The impact of personal, housing, and neighbourhood factors on personal wellbeing

This study assesses how subjective wellbeing is related to housing and neighbourhood characteristics, controlling for personal variables. The secondary data analysis was based on the English Housing Survey, 2017: Housing Stock Data and the English Housing Survey: Fuel Poverty Dataset, 2017, collected in the period April 2016 to March 2018 (N= 9205). Subjective wellbeing was measured with four variables-life satisfaction, the perception of things being worthwhile in life, feeling happy and feeling anxious-that were dichotomized into low and high wellbeing. Logistic regression analysis showed that personal variables are most strongly related to wellbeing but that both housing and neighbourhood variables are also significantly related to it. Finding it difficult to keep the living room warm, being in fuel poverty, and finding it difficult to meet heating costs were associated with lower wellbeing. Low area satisfaction and not feeling safe were also significantly associated with lower wellbeing. The effects of variables are not constant across all four wellbeing measures used which raises the question ‘which wellbeing’should be addressed

Methods for enhancing the reproducibility of biomedical research findings using electronic health records.

BACKGROUND: The ability of external investigators to reproduce published scientific findings is critical for the evaluation and validation of biomedical research by the wider community. However, a substantial proportion of health research using electronic health records (EHR), data collected and generated during clinical care, is potentially not reproducible mainly due to the fact that the implementation details of most data preprocessing, cleaning, phenotyping and analysis approaches are not systematically made available or shared. With the complexity, volume and variety of electronic health record data sources made available for research steadily increasing, it is critical to ensure that scientific findings from EHR data are reproducible and replicable by researchers. Reporting guidelines, such as RECORD and STROBE, have set a solid foundation by recommending a series of items for researchers to include in their research outputs. Researchers however often lack the technical tools and methodological approaches to actuate such recommendations in an efficient and sustainable manner. RESULTS: In this paper, we review and propose a series of methods and tools utilized in adjunct scientific disciplines that can be used to enhance the reproducibility of research using electronic health records and enable researchers to report analytical approaches in a transparent manner. Specifically, we discuss the adoption of scientific software engineering principles and best-practices such as test-driven development, source code revision control systems, literate programming and the standardization and re-use of common data management and analytical approaches. CONCLUSION: The adoption of such approaches will enable scientists to systematically document and share EHR analytical workflows and increase the reproducibility of biomedical research using such complex data sources

Preeclampsia and Cardiovascular Disease in a Large UK Pregnancy Cohort of Linked Electronic Health Records: A CALIBER Study.

BACKGROUND: The associations between pregnancy hypertensive disorders and common cardiovascular disorders have not been investigated at scale in a contemporaneous population. We aimed to investigate the association between preeclampsia, hypertensive disorders of pregnancy, and subsequent diagnosis of 12 different cardiovascular disorders. METHODS: We used linked electronic health records from 1997 to 2016 to recreate a UK population-based cohort of 1.3 million women, mean age at delivery 28 years, with nearly 1.9 million completed pregnancies. We used multivariable Cox models to determine the associations between hypertensive disorders of pregnancy, and preeclampsia alone (term and preterm), with 12 cardiovascular disorders in addition to chronic hypertension. We estimated the cumulative incidence of a composite end point of any cardiovascular disorder according to preeclampsia exposure. RESULTS: During the 20-year study period, 18 624 incident cardiovascular disorders were observed, 65% of which had occurred in women under 40 years. Compared to women without hypertension in pregnancy, women who had 1 or more pregnancies affected by preeclampsia had a hazard ratio of 1.9 (95% confidence interval 1.53-2.35) for any stroke, 1.67 (1.54-1.81) for cardiac atherosclerotic events, 1.82 (1.34-2.46) for peripheral events, 2.13 (1.64-2.76) for heart failure, 1.73 (1.38-2.16) for atrial fibrillation, 2.12 (1.49-2.99) for cardiovascular deaths, and 4.47 (4.32-4.62) for chronic hypertension. Differences in cumulative incidence curves, according to preeclampsia status, were apparent within 1 year of the first index pregnancy. Similar patterns of association were observed for hypertensive disorders of pregnancy, while preterm preeclampsia conferred slightly further elevated risks. CONCLUSIONS: Hypertensive disorders of pregnancy, including preeclampsia, have a similar pattern of increased risk across all 12 cardiovascular disorders and chronic hypertension, and the impact was evident soon after pregnancy. Hypertensive disorders of pregnancy should be considered as a natural screening tool for cardiovascular events, enabling cardiovascular risk prevention through national initiatives

