Biology subjects

Sudlow, C.

Publications and source records attributed to Sudlow, C..

7 recordsLinked to original sources

bioRxiv2022

Refining epigenetic prediction of chronological and biological age

Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to refine predictors and improve understanding of the epigenomic architecture of cAge and bAge. First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to improve cAge prediction, we use methylation data from 24,673 participants from the Generation Scotland (GS) study, the Lothian Birth Cohorts (LBC) of 1921 and 1936 and 8 publicly available datasets. Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection/dimensionality reduction in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross validation framework, we arrive at an improved cAge predictor (median absolute error = 2.3 years across 10 cohorts). In addition, we train a predictor of bAge on 1,214 all-cause mortality events in GS, based on epigenetic surrogates for 109 plasma proteins and the 8 component parts of GrimAge, the current best epigenetic predictor of all-cause mortality. We test this predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Womens Health Initiative study) where it outperforms GrimAge in its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 x 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 x 10-60). Finally, we introduce MethylBrowsR, an online tool to visualize epigenome-wide CpG-age associations.

genomics↗

bioRxiv2019

Analyzing the heterogeneity of rule-based EHR phenotyping algorithms in CALIBER and the UK Biobank

Electronic Health Records (EHR) are data generated during routine interactions across healthcare settings and contain rich, longitudinal information on diagnoses, symptoms, medications, investigations and tests. A primary use-case for EHR is the creation of phenotyping algorithms used to identify disease status, onset and progression or extraction of information on risk factors or biomarkers. Phenotyping however is challenging since EHR are collected for different purposes, have variable data quality and often require significant harmonization. While considerable effort goes into the phenotyping process, no consistent methodology for representing algorithms exists in the UK. Creating a national repository of curated algorithms can potentially enable algorithm dissemination and reuse by the wider community. A critical first step is the creation of a robust minimum information standard for phenotyping algorithm components (metadata, implementation logic, validation evidence) which involves identifying and reviewing the complexity and heterogeneity of current UK EHR algorithms. In this study, we analyzed all available EHR phenotyping algorithms (n=70) from two large-scale contemporary EHR resources in the UK (CALIBER and UK Biobank). We documented EHR sources, controlled clinical terminologies, evidence of algorithm validation, representation and implementation logic patterns. Understanding the heterogeneity of UK EHR algorithms and identifying common implementation patterns will facilitate the design of a minimum information standard for representing and curating algorithms nationally and internationally.

bioinformatics↗

bioRxiv2019

UK phenomics platform for developing and validating EHR phenotypes: CALIBER

ObjectiveElectronic health records are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems and collected for purposes other than medical research. We describe an approach for developing, validating and sharing reproducible phenotypes from national structured Electronic Health Records (EHR) in the UK with applications for translational research. Materials and MethodsWe implemented a rule-based phenotyping framework, with up to six approaches of validation. We applied our framework to a sample of 15 million individuals in a national EHR data source (population based primary care, all ages) linked to hospitalization and death records in England. Data comprised continuous measurements such as blood pressure, medication information and coded diagnoses, symptoms, procedures and referrals, recorded using five controlled clinical terminologies: a) Read (primary care, subset of SNOMED-CT), b) ICD-9, ICD-10 (secondary care diagnoses and cause of mortality), c) OPCS-4 (hospital surgical procedures) and d) Gemscript Drug Codes. ResultsThe open-access CALIBER Portal (https://www.caliberresearch.org/portal) demonstrates phenotyping algorithms for 50 diseases, syndromes, biomarkers and lifestyle risk factors and provides up to six validation layers. These phenotyping algorithms have been used by 40 national/international research groups in 60 peer-reviewed publications. ConclusionHerein, we describe the UK EHR phenomics approach, CALIBER, with initial evidence of validity and use, as an important step towards international use of UK EHR data for health research.

epidemiology↗

bioRxiv2018

Clinical diagnosis of TIA or stroke and prognosis in patients with neurological symptoms: a rapid access clinic cohort

BackgroundThe long-term risk of stroke or MI in patients with minor neurological symptoms who are not clinically diagnosed with transient ischaemic attack (TIA) or minor stroke is uncertain.\n\nMethodsWe used data from a rapid access clinic for patients with suspected TIA or minor stroke and follow-up from four overlapping data sources for a diagnosis of ischaemic or haemorrhagic stroke, myocardial infarction, major haemorrhage and death. We identified patients with and without a clinical diagnosis of TIA or minor stroke. We estimated hazard ratios of stroke, MI and death in early and late time periods.\n\nResults5,997 patients were seen from 2004-2013, who were diagnosed TIA or minor stroke (n=3604, 60%) or with other diagnoses (n=2392, 40%). By 5 years the proportion of patients who had a subsequent ischaemic stroke or MI, in patients with a clinical diagnosis of minor stroke or TIA was 19% [95% confidence interval (CI): 17-20%], and in patients with other diagnoses was 10% (95%CI: 8-15%). Patients with clinical diagnosis of TIA or minor stroke had three times the hazard of stroke or MI compared to patients with other diagnoses (HR 2.83 95%CI:2.13-3.76, adjusted age and sex) by 90 days post-event; however from 90 days to end of follow up, this difference was attenuated (HR 1.52, 95%CI:1.25-1.86). Older patients and those who had a history of vascular disease had a high risk of stroke or MI, whether or not they were diagnosed with minor stroke or TIA.\n\nConclusionsCareful attention to vascular risk factors in patients presenting with transient or minor neurological symptoms not thought to be due to stroke or TIA is justified, particularly those who are older or have a history of vascular disease.

