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Background: Elevated serum uric acid (sUA) is associated with heart failure (HF)., Hypothesis: Urate-lowering therapy (ULT) in HF is associated with lower risk of HF hospitalization (hHF) and mortality., Methods: Data on patients with HF and gout or hyperuricemia in the Clinical Practice Research Datalink database linked to the Hospital Episode Statistics and the Office for National Statistics in the United Kingdom were analyzed. Risks of hHF and all-cause mortality or cardiovascular-related mortality by ULT exposure (ULT initiated within ≤6 months of gout or hyperuricemia diagnosis) were analyzed in a propensity score-matched cohort using adjusted Cox proportional hazards regression models., Results: Of 2174 propensity score-matched pairs, patients were predominantly male, aged >70 years, with mean ± standard deviation sUA 9.3 ± 1.8 (ULT-exposed) and 9.4 ± 1.9 mg/dL (ULT-unexposed). At 5 years, ULT-exposed patients had a 43% lower risk of hHF or all-cause mortality (adjusted hazard ratio [HR]: 0.57; 95% confidence interval [CI]: 0.51-0.65) and a 19% lower risk of hHF or cardiovascular-related mortality (adjusted HR: 0.81; 95% CI: 0.71-0.92) versus no ULT exposure., Conclusion: ULT was associated with reduced risk of adverse clinical outcomes in patients with HF and gout or hyperuricemia over 5 years., (© 2024 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC.)

We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property of interest. This problem arises in discovery processes that rely on screening funnels for trading off the overall costs against throughput and accuracy. Typically, individual stages in these processes are loosely connected and each one generates data at different scale and fidelity. We consider this setup holistically and demonstrate empirically that existing transfer learning techniques for graph neural networks are generally unable to harness the information from multi-fidelity cascades. Here, we propose several effective transfer learning strategies and study them in transductive and inductive settings. Our analysis involves a collection of more than 28 million unique experimental protein-ligand interactions across 37 targets from drug discovery by high-throughput screening and 12 quantum properties from the dataset QMugs. The results indicate that transfer learning can improve the performance on sparse tasks by up to eight times while using an order of magnitude less high-fidelity training data. Moreover, the proposed methods consistently outperform existing transfer learning strategies for graph-structured data on drug discovery and quantum mechanics datasets., (© 2024. The Author(s).)

Rationale: Bronchiectasis is a chronic, progressive disease of bronchial dilation, inflammation, and scarring leading to impaired mucociliary clearance and increased susceptibility to infection. Identified causes include previous severe respiratory infections. A small, single-center UK study demonstrated a reduction in bronchiectasis exacerbations during the first year of the coronavirus disease (COVID-19) pandemic. No studies have been conducted in a U.S. (commercially insured) cohort to date. Objectives: To explore the impact of the COVID-19 pandemic on the frequency of exacerbations in a large cohort of commercially insured U.S. patients with bronchiectasis by testing the hypothesis that U.S. patients with bronchiectasis had fewer exacerbations during the pandemic. Methods: This retrospective observational cohort study used health insurance claims data from Optum's deidentified Clinformatics Data Mart database, which included U.S. patients and their covered dependents. Eligible patients were ⩾18 years of age with bronchiectasis; patients with other respiratory conditions were excluded. The main study cohort excluded patients with frequent asthma and/or chronic obstructive pulmonary disease diagnoses. The primary objective was to compare the bronchiectasis exacerbation rates before and during the COVID-19 pandemic. Results: The median number of exacerbations per patient per year decreased significantly from the year before the COVID-19 pandemic to the first year of the pandemic (1 vs. 0; P  < 0.01). More patients had zero exacerbations during the first year of the pandemic than the year prior (57% vs. 24%; McNemar's chi-square = 122.56; P  < 0.01). Conclusions: In a U.S. population-based study of patients with International Classification of Diseases codes for bronchiectasis, the rate of exacerbations during Year 1 of the COVID-19 pandemic was reduced compared with the 2-year time period preceding the pandemic.

Objective: Cirrhosis describes the end-stage of chronic liver disease. Irreversible changes in the liver cause portal hypertension, which can progress to serious complications and death. Only a few studies with small sample sizes have investigated the prognosis of cirrhosis with portal hypertension. We used electronic healthcare records to examine liver-related outcomes in patients with diagnosed/suspected portal hypertension., Design: This retrospective observational cohort study used secondary health data between 1 January 2017 and 3 December 2020 from the TriNetX Network, a federated electronic healthcare records platform. Three patient groups with cirrhosis and diagnosed/suspected portal hypertension were identified ('most severe', 'moderate severity' and 'least severe'). Outcomes studied individually and as a composite were variceal haemorrhage, hepatic encephalopathy, complications of ascites and recorded mortality up to 24 months., Results: There were 13 444, 23 299, and 23 836 patients in the most severe, moderate severity and least severe groups, respectively. Mean age was similar across groups; most participants were white. The most common individual outcomes at 24 months were variceal haemorrhage in the most severe group, recorded mortality and hepatic encephalopathy in the moderate severity group, and recorded mortality in the least severe group. Recorded mortality rate was similar across groups. For the composite outcome, cumulative incidence was 59% in the most severe group at 6 months. Alcohol-associated liver disease and metabolic-associated steatohepatitis were significantly associated with the composite outcome across groups., Conclusion: Our analysis of a large dataset from electronic healthcare records illustrates the poor prognosis of patients with diagnosed/suspected portal hypertension., Competing Interests: Competing interests: NHCL was employed by AstraZeneca during the majority of her contributions. SJK is an employee of and owns shares in AstraZeneca. SC is an employee of ZS and worked as a contractor for AstraZeneca. SA is an employee of ZS and worked as a contractor for AstraZeneca. DMB is an employee of and owns shares in AstraZeneca. PRH was employed by AstraZeneca during the majority of his contributions and owned shares in AstraZeneca at the time the work was conducted. VERP is an employee of and owns shares in AstraZeneca. PJG is an employee of and owns shares in AstraZeneca. PA is an employee of and owns shares in AstraZeneca., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Atom-centred neural networks represent the state-of-the-art for approximating the quantum chemical properties of molecules, such as internal energies. While the design of machine learning architectures that respect chemical principles has continued to advance, the final atom pooling operation that is necessary to convert from atomic to molecular representations in most models remains relatively undeveloped. The most common choices, sum and average pooling, compute molecular representations that are naturally a good fit for many physical properties, while satisfying properties such as permutation invariance which are desirable from a geometric deep learning perspective. However, there are growing concerns that such simplistic functions might have limited representational power, while also being suboptimal for physical properties that are highly localised or intensive. Based on recent advances in graph representation learning, we investigate the use of a learnable pooling function that leverages an attention mechanism to model interactions between atom representations. The proposed pooling operation is a drop-in replacement requiring no changes to any of the other architectural components. Using SchNet and DimeNet++ as starting models, we demonstrate consistent uplifts in performance compared to sum and mean pooling and a recent physics-aware pooling operation designed specifically for orbital energies, on several datasets, properties, and levels of theory, with up to 85% improvements depending on the specific task., (© 2023. The Author(s).)