Subsequent event risk in individuals with established coronary heart disease

Background: The Genetics of Subsequent Coronary Heart Disease (GENIUS-CHD) consortium was established to facilitate discovery and validation of genetic variants and biomarkers for risk of subsequent CHD events, in individuals with established CHD.Methods: The consortium currently includes 57 studies from 18 countries, recruiting 185 614 participants with either acute coronary syndrome, stable CHD, or a mixture of both at baseline. All studies collected biological samples and followed-up study participants prospectively for subsequent events.Results: Enrollment into the individual studies took place between 1985 to present day with a duration of follow-up ranging from 9 months to 15 years. Within each study, participants with CHD are predominantly of self-reported European descent (38%-100%), mostly male (44%-91%) with mean ages at recruitment ranging from 40 to 75 years. Initial feasibility analyses, using a federated analysis approach, yielded expected associations between age (hazard ratio, 1.15; 95% CI, 1.14-1.16) per 5-year increase, male sex (hazard ratio, 1.17; 95% CI, 1.13-1.21) and smoking (hazard ratio, 1.43; 95% CI, 1.35-1.51) with risk of subsequent CHD death or myocardial infarction and differing associations with other individual and composite cardiovascular endpoints.Conclusions: GENIUS-CHD is a global collaboration seeking to elucidate genetic and nongenetic determinants of subsequent event risk in individuals with established CHD, to improve residual risk prediction and identify novel drug targets for secondary prevention. Initial analyses demonstrate the feasibility and reliability of a federated analysis approach. The consortium now plans to initiate and test novel hypotheses as well as supporting replication and validation analyses for other investigators

Accelerated DNA methylation age plays a role in the impact of cardiovascular risk factors on the human heart

BACKGROUND: DNA methylation (DNAm) age acceleration (AgeAccel) and cardiac age by 12-lead advanced electrocardiography (A-ECG) are promising biomarkers of biological and cardiac aging, respectively. We aimed to explore the relationships between DNAm age and A-ECG heart age and to understand the extent to which DNAm AgeAccel relates to cardiovascular (CV) risk factors in a British birth cohort from 1946. RESULTS: We studied four DNAm ages (AgeHannum, AgeHorvath, PhenoAge, and GrimAge) and their corresponding AgeAccel. Outcomes were the results from two publicly available ECG-based cardiac age scores: the Bayesian A-ECG-based heart age score of Lindow et al. 2022 and the deep neural network (DNN) ECG-based heart age score of Ribeiro et al. 2020. DNAm AgeAccel was also studied relative to results from two logistic regression-based A-ECG disease scores, one for left ventricular (LV) systolic dysfunction (LVSD), and one for LV electrical remodeling (LVER). Generalized linear models were used to explore the extent to which any associations between biological cardiometabolic risk factors (body mass index, hypertension, diabetes, high cholesterol, previous cardiovascular disease [CVD], and any CV risk factor) and the ECG-based outcomes are mediated by DNAm AgeAccel. We derived the total effects, average causal mediation effects (ACMEs), average direct effects (ADEs), and the proportion mediated [PM] with their 95% confidence intervals [CIs]. 498 participants (all 60-64 years) were included, with the youngest ECG heart age being 27 and the oldest 90. When exploring the associations between cardiometabolic risk factors and Bayesian A-ECG cardiac age, AgeAccelPheno appears to be a partial mediator, as ACME was 0.23 years [0.01, 0.52] p = 0.028 (i.e., PM≈18%) for diabetes, 0.34 [0.03, 0.74] p = 0.024 (i.e., PM≈15%) for high cholesterol, and 0.34 [0.03, 0.74] p = 0.024 (PM≈15%) for any CV risk factor. Similarly, AgeAccelGrim mediates ≈30% of the relationship between diabetes or high cholesterol and the DNN ECG-based heart age. When exploring the link between cardiometabolic risk factors and the A-ECG-based LVSD and LVER scores, it appears that AgeAccelPheno or AgeAccelGrim mediate 10-40% of these associations. CONCLUSION: By the age of 60, participants with accelerated DNA methylation appear to have older, weaker, and more electrically impaired hearts. We show that the harmful effects of CV risk factors on cardiac age and health, appear to be partially mediated by DNAm AgeAccelPheno and AgeAccelGrim. This highlights the need to further investigate the potential cardioprotective effects of selective DNA methyltransferases modulators

