Classifying the unclassifiable – A Delphi study to reach consensus on the fibrotic nature of diseases

Background Traditionally, clinical research has focused on individual fibrotic diseases or fibrosis in a particular organ. However, it is possible for people to have multiple fibrotic diseases. While multi-organ fibrosis may suggest shared pathogenic mechanisms, yet there is no consensus on what constitutes a fibrotic disease and therefore fibrotic multimorbidity. Aim A Delphi study was performed to reach consensus on which diseases may be described as fibrotic. Methods Participants were asked to rate a list of diseases, sub-grouped according to eight body regions, as ‘fibrotic manifestation always present’, ‘can develop fibrotic manifestations’, ‘associated with fibrotic manifestations’ or ‘not fibrotic nor associated’. Classifications of ‘fibrotic manifestation always present’ and ‘can develop fibrotic manifestations’ were merged and termed ‘fibrotic’. Clinical consensus was defined according to the interquartile range, having met a minimum number of responses. Clinical agreement was used for classification where diseases did not meet the minimum number of responses (required for consensus measure), were only classified if there was 100% consensus on disease classification. Results After consulting experts, searching the literature and coding dictionaries, a total of 323 non-overlapping diseases which might be considered fibrotic were identified; 92 clinical specialists responded to the first round of the survey. Over three survey rounds, 240 diseases were categorized as fibrotic via clinical consensus and 25 additional diseases through clinical agreement. Conclusion Using a robust methodology, an extensive list of diseases was classified. The findings lay the foundations for studies estimating the burden of fibrotic multimorbidity, as well as investigating shared mechanisms and therapies

Using Routinely Collected Electronic Healthcare Record Data to Investigate Fibrotic Multimorbidity in England