High-throughput screening (HTS), as one of the key techniques in drug discovery, is frequently used to identify promising drug candidates in a largely automated and cost-effective way. One of the necessary conditions for successful HTS campaigns is a large and diverse compound library, enabling hundreds of thousands of activity measurements per project. Such collections of data hold great promise for computational and experimental drug discovery efforts, especially when leveraged in combination with modern deep learning techniques, and can potentially lead to improved drug activity predictions and cheaper and more effective experimental design. However, existing collections of machine-learning-ready public datasets do not exploit the multiple data modalities present in real-world HTS projects. Thus, the largest fraction of experimental measurements, corresponding to hundreds of thousands of "noisy" activity values from primary screening, are effectively ignored in the majority of machine learning models of HTS data. To address these limitations, we introduce Multifidelity PubChem BioAssay (MF-PCBA), a curated collection of 60 datasets that includes two data modalities for each dataset, corresponding to primary and confirmatory screening, an aspect that we call multifidelity . Multifidelity data accurately reflect real-world HTS conventions and present a new, challenging task for machine learning: the integration of low- and high-fidelity measurements through molecular representation learning, taking into account the orders-of-magnitude difference in size between the primary and confirmatory screens. Here we detail the steps taken to assemble MF-PCBA in terms of data acquisition from PubChem and the filtering steps required to curate the raw data. We also provide an evaluation of a recent deep-learning-based method for multifidelity integration across the introduced datasets, demonstrating the benefit of leveraging all HTS modalities, and a discussion in terms of the roughness of the molecular activity landscape. In total, MF-PCBA contains over 16.6 million unique molecule-protein interactions. The datasets can be easily assembled by using the source code available at https://github.com/davidbuterez/mf-pcba.

Rationale: In chronic obstructive pulmonary disease (COPD), inhaled corticosteroids (ICS) are associated with pneumonia, highlighting the importance of investigating subgroups of patients who may benefit from prolonged ICS use. Despite this, the WISDOM (Withdrawal of Inhaled Steroids during Optimized Bronchodilator Management) trial found a greater decline in forced expiratory volume in 1 second (FEV 1 ) in patients with COPD who withdrew from ICS compared with patients who remained on triple therapy. Objectives: We investigated the association between ICS withdrawal and the rate of FEV 1 decline in patients with COPD using routinely collected electronic healthcare records. Methods: Using CPRD (Clinical Practice Research Datalink) Aurum and Hospital episode statistics, we included patients with COPD who had been on triple therapy for at least 4 months. Patients were categorized into those who withdrew from ICS and those who remained on triple therapy during follow-up. Three cohorts were created: 1 ) patients meeting the WISDOM trial eligibility criteria; 2 ) patients with COPD not restricted by the WISDOM trial eligibility criteria; and 3 ) patients who would have been excluded from the WISDOM trial on the basis of their comorbidities. Mixed linear regression was used to model the association between ICS withdrawal and the rate of FEV 1 decline (ml/year) adjusted for baseline characteristics. Results: A total of 6,008 patients with COPD met the WISDOM eligibility criteria, of which 9.0% withdrew from ICS. Mean rates of FEV 1 declined -7.8 ml/year (95% confidence interval [CI], -19.7 to 4.1) for withdrawers and -15.2 ml/year (95% CI, -18.7 to -11.8) for those who remained on triple therapy (difference, P  = 0.264). A total of 60,645 patients with COPD were not restricted by the WISDOM eligibility criteria. The mean rate of FEV 1 decline was -32.6 ml/year (95% CI, -33.6 to -31.5) for withdrawers and -36.4 ml/year (95% CI, -39.4 to -33.4) for those who remained on triple therapy. A total of 32,882 patients with COPD were included in the last population representing those who would have been excluded from the WISDOM trial because of their comorbidities. The mean rate of FEV 1 decline was -29.4 ml/year (95% CI, -30 to -28.1) in withdrawers and -31.3 ml/year (95% CI, -35 to -27.5) in those who remained on triple therapy. Conclusions: The rate of FEV 1 decline was similar between patients on triple therapy and patients who withdrew from ICS regardless of the specific COPD population studied. In routine clinical practice, few patients with COPD meet WISDOM eligibility criteria, and few patients are withdrawn from ICS.