Risk of mortality and cardiovascular events following macrolide prescription in chronic rhinosinusitis patients: a cohort study using linked primary care electronic health records

Background: Macrolide antibiotics have demonstrated important anti-inflammatory and immunomodulatory properties in chronic rhinosinusitis (CRS) patients. However, reports of increased risks of cardiovascular events have led to safety concerns. We investigated the risk of all-cause and cardiac death, and cardiovascular outcomes, associated with macrolide use. Methodology: Observational cohort (1997-2016) using linked data from the Clinical Practice Research Datalink, Hospital Episodes Statistics, and the Office for National Statistics. Patients aged 16-80 years with CRS prescribed a macrolide antibiotic or penicillin were included, comparing prescriptions for macrolide antibiotics to penicillin. Outcomes were all-cause mortality, cardiac death, myocardial infarction, stroke, diagnosis of peripheral vascular disease, and cardiac arrhythmia. Results: Analysis included 320,798 prescriptions received by 66,331 patients. There were 3,251 deaths, 815 due to cardiovascular causes, 925 incident myocardial infarctions, 859 strokes, 637 diagnoses of peripheral vascular disease, and 1,436 cardiac arrhythmias. A non-statistically significant trend towards increased risk of myocardial infarction during the first 30 days following macrolide prescription was observed (fully adjusted hazard ratio 1.60, 95% confidence interval: 0.95, 2.68, p=0.08). No statistically significant short- or long-term risks were observed for macrolide prescription. No significant risks were identified for clarithromycin in particular. Conclusions: Although not statistically significant, our best estimates suggest an increased short-term risk of myocardial infarction in patients with CRS following macrolide prescription, supporting previous observational evidence. However, confounding by indication remains a possible explanation for this apparent increased risk. We found no evidence of longer term increased risks

UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER

Objective: Electronic health records (EHRs) 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 EHR in the United Kingdom with applications for translational research. Materials and Methods: We implemented a rule-based phenotyping framework, with up to 6 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 (for example, blood pressure; medication information; coded diagnoses, symptoms, procedures, and referrals), recorded using 5 controlled clinical terminologies: (1) read (primary care, subset of SNOMED-CT [Systematized Nomenclature of Medicine Clinical Terms]), (2) International Classification of Diseases–Ninth Revision and Tenth Revision (secondary care diagnoses and cause of mortality), (3) Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures, Fourth Revision (hospital surgical procedures), and (4) DMþD prescription codes. Results: Using the CALIBER phenotyping framework, we created algorithms for 51 diseases, syndromes, biomarkers, and lifestyle risk factors and provide up to 6 validation approaches. The EHR phenotypes are curated in the open-access CALIBER Portal (https://www.caliberresearch.org/portal) and have been used by 40 national and international research groups in 60 peer-reviewed publications. Conclusions: We describe a UK EHR phenomics approach within the CALIBER EHR data platform with initial evidence of validity and use, as an important step toward international use of UK EHR data for health research

Blood-based epigenome-wide analysis of chronic low-grade inflammation across diverse population cohorts

Chronic inflammation is a hallmark of ageing and age-related disease states. The effectiveness of inflammatory proteins such as C-reactive protein (CRP) in assessing long-term inflammation is hindered by their phasic nature. DNA methylation (DNAm) signatures of CRP may act as more reliable markers of chronic inflammation. We show that inter-individual differences in DNAm capture 50% of the variance in circulating CRP (N=17,936, Generation Scotland). We develop a series of DNAm predictors of CRP using state-of-the-art algorithms. An elastic net regression-based predictor outperformed competing methods and explained 17% of phenotypic variance in the Lothian Birth Cohort 1936, doubling that of existing DNAm predictors. DNAm predictors performed comparably in three additional test cohorts, including individuals of European and South Asian ancestries and from childhood to later-life. The newly-described predictor surpassed assay-measured CRP and a genetic risk score in its associations with 28 health outcomes. Our findings forge new avenues for assessing chronic inflammation in diverse populations

A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service.

