Quantifying the contribution of established risk factors to cardiovascular mortality differences between Russia and Norway.

Surprisingly few attempts have been made to quantify the simultaneous contribution of well-established risk factors to CVD mortality differences between countries. We aimed to develop and critically appraise an approach to doing so, applying it to the substantial CVD mortality gap between Russia and Norway using survey data in three cities and mortality risks from the Emerging Risk Factor Collaboration. We estimated the absolute and relative differences in CVD mortality at ages 40-69 years between countries attributable to the risk factors, under the counterfactual that the age- and sex-specific risk factor profile in Russia was as in Norway, and vice-versa. Under the counterfactual that Russia had the Norwegian risk factor profile, the absolute age-standardized CVD mortality gap would decline by 33.3% (95% CI 25.1-40.1) among men and 22.1% (10.4-31.3) among women. In relative terms, the mortality rate ratio (Russia/Norway) would decline from 9-10 to 7-8. Under the counterfactual that Norway had the Russian risk factor profile, the mortality gap reduced less. Well-established CVD risk factors account for a third of the male and around a quarter of the female CVD mortality gap between Russia and Norway. However, these estimates are based on widely held epidemiological assumptions that deserve further scrutiny

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

Funder: Kidney Cancer UKFunder: Yorkshire Cancer ResearchFunder: Mark Foundation For Cancer Research; Id: http://dx.doi.org/10.13039/100014599Funder: The Rosetrees TrustFunder: Urology Foundation; Id: http://dx.doi.org/10.13039/501100007568Objectives: 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. Population level data were used to estimate model performance in a screening scenario for a range of risk thresholds (6‐year risk: 0.1–1.0%). Results: In all, 10 models had reasonable discrimination (area under the receiver‐operating characteristic curve >0.60), although some had poor calibration. Modelling demonstrated similar performance of the best models over a range of thresholds. The models showed an improvement in ability to identify cases compared to age‐ and sex‐based screening. All the models performed less well in women than men. Conclusions: The present study is the first comprehensive external validation of risk models for kidney cancer. The best‐performing models are better at identifying individuals at high risk of kidney cancer than age and sex alone; however, the benefits are relatively small. Feasibility studies are required to determine applicability to a screening programme

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. RESULTS: Formal CVD risk assessment on all adults would identify 76% and 49% of future CVD events amongst men and women respectively, and 93 (95% CI: 90, 95) men and 279 (95% CI: 259, 297) women would need to be screened (NNS) to prevent one CVD event. In contrast, if eHEART was first used to prioritise individuals for formal CVD risk assessment, we would identify 73% and 47% of future events amongst men and women respectively, and a NNS of 75 (95% CI: 72, 77) men and 162 (95% CI: 150, 172) women. Replacing the age- and sex-specific prioritisation thresholds with a 10% threshold identify around 10% less events. CONCLUSIONS: The use of prioritisation tools with age- and sex-specific thresholds could lead to more efficient CVD assessment programmes with only small reductions in effectiveness at preventing new CVD events

Natriuretic peptides and integrated risk assessment for cardiovascular disease. an individual-participant-data meta-analysis

BACKGROUND: Guidelines for primary prevention of cardiovascular diseases focus on prediction of coronary heart disease and stroke. We assessed whether or not measurement of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) concentration could enable a more integrated approach than at present by predicting heart failure and enhancing coronary heart disease and stroke risk assessment. METHODS: In this individual-participant-data meta-analysis, we generated and harmonised individual-participant data from relevant prospective studies via both de-novo NT-proBNP concentration measurement of stored samples and collection of data from studies identified through a systematic search of the literature (PubMed, Scientific Citation Index Expanded, and Embase) for articles published up to Sept 4, 2014, using search terms related to natriuretic peptide family members and the primary outcomes, with no language restrictions. We calculated risk ratios and measures of risk discrimination and reclassification across predicted 10 year risk categories (ie, <5%, 5% to <7·5%, and ≥7·5%), adding assessment of NT-proBNP concentration to that of conventional risk factors (ie, age, sex, smoking status, systolic blood pressure, history of diabetes, and total and HDL cholesterol concentrations). Primary outcomes were the combination of coronary heart disease and stroke, and the combination of coronary heart disease, stroke, and heart failure. FINDINGS: We recorded 5500 coronary heart disease, 4002 stroke, and 2212 heart failure outcomes among 95 617 participants without a history of cardiovascular disease in 40 prospective studies. Risk ratios (for a comparison of the top third vs bottom third of NT-proBNP concentrations, adjusted for conventional risk factors) were 1·76 (95% CI 1·56-1·98) for the combination of coronary heart disease and stroke and 2·00 (1·77-2·26) for the combination of coronary heart disease, stroke, and heart failure. Addition of information about NT-proBNP concentration to a model containing conventional risk factors was associated with a C-index increase of 0·012 (0·010-0·014) and a net reclassification improvement of 0·027 (0·019-0·036) for the combination of coronary heart disease and stroke and a C-index increase of 0·019 (0·016-0·022) and a net reclassification improvement of 0·028 (0·019-0·038) for the combination of coronary heart disease, stroke, and heart failure. INTERPRETATION: In people without baseline cardiovascular disease, NT-proBNP concentration assessment strongly predicted first-onset heart failure and augmented coronary heart disease and stroke prediction, suggesting that NT-proBNP concentration assessment could be used to integrate heart failure into cardiovascular disease primary prevention