Background: Electronic healthcare records (EHR) are increasingly used in epidemiological studies but are often viewed as lacking quality compared to randomised control trials and prospective cohorts. Studies of patients with chronic obstructive pulmonary disease (COPD) often use the rate of forced expiratory volume in 1 second (FEV 1 ) decline as an outcome; however, its definition and robustness in EHR have not been investigated. We aimed to investigate how the rate of FEV 1 decline differs by the criteria used in an EHR database., Methods: Clinical Practice Research Datalink and Hospital Episode Statistics were used. Patient populations were defined using 8 sets of criteria around repeated FEV 1 measurements. At a minimum, patients had a diagnosis of COPD, were ≥35 years old, were current or ex-smokers, and had data recorded from 2004. FEV 1 measurements recorded during follow-up were identified. Thereafter, eight populations were defined based on criteria around: i) the exclusion of patients or individual measurements with potential measurement error; ii) minimum number of FEV 1 measurements; iii) minimum time interval between measurements; iv) specific timing of measurements; v) minimum follow-up time; and vi) the use of linked data. For each population, the rate of FEV 1 decline was estimated using mixed linear regression., Results: For 7/8 patient populations, rates of FEV 1 decline (age and sex adjusted) were similar and ranged from -18.7mL/year (95% CI -19.2 to -18.2) to -16.5mL/year (95% CI -17.3 to -15.7). Rates of FEV 1 decline in populations that excluded patients with potential measurement error ranged from -79.4mL/year (95% CI -80.7 to -78.2) to -46.8mL/year (95% CI -47.6 to -46.0)., Conclusion: FEV 1 decline remained similar in a COPD population regardless of number of FEV 1 measurements, time intervals between measurements, follow-up period, exclusion of specific FEV 1 measurements, and linkage to HES. However, exclusion of individuals with questionable data led to selection bias and faster rates of decline., Competing Interests: HW reports grants from GSK, AZ, and BI, outside the submitted work; SK reports grants from Medical Research Council, during the conduct of the study; personal fees from Roche Diagnostics, personal fees from DIADEM, personal fees from AstraZeneca, outside the submitted work; JQ reports grants from British Lung Foundation and personal fees from AZ, Asthma UK, BI, Bayer, GSK, MRC, and Chiesi, outside the submitted work. The authors report no other conflicts of interest in this work., (© 2021 Whittaker et al.)

Accelerated lung function decline has been associated with increased risk of cardiovascular disease (CVD) in a general population, but little is known about this association in chronic obstructive pulmonary disease (COPD). We investigated the association between accelerated lung function decline and CVD outcomes and mortality in a primary care COPD population.COPD patients without a history of CVD were identified in the Clinical Practice Research Datalink (CPRD)-GOLD primary care dataset (n=36 382). Accelerated decline in forced expiratory volume in 1 s (FEV 1 ) was defined using the fastest quartile of the COPD population's decline. A Cox regression was used to assess the association between baseline accelerated FEV 1 decline and a composite CVD outcome over follow-up (myocardial infarction, ischaemic stroke, heart failure, atrial fibrillation, coronary artery disease and CVD mortality). The model was adjusted for age, sex, smoking status, body mass index, history of asthma, hypertension, diabetes, statin use, Modified Medical Research Council (mMRC) dyspnoea score, exacerbation frequency and baseline FEV 1 % predicted.6110 COPD patients (16.8%) had a CVD event during follow-up; median length of follow-up was 3.6 years (interquartile range (IQR) 1.7-6.1 years). Median rate of FEV 1 decline was -19.4 mL·year -1 (IQR -40.5-1.9); 9095 patients (25%) had accelerated FEV 1 decline (> -40.5 mL·year -1 ), 27 287 (75%) did not (≤ -40.5 mL·year -1 ). Risk of CVD and mortality was similar between patients with and without accelerated FEV 1 decline (HR adj 0.98, 95% CI 0.90-1.06). Corresponding risk estimates were 0.99 (95% CI 0.83-1.20) for heart failure, 0.89 (95% CI 0.70-1.12) for myocardial infarction, 1.01 (95% CI 0.82-1.23) for stroke, 0.97 (95% CI 0.81-1.15) for atrial fibrillation, 1.02 (95% CI 0.87-1.19) for coronary artery disease and 0.94 (95% CI 0.71-1.25) for CVD mortality. Rather, risk of CVD was associated with a mMRC score ≤2 and two or more exacerbations in the year prior.CVD outcomes and mortality were associated with exacerbation frequency and severity and increased mMRC dyspnoea score but not with accelerated FEV 1 decline., Competing Interests: Conflict of interest: H.R. Whittaker reports grants from GSK, AstraZeneca and BI, outside the submitted work. Conflict of interest: C. Bloom reports funding from AstraZeneca, Chiesi and Asthma UK, outside the submitted work. Conflict of interest: A. Morgan has nothing to disclose. Conflict of interest: D. Jarvis reports grants from the European Union and Cystic Fibrosis Trust, outside the submitted work. Conflict of interest: S.J. Kiddle reports grants from the Medical Research Council (grant number MR/P021573/1), during the conduct of the study; and personal fees from Roche Diagnostics and DIADEM, outside the submitted work. Conflict of interest: J.K. Quint reports grants from the British Lung Foundation, during the conduct of the study; grants and personal fees from AstraZeneca, BI, Bayer and GSK, and grants from Asthma UK, the Medical Research Council and Chiesi, outside the submitted work., (Copyright ©ERS 2021.)

