Conventional and regionally distinctive risk factors for first-onset myocardial infarction: the Bangladesh Risk of Acute Vascular Events (BRAVE) case-control study.

Background: South Asians may be particularly susceptible to premature myocardial infarction (MI) owing both to conventional cardiovascular risk factors and practices distinctive to South Asia. Identifying modifiable risk factors for MI in these populations could inform prevention strategies. We have, therefore, studied conventional risk factors and other characteristics in relation to occurrence of first MI in Bangladesh.

Evaluating the cost-effectiveness of polygenic risk score-stratified screening for abdominal aortic aneurysm.

As the heritability of abdominal aortic aneurysm (AAA) is high and AAA partially shares genetic architecture with other cardiovascular diseases, genetic information could help inform AAA screening strategies. Exploiting pleiotropy and meta-analysing summary data from large studies, we construct a polygenic risk score (PRS) for AAA. Leveraging related traits improves PRS performance (R) by 22.

Integrated clinical risk prediction of type 2 diabetes with a multifactorial polygenic risk score.

Combining information from multiple GWASs for a disease and its risk factors has proven a powerful approach for development of polygenic risk scores (PRSs). This may be particularly useful for type 2 diabetes (T2D), a highly polygenic and heterogeneous disease where the additional predictive value of a PRS is unclear. Here, we use a meta-scoring approach to develop a metaPRS for T2D that incorporated genome-wide associations from both European and non-European genetic ancestries and T2D risk factors.

Age and sex specific thresholds for risk stratification of cardiovascular disease and clinical decision making: prospective open cohort study.

Objective: To quantify the potential advantages of using 10 year risk prediction models for cardiovascular disease, in combination with risk thresholds specific to both age and sex, to identify individuals at high risk of cardiovascular disease for allocation of statin treatment.

Design: Prospective open cohort study.

Setting: Primary care data from the UK Clinical Practice Research Datalink GOLD, linked with hospital admissions from Hospital Episode Statistics and national mortality records from the Office for National Statistics in England, 1 January 2006 to 31 May 2019.

Risk estimation for the primary prevention of cardiovascular disease: considerations for appropriate risk prediction model selection.

Cardiovascular diseases remain the number one cause of death globally. Cardiovascular disease risk scores are an integral tool in primary prevention, being used to identify individuals at the highest risk and guide the assignment of preventive interventions. Available risk scores differ substantially in terms of the population sample data sources used for their derivation and, consequently, in the absolute risks they assign to individuals.

Prediction of individual lifetime cardiovascular risk and potential treatment benefit: development and recalibration of the LIFE-CVD2 model to four European risk regions.

Aims: The 2021 European Society of Cardiology prevention guidelines recommend the use of (lifetime) risk prediction models to aid decisions regarding initiation of prevention. We aimed to update and systematically recalibrate the LIFEtime-perspective CardioVascular Disease (LIFE-CVD) model to four European risk regions for the estimation of lifetime CVD risk for apparently healthy individuals.

Methods And Results: The updated LIFE-CVD (i.

Prioritising cardiovascular disease risk assessment to high risk individuals based on primary care records.

Objective: To provide quantitative evidence for systematically prioritising individuals for full formal cardiovascular disease (CVD) risk assessment using primary care records with a novel tool (eHEART) with age- and sex- specific risk thresholds.

Methods And Analysis: eHEART was derived using landmark Cox models for incident CVD with repeated measures of conventional CVD risk predictors in 1,642,498 individuals from the Clinical Practice Research Datalink. Using 119,137 individuals from UK Biobank, we modelled the implications of initiating guideline-recommended statin therapy using eHEART with age- and sex-specific prioritisation thresholds corresponding to 5% false negative rates to prioritise adults aged 40-69 years in a population in England for invitation to a formal CVD risk assessment.

Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment.

Background The aim of this study was to provide quantitative evidence of the use of polygenic risk scores for systematically identifying individuals for invitation for full formal cardiovascular disease (CVD) risk assessment. Methods and Results A total of 108 685 participants aged 40 to 69 years, with measured biomarkers, linked primary care records, and genetic data in UK Biobank were used for model derivation and population health modeling. Prioritization tools using age, polygenic risk scores for coronary artery disease and stroke, and conventional risk factors for CVD available within longitudinal primary care records were derived using sex-specific Cox models.

The relevance of competing risk adjustment in cardiovascular risk prediction models for clinical practice.

Background: Many models developed for predicting the risk of cardiovascular disease (CVD) are adjusted for the competing risk of non-CVD mortality, which has been suggested to reduce potential overestimation of cumulative incidence in populations where the risk of competing events is high. The objective was to evaluate and illustrate the clinical impact of competing risk adjustment when deriving a CVD prediction model in a high-risk population.

Methods And Results: Individuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort-Secondary Manifestations of ARTerial disease (UCC-SMART).

