Burke, Gregory L.

Moritz F Sinner, 2012 Finalist and Presenting Author

Background: Hepatocyte growth factor (HGF) is a cytokine released in response to vascular injury and a novel biomarker of cardiovascular disease (CVD) risk. However, the relationship between ideal cardiovascular health (CVH) and HGF is unknown. We examined whether ideal CVH is associated with lower HGF levels in a multi-ethnic cohort of adults free from clinical CVD at baseline. Methods: We analyzed data from the MESA study of 6,490 men and women aged 45-84 years. The independent variable was the CVH score derived from 7 metrics (smoking, body mass index, physical activity, diet, total cholesterol, blood pressure and blood glucose). Each metric was scored 0 points (poor), 1 point (intermediate) and 2 points (ideal). The total CVH score ranged from 0-14. An inadequate score was 0-8, average, 9-10 and optimal, 11-14. The number of ideal metrics was also counted. The dependent variable was logarithmically transformed HGF. We examined the association between the CVH score and HGF using linear regression models adjusted for age, sex, race/ethnicity, education, income, health insurance and study site. Results: The mean (SD) age of participants was 62 (10) years. Fifty-three percent were women. Participants with optimal CVH scores had the lowest HGF concentration [Median (IQR): 807 (678-962) pg/mL] compared to those with average [870 (740-1,036)] and inadequate scores [969 (821-1,159)]. A one-unit increment in the CVH score was significantly associated with a 3% lower HGF concentration (Table). Average and optimal CVH scores were also significantly associated with 8% and 12% lower HGF concentrations, respectively, compared to inadequate scores. Additionally, a greater number of ideal metrics was associated with lower HGF concentrations. Interactions by age, sex and race/ethnicity were not significant. Conclusion: In this ethnically diverse cohort, optimal CVH was significantly associated with lower HGF levels. Interventions aimed at promoting ideal CVH may reduce vascular injury as indicated by lower serum HGF levels.

Background Tools for the prediction of atrial fibrillation ( AF ) may identify high‐risk individuals more likely to benefit from preventive interventions and serve as a benchmark to test novel putative risk factors. Methods and Results Individual‐level data from 3 large cohorts in the U nited S tates (Atherosclerosis Risk in Communities [ ARIC ] study, the Cardiovascular Health Study [ CHS ], and the Framingham Heart Study [ FHS ]), including 18 556 