Background: Estimates for lung function decline in chronic obstructive pulmonary disease (COPD) have differed by study setting and have not been described in a UK primary care population., Purpose: To describe rates of FEV 1 and FVC decline in COPD and investigate characteristics associated with accelerated decline., Patients and Methods: Current/ex-smoking COPD patients (35 years+) who had at least 2 FEV 1 or FVC measurements ≥6 months apart were included using Clinical Practice Research Datalink. Patients were followed up for a maximum of 13 years. Accelerated rate of lung function decline was defined as the fastest quartile of decline using mixed linear regression, and association with baseline characteristics was investigated using logistic regression., Results: A total of 72,683 and 50,649 COPD patients had at least 2 FEV 1 or FVC measurements, respectively. Median rates of FEV 1 and FVC changes or decline were -18.1mL/year (IQR: -31.6 to -6.0) and -22.7mL/year (IQR: -39.9 to -6.7), respectively. Older age, high socioeconomic status, being underweight, high mMRC dyspnoea and frequent AECOPD or severe AECOPD were associated with an accelerated rate of FEV 1 and FVC decline. Current smoking, mild airflow obstruction and inhaled corticosteroid treatment were additionally associated with accelerated FEV 1 decline whilst women, sputum production and severe airflow obstruction were associated with accelerated FVC decline., Conclusion: Rate of FEV 1 and FVC decline was similar and showed similar heterogeneity. Whilst FEV 1 and FVC shared associations with baseline characteristics, a few differences highlighted the importance of both lung function measures in COPD progression. We identified important characteristics that should be monitored for disease progression., Competing Interests: HRW & JKQ received funding from GSK for this work. JKQ also reports grants from Asthma UK, personal fees from AZ, MRC, Chiesi, BI, and The Health Foundation, outside the submitted work. JMP is a full-time employee of GSK and holds shares in GSK. SJK reports personal fees from DIADEM and AstraZeneca, outside the submitted work. The authors report no other conflicts of interest in this work., (© 2020 Whittaker et al.)

Accurate prognosis information after a diagnosis of chronic obstructive pulmonary disease (COPD) would facilitate earlier and better informed decisions about the use of prevention strategies and advanced care plans. We therefore aimed to develop and validate an accurate prognosis model for incident COPD cases using only information present in general practitioner (GP) records at the point of diagnosis. Incident COPD patients between 2004-2012 over the age of 35 were studied using records from 396 general practices in England. We developed a model to predict all-cause five-year mortality at the point of COPD diagnosis, using 47,964 English patients. Our model uses age, gender, smoking status, body mass index, forced expiratory volume in 1-second (FEV1) % predicted and 16 co-morbidities (the same number as the Charlson Co-morbidity Index). The performance of our chosen model was validated in all countries of the UK (N = 48,304). Our model performed well, and performed consistently in validation data. The validation area under the curves in each country varied between 0.783-0.809 and the calibration slopes between 0.911-1.04. Our model performed better in this context than models based on the Charlson Co-morbidity Index or Cambridge Multimorbidity Score. We have developed and validated a model that outperforms general multimorbidity scores at predicting five-year mortality after COPD diagnosis. Our model includes only data routinely collected before COPD diagnosis, allowing it to be readily translated into clinical practice, and has been made available through an online risk calculator (https://skiddle.shinyapps.io/incidentcopdsurvival/)., Competing Interests: Dr. Kiddle reports grants from Medical Research Council, during the conduct of the study; personal fees from Roche Diagnostics and DIADEM, outside the submitted work. After completing this work, but before manuscript submission Dr. Kiddle became an employee of AstraZeneca. Ms. Whittaker reports grants from GlaxoSmithKline, during the conduct of the study. Dr. Seaman has nothing to disclose. Dr. Quint reports grants from MRC, grants from The Health Foundation, grants from BLF, grants and personal fees from GSK, grants and personal fees from BI, grants and personal fees from Insmed, grants and personal fees from AZ, personal fees from Chiesi, personal fees from Teva, outside the submitted work. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