Georgie M Massen,1 Hannah R Whittaker,1 Sarah Cook,1 Gisli Jenkins,2 Richard J Allen,3,4 Louise V Wain,3,4 Iain Stewart,2 Jennifer K Quint,1 On behalf of DEMISTIFI consortiumAndrew Thorley, Anna Duckworth, Ali-Reza Mohammadi-Nejad, Aloysious Aravinthan, Anthony Harbottle, Armando Mendez Villalon, Chris Scotton, Christopher Denton, Daniel Lea, Dorothee Auer, Ebrima Joof, Eleanor Cox, Elizabeth Eves, Elizabeth Robertson, Emma Blamont, Fasihul Khan, Georgie Massen, Gina Parcesepe, Gisli Jenkins, Gordon Moran, Guruprasad Aithal, Hilary Longhurst, Iain Stewart, Jane Paxton, Jennifer Quint, Karen Piper Hanley, Kate Frost, Leo Casmino, Lisa Chakrabarti, Louise Wain, Margot Roeth, Maria Kaisar, Martin Craig, Michael Nation, Mohammad Alireza Kisomi, Mujdat Zeybel, Neil Guha, Nicholas Selby, Nick Oliver, Nick Selby, Olivia C Leavy, Penny Gowland, Philip Quinlan, Rachel Chambers, Richard Allen, Richard Hubbard, Rob Slack, Rutger Ploeg, Sam Moss, Sara Fawaz, Scott Turner, Shauntelle Quammie, Simon Johnson, Stamatios N Sotiropoulos, Stuart Astbury, Susan Francis, Tom Giles, Valerie Quinn, Wendy Adams, Xin Chen, Zhendi Gong 1School of Public Health, Imperial College London, London, UK; 2National Heart and Lung Institute, Imperial College London, London, UK; 3Department of Population Health Sciences, University of Leicester, Leicester, UK; 4NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK; National Heart and Lung Institute, Imperial College London, London, United Kingdom; University of Exeter, Exeter, United Kingdom; Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham NG7 2UH, United Kingdom; Institute for Health Research (NIHR) Nottingham Biomedical Research Ctr, Queens Medical Ctr, Nottingham, United Kingdom; Nottingham Digestive Diseases Centre, Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK; Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK; Patient and Public Involvement and Engagement, Nottingham University Hospitals, Nottingham, United Kingdom; Digital Research Service, University of Nottingham, Nottingham, United Kingdom; Department of Clinical and Biomedical Sciences, University of Exeter,Exeter, United Kingdom; Centre for Rheumatology, Royal Free Hospital and University College London, London, UK; Digital Research Service, University of Nottingham, Nottingham, United Kingdom; Mental Health & Clinical Neurosciences,School of Medicine, University ofNottingham, Nottingham, UK; Sir Peter Mansfield Imaging Centre,School of Medicine, University ofNottingham, Nottingham, UK; NIHR Nottingham Biomedical ResearchCentre, Queen’s Medical Centre,University of Nottingham, Nottingham,UK; School of Life Sciences, University of Nottingham, Nottingham, United Kingdom, 2National Public Health Laboratories; Ministry of Health and Social Welfare, Banjul, The Gambia; Sir Peter Mansfield Imaging Centre, School of Physics & Astronomy, University of Nottingham, Nottingham, UK; NIHR Nottingham BRC, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK; Diabetes UK, UK; Diabetes UK, UK; Scleroderma and Raynaud’s UK, UK; Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, UK; Imperial College London, London, United Kingdom; Department of Population Health Sciences, University of Leicester, Leicester, UK; NIHR Leicester Biomedical Research Centre, Leicester, UK; Gisli Jenkins, Margaret Turner Warwick Centre for Fibrosing Lung Disease, National Heart and Lung Institute, Imperial College London, United Kingdom; Gordon W. Moran, NIHR Nottingham BRC, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK; Guruprasad P. Aithal, NIHR Nottingham BRC, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK; Dyskeratosis Congenita (DC) Action, UK; National Heart and Lung Institute, Imperial College London, London, UK; Dyskeratosis Congenita (DC) Action, UK; National Heart and Lung Institute, Imperial College London, London, UK; Division of Gastroenterology and Hepatology, Manchester University NHS Foundation Trust, Manchester, UK; Patient and Public Involvement and Engagement, Nottingham University Hospitals, Nottingham, United Kingdom; Sarcoidosis UK, UK; School of Veterinary Medicine and Science, Sutton Bonington Campus, University of Nottingham, Nottingham, UK; Medical Research Council Versus Arthritis Centre for Musculoskeletal Ageing Research, Nottingham, UK; Department of Population Health Sciences, University of Leicester, Leicester, UK; NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK; University of Nottingham, Nottingham, UK; Nuffield Department of Surgical Sciences, University of Oxford; Sir Peter Mansfield Imaging Center, School of Medicine, University of Nottingham, Nottingham, UK ; Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Quantified Imaging, London, UK; Kidney Research UK, UK; Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham NG7 2UH, United Kingdom; NationalInstitute for Health Research (NIHR) Nottingham Biomedical Research Ctr, Queens Medical Ctr, Nottingham, United Kingdom; NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust & University of Nottingham, Nottingham, UK; NIHR Nottingham BRC, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK; Centre for Kidney Research and Innovation, University of Nottingham, Royal Derby Hospital Campus,Derby, UK; Department of Medicine, Imperial College London, London, UK; Centre for Kidney Research and Innovation, School of Medicine, University of Nottingham; Department of Non-communicable Disease Epidemiology, The London School of Hygiene and Tropical Medicine, London, UK; Department of Health Sciences, University of Leicester,Leicester, UK; Universiy of Nottingham, Sir Peter Mansfield Imaging Centre, Nottingham, United Kingdom; The Digital Research Service, University of Nottingham, UK; Centre for Inflammation and Tissue Repair, University College London, London, UK; Department of Population Health Sciences, University of Leicester, Leicester, UK, 2NIHR Leicester Biomedical Research Centre, Leicester, UK; University of Nottingham, Nottingham, UK; Galecto, Stevenage, Hertfordshire, UK; Nuffield Department of Surgical Sciences, University of Oxford, and Biomedical Research Centre Oxford; Imperial College London, London, United Kingdom; University of Nottingham, Nottingham, UK; NIHR Nottingham BRC, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK; University of Norringham, Nottingham, United Kingdom; Centre for Respiratory Research, NIHRRespiratory Biomedical Research Centre,School of Medicine, Biodiscovery Institute,University Park, University of Nottingham,Nottingham, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK; Nottingham Digestive Diseases Centre, Translational Medical Sciences, School of Medicine, University of Nottingham, Nottingham, UK; NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK; Sir Peter Mansfield Imaging Centre, School of Physics, University of Nottingham, UK; The Digital Research Service & The Advanced Data Analysis Centre, University of Nottingham, UK; Margaret Turner Warwick Centre for Fibrosing Lung Disease, National Heart and Lung Institute, Imperial College London, United Kingdom; Action for Pulmonary Fibrosis, United Kingdom; School of Computer Science, University of Nottingham, UK; School of Computer Science, University of Nottingham, UKCorrespondence: Georgie M Massen, Email [email protected]: Electronic healthcare records (EHRs) are used to document diagnoses, symptoms, tests, and prescriptions. Though not primarily collected for research purposes, owing to the size of the data as well as the depth of information collected, they have been used extensively to conduct epidemiological research. The Clinical Practice Research Datalink (CPRD) is an EHR database containing representative data of the UK population with regard to age, sex, race, and social deprivation measures. Fibrotic conditions are characterised by excessive scarring, contributing towards organ dysfunction and eventual organ failure. Fibrosis is associated with ageing as well as many other factors, it is hypothesised that fibrotic conditions are caused by the same underlying pathological mechanism. We calculated the prevalence of fibrotic conditions (as defined in a previous Delphi survey of clinicians) as well as the prevalence of fibrotic multimorbidity (the proportion of people with multiple fibrotic conditions).Methods: We included a random sample of 993,370 UK adults, alive, and enrolled at a UK general practice, providing data to the CPRD Aurum database as of 1st of January 2015. Individuals had to be eligible for linkage to hospital episode statistics (HES) and ONS death registration. We calculated the point prevalence of fibrotic conditions and multi-morbid fibrosis on the 1st of January 2015. Using death records of those who died in 2015, we investigated the prevalence of fibrosis associated death. We explored the most commonly co-occurring fibrotic conditions and determined the settings in which diagnoses were commonly made (primary care, secondary care or after death).Results: The point prevalence of any fibrotic condition was 21.46%. In total, 6.00% of people had fibrotic multimorbidity. Of the people who died in 2015, 34.82% had a recording of a fibrotic condition listed on their death certificate.Conclusion: The key finding was that fibrotic multimorbidity affects approximately 1 in 16 people.Plain Language Summary: Fibrotic conditions are scarring conditions which impact the way an organ functions and eventually lead to organ failure. We studied routinely collected health data from GPs, hospitals, and death certificates to estimate the percentage of UK adults who had fibrotic diseases. We found that 1 in 5 people had at least one fibrotic disease, and we also found that 1 in 16 people had more than one fibrotic disease.Keywords: fibrosis, CPRD, electronic health records, multimorbidity, fibrotic multimorbidity, ON

