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Aims The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe.Methods and results We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low- risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries.Conclusion SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe. Acknowledgements We thank investigators and participants of the several studies that contributed data to the Emerging Risk Factors Collaboration (ERFC). This research has been conducted using the UK Biobank Resource under Application Number 26865. Data from the Clinical Practice Research Datalink (CPRD) were obtained under licence from the UK Medicines and Healthcare products Regulatory Agency (protocol 162RMn2). CPRD uses data provided by patients and collected by the NHS as part of their care and support. We thank all EPIC participants and staff for their contribution to the study, the laboratory teams at the Medical Research Council Epidemiology Unit for sample management and Cambridge Genomic Services for genotyping, Sarah Spackman for data management and the team at the EPIC-CVD Coordinating Centre for study co-ordination and administration. Funding The ERFC co-ordinating centre was underpinned by programme grants from the British Heart Foundation (SP/09/002; RG/13/13/30194; RG/18/13/33946), BHF Centre of Research Excellence (RE/18/1/34212), the UK Medical Research Council (MR/L003120/1), and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC1215-20014), with project-specific support received from the UK NIHR [*], British United Provident Association UK Foundation and an unrestricted educational grant from GlaxoSmithKline. A variety of funding sources have supported recruitment, follow-up, and laboratory measurements in the studies contributing data to the ERFC, which are listed on the ERFC website (www.phpc.cam.ac.uk/ceu/erfc/list-of-studies). *The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome. The MORGAM Project has received funding from EU projects MORGAM (Biomed BMH4-CT98-3183), GenomEUtwin (FP5, QLG2-CT-2002-01254), ENGAGE (FP7, HEALTH-F4-2007-201413),CHANCES (FP7, HEALTH-F3-2010-242244), BiomarCaRE (FP7,HEALTH-F2-2011-278913), euCanSHare (Horizon 2020, No. 825903) and AFFECT-EU (Horizon 2020, No. 847770); and Medical Research Council, London (G0601463, No. 80983: Biomarkers in the MORGAM Populations). This has supported central coordination, workshops and part of the activities of the MORGAM Data Centre, the MORGAM Laboratories and the MORGAM Participating Centres EPIC-CVD was funded by the European Research Council (268834), and the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). This work was supported by the Estonian Research Council grant PUTs (PRG687, PUT1660, PUT1665, PRG184), by European Union through the European Regional Development Fund project no. MOBERA5 (Norface Network project no 462.16.107), by the Green ICT programme under Norway Grants 2014 – 2021 (grant number EU53928), by the European Union through Horizon 2020 grant no. 810645 and through the European Regional Development Fund (Project No. 2014-2020.4.01.16-0125) and by the PRECISE4Q consortium. PRECISE4Q project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement 777107. This work was partly funded through the CoMorMent project. CoMorMent has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement 847776. The KORA study was initiated and financed by the Helmholtz Zentrum Mu¨nchen—German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. The KORA study was supported by a research grant from the Virtual Institute of Diabetes Research (Helmholtz Zentrum Mu¨nchen), the Clinical Cooperation Group Diabetes between Ludwig-Maximilians-Universita¨t Mu¨nchen and Helmholtz Zentrum Mu¨nchen, and by the German Diabetes Center (DDZ). The HAPIEE project, Institute, was supported by grants from the Wellcome Trust (064947/Z/01/Z; WT081081) and US National Institute on Aging (1R01 and AG23522). The co-ordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Ge´ne´rale de l’Education Nationale, Institut National de la Sante´ et de la Recherche Me´dicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch 2448 SCORE2 working group and ESC Cardiovascular Risk Collaboration Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS)—Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucı´a, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology—ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Ska˚ne and Va¨sterbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford) (United Kingdom)

