Miguel Marino1, Yi Li2, Michael J Pencina3, Ralph B D'Agostino4, Lisa F Berkman5, Orfeu M Buxton6. 1. Department of Family Medicine, Department of Public Health and Preventive Medicine, Division of Biostatistics, Oregon Health Science University, Portland, Oregon. Electronic address: marinom@ohsu.edu. 2. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan. 3. Duke Clinical Research Institute and Department of Biostatistics and Bioinformatics and Framingham Heart Study, Duke University, Durham, North Carolina. 4. Department of Mathematics/Statistics, Biostatistics and Epidemiology, Boston University. 5. Harvard Center for Population and Development Studies, Cambridge, Massachusetts. 6. Department of Medicine, Brigham and Women's Hospital, Department of Social and Behavioral Sciences, Harvard School of Public Health, Division of Sleep Medicine, Harvard Medical School Boston; Department of Biobehavioral Health, Pennsylvania State University, University Park, Pennsylvania.
Abstract
BACKGROUND: Sensitive general cardiometabolic risk assessment tools of modifiable risk factors would be helpful and practical in a range of primary prevention interventions or for preventive health maintenance. PURPOSE: To develop and validate a cumulative general cardiometabolic risk score that focuses on non-self-reported modifiable risk factors such as glycosylated hemoglobin (HbA1c) and BMI so as to be sensitive to small changes across a span of major modifiable risk factors, which may not individually cross clinical cut-off points for risk categories. METHODS: We prospectively followed 2,359 cardiovascular disease (CVD)-free subjects from the Framingham offspring cohort over a 14-year follow-up. Baseline (fifth offspring examination cycle) included HbA1c and cholesterol measurements. Gender-specific Cox proportional hazards models were considered to evaluate the effects of non-self-reported modifiable risk factors (blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking, BMI, and HbA1c) on general CVD risk. We constructed 10-year general cardiometabolic risk score functions and evaluated its predictive performance in 2012-2013. RESULTS: HbA1c was significantly related to general CVD risk. The proposed cardiometabolic general CVD risk model showed good predictive performance as determined by cross-validated discrimination (male C-index=0.703, 95% CI=0.668, 0.734; female C-index=0.762, 95% CI=0.726, 0.801) and calibration (lack-of-fit chi-square=9.05 [p=0.338] and 12.54 [p=0.128] for men and women, respectively). CONCLUSIONS: This study presents a risk factor algorithm that provides a convenient and informative way to quantify cardiometabolic risk on the basis of modifiable risk factors that can motivate an individual's commitment to prevention and intervention.
BACKGROUND: Sensitive general cardiometabolic risk assessment tools of modifiable risk factors would be helpful and practical in a range of primary prevention interventions or for preventive health maintenance. PURPOSE: To develop and validate a cumulative general cardiometabolic risk score that focuses on non-self-reported modifiable risk factors such as glycosylated hemoglobin (HbA1c) and BMI so as to be sensitive to small changes across a span of major modifiable risk factors, which may not individually cross clinical cut-off points for risk categories. METHODS: We prospectively followed 2,359 cardiovascular disease (CVD)-free subjects from the Framingham offspring cohort over a 14-year follow-up. Baseline (fifth offspring examination cycle) included HbA1c and cholesterol measurements. Gender-specific Cox proportional hazards models were considered to evaluate the effects of non-self-reported modifiable risk factors (blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking, BMI, and HbA1c) on general CVD risk. We constructed 10-year general cardiometabolic risk score functions and evaluated its predictive performance in 2012-2013. RESULTS: HbA1c was significantly related to general CVD risk. The proposed cardiometabolic general CVD risk model showed good predictive performance as determined by cross-validated discrimination (male C-index=0.703, 95% CI=0.668, 0.734; female C-index=0.762, 95% CI=0.726, 0.801) and calibration (lack-of-fit chi-square=9.05 [p=0.338] and 12.54 [p=0.128] for men and women, respectively). CONCLUSIONS: This study presents a risk factor algorithm that provides a convenient and informative way to quantify cardiometabolic risk on the basis of modifiable risk factors that can motivate an individual's commitment to prevention and intervention.
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