34 research outputs found

    Methods of data collection and definitions of cardiac outcomes in the Rotterdam Study

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    The prevalence of cardiovascular diseases is rising. Therefore, adequate risk prediction and identification of its determinants is increasingly important. The Rotterdam Study is a prospective population-based cohort study ongoing since 1990 in the city of Rotterdam, The Netherlands. One of the main targets of the Rotterdam Study is to identify the determinants and prognosis of cardiovascular diseases. Case finding in epidemiological studies is strongly depending on various sources of followup and clear outcome definitions. The sources used for collection of data in the Rotterdam Study are diverse and the definitions of outcomes in the Rotterdam Study have changed due to the introduction of novel diagnostics and therapeutic interventions. This article gives the methods for data collection and the up-to-date definitions of the cardiac outcomes based on international guidelines, including the recently adopted cardiovascular disease mortality definitions. In all, detailed description of cardiac outcome definitions enhances the possibility to make comparisons with other studies in the field of cardiovascular research and may increase the strength of collaborations

    The association of innate and adaptive immunity, subclinical atherosclerosis, and cardiovascular disease in the Rotterdam Study: A prospective cohort study

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    BACKGROUND: Atherosclerotic cardiovascular disease (ASCVD) is driven by multifaceted contributions of the immune system. However, the dysregulation of immune cells that leads to ASCVD is poorly understood. We determined the association of components of innate and adaptive immunity longitudinally with ASCVD, and assessed whether arterial calcifications play a role in this association. METHODS AND FINDINGS: Granulocyte (innate immunity) and lymphocyte (adaptive immunity) counts were determined 3 times (2002-2008, mean age 65.2 years; 2009-2013, mean age 69.0 years; and 2014-2015, mean age 78.5 years) in participants of the population-based Rotterdam Study without ASCVD at baseline. Participants were followed-up for ASCVD or death until 1 January 2015. A random sample of 2,366 underwent computed tomography at baseline to quantify arterial calcification volume in 4 vessel beds. We studied the association between immunity components with risk of ASCVD and assessed whether immunity components were related to arterial calcifications at baseline. Of 7,730 participants (59.4% women), 801 developed ASCVD during a median follow-up of 8.1 years. Having an increased granulocyte count increased ASCVD risk (adjusted hazard ratio for doubled granulocyte count [95% CI] = 1.78 [1.34-2.37], P < 0.001). Higher granulocyte counts were related to larger calcification volumes in all vessels, most prominently in the coronary arteries (mean difference in calcium volume [mm3] per SD increase in granulocyte count [95% CI] = 32.3 [9.9-54.7], P < 0.001). Respectively, the association between granulocyte count and incident coronary heart disease and stroke was partly mediated by coronary artery calcification (overall proportion mediated [95% CI] = 19.0% [-10% to 32.3%], P = 0.08) and intracranial artery calcification (14.9% [-10.9% to 19.1%], P = 0.05). A limitation of our study is that studying the etiology of ASCVD remains difficult within an epidemiological setting due to the limited availability of surrogates for innate and especially adaptive immunity. CONCLUSIONS: In this study, we found that an increased granulocyte count was associated with a higher risk of ASCVD in the general population. Moreover, higher levels of granulocytes were associated with larger volumes of arterial calcification. Arterial calcifications may explain a proportion of the link between granulocytes and ASCVD

    Changes in the Diagnosis of Stroke and Cardiovascular Conditions in Primary Care During First 2 COVID-19 Waves in the Netherlands

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    BACKGROUND AND OBJECTIVES: Although there is evidence of disruption in acute cerebrovascular and cardiovascular care during the coronavirus disease 2019 (COVID-19) pandemic, its downstream effect in primary care is less clear. We investigated how the pandemic affected utilization of cerebrovascular and cardiovascular care in general practices (GPs) and determined changes in GP-recorded diagnoses of selected cerebrovascular and cardiovascular outcomes. METHODS: From electronic health records of 166,929 primary care patients aged 30 or over within the Rotterdam region, the Netherlands, we extracted the number of consultations related to cerebrovascular and cardiovascular care, and first diagnoses of selected cerebrovascular and cardiovascular risk factors (hypertension, diabetes, lipid disorders), conditions, and events (angina, atrial fibrillation, TIA, myocardial infarction, stroke). We quantified changes in those outcomes during the first COVID-19 wave (March–May 2020) and thereafter (June–December 2020) by comparing them to the same period in 2016–2019. We also estimated the number of potentially missed diagnoses for each outcome. RESULTS: The number of GP consultations related to cerebrovascular and cardiovascular care declined by 38% (0.62, 95% confidence interval 0.56–0.68) during the first wave, as compared to expected counts based on prepandemic levels. Substantial declines in the number of new diagnoses were observed for cerebrovascular events: 37% for TIA (0.63, 0.41–0.96) and 29% for stroke (0.71, 0.59–0.84), while no significant changes were observed for cardiovascular events (myocardial infarction [0.91, 0.74–1.14], angina [0.77, 0.48–1.25]). The counts across individual diagnoses recovered following June 2020, but the number of GP consultations related to cerebrovascular and cardiovascular care remained lower than expected throughout the June to December period (0.93, 0.88–0.98). DISCUSSION: While new diagnoses of acute cardiovascular events remained stable during the COVID-19 pandemic, diagnoses of cerebrovascular events declined substantially compared to prepandemic levels, possibly due to incorrect perception of risk by patients. These findings emphasize the need to improve symptom recognition of cerebrovascular events among the general public and to encourage urgent presentation despite any physical distancing measures