epidemiology↗

bioRxiv2018

Hierarchical Complexity of the Adult Human Structural Connectome

The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology.

systems biology↗

bioRxiv2018

The Subtype Specificity of Genetic Loci Associated with Stroke in 16,664 cases and 32,792 controls

BackgroundGenome-wide association studies have identified multiple loci associated with stroke. However, the specific stroke subtypes affected, and whether loci influence both ischaemic and haemorrhagic stroke, remains unknown. For loci associated with stroke, we aimed to infer the combination of stroke subtypes likely to be affected, and in doing so assess the extent to which such loci have homogeneous effects across stroke subtypes.\n\nMethodsWe performed Bayesian multinomial regression in 16,664 stroke cases and 32,792 controls of European ancestry to determine the most likely combination of stroke subtypes affected for loci with published genome-wide stroke associations, using model selection. Cases were subtyped under two commonly used stroke classification systems, Trial of Org 10172 Acute Stroke Treatment (TOAST) and Causative Classification of Stroke (CCS). All individuals had genotypes imputed to the Haplotype Reference Consortium 1.1 Panel.\n\nResultsSixteen loci were considered for analysis. Seven loci influenced both haemorrhagic and ischaemic stroke, three of which influenced ischaemic and haemorrhagic subtypes under both TOAST and CCS. Under CCS, 4 loci influenced both small vessel stroke and intracerebral haemorrhage. An EDNRA locus demonstrated opposing effects on ischaemic and haemorrhagic stroke. No loci were predicted to influence all stroke subtypes in the same direction and only one locus (12q24) was predicted to influence all ischaemic stroke subtypes.\n\nConclusionsHeterogeneity in the influence of stroke-associated loci on stroke subtypes is pervasive, reflecting differing causal pathways. However, overlap exists between haemorrhagic and ischaemic stroke, which may reflect shared pathobiology predisposing to small vessel arteriopathy. Stroke is a complex, heterogeneous disorder requiring tailored analytic strategies to decipher genetic mechanisms.

genetics↗

bioRxiv2018

Can we use routinely collected hospital and GP data for epidemiological study of common hand conditions? A UK Biobank based validation project

ObjectiveRoutine health records can be of great value in epidemiological and genetic studies if they are able to reliably identify true disease cases, especially when linked to large cohort studies. Little research has been undertaken into whether coding within UK electronic health records (EHR) is able to accurately identify clinical disease cases of common hand conditions. There is therefore a relative paucity of hand surgical research using EHRs due to concerns that cases cannot be accurately identified.\n\nThe aim of this study was to investigate the accuracy of hospital and primary care coding of routine EHRs for carpal tunnel syndrome (CTS) and base of thumb osteoarthritis (BTOA). Self-reported disease state as recorded in UK Biobank, a large prospective cohort study was also investigated.\n\nMethodsCode lists for each condition were generated by a team of clinicians, clinical coders and epidemiologists. All patients recruited to UK Biobank in one geographical region (Lothian, Scotland) where linked primary and secondary care coded datasets available were included. A decision- making algorithm was designed to define an administratively-confirmed or a clinically confirmed disease case. Patient electronic medical records (EMRs) were independently interrogated by two clinicians and inter-observer reliability calculated.\n\nResultsOf the 17,201 Biobank participants in NHS Lothian, 268 had at least one code for CTS and 82 for BTOA. For CTS, 159 cases were confirmed, 100 cases had insufficient information and 9 cases were refuted. Excluding missing data, the positive predictive value (PPV) for true clinical disease cases was 96% for incident disease (90% for prevalent disease; overall 94%).\n\nFor BTOA, 27 cases were confirmed, 46 cases had insufficient information, and 9 cases were refuted. Excluding missing data, PPV for incident disease was 81% (prevalent disease 56%, overall PPV 75%). Interrogation of the disease cases with insufficient information noted a large proportion arising from primary care and self-report coding systems.\n\nAnalyzing code combinations revealed that secondary care codes had the highest PPV for CTS and BTOA, emphasizing a more robust evaluation of PPV for patients requiring hospital based care. Overall, inter-observer reliability was good, with agreement in 90% of cases (Cohens kappa of 0.79) for clinical disease cases in CTS and agreement of 98%, (kappa 0.96) for BTOA.\n\nConclusionsWe have demonstrated that coding within UK Biobank is of sufficient quality to enable use of the resource for epidemiological and genetic research into common hand conditions, and that EMRs can be used for manual validation of UK health coding systems. Further work is needed to consider potential regional and interdisciplinary differences in coding practice, in strategies for dealing with missing data in EHRs, and to validate coding of common hand conditions in primary care.

epidemiology↗