Improving 10-year cardiovascular risk prediction in apparently healthy people: flexible addition of risk modifiers on top of SCORE2.

Aims: In clinical practice, factors associated with cardiovascular disease (CVD) like albuminuria, education level, or coronary artery calcium (CAC) are often known, but not incorporated in cardiovascular risk prediction models. The aims of the current study were to evaluate a methodology for the flexible addition of risk modifying characteristics on top of SCORE2 and to quantify the added value of several clinically relevant risk modifying characteristics.

Methods And Results: Individuals without previous CVD or DM were included from the UK Biobank; Atherosclerosis Risk in Communities (ARIC); Multi-Ethnic Study of Atherosclerosis (MESA); European Prospective Investigation into Cancer, The Netherlands (EPIC-NL); and Heinz Nixdorf Recall (HNR) studies (n = 409 757) in whom 16 166 CVD events and 19 149 non-cardiovascular deaths were observed over exactly 10.

Estimating the Effectiveness of Kidney Cancer Screening Within Lung Cancer Screening Programmes: A Validation in UK Biobank.

In the absence of population-based screening, addition of screening for kidney cancer to lung cancer screening could provide an efficient and low-resource means to improve early detection. In this study, we used the UK Biobank cohort (n = 442 865) to determine the performance of the Yorkshire Lung Cancer Screening Trial (YLST) eligibility criteria for selecting individuals for kidney cancer screening. We measured the performance of two models widely used to determine eligibility for lung cancer screening (PLCO[m2012] and the Liverpool-Lung-Project-v2) and the performance of the combined YLST criteria.

Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants.

Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy in approximately 121,000 UK Biobank participants has recently been released as a community resource, comprising absolute concentrations and ratios of 249 circulating metabolites, lipids, and lipoprotein sub-fractions. Here we identify and characterise additional sources of unwanted technical variation influencing individual biomarkers in the data available to download from UK Biobank. These included sample preparation time, shipping plate well, spectrometer batch effects, drift over time within spectrometer, and outlier shipping plates.

Mild-to-Moderate Kidney Dysfunction and Cardiovascular Disease: Observational and Mendelian Randomization Analyses.

Background: End-stage renal disease is associated with a high risk of cardiovascular events. It is unknown, however, whether mild-to-moderate kidney dysfunction is causally related to coronary heart disease (CHD) and stroke.

Methods: Observational analyses were conducted using individual-level data from 4 population data sources (Emerging Risk Factors Collaboration, EPIC-CVD [European Prospective Investigation into Cancer and Nutrition-Cardiovascular Disease Study], Million Veteran Program, and UK Biobank), comprising 648 135 participants with no history of cardiovascular disease or diabetes at baseline, yielding 42 858 and 15 693 incident CHD and stroke events, respectively, during 6.

Estimating individual lifetime risk of incident cardiovascular events in adults with Type 2 diabetes: an update and geographical calibration of the DIAbetes Lifetime perspective model (DIAL2).

Aims: The 2021 European Society of Cardiology cardiovascular disease (CVD) prevention guidelines recommend the use of (lifetime) risk prediction models to aid decisions regarding intensified preventive treatment options in adults with Type 2 diabetes, e.g. the DIAbetes Lifetime perspective model (DIAL model).

Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP.

Aims: The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach.

Methods: In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP.

Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records.

Background: Cardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes.

Methods: We used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004-2017.

Validation and public health modelling of risk prediction models for kidney cancer using the UK Biobank.

Objectives: To externally validate risk models for the detection of kidney cancer, as early detection of kidney cancer improves survival and stratifying the population using risk models could enable an individually tailored screening programme.

Methods: We validated the performance of 30 existing phenotypic models predicting the risk of kidney cancer in the UK Biobank cohort (n = 450 687). We compared the discrimination and calibration of models for men, women, and a mixed-sex cohort.

Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment.

Cardiovascular disease (CVD) risk-prediction models are used to identify high-risk individuals and guide statin initiation. However, these models are usually derived from individuals who might initiate statins during follow-up. We present a simple approach to address statin initiation to predict "statin-naive" CVD risk.

Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses.

Background: Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD.

Methods And Findings: Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.

Association Between Depressive Symptoms and Incident Cardiovascular Diseases.

Importance: It is uncertain whether depressive symptoms are independently associated with subsequent risk of cardiovascular diseases (CVDs).

Objective: To characterize the association between depressive symptoms and CVD incidence across the spectrum of lower mood.

Design, Setting, And Participants: A pooled analysis of individual-participant data from the Emerging Risk Factors Collaboration (ERFC; 162 036 participants; 21 cohorts; baseline surveys, 1960-2008; latest follow-up, March 2020) and the UK Biobank (401 219 participants; baseline surveys, 2006-2010; latest follow-up, March 2020).