The pathogenesis of autoimmune thyroid diseases (AITD) is poorly understood and the association between different immune features and the germline variants involved in AITD are yet unclear. We previously observed systemic depletion of IgG core fucosylation and antennary α1,2 fucosylation in peripheral blood mononuclear cells in AITD, correlated with anti-thyroid peroxidase antibody (TPOAb) levels. Fucose depletion is known to potentiate strong antibody-mediated NK cell activation and enhanced target antigen-expressing cell killing. In autoimmunity, this may translate to autoantibody-mediated immune cell recruitment and attack of self-antigen expressing normal tissues. Hence, we investigated the crosstalk between immune cell traits, secreted proteins, genetic variants and the glycosylation patterns of serum IgG, in a multi-omic and cross-sectional study of 622 individuals from the TwinsUK cohort, 172 of whom were diagnosed with AITD. We observed associations between two genetic variants (rs505922 and rs687621), AITD status, the secretion of Desmoglein-2 protein, and the profile of two IgG N-glycan traits in AITD, but further studies need to be performed to better understand their crosstalk in AITD. On the other side, enhanced afucosylated IgG was positively associated with activatory CD335 - CD314 + CD158b + NK cell subsets. Increased levels of the apoptosis and inflammation markers Caspase-2 and Interleukin-1α positively associated with AITD. Two genetic variants associated with AITD, rs1521 and rs3094228, were also associated with altered expression of the thyrocyte-expressed ligands known to recognize the NK cell immunoreceptors CD314 and CD158b. Our analyses reveal a combination of heightened Fc-active IgG antibodies, effector cells, cytokines and apoptotic signals in AITD, and AITD genetic variants associated with altered expression of thyrocyte-expressed ligands to NK cell immunoreceptors. Together, TPOAb responses, dysregulated immune features, germline variants associated with immunoactivity profiles, are consistent with a positive autoreactive antibody-dependent NK cell-mediated immune response likely drawn to the thyroid gland in AITD.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Rate of FEV 1 decline in COPD is heterogeneous and the extent to which inhaled corticosteroids (ICS) influence the rate of decline is unclear. The majority of previous reviews have investigated specific ICS and non-ICS inhalers and have consisted of randomised control trials (RCTs), which have specific inclusion and exclusion criteria and short follow up times. We aimed to investigate the association between change in FEV 1 and ICS-containing medications in COPD patients over longer follow up times.MEDLINE and EMBASE were searched and literature comparing change in FEV 1 in COPD patients taking ICS-containing medications with patients taking non-ICS-containing medications were identified. Titles, abstract, and full texts were screened and information extracted using the PICO checklist. Risk of bias was assessed using the Cochrane Risk of Bias tool and a descriptive synthesis of the literature was carried out due to high heterogeneity of included studies.Seventeen studies met our inclusion criteria. We found that the difference in change in FEV 1 in people using ICS and non-ICS containing medications depended on the study follow-up time. Shorter follow-up studies (1 year or less) were more likely to report an increase in FEV 1 from baseline in both patients on ICS and in patients on non-ICS-containing medications, with the majority of these studies showing a greater increase in FEV 1 in patients on ICS-containing medications. Longer follow-up studies (greater than 1 year) were more likely to report a decline in FEV 1 from baseline in patients on ICS and in patients on non-ICS containing medications but rates of FEV 1 decline were similar.Further studies are needed to better understand changes in FEV 1 when ICS-containing medications are prescribed and to determine whether ICS-containing medications influence rate of decline in FEV 1 in the long term. Results from inclusive trials and observational patient cohorts may provide information more generalisable to a population of COPD patients.

Background: Inhaled corticosteroid (ICS)-containing medications slow rate of decline of FEV 1 . Blood eosinophil (EOS) levels are associated with the degree of exacerbation reduction with ICS. Purpose: We investigated whether FEV 1 decline differs between patients with and without ICS, stratified by blood EOS level. Patients and methods: The UK Clinical Practice Research Datalink (primary care records) and Hospital Episode Statistics (hospital records) were used to identify COPD patients aged 35 years or older, who were current or ex-smokers with ≥2 FEV 1 measurements ≥6 months apart. Prevalent ICS use and the nearest EOS count to start of follow-up were identified. Patients were classified at baseline as higher stratum EOS (≥150 cell/µL) on ICS; higher stratum EOS not on ICS; lower stratum EOS (<150 cells/µL) on ICS; and lower stratum EOS not on ICS. In addition, an incident ICS cohort was used to investigate the rate of FEV 1 change by EOS and incident ICS use. Mixed-effects linear regression was used to compare rates of FEV 1 change in mL/year. Results: A total of 26,675 COPD patients met our inclusion criteria (median age 69, 46% female). The median duration of follow up was 4.2 years. The rate of FEV 1 change in prevalent ICS users was slower than non-ICS users (-12.6 mL/year vs -21.1 mL/year; P  =0.001). The rate of FEV 1 change was not significantly different when stratified by EOS level. The rate of FEV 1 change in incident ICS users increased (+4.2 mL/year) vs -21.2 mL/year loss in non-ICS users; P <0.001. In patients with high EOS, incident ICS patients showed an increase in FEV 1 (+12 mL/year) compared to non-ICS users whose FEV 1 decreased (-20.8 mL/year); P <0.001. No statistical difference was seen in low EOS patients. Incident ICS use is associated with an improvement in FEV 1 change, however, over time this association is lost. Conclusion: Regardless of blood EOS level, prevalent ICS use is associated with slower rates of FEV 1 decline in COPD., Competing Interests: Miss Whittaker reports grants from GlaxoSmithKline, during the conduct of the study; Dr Müllerova was employed by and reported personal fees from GlaxoSmithKline, during the conduct of the study; Dr Jarvis reports grants from European Union and Cystic Fibrosis Trust, outside the submitted work; she also reports grants from GlaxoSmithKline, during the conduct of the study. Dr Barnes is employed, holds shares and reports personal fees from GlaxoSmithKline, outside the submitted work; he also received grants from GlaxoSmithKline, during the conduct of the study. Dr Jones is employed by and reports personal fees from GlaxoSmithKline, during the conduct of the study; Dr Compton is employed, holds shares and reports personal fees from GlaxoSmithKline, during the conduct of the study; Dr Kiddle reports grants from MRC, during the conduct of the study; grants from Roche Diagnostics, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council, National Institute for Health Research, Alzheimer’s Society, Eli Lily, and Jannsen, outside the submitted work; Dr Kiddle was supported by a MRC Career Development Award (MR/PO21573/1). Dr Quint reports grants from GlaxoSmithKline, during the conduct of the study; grants from The Health Foundation, MRC, Wellcome Trust, British Lung Foundation, Insmed, AstraZeneca, Bayer, Boehringer Ingelheim, Chiesi, Teva, and GlaxoSmithKline, outside the submitted work. The authors report no other conflicts of interest in this work.

Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (r g  = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.

