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      Race/Ethnic Differences in the Associations of the Framingham Risk Factors with Carotid IMT and Cardiovascular Events

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      1 , 2 , 3 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 3 , 13 , 14 , 15 , 16 , 17 , 16 , 2 , 18 , 1 , 19 , 20 , 21 , 22 , 23 , 24 , 1 , 3 , 24 , 25 , 25 , 26 , 14 , 27 , 28 , 29 , 30 , 3 , 1 , 3 , *
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          Abstract

          Background

          Clinical manifestations and outcomes of atherosclerotic disease differ between ethnic groups. In addition, the prevalence of risk factors is substantially different. Primary prevention programs are based on data derived from almost exclusively White people. We investigated how race/ethnic differences modify the associations of established risk factors with atherosclerosis and cardiovascular events.

          Methods

          We used data from an ongoing individual participant meta-analysis involving 17 population-based cohorts worldwide. We selected 60,211 participants without cardiovascular disease at baseline with available data on ethnicity (White, Black, Asian or Hispanic). We generated a multivariable linear regression model containing risk factors and ethnicity predicting mean common carotid intima-media thickness (CIMT) and a multivariable Cox regression model predicting myocardial infarction or stroke. For each risk factor we assessed how the association with the preclinical and clinical measures of cardiovascular atherosclerotic disease was affected by ethnicity.

          Results

          Ethnicity appeared to significantly modify the associations between risk factors and CIMT and cardiovascular events. The association between age and CIMT was weaker in Blacks and Hispanics. Systolic blood pressure associated more strongly with CIMT in Asians. HDL cholesterol and smoking associated less with CIMT in Blacks. Furthermore, the association of age and total cholesterol levels with the occurrence of cardiovascular events differed between Blacks and Whites.

          Conclusion

          The magnitude of associations between risk factors and the presence of atherosclerotic disease differs between race/ethnic groups. These subtle, yet significant differences provide insight in the etiology of cardiovascular disease among race/ethnic groups. These insights aid the race/ethnic-specific implementation of primary prevention.

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          Most cited references38

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          General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study.

            Although more than 80% of the global burden of cardiovascular disease occurs in low-income and middle-income countries, knowledge of the importance of risk factors is largely derived from developed countries. Therefore, the effect of such factors on risk of coronary heart disease in most regions of the world is unknown. We established a standardised case-control study of acute myocardial infarction in 52 countries, representing every inhabited continent. 15152 cases and 14820 controls were enrolled. The relation of smoking, history of hypertension or diabetes, waist/hip ratio, dietary patterns, physical activity, consumption of alcohol, blood apolipoproteins (Apo), and psychosocial factors to myocardial infarction are reported here. Odds ratios and their 99% CIs for the association of risk factors to myocardial infarction and their population attributable risks (PAR) were calculated. Smoking (odds ratio 2.87 for current vs never, PAR 35.7% for current and former vs never), raised ApoB/ApoA1 ratio (3.25 for top vs lowest quintile, PAR 49.2% for top four quintiles vs lowest quintile), history of hypertension (1.91, PAR 17.9%), diabetes (2.37, PAR 9.9%), abdominal obesity (1.12 for top vs lowest tertile and 1.62 for middle vs lowest tertile, PAR 20.1% for top two tertiles vs lowest tertile), psychosocial factors (2.67, PAR 32.5%), daily consumption of fruits and vegetables (0.70, PAR 13.7% for lack of daily consumption), regular alcohol consumption (0.91, PAR 6.7%), and regular physical activity (0.86, PAR 12.2%), were all significantly related to acute myocardial infarction (p<0.0001 for all risk factors and p=0.03 for alcohol). These associations were noted in men and women, old and young, and in all regions of the world. Collectively, these nine risk factors accounted for 90% of the PAR in men and 94% in women. Abnormal lipids, smoking, hypertension, diabetes, abdominal obesity, psychosocial factors, consumption of fruits, vegetables, and alcohol, and regular physical activity account for most of the risk of myocardial infarction worldwide in both sexes and at all ages in all regions. This finding suggests that approaches to prevention can be based on similar principles worldwide and have the potential to prevent most premature cases of myocardial infarction.
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              2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk

              Supplemental Digital Content is available in the text.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2 July 2015
                2015
                : 10
                : 7
                : e0132321
                Affiliations
                [1 ]Department of Experimental Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
                [2 ]Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands
                [3 ]Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
                [4 ]Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht, The Netherlands
                [5 ]Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands
                [6 ]Institute of Cardiovascular Science, faculty of Population Health Sciences, University College London, London, United Kingdom
                [7 ]Department of Cardiac Sciences and Libin Cardiovascular Institute of Alberta, University of Calgary, Alberta, Canada
                [8 ]Department of Epidemiology and Public Health University College London, London, United Kingdom
                [9 ]Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
                [10 ]Dept of Clinical Sciences in Malmö, Lund University, Skåne University Hospital, Malmö, Sweden
                [11 ]Department of Biostatistical Sciences and Neurology, Wake Forest School of Medicine, Winston-Salem, NC, United States of America
                [12 ]Department of General Internal Medicine, Division of Vascular Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
                [13 ]University of Malaya Medical Center, Kuala Lumpur, Malaysia
                [14 ]Dept of Clinical Sciences in Malmö, Lund University, Skåne University Hospital, Malmö, Sweden
                [15 ]Department of General Internal Medicine, Division of Vascular Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
                [16 ]Osaka Medical Center for Health Science and Promotion, Osaka, Japan
                [17 ]Department of Neurology, Tokyo Women Medical University, Tokyo, Japan
                [18 ]Cardiovascular Research Institute & Surgery, Singapore, Singapore
                [19 ]Department of Medicine, Division of Cardiology and Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada
                [20 ]Department of Neurology, University Hospital, Goethe-University, Frankfurt am Main, Germany
                [21 ]Brain and Circulation Research Group, Department of Clinical Medicine, University of Tromsø, Tromsø, Norway
                [22 ]Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
                [23 ]Stroke Center, Department of Neurology, Osaka University Graduate School of Medicine, Osaka, Japan
                [24 ]Department of Radiology, Tufts Medical Center, Boston, MA, United States of America
                [25 ]Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
                [26 ]Cardiology Division, Department of Internal Medicine, University of Virginia, Charlottesville, VA, United States of America
                [27 ]Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, United States of America
                [28 ]MAS-Metabolic Analytical Services Oy, Helsinki, Finland
                [29 ]Department of Neurology, University Hospital, Goethe-University, Frankfurt am Main, Germany and Department of Neurology Klinikum Herford, Germany
                [30 ]Department of Internal Medicine and Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, The Netherlands
                University of Pittsburgh Center for Vaccine Research, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: CMG KAG MJCE MLB HMdR. Performed the experiments: CMG KAG. Analyzed the data: KAG CMG. Wrote the paper: CMG KAG IEH MLB HMdR. Contributed to data acquisition, interpretation of analyses and editing scientific content of the manuscript: CMG KAG IEH MJCE FWA TJA ARB JMD GE GWE JdG DEG BH SH AI KK AK DPVK EML MWL EBM GN SO DHO GP SAEP J.F. Polak J.F. Price CR CMR MR TR JTS MS CDAS MLB HMdR.

                Article
                PONE-D-15-14168
                10.1371/journal.pone.0132321
                4489855
                26134404
                e4bfd4a7-9b09-4250-9fb4-a1f7abeab042
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 8 April 2015
                : 12 June 2015
                Page count
                Figures: 2, Tables: 3, Pages: 13
                Funding
                The USE-IMT-project is supported by a grant from the Netherlands Organisation for Health Research and Development (ZonMw 200320003). This research was in part supported by NIH contracts N01-HC-95159 to N01-HC-95167 (MESA) and HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086 (CHS).
                Categories
                Research Article
                Custom metadata
                All relevant summary data are provided in the paper and its supplemental information. Unfortunately, the raw data cannot be made publicly available due to ethical and legal restrictions imposed by the IRBs of the centers participating in the USE-IMT collaboration. The de-identified data is traceable to the participating centers (through the number of participating patients) which did not explicitly approve of raw data publication. However, all interested readers may request data without restriction from Dr. Hester M. den Ruijter ( h.m.denruijter-2@ 123456umcutrecht.nl ) the study coordinator of USE-IMT.

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