    Lifetime risk to progress from pre-diabetes to type 2 diabetes among women and men: comparison between American

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    INTRODUCTION: Pre-diabetes, a status conferring high risk of overt diabetes, is defined differently by the American Diabetes Association (ADA) and the WHO. We investigated the impact of applying definitions of pre-diabetes on lifetime risk of diabetes in women and men from the general population. RESEARCH DESIGN AND METHODS: We used data from 8844 women without diabetes and men aged ≥45 years from the prospective population-based Rotterdam Study in the Netherlands. In both gender groups, we calculated pre-diabetes prevalence according to ADA and WHO criteria and estimated the 10-year and lifetime risk to progress to overt diabetes with adjustment for competing risk of death. RESULTS: Out of 8844 individuals, pre-diabetes was identified in 3492 individuals (prevalence 40%, 95% CI 38% to 41%) according to ADA and 1382 individuals (prevalence 16%, 95% CI 15% to 16%) according to WHO criteria. In both women and men and each age category, ADA prevalence estimates doubled WHO-defined pre-diabetes. For women and men aged 45 years having ADA-defined pre-diabetes, the 10-year risk of diabetes was 14.2% (95% CI 6.0% to 22.5%) and 9.2% (95% CI 3.4% to 15.0%) compared with 23.2% (95% CI 6.8% to 39.6%) and 24.6% (95% CI 8.4% to 40.8%) in women and men with WHO-defined pre-diabetes. At age 45 years, the remaining lifetime risk to progress to overt diabetes was 57.5% (95% CI 51.8% to 63.2%) vs 80.2% (95% CI 74.1% to 86.3%) in women and 46.1% (95% CI 40.8% to 51.4%) vs 68.4% (95% CI 58.3% to 78.5%) in men with pre-diabetes according to ADA and WHO definitions, respectively. CONCLUSION: Prevalence of pre-diabetes differed considerably in both women and men when applying ADA and WHO pre-diabetes definitions. Women with pre-diabetes had higher lifetime risk to progress to diabetes. The lifetime risk of diabetes was lower in women and men with ADA-defined pre-diabetes as compared with WHO. Improvement of pre-diabetes definition considering appropriate sex-specific and age-specific glycemic thresholds may lead to better identification of individuals at high risk of diabetes

    Association of Cardiometabolic Multimorbidity With Mortality.

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    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

    Association of Cardiometabolic Multimorbidity With Mortality.

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    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

    Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function

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    BACKGROUND: Understanding the genetic architecture of cardiac structure and function may help to prevent and treat heart disease. This investigation sought to identify common genetic variations associated with inter-individual variability in cardiac structure and function. METHODS: A GWAS meta-analysis of echocardiographic traits was performed, including 46,533 individuals from 30 studies (EchoGen consortium). The analysis included 16 traits of left ventricular (LV) structure, and systolic and diastolic function. RESULTS: The discovery analysis included 21 cohorts for structural and systolic function traits (n = 32,212) and 17 cohorts for diastolic function traits (n = 21,852). Replication was performed in 5 cohorts (n = 14,321) and 6 cohorts (n = 16,308), respectively. Besides 5 previously reported loci, the combined meta-analysis identified 10 additional genome-wide significant SNPs: rs12541595 near MTSS1 and rs10774625 in ATXN2 for LV end-diastolic internal dimension; rs806322 near KCNRG, rs4765663 in CACNA1C, rs6702619 near PALMD, rs7127129 in TMEM16A, rs11207426 near FGGY, rs17608766 in GOSR2, and rs17696696 in CFDP1 for aortic root diameter; and rs12440869 in IQCH for Doppler transmitral A-wave peak velocity. Findings were in part validated in other cohorts and in GWAS of related disease traits. The genetic loci showed associations with putative signaling pathways, and with gene expression in whole blood, monocytes, and myocardial tissue. CONCLUSION: The additional genetic loci identified in this large meta-analysis of cardiac structure and function provide insights into the underlying genetic architecture of cardiac structure and warrant follow-up in future functional studies. FUNDING: For detailed information per study, see Acknowledgments.This work was supported by a grant from the US National Heart, Lung, and Blood Institute (N01-HL-25195; R01HL 093328 to RSV), a MAIFOR grant from the University Medical Center Mainz, Germany (to PSW), the Center for Translational Vascular Biology (CTVB) of the Johannes Gutenberg-University of Mainz, and the Federal Ministry of Research and Education, Germany (BMBF 01EO1003 to PSW). This work was also supported by the research project Greifswald Approach to Individualized Medicine (GANI_MED). GANI_MED was funded by the Federal Ministry of Education and Research and the Ministry of Cultural Affairs of the Federal State of Mecklenburg, West Pomerania (contract 03IS2061A). We thank all study participants, and the colleagues and coworkers from all cohorts and sites who were involved in the generation of data or in the analysis. We especially thank Andrew Johnson (FHS) for generation of the gene annotation database used for analysis. We thank the German Center for Cardiovascular Research (DZHK e.V.) for supporting the analysis and publication of this project. RSV is a member of the Scientific Advisory Board of the DZHK. Data on CAD and MI were contributed by CARDIoGRAMplusC4D investigators. See Supplemental Acknowledgments for consortium details. PSW, JFF, AS, AT, TZ, RSV, and MD had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis
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