Alzheimer's disease (AD)-a complex disease showing multiple pathomechanistic alterations-is triggered by nonlinear dynamic interactions of genetic/epigenetic and environmental risk factors, which, ultimately, converge into a biologically heterogeneous disease. To tackle the burden of AD during early preclinical stages, accessible blood-based biomarkers are currently being developed. Specifically, next-generation clinical trials are expected to integrate positive and negative predictive blood-based biomarkers into study designs to evaluate, at the individual level, target druggability and potential drug resistance mechanisms. In this scenario, systems biology holds promise to accelerate validation and qualification for clinical trial contexts of use-including proof-of-mechanism, patient selection, assessment of treatment efficacy and safety rates, and prognostic evaluation. Albeit in their infancy, systems biology-based approaches are poised to identify relevant AD "signatures" through multifactorial and interindividual variability, allowing us to decipher disease pathophysiology and etiology. Hopefully, innovative biomarker-drug codevelopment strategies will be the road ahead towards effective disease-modifying drugs.
., (© 2019, AICH – Servier GroupCopyright © 2019 AICH – Servier Group. All rights reserved.)

Background: Blood biomarkers may aid in recruitment to clinical trials of Alzheimer's disease (AD) modifying therapeutics by triaging potential trials participants for amyloid positron emission tomography (PET) or cerebrospinal fluid (CSF) Aβ and tau tests. Objective: To discover a plasma proteomic signature associated with CSF and PET measures of AD pathology. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics were performed in plasma from participants with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD, recruited to the Amsterdam Dementia Cohort, stratified by CSF Tau/Aβ 42 ( n = 50). Technical replication and independent validation were performed by immunoassay in plasma from SCD, MCI, and AD participants recruited to the Amsterdam Dementia Cohort with CSF measures ( n = 100), MCI participants enrolled in the GE067-005 study with [ 18 F]-Flutemetamol PET amyloid measures ( n = 173), and AD, MCI and cognitively healthy participants from the EMIF 500 study with CSF Aβ 42 measurements ( n = 494). Results: 25 discovery proteins were nominally associated with CSF Tau/Aβ 42 ( P < 0.05) with associations of ficolin-2 (FCN2), apolipoprotein C-IV and fibrinogen β chain confirmed by immunoassay ( P < 0.05). In the GE067-005 cohort, FCN2 was nominally associated with PET amyloid ( P < 0.05) replicating the association with CSF Tau/Aβ 42 . There were nominally significant associations of complement component 3 with PET amyloid, and apolipoprotein(a), apolipoprotein A-I, ceruloplasmin, and PPY with MCI conversion to AD (all P < 0.05). In the EMIF 500 cohort FCN2 was trending toward a significant relationship with CSF Aβ 42 ( P ≈ 0.05), while both A1AT and clusterin were nominally significantly associated with CSF Aβ 42 (both P < 0.05). Conclusion: Associations of plasma proteins with multiple measures of AD pathology and progression are demonstrated. To our knowledge this is the first study to report an association of FCN2 with AD pathology. Further testing of the proteins in larger independent cohorts will be important.

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine - most notably, oncology and cardiovascular diseases - allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic-industry co-development pathway from identification and assay development to validation for clinical use.

Ever since the discovery of APOEɛ4 around 25 years ago, researchers have been excited about the potential of a blood test for Alzheimer's disease (AD). Since then researchers have looked for genetic, protein, metabolite, and/or gene expression markers of AD and related phenotypes. However, no blood test for AD is yet being used in the clinical setting. We first review the trends and challenges in AD blood biomarker research, before giving our personal recommendations to help researchers overcome these challenges. While some degree of consistency and replication has been seen across independent studies, several high-profile studies have seemingly failed to replicate. Partly due to academic incentives, there is a reluctance in the field to report predictive ability, to publish negative findings, and to independently replicate the work of others. If this can be addressed, then we will know sooner whether a blood test for AD or related phenotypes with clinical utility can be developed.

Background: The search for a biomarker of Alzheimer's disease (AD) pathology (amyloid-β (Aβ) and tau) is ongoing, with the best markers currently being measurements of Aβ and tau in cerebrospinal fluid (CSF) and via positron emission tomography (PET) scanning. These methods are relatively invasive, costly, and often have high screening failure rates. Consequently, research is aiming to elucidate blood biomarkers of Aβ and tau., Objective: This study aims to investigate a case/control polygenic risk score (PGRS) as a marker of tau and investigate blood markers of a combined Aβ and tau outcome for the first time. A sub-study also considers plasma tau as markers of Aβ and tau pathology in CSF., Methods: We used data from the EDAR*, DESCRIPA**, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts in a logistic regression analysis to investigate blood markers of Aβ and tau in CSF. In particular, we investigated the extent to which a case/control PGRS is predictive of CSF tau, CSF amyloid, and a combined amyloid and tau outcome. The predictive ability of models was compared to that of age, gender, and APOE genotype ('basic model')., Results: In EDAR and DESCRIPA test data, inclusion of a case/control PGRS was no more predictive of Aβ, and a combined Aβ and tau endpoint than the basic models (accuracies of 66.0%, and 73.3% respectively). The tau model saw a small increase in accuracy compared to basic models (59.6%). ADNI 2 test data also showed a slight increase in accuracy for the Aβ model when compared to the basic models (61.4%)., Conclusion: We see some evidence that a case/control PGRS is marginally more predictive of Aβ and tau pathology than the basic models. The search for predictive factors of Aβ and tau pathologies, above and beyond demographic information, is still ongoing. Better understanding of AD risk alleles, development of more sensitive assays, and studies of larger sample size are three avenues that may provide such factors. However, the clinical utility of possible predictors of brain Aβ and tau pathologies must also be investigated.*'Beta amyloid oligomers in the early diagnosis of AD and as marker for treatment response'**'Development of screening guidelines and criteria for pre-dementia Alzheimer's disease'.

