European Alzheimer’s Disease Initiative was supported by a LABEX (Laboratory of Excellence Program Investment for the Future) DISTALZ grant, the Institut National de la Santé et de la Recherche Médicale, Institut Pasteur de Lille, Université de Lille 2 and the Lille University Hospital. The GERAD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium was supported by the MRC (grant no. 503480), ARUK (grant no. 503176), the Wellcome Trust (grant no. 082604/2/07/Z) and the German Federal Ministry of Education and Research (Competence Network Dementia grant nos. 01GI0102, 01GI0711 and 01GI0420). The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium was partly supported by NIH/NIA grant no. R01 AG033193; NIA grant no. AG081220; AGES contract no. N01-AG-12100; National Heart, Lung and Blood Institute grant no. R01 HL105756; the Icelandic Heart Association; and the Erasmus Medical Center and Erasmus University. The Alzheimer’s Disease Genetics Consortium was supported by NIH/NIA grant nos. U01 AG032984, U24 AG021886 and U01 AG016976; and Alzheimer’s Association grant no. ADGC-10-196728.
ANM data are accessible via Synapse (https://www.synapse.org/Synapse:syn22252881). The AddNeuroMed study was supported by InnoMed (Innovative Medicines in Europe)—an Integrated Project funded by the European Union of the Sixth Framework program priority FP6-2004-LIFESCIHEALTH-5, Life Sciences, Genomics and Biotechnology for Health.
Compensation was not provided for participants in any of the above studies.
We also thank and acknowledge the contribution and use of the CREATE high-performance computing cluster at King’s College London (King’s Computational Research, Engineering and Technology Environment (CREATE); retrieved 23 May 2023 from https://doi.org/10.18742/rnvf-m076).
The analysis flowchart in Fig. 1 was created and licensed in BioRender (https://www.BioRender.com/z76j516).
The ethics committee/IRB of King’s College London gave ethical approval for this work. Ethical approval for the UK Biobank study was granted by the National Information Governance Board for Health and Social Care and the NHS North West Multicentre Research Ethics Committee (11/NW/0382). Data access permission was granted under UK Biobank application 18177. Written informed consent was obtained from all participants by UK Biobank. The GLAD Study was approved by the London–Fulham Research Ethics Committee on 21 August 2018 (REC ref. 18/LO/1218) following a full review by the committee. The NIHR BioResource has been approved as a Research Tissue Bank by the East of England–Cambridge Central Committee (REC ref. 17/EE/0025). The PROTECT study received ethical approval from the UK London Bridge National Research Ethics Committee (ref. 13/LO/1578). Data were obtained from ADNI, AddNeuroMed and GERAD1 following formal request to each consortia. Permission was granted for the use of data
Intravenous or nebulised magnesium sulphate versus standard therapy for severe acute asthma (3Mg trial): a double-blind, randomised controlled trial
No full textBACKGROUND: Neuraminidase inhibitors were widely used during the 2009-10 influenza A H1N1 pandemic, but evidence for their effectiveness in reducing mortality is uncertain. We did a meta-analysis of individual participant data to investigate the association between use of neuraminidase inhibitors and mortality in patients admitted to hospital with pandemic influenza A H1N1pdm09 virus infection. METHODS: We assembled data for patients (all ages) admitted to hospital worldwide with laboratory confirmed or clinically diagnosed pandemic influenza A H1N1pdm09 virus infection. We identified potential data contributors from an earlier systematic review of reported studies addressing the same research question. In our systematic review, eligible studies were done between March 1, 2009 (Mexico), or April 1, 2009 (rest of the world), until the WHO declaration of the end of the pandemic (Aug 10, 2010); however, we continued to receive data up to March 14, 2011, from ongoing studies. We did a meta-analysis of individual participant data to assess the association between neuraminidase inhibitor treatment and mortality (primary outcome), adjusting for both treatment propensity and potential confounders, using generalised linear mixed modelling. We assessed the association with time to treatment using time-dependent Cox regression shared frailty modelling. FINDINGS: We included data for 29,234 patients from 78 studies of patients admitted to hospital between Jan 2, 2009, and March 14, 2011. Compared with no treatment, neuraminidase inhibitor treatment (irrespective of timing) was associated with a reduction in mortality risk (adjusted odds ratio [OR] 0·81; 95% CI 0·70-0·93; p=0·0024). Compared with later treatment, early treatment (within 2 days of symptom onset) was associated with a reduction in mortality risk (adjusted OR 0·48; 95% CI 0·41-0·56; p<0·0001). Early treatment versus no treatment was also associated with a reduction in mortality (adjusted OR 0·50; 95% CI 0·37-0·67; p<0·0001). These associations with reduced mortality risk were less pronounced and not significant in children. There was an increase in the mortality hazard rate with each day's delay in initiation of treatment up to day 5 as compared with treatment initiated within 2 days of symptom onset (adjusted hazard ratio [HR 1·23] [95% CI 1·18-1·28]; p<0·0001 for the increasing HR with each day's delay). INTERPRETATION: We advocate early instigation of neuraminidase inhibitor treatment in adults admitted to hospital with suspected or proven influenza infection. FUNDING: F Hoffmann-La Roche