Erratum to: https://doi.org/10.1016/j.jalz.2019.01.002

Background: Long working hours might increase the risk of cardiovascular disease, but prospective evidence is scarce, imprecise, and mostly limited to coronary heart disease. We aimed to assess long working hours as a risk factor for incident coronary heart disease and stroke. Methods: We identified published studies through a systematic review of PubMed and Embase from inception to Aug 20, 2014. We obtained unpublished data for 20 cohort studies from the Individual-Participant-Data Meta-analysis in Working Populations (IPD-Work) Consortium and open-access data archives. We used cumulative random-effects meta-analysis to combine effect estimates from published and unpublished data. Findings: We included 25 studies from 24 cohorts in Europe, the USA, and Australia. The meta-analysis of coronary heart disease comprised data for 603 838 men and women who were free from coronary heart disease at baseline; the meta-analysis of stroke comprised data for 528 908 men and women who were free from stroke at baseline. Follow-up for coronary heart disease was 5·1 million person-years (mean 8·5 years), in which 4768 events were recorded, and for stroke was 3·8 million person-years (mean 7·2 years), in which 1722 events were recorded. In cumulative meta-analysis adjusted for age, sex, and socioeconomic status, compared with standard hours (35-40 h per week), working long hours (≥55 h per week) was associated with an increase in risk of incident coronary heart disease (relative risk [RR] 1·13, 95% CI 1·02-1·26; p=0·02) and incident stroke (1·33, 1·11-1·61; p=0·002). The excess risk of stroke remained unchanged in analyses that addressed reverse causation, multivariable adjustments for other risk factors, and different methods of stroke ascertainment (range of RR estimates 1·30-1·42). We recorded a dose-response association for stroke, with RR estimates of 1·10 (95% CI 0·94-1·28; p=0·24) for 41-48 working hours, 1·27 (1·03-1·56; p=0·03) for 49-54 working hours, and 1·33 (1·11-1·61; p=0·002) for 55 working hours or more per week compared with standard working hours (ptrend

Background Body-mass index (BMI) and diabetes have increased worldwide, whereas global average blood pressure and cholesterol have decreased or remained unchanged in the past three decades. We quantified how much of the effects of BMI on coronary heart disease and stroke are mediated through blood pressure, cholesterol, and glucose, and how much is independent of these factors. Methods We pooled data from 97 prospective cohort studies that collectively enrolled 1·8 million participants between 1948 and 2005, and that included 57 161 coronary heart disease and 31 093 stroke events. For each cohort we excluded participants who were younger than 18 years, had a BMI of lower than 20 kg/m2, or who had a history of coronary heart disease or stroke. We estimated the hazard ratio (HR) of BMI on coronary heart disease and stroke with and without adjustment for all possible combinations of blood pressure, cholesterol, and glucose. We pooled HRs with a random-effects model and calculated the attenuation of excess risk after adjustment for mediators. Findings The HR for each 5 kg/m2 higher BMI was 1·27 (95% CI 1·23-1·31) for coronary heart disease and 1·18 (1·14-1·22) for stroke after adjustment for confounders. Additional adjustment for the three metabolic risk factors reduced the HRs to 1·15 (1·12-1·18) for coronary heart disease and 1·04 (1·01-1·08) for stroke, suggesting that 46% (95% CI 42-50) of the excess risk of BMI for coronary heart disease and 76% (65-91) for stroke is mediated by these factors. Blood pressure was the most important mediator, accounting for 31% (28-35) of the excess risk for coronary heart disease and 65% (56-75) for stroke. The percentage excess risks mediated by these three mediators did not differ significantly between Asian and western cohorts (North America, western Europe, Australia, and New Zealand). Both overweight (BMI ≥25 to