Objective: Peripheral protein biomarkers of Alzheimer's disease (AD) may help identify novel treatment avenues by allowing early diagnosis, recruitment to clinical trials, and treatment initiation. The purpose of this review was to determine which proteins have been found to be differentially expressed in the AD brain and whether these proteins are also found within the blood of AD patients., Methods: A two-stage approach was conducted. The first stage involved conducting a systematic search to identify discovery-based brain proteomic studies of AD. The second stage involved comparing whether proteins found to be differentially expressed in AD brain were also differentially expressed in the blood., Results: Across 11 discovery based brain proteomic studies 371 proteins were at different levels in the AD brain. Nine proteins were frequently found, defined as appearing in at least three separate studies. Of these proteins heat-shock cognate 71 kDa, ubiquitin carboxyl-terminal hydrolase isozyme L1, and 2',3'-cyclic nucleotide 3' phosphodiesterase alone were found to share a consistent direction of change, being consistently upregulated in studies they appeared in. Eighteen proteins seen as being differentially expressed within the AD brain were present in blood proteomic studies of AD. Only complement C4a was seen multiple times within both the blood and brain proteomic studies., Interpretation: We report a number of proteins appearing in both the blood and brain of AD patients. Of these proteins, C4a may be a good candidate for further follow-up in large-scale replication efforts.

Increasingly, clinical trials for Alzheimer's disease (AD) are being conducted earlier in the disease phase and with biomarker confirmation using in vivo amyloid PET imaging or CSF tau and Aβ measures to quantify pathology. However, making such a pre-clinical AD diagnosis is relatively costly and the screening failure rate is likely to be high. Having a blood-based marker that would reduce such costs and accelerate clinical trials through identifying potential participants with likely pre-clinical AD would be a substantial advance. In order to seek such a candidate biomarker, discovery phase proteomic analyses using 2DGE and gel-free LC-MS/MS for high and low molecular weight analytes were conducted on longitudinal plasma samples collected over a 12-year period from non-demented older individuals who exhibited a range of 11C-PiB PET measures of amyloid load. We then sought to extend our discovery findings by investigating whether our candidate biomarkers were also associated with brain amyloid burden in disease, in an independent cohort. Seven plasma proteins, including A2M, Apo-A1, and multiple complement proteins, were identified as pre-clinical biomarkers of amyloid burden and were consistent across three time points (p <  0.05). Five of these proteins also correlated with brain amyloid measures at different stages of the disease (q <  0.1). Here we show that it is possible to detect a plasma based biomarker signature indicative of AD pathology at a stage long before the onset of clinical disease manifestation. As in previous studies, acute phase reactants and inflammatory markers dominate this signature.

In this exploratory neuroimaging-proteomic study, we aimed to identify CSF proteins associated with AD and test their prognostic ability for disease classification and MCI to AD conversion prediction. Our study sample consisted of 295 subjects with CSF multi-analyte panel data and MRI at baseline downloaded from ADNI. Firstly, we tested the statistical effects of CSF proteins (n = 83) to measures of brain atrophy, CSF biomarkers, ApoE genotype and cognitive decline. We found that several proteins (primarily CgA and FABP) were related to either brain atrophy or CSF biomarkers. In relation to ApoE genotype, a unique biochemical profile characterised by low CSF levels of Apo E was evident in ε4 carriers compared to ε3 carriers. In an exploratory analysis, 3/83 proteins (SGOT, MCP-1, IL6r) were also found to be mildly associated with cognitive decline in MCI subjects over a 4-year period. Future studies are warranted to establish the validity of these proteins as prognostic factors for cognitive decline. For disease classification, a subset of proteins (n = 24) combined with MRI measurements and CSF biomarkers achieved an accuracy of 95.1% (Sensitivity 87.7%; Specificity 94.3%; AUC 0.95) and accurately detected 94.1% of MCI subjects progressing to AD at 12 months. The subset of proteins included FABP, CgA, MMP-2, and PPP as strong predictors in the model. Our findings suggest that the marker of panel of proteins identified here may be important candidates for improving the earlier detection of AD. Further targeted proteomic and longitudinal studies would be required to validate these findings with more generalisability.

To elucidate the proteomic features of aging in plasma, the subproteome targeted by the SOMAscan assay was profiled in blood samples from 202 females from the TwinsUK cohort. Findings were replicated in 677 independent individuals from the AddNeuroMed, Alzheimer's Research UK, and Dementia Case Registry cohorts. Results were further validated using RNAseq data from whole blood in TwinsUK and the most significant proteins were tested for association with aging-related phenotypes after adjustment for age. Eleven proteins were associated with chronological age and were replicated at protein level in an independent population. These were further investigated at gene expression level in 384 females from the TwinsUK cohort. The two most strongly associated proteins were chordin-like protein 1 (meta-analysis β [SE] = 0.013 [0.001], p = 3.66 × 10(-46)) and pleiotrophin (0.012 [0.005], p = 3.88 × 10(-41)). Chordin-like protein 1 was also significantly correlated with birthweight (0.06 [0.02], p = 0.005) and with the individual Framingham 10-years cardiovascular risk scores in TwinsUK (0.71 [0.18], p = 9.9 × 10(-5)). Pleiotrophin is a secreted growth factor with a plethora of functions in multiple tissues and known to be a marker for cardiovascular risk and osteoporosis. Our study highlights the importance of proteomics to identify some molecular mechanisms involved in human health and aging., (© The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America.)