Are your covariates under control? How normalization can re-introduce covariate effects

Many statistical tests rely on the assumption that the residuals of a model are normally distributed. Rank-based inverse normal transformation (INT) of the dependent variable is one of the most popular approaches to satisfy the normality assumption. Studies regularly adjust for covariates and then normalize the residuals. This study investigated the effect of regressing covariates against the dependent variable and then applying rank-based INT to the residuals. The correlation between the dependent variable and covariates at each stage of processing was assessed. An alternative approach was tested of applying rank-based INT to the dependent variable before regressing covariates was tested. Analyses based on both simulated and real data examples demonstrated that applying rank-based INT to the dependent variable residuals after regressing out covariates re-introduces a linear correlation between the dependent variable and covariates in almost all situations. This will increase type-1 errors and reduce power. Our proposed alternative approach, where rank-based INT was applied prior to controlling for covariate effects, gave residuals that were normally distributed and linearly uncorrelated with covariates. This approach is therefore recommended

Defining genetic risk factors for scleroderma-associated interstitial lung disease

Although several genetic associations with scleroderma (SSc) are defined, very little is known on genetic susceptibility to SSc-associated interstitial lung disease (SSc-ILD). A number of common polymorphisms have been associated with SSc-ILD, but most have not been replicated in separate populations. Four SNPs in IRF5, and one in each of STAT4, CD226 and IRAK1, selected as having been previously the most consistently associated with SSc-ILD, were genotyped in 612 SSc patients, of European descent, of whom 394 had ILD. The control population (n = 503) comprised individuals of European descent from the 1000 Genomes Project. After Bonferroni correction, two of the IRF5 SNPs, rs2004640 (OR (95% CI)1.30 (1.10–1.54), p^{corr} = 0.015) and rs10488631 (OR 1.48 (1.14–1.92), p^{corr} = 0.022), and the STAT4 SNP rs7574865 (OR 1.43 (1.18–1.73), p^{corr} = 0.0015) were significantly associated with SSc compared with controls. However, none of the SNPs were significantly different between patients with SSc-ILD and controls. Two SNPs in IRF5, rs10488631 (OR 1.72 (1.24–2.39), p^{corr} = 0.0098), and rs2004640 (OR 1.39 (1.11–1.75), p^{corr} = 0.03), showed a significant difference in allele frequency between controls and patients without ILD, as did STAT4 rs7574865 (OR 1.86 (1.45–2.38), p^{corr} = 6.6 × 10^{-6}). A significant difference between SSc with and without ILD was only observed for STAT4 rs7574865, being less frequent in patients with ILD (OR 0.66 (0.51–0.85), p^{corr} = 0.0084). In conclusion, IRF5 rs2004640 and rs10488631, and STAT4 rs7574865 were significantly associated with SSc as a whole. Only STAT4 rs7574865 showed a significant difference in allele frequency in SSc-ILD, with the T allele being protective against ILD