Association between depressive symptoms and incident cardiovascular diseases

Importance: It is uncertain whether depressive symptoms are independently associated with subsequent risk of cardiovascular diseases (CVD). 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 UK Biobank (UKB; 401,219 participants; baseline surveys, 2006-2010; latest follow-up, March 2020). Eligible participants had information about self-reported depressive symptoms and no CVD history at baseline. Exposure: Depressive symptoms were recorded using validated instruments. ERFC scores were harmonized across studies to a scale representative of the Centre for Epidemiological Studies Depression scale (CES-D; range 0-60; ≥16 indicates possible depressive disorder). UKB recorded the Patient Health Questionnaire-2 (PHQ-2; range 0-6; ≥3 indicates possible depressive disorder). Main Outcomes and Measures: Primary outcomes were incident fatal/nonfatal coronary heart disease (CHD), stroke and CVD (composite of CHD and stroke). Hazard ratios (HRs) per 1-SD higher log-CES-D or PHQ-2 adjusted for age, sex, smoking and diabetes were reported. Results: Among 162,036 participants from the ERFC, 73% were female, mean (SD) age at baseline was 63 (9) years, and 5,078 CHD and 3,932 stroke events were recorded (median follow-up, 9.5-years). Associations with CHD, stroke and CVD were log-linear. HRs (95%CI) per 1SD higher depression score for CHD, stroke and CVD respectively were 1.07 (1.03-1.11), 1.05 (1.01-1.10), and 1.06 (1.04-1.08). This reflects, 36 versus 29 CHD events, 28 versus 25 stroke events, and 63 versus 54 CVD events per 1000 individuals over 10 years in the highest versus lowest quintile of CES-D (geometric mean CES-D score, 19 versus 1). Among 401,219 participants from the UKB, 55% were female, mean baseline age was 56 (8) years, and 4607 CHD and 3253 stroke events were recorded (median follow-up, 8.1-years). HRs per 1SD higher depression score for CHD, stroke and CVD respectively were 1.11 (1.08-1.14), 1.10 (1.06-1.14) and 1.10 (1.08-1.13). This reflects, 21 versus 14 CHD events, 15 versus 10 stroke events, and 36 versus 25 CVD events per 1000 individuals over 10 years in those with PHQ2 ≥4 versus 0. The magnitude and statistical significance of the HRs were not materially changed after adjustment for additional risk factors. Conclusions and Relevance: In a pooled analysis of 563,255 participants in 22 cohorts, baseline depressive symptoms were associated with CVD incidence, including at symptom levels below the threshold indicative of a depressive disorder. However, the magnitude of associations was modest.Lisa Pennells, Stephen Kaptoge and Sarah Spackman are funded by a British Heart Foundation Programme Grant (RG/18/13/33946). Steven Bell was funded by the National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). Tom Bolton is funded by the National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). Angela Wood is supported by a BHF-Turing Cardiovascular Data Science Award and by the EC-Innovative Medicines Initiative (BigData@Heart). John Danesh holds a British Heart Foundation Professorship and a National Institute for Health Research Senior Investigator Award.* *The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care

The Bangladesh Risk of Acute Vascular Events (BRAVE) Study: objectives and design.