Background: Measures of neocortical amyloid burden (NAB) identify individuals who are at substantially greater risk of developing Alzheimer's disease (AD). Blood-based biomarkers predicting NAB would have great utility for the enrichment of AD clinical trials, including large-scale prevention trials., Methods: Nontargeted proteomic discovery was applied to 78 subjects from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing with a range of NAB values. Technical and independent replications were performed by immunoassay., Results: Seventeen discovery candidates were selected for technical replication. α2-Macroglobulin, fibrinogen γ-chain (FGG), and complement factor H-related protein 1 were confirmed to be associated with NAB. In an independent cohort, FGG plasma levels combined with age predicted NAB had a sensitivity of 59% and specificity of 78%., Conclusion: A single blood protein, FGG, combined with age, was shown to relate to NAB and therefore could have potential for enrichment of clinical trial populations.

Blood proteins and their complexes have become the focus of a great deal of interest in the context of their potential as biomarkers of Alzheimer's disease (AD). We used a SOMAscan assay for quantifying 1001 proteins in blood samples from 331 AD, 211 controls, and 149 mild cognitive impaired (MCI) subjects. The strongest associations of protein levels with AD outcomes were prostate-specific antigen complexed to α1-antichymotrypsin (AD diagnosis), pancreatic prohormone (AD diagnosis, left entorhinal cortex atrophy, and left hippocampus atrophy), clusterin (rate of cognitive decline), and fetuin B (left entorhinal atrophy). Multivariate analysis found that a subset of 13 proteins predicted AD with an accuracy of area under the curve of 0.70. Our replication of previous findings provides further evidence that levels of these proteins in plasma are truly associated with AD. The newly identified proteins could be potential biomarkers and are worthy of further investigation., (Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.)

Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g. high correlation of expression profiles across multiple time series datasets. However, numbers of up- or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation., Results: Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions., Availability and Implementation: A Matlab implementation of Wigwams, complete with graphical user interfaces and documentation, is available at: warwick.ac.uk/wigwams. .

A blood-based protein biomarker, or set of protein biomarkers, that could predict onset and progression of Alzheimer's disease (AD) would have great utility; potentially clinically, but also for clinical trials and especially in the selection of subjects for preventative trials. We reviewed a comprehensive list of 21 published discovery or panel-based (> 100 proteins) blood proteomics studies of AD, which had identified a total of 163 candidate biomarkers. Few putative blood-based protein biomarkers replicate in independent studies but we found that some proteins do appear in multiple studies; for example, four candidate biomarkers are found to associate with AD-related phenotypes in five independent research cohorts in these 21 studies: α-1-antitrypsin, α-2-macroglobulin, apolipoprotein E, and complement C3. Using SomaLogic's SOMAscan proteomics technology, we were able to conduct a large-scale replication study for 94 of the 163 candidate biomarkers from these 21 published studies in plasma samples from 677 subjects from the AddNeuroMed (ANM) and the Alzheimer's Research UK/Maudsley BRC Dementia Case Registry at King's Health Partners (ARUK/DCR) research cohorts. Nine of the 94 previously reported candidates were found to associate with AD-related phenotypes (False Discovery Rate (FDR) q-value < 0.1). These proteins show sufficient replication to be considered for further investigation as a biomarker set. Overall, we show that there are some signs of a replicable signal in the range of proteins identified in previous studies and we are able to further replicate some of these. This suggests that AD pathology does affect the blood proteome with some consistency.

Transcriptional reprogramming forms a major part of a plant's response to pathogen infection. Many individual components and pathways operating during plant defense have been identified, but our knowledge of how these different components interact is still rudimentary. We generated a high-resolution time series of gene expression profiles from a single Arabidopsis thaliana leaf during infection by the necrotrophic fungal pathogen Botrytis cinerea. Approximately one-third of the Arabidopsis genome is differentially expressed during the first 48 h after infection, with the majority of changes in gene expression occurring before significant lesion development. We used computational tools to obtain a detailed chronology of the defense response against B. cinerea, highlighting the times at which signaling and metabolic processes change, and identify transcription factor families operating at different times after infection. Motif enrichment and network inference predicted regulatory interactions, and testing of one such prediction identified a role for TGA3 in defense against necrotrophic pathogens. These data provide an unprecedented level of detail about transcriptional changes during a defense response and are suited to systems biology analyses to generate predictive models of the gene regulatory networks mediating the Arabidopsis response to B. cinerea.

Motivation: Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose. However, gene regulatory events may induce complex temporal features in a gene expression profile, including time delays, inversions and transient correlations, which are not well accounted for by current clustering methods. As the cost of microarray experiments continues to fall, the temporal resolution of time course studies is increasing. This has led to a need to take account of detailed temporal features of this kind. Thus, while standard clustering methods are both widely used and much studied, their shared shortcomings with respect to such temporal features motivates the work presented here., Results: Here, we introduce a temporal clustering approach for high-dimensional gene expression data which takes account of time delays, inversions and transient correlations. We do so by exploiting a recently introduced, message-passing-based algorithm called Affinity Propagation (AP). We take account of temporal features of interest following an approximate but efficient dynamic programming approach due to Qian et al. The resulting approach is demonstrably effective in its ability to discern non-obvious temporal features, yet efficient and robust enough for routine use as an exploratory tool. We show results on validated transcription factor-target pairs in yeast and on gene expression data from a study of Arabidopsis thaliana under pathogen infection. The latter reveals a number of biologically striking findings., Availability: Matlab code for our method is available at http://www.wsbc.warwick.ac.uk/stevenkiddle/tcap.html.