Objective: The association between obesity and coronary heart disease (CHD) may have changed over time, for example due to improved pharmacological treatment of CHD risk factors. This meta-analysis of 31 prospective cohort studies explores the influence of calendar period on CHD risk associated with body mass index (BMI). Design and Methods: The relative risks (RRs) of CHD for a five-BMI-unit increment and BMI categories were pooled by means of random effects models. Meta-regression analysis was used to examine the influence of calendar period (>1985 v =1985) in univariate and multivariate analyses (including mean population age as a covariate). Results: The age, sex, and smoking adjusted RR (95% confidence intervals) of CHD for a five-BMI-unit increment was 1.28(1.22:1.34). For underweight, overweight and obesity, the RRs (compared to normal weight) were 1.11(0.91:1.36), 1.31(1.22:1.41), and 1.78(1.55:2.04), respectively. The univariate analysis indicated 31% (95%CI: -56:0) lower RR of CHD associated with a five-BMI-unit increment and a 51% (95%CI: -78: -14)) lower RR associated with obesity in studies starting after 1985 (n = 15 and 10, respectively) compared to studies starting in or before 1985 (n = 16 and 10). However, in the multivariate analysis, only mean population age was independently associated with the RRs for a five-BMI-unit increment and obesity (-29(95%CI: -55: -5)) and -31(95%CI: -66:3), respectively) per 10-year increment in mean age). Conclusion: This study provides no consistent evidence for a difference in the association between BMI and CHD by calendar period. The mean population age seems to be the most important factor that modifies the association between the risk of CHD and BMI, in which the RR decreases with increasing age.

Background: The extent to which moderate overweight (body mass index [BMI], 25.0-29.9 [calculated as weight in kilograms divided by height in meters squared]) and obesity (BMI,≥30.0) are associated with increased risk of coronary heart disease (CHD) through adverse effects on blood pressure and cholesterol levels is unclear, as is the risk of CHD that remains after these mediating effects are considered. Methods: Relative risks (RRs) of CHD associated with moderate overweight and obesity with and without adjustment for blood pressure and cholesterol concentrations were calculated by the members of a collaboration of prospective cohort studies of healthy, mainly white persons and pooled by means of random-effects models (RRs for categories of BMI in 14 cohorts and for continuous BMI in 21 cohorts; total N=302 296). Results: A total of 18 000 CHD events occurred during follow-up. The age-, sex-, physical activity-, and smoking-adjusted RRs (95% confidence intervals) for moderate overweight and obesity compared with normal weight were 1.32 (1.24-1.40) and 1.81 (1.56-2.10), respectively. Additional adjustment for blood pressure and cholesterol levels reduced the RR to 1.17 (1.11-1.23) for moderate overweight and to 1.49 (1.32-1.67) for obesity. The RR associated with a 5-unit BMI increment was 1.29 (1.22-1.35) before and 1.16 (1.11-1.21) after adjustment for blood pressure and cholesterol levels. Conclusions: Adverse effects of overweight on blood pressure and cholesterol levels could account for about 45% of the increased risk of CHD. Even for moderate overweight, there is a significant increased risk of CHD independent of these traditional risk factors, although confounding (eg, by dietary factors) cannot be completely ruled out. ©2007 American Medical Association. All rights reserved.

ObjectiveWhile several studies have reported on the relation of diabetes status with pancreatic cancer risk, the predictive value of this disorder for other malignancies is unclear. Methods: The Whitehall study, a 25year follow-up for mortality experience of 18,006 men with data on post-challenge blood glucose and self-reported diabetes, allowed us to address these issues. Results: There were 2158 cancer deaths at follow-up. Of the 15 cancer outcomes, diabetes status was positively associated with mortality from carcinoma of the pancreas and liver, while the relationship with lung cancer was inverse, after controlling for a range of potential covariates and mediators which included obesity and socioeconomic position. After excluding deaths occurring in the first 10years of follow-up to examine the effect of reverse causality, the magnitude of the relationships for carcinoma of the pancreas and lung was little altered, while for liver cancer it was markedly attenuated. Conclusions: In the present study, diabetes status was related to pancreatic, liver, and lung cancer risk. Cohorts with serially collected data on blood glucose and covariates are required to further examine this area.