During recent decades, Bangladesh has experienced a rapid epidemiological transition from communicable to non-communicable diseases. Coronary heart disease (CHD), with myocardial infarction (MI) as its main manifestation, is a major cause of death in the country. However, there is limited reliable evidence about its determinants in this population. The Bangladesh Risk of Acute Vascular Events (BRAVE) study is an epidemiological bioresource established to examine environmental, genetic, lifestyle and biochemical determinants of CHD among the Bangladeshi population. By early 2015, the ongoing BRAVE study had recruited over 5000 confirmed first-ever MI cases, and over 5000 controls "frequency-matched" by age and sex. For each participant, information has been recorded on demographic factors, lifestyle, socioeconomic, clinical, and anthropometric characteristics. A 12-lead electrocardiogram has been recorded. Biological samples have been collected and stored, including extracted DNA, plasma, serum and whole blood. Additionally, for the 3000 cases and 3000 controls initially recruited, genotyping has been done using the CardioMetabochip+ and the Exome+ arrays. The mean age (standard deviation) of MI cases is 53 (10) years, with 88 % of cases being male and 46 % aged 50 years or younger. The median interval between reported onset of symptoms and hospital admission is 5 h. Initial analyses indicate that Bangladeshis are genetically distinct from major non-South Asian ethnicities, as well as distinct from other South Asian ethnicities. The BRAVE study is well-placed to serve as a powerful resource to investigate current and future hypotheses relating to environmental, biochemical and genetic causes of CHD in an important but under-studied South Asian population.The Gates Cambridge Trust has supported Dr Chowdhury. Epidemiological fieldwork in BRAVE has been supported by grants to investigators at the Cardiovascular Epidemiology Unit, University of Cambridge. The Cardiovascular Epidemiology Unit is underpinned by programme grants from the British Heart Foundation (RG/13/13/30194), the UK Medical Research Council (MR/L003120/1), and the UK National Institute of Health Research Cambridge Biomedical Research Centre. BRAVE has received support for genetic assays from the European Research Council (ERC-2010-AdG-20100317), European Commission Framework 7 (Grant Agreement number: 279233), and the Cambridge British Heart Foundation Centre for Excellence in Cardiovascular Science; We would like to acknowledge the contributions of the following individuals: Cardiology Research Group in Bangladesh Mohammad Afzalur Rahman, Mohammad Abdul Kader Akanda, M Atahar Ali, Mir Jamal Uddin, SM Siddiqur Rahman, Amal Kumar Choudhury, Md. Mamunur Rashid, Nazir Ahmed Chowdhury, Mohammad Abdullahel Baqui, Kajal Kumar Karmoker, Mohammad Golam Azam; Setting up/implementation of fieldwork in Bangladesh Abbas Bhuiya, Susmita Chowdhury, Kamrun Nahar, Neelima Das, Proshon Roy, Sumona Ferdous, Taposh Kumar Biswas, Abu Sadat Mohammad Sayed Sharif, Ranjit Shingha, Rose Jinnath Tomas, Babulal Parshei, Mabubur Rahman, Mohammad Emon Hossain, Akhirunnesa Mily, AK Mottashir Ahmed, Sati Chowdhury, Sushila Roy, Dipak Kanti Chowdhury, Swapan Kumar Roy; Epidemiological/statistical support in Cambridge Stephen Kaptoge, Simon Thompson, Angela Wood, Narinder Bansal, Anna Ramond, Clare Oliver-Williams, Marinka Steur, Linda O’Keeffe, Eleni Sofianopoulou, Setor Kunutsor, Donal Gorman, Oscar H Franco, Malcolm Legget, Pinal Patel, Marc Suhrcke, Sylvaine Bruggraber, Jonathan Powell; Data management Matthew Walker, Steve Ellis, Shawkat Jahangir, Habibur Rahman, Rifat Hasan Shammi, Shafqat Ullah, Mohammad Abdul Matin and Administration Beth Collins, Hannah Lombardi, Binder Kaur, Rachel Henry, Marilena Papanikolaou, Robert Smith, Abdul Wazed, Robert Williams, Julie Jenkins, Keith Hoddy.This is the final published version of the article. It was originally published in the European Journal of Epidemiology (Chowdhury R, et al., European Journal of Epidemiology, 2015, doi:10.1007/s10654-015-0037-2). The final version is available at http://dx.doi.org/10.1007/s10654-015-0037-

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)