Background: To effectively prevent, detect, and treat health conditions that affect people during their lifecourse, health-care professionals and researchers need to know which sections of the population are susceptible to which health conditions and at which ages. Hence, we aimed to map the course of human health by identifying the 50 most common health conditions in each decade of life and estimating the median age at first diagnosis. Methods: We developed phenotyping algorithms and codelists for physical and mental health conditions that involve intensive use of health-care resources. Individuals older than 1 year were included in the study if their primary-care and hospital-admission records met research standards set by the Clinical Practice Research Datalink and they had been registered in a general practice in England contributing up-to-standard data for at least 1 year during the study period. We used linked records of individuals from the CALIBER platform to calculate the sex-standardised cumulative incidence for these conditions by 10-year age groups between April 1, 2010, and March 31, 2015. We also derived the median age at diagnosis and prevalence estimates stratified by age, sex, and ethnicity (black, white, south Asian) over the study period from the primary-care and secondary-care records of patients. Findings: We developed case definitions for 308 disease phenotypes. We used records of 2 784 138 patients for the calculation of cumulative incidence and of 3 872 451 patients for the calculation of period prevalence and median age at diagnosis of these conditions. Conditions that first gained prominence at key stages of life were: atopic conditions and infections that led to hospital admission in children (<10 years); acne and menstrual disorders in the teenage years (10-19 years); mental health conditions, obesity, and migraine in individuals aged 20-29 years; soft-tissue disorders and gastro-oesophageal reflux disease in individuals aged 30-39 years; dyslipidaemia, hypertension, and erectile dysfunction in individuals aged 40-59 years; cancer, osteoarthritis, benign prostatic hyperplasia, cataract, diverticular disease, type 2 diabetes, and deafness in individuals aged 60-79 years; and atrial fibrillation, dementia, acute and chronic kidney disease, heart failure, ischaemic heart disease, anaemia, and osteoporosis in individuals aged 80 years or older. Black or south-Asian individuals were diagnosed earlier than white individuals for 258 (84%) of the 308 conditions. Bone fractures and atopic conditions were recorded earlier in male individuals, whereas female individuals were diagnosed at younger ages with nutritional anaemias, tubulointerstitial nephritis, and urinary disorders. Interpretation: We have produced the first chronological map of human health with cumulative-incidence and period-prevalence estimates for multiple morbidities in parallel from birth to advanced age. This can guide clinicians, policy makers, and researchers on how to formulate differential diagnoses, allocate resources, and target research priorities on the basis of the knowledge of who gets which diseases when. We have published our phenotyping algorithms on the CALIBER open-access Portal which will facilitate future research by providing a curated list of reusable case definitions. Funding: Wellcome Trust, National Institute for Health Research, Medical Research Council, Arthritis Research UK, British Heart Foundation, Cancer Research UK, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Department of Health and Social Care (England), Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), Economic and Social Research Council, Engineering and Physical Sciences Research Council, National Institute for Social Care and Health Research, and The Alan Turing Institute

Data-driven identification of ageing-related diseases from electronic health records.

Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49.7 years (s.d. 18.6), 51% female, 70% white ethnicity). We grouped 278 high-burden diseases into nine main clusters according to their patterns of disease onset, using a hierarchical agglomerative clustering algorithm. Four of these clusters, encompassing 207 diseases spanning diverse organ systems and clinical specialties, had rates of disease onset that clearly increased with chronological age. However, the ages of onset for these four clusters were strikingly different, with median age of onset 82 years (IQR 82-83) for Cluster 1, 77 years (IQR 75-77) for Cluster 2, 69 years (IQR 66-71) for Cluster 3 and 57 years (IQR 54-59) for Cluster 4. Fitting to ageing-related actuarial models confirmed that the vast majority of these 207 diseases had a high probability of being ageing-related. Cardiovascular diseases and cancers were highly represented, while benign neoplastic, skin and psychiatric conditions were largely absent from the four ageing-related clusters. Our framework identifies and clusters ARDs and can form the basis for fundamental and translational research into ageing pathways