Telomere length and risk of idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease: a mendelian randomisation study

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease accounting for 1% of UK deaths. In the familial form of pulmonary fibrosis, causal genes have been identified in about 30% of cases, and a majority of these causal genes are associated with telomere maintenance. Prematurely shortened leukocyte telomere length is associated with IPF and chronic obstructive pulmonary disease (COPD), a disease with similar demographics and shared risk factors. Using mendelian randomisation, we investigated evidence supporting a causal role for short telomeres in IPF and COPD. METHODS: Mendelian randomisation inference of telomere length causality was done for IPF (up to 1369 cases) and COPD (13 538 cases) against 435 866 controls of European ancestry in UK Biobank. Polygenic risk scores were calculated and two-sample mendelian randomisation analyses were done using seven genetic variants previously associated with telomere length, with replication analysis in an IPF cohort (2668 cases vs 8591 controls) and COPD cohort (15 256 cases vs 47 936 controls). FINDINGS: In the UK Biobank, a genetically instrumented one-SD shorter telomere length was associated with higher odds of IPF (odds ratio [OR] 4·19, 95% CI 2·33-7·55; p=0·0031) but not COPD (1·07, 0·88-1·30; p=0·51). Similarly, an association was found in the IPF replication cohort (12·3, 5·05-30·1; p=0·0015) and not in the COPD replication cohort (1·04, 0·71-1·53; p=0·83). Meta-analysis of the two-sample mendelian randomisation results provided evidence inferring that shorter telomeres cause IPF (5·81 higher odds of IPF, 95% CI 3·56-9·50; p=2·19 × 10-12). There was no evidence to infer that telomere length caused COPD (OR 1·07, 95% CI 0·90-1·27; p=0·46). INTERPRETATION: Cellular senescence is hypothesised as a major driving force in IPF and COPD; telomere shortening might be a contributory factor in IPF, suggesting divergent mechanisms in COPD. Defining a key role for telomere shortening enables greater focus in telomere-related diagnostics, treatments, and the search for a cure in IPF. Investigation of therapies that improve telomere length is warranted. FUNDING: Medical Research Council.National Institute for Health Research (NIHR

The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers (UK-REACH): Protocol for a prospective longitudinal cohort study of healthcare and ancillary workers in UK healthcare settings

Introduction: The COVID-19 pandemic has resulted in significant morbidity and mortality, and has devastated economies in many countries. Amongst the groups identified as being at increased risk from COVID-19 are healthcare workers (HCWs) and ethnic minority groups. Emerging evidence suggests HCWs from ethnic minority groups are at increased risk of adverse COVID-19-related physical and mental health outcomes. To date there has been no large-scale analysis of these risks in UK healthcare workers or ancillary workers in healthcare settings, stratified by ethnicity or occupation type, and adjusted for potential confounders. This paper reports the protocol for a prospective longitudinal questionnaire study of UK HCWs, as part of the UK-REACH programme (The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers). Methods and analysis: A baseline questionnaire with follow-up questionnaires at 4 and 8 months will be administered to a national cohort of UK healthcare workers and ancillary workers in healthcare settings, and those registered with UK healthcare regulators. With consent, data will be linked to health records, and participants followed up for 25 years. Univariate associations between ethnicity and primary outcome measures (clinical COVID-19 outcomes, and physical and mental health) and key confounders/explanatory variables will be tested, followed by multivariable analyses to test for associations between ethnicity and key outcomes adjusted for the confounder/explanatory variables, with interactions included as appropriate. Using follow-up data, multilevel models will be used to model changes over time by ethnic group, facilitating understanding of absolute and relative risks in different ethnic groups, and generalisability of findings. Ethics and dissemination: The study is approved by Health Research Authority (reference 20/HRA/4718), and carries minimal risk to participants. We aim to manage the small risk of participant distress due to being asked questions on sensitive topics by clearly indicating on the participant information sheet that the questionnaire covers sensitive topics and that participants are under no obligation to answer these, or indeed any other, questions, and by providing links to support organisations. Results will be disseminated with reports to Government and papers uploaded to pre-print servers and submitted to peer reviewed journals. Registration details Trial ID: ISRCTN11811602 STRENGTHS AND LIMITATIONS OF THIS STUDY National, UK-wide, study, aiming to capture variety of healthcare worker job roles including ancillary workers in healthcare settings. Longitudinal study including three waves of questionnaire data collection, and linkage to administrative data over 25 years, with consent. Unique support from all major UK healthcare worker regulators, relevant healthcare worker organisations, and a Professional Expert Panel to increase participant uptake and the validity of findings. Potential for self-selection bias and low response rates, and the use of electronic invitations and online data collection makes it harder to reach ancillary workers without regular access to work email addresses

Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort

BACKGROUND: Idiopathic pulmonary fibrosis is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in forced vital capacity (FVC) is the main indicator of progression; however, missingness prevents long-term analysis of patterns in lung function. We aimed to identify distinct clusters of lung function trajectory among patients with idiopathic pulmonary fibrosis using machine learning techniques. METHODS: We did a secondary analysis of longitudinal data on FVC collected from a cohort of patients with idiopathic pulmonary fibrosis from the PROFILE study; a multicentre, prospective, observational cohort study. We evaluated the imputation performance of conventional and machine learning techniques to impute missing data and then analysed the fully imputed dataset by unsupervised clustering using self-organising maps. We compared anthropometric features, genomic associations, serum biomarkers, and clinical outcomes between clusters. We also performed a replication of the analysis on data from a cohort of patients with idiopathic pulmonary fibrosis from an independent dataset, obtained from the Chicago Consortium. FINDINGS: 415 (71%) of 581 participants recruited into the PROFILE study were eligible for further analysis. An unsupervised machine learning algorithm had the lowest imputation error among tested methods, and self-organising maps identified four distinct clusters (1-4), which was confirmed by sensitivity analysis. Cluster 1 comprised 140 (34%) participants and was associated with a disease trajectory showing a linear decline in FVC over 3 years. Cluster 2 comprised 100 (24%) participants and was associated with a trajectory showing an initial improvement in FVC before subsequently decreasing. Cluster 3 comprised 113 (27%) participants and was associated with a trajectory showing an initial decline in FVC before subsequent stabilisation. Cluster 4 comprised 62 (15%) participants and was associated with a trajectory showing stable lung function. Median survival was shortest in cluster 1 (2·87 years [IQR 2·29-3·40]) and cluster 3 (2·23 years [1·75-3·84]), followed by cluster 2 (4·74 years [3·96-5·73]), and was longest in cluster 4 (5·56 years [5·18-6·62]). Baseline FEV1 to FVC ratio and concentrations of the biomarker SP-D were significantly higher in clusters 1 and 3. Similar lung function clusters with some shared anthropometric features were identified in the replication cohort. INTERPRETATION: Using a data-driven unsupervised approach, we identified four clusters of lung function trajectory with distinct clinical and biochemical features. Enriching or stratifying longitudinal spirometric data into clusters might optimise evaluation of intervention efficacy during clinical trials and patient management. FUNDING: National Institute for Health and Care Research, Medical Research Council, and GlaxoSmithKline

Targeted sequencing of lung function loci in chronic obstructive pulmonary disease cases and controls

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide; smoking is the main risk factor for COPD, but genetic factors are also relevant contributors. Genome-wide association studies (GWAS) of the lung function measures used in the diagnosis of COPD have identified a number of loci, however association signals are often broad and collectively these loci only explain a small proportion of the heritability. In order to examine the association with COPD risk of genetic variants down to low allele frequencies, to aid fine-mapping of association signals and to explain more of the missing heritability, we undertook a targeted sequencing study in 300 COPD cases and 300 smoking controls for 26 loci previously reported to be associated with lung function. We used a pooled sequencing approach, with 12 pools of 25 individuals each, enabling high depth (30x) coverage per sample to be achieved. This pooled design maximised sample size and therefore power, but led to challenges during variant-calling since sequencing error rates and minor allele frequencies for rare variants can be very similar. For this reason we employed a rigorous quality control pipeline for variant detection which included the use of 3 independent calling algorithms. In order to avoid false positive associations we also developed tests to detect variants with potential batch effects and removed them before undertaking association testing. We tested for the effects of single variants and the combined effect of rare variants within a locus. We followed up the top signals with data available (only 67% of collapsing methods signals) in 4,249 COPD cases and 11,916 smoking controls from UK Biobank. We provide suggestive evidence for the combined effect of rare variants on COPD risk in TNXB and in sliding windows within MECOM and upstream of HHIP. These findings can lead to an improved understanding of the molecular pathways involved in the development of COPD

Corrigendum to: Cohort profile: Extended Cohort for E-health, Environment and DNA (EXCEED)

This is a correction to: International Journal of Epidemiology, Volume 48, Issue 3, June 2019, Pages 678–679j, https://doi.org/10.1093/ije/dyz07