Background: The 2021 ESC 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). The aim of this study was to update the DIAL-model using contemporary and representative registry data (DIAL2) and to systematically calibrate the model for use in other European countries. Methods and Results: The DIAL2 model was derived in 467,856 people with type 2 diabetes without a history of CVD from the Swedish National Diabetes Register, with a median follow-up of 7.3 years (IQR 4.0-10.6 years) and comprising 63,824 CVD (including fatal CVD, nonfatal stroke and nonfatal myocardial infarction) events and 66,048 non-CVD mortality events. The model was systematically recalibrated to Europe’s low and moderate risk region using contemporary incidence data and mean risk factor distributions. The recalibrated DIAL2 model was externally validated in 218,267 individuals with type 2 diabetes from the Scottish Care Information – Diabetes (SCID) and Clinical Practice Research Datalink (CPRD). In these individuals, 43,074 CVD events and 27,115 non-CVD fatal events were observed. The DIAL2 model discriminated well, with C-indices of 0.732 (95%CI 0.726-0.739) in CPRD and 0.700 (95%CI 0.691-0.709) in SCID. Interpretation: The recalibrated DIAL2 model provides a useful tool for the prediction of CVD-free life expectancy and lifetime CVD risk for people with type 2 diabetes without previous CVD in the European low and moderate risk regions. These long-term individualized measures of CVD risk are well suited for shared decision making in clinical practice as recommended by the 2021 CVD ESC prevention guidelines

Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (Cindex) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction

Association of Cardiometabolic Multimorbidity With Mortality.

IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy. RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity

Cardiovascular Risk Factors Associated With Venous Thromboembolism.

IMPORTANCE: It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). OBJECTIVE: To estimate the associations of major cardiovascular risk factors with VTE, ie, deep vein thrombosis and pulmonary embolism. DESIGN, SETTING, AND PARTICIPANTS: This study included individual participant data mostly from essentially population-based cohort studies from the Emerging Risk Factors Collaboration (ERFC; 731 728 participants; 75 cohorts; years of baseline surveys, February 1960 to June 2008; latest date of follow-up, December 2015) and the UK Biobank (421 537 participants; years of baseline surveys, March 2006 to September 2010; latest date of follow-up, February 2016). Participants without cardiovascular disease at baseline were included. Data were analyzed from June 2017 to September 2018. EXPOSURES: A panel of several established cardiovascular risk factors. MAIN OUTCOMES AND MEASURES: Hazard ratios (HRs) per 1-SD higher usual risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (VTE, 1041; coronary heart disease [CHD], 25 131) and incident fatal/nonfatal outcomes in UK Biobank (VTE, 2321; CHD, 3385). Hazard ratios were adjusted for age, sex, smoking status, diabetes, and body mass index (BMI). RESULTS: Of the 731 728 participants from the ERFC, 403 396 (55.1%) were female, and the mean (SD) age at the time of the survey was 51.9 (9.0) years; of the 421 537 participants from the UK Biobank, 233 699 (55.4%) were female, and the mean (SD) age at the time of the survey was 56.4 (8.1) years. Risk factors for VTE included older age (ERFC: HR per decade, 2.67; 95% CI, 2.45-2.91; UK Biobank: HR, 1.81; 95% CI, 1.71-1.92), current smoking (ERFC: HR, 1.38; 95% CI, 1.20-1.58; UK Biobank: HR, 1.23; 95% CI, 1.08-1.40), and BMI (ERFC: HR per 1-SD higher BMI, 1.43; 95% CI, 1.35-1.50; UK Biobank: HR, 1.37; 95% CI, 1.32-1.41). For these factors, there were similar HRs for pulmonary embolism and deep vein thrombosis in UK Biobank (except adiposity was more strongly associated with pulmonary embolism) and similar HRs for unprovoked vs provoked VTE. Apart from adiposity, these risk factors were less strongly associated with VTE than CHD. There were inconsistent associations of VTEs with diabetes and blood pressure across ERFC and UK Biobank, and there was limited ability to study lipid and inflammation markers. CONCLUSIONS AND RELEVANCE: Older age, smoking, and adiposity were consistently associated with higher VTE risk.This research has been conducted using the UK Biobank resource under Application Number 26865. This work was supported by underpinning grants from the UK Medical Research Council (grant G0800270), the British Heart Foundation (grant SP/09/002), the British Heart Foundation Cambridge Cardiovascular Centre of Excellence, UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council (grant 268834), the European Commission Framework Programme 7 (grant HEALTH-F2-2012-279233), and Health Data Research UK. Dr Danesh holds a British Heart Foundation Personal Chair and a National Institute for Health Research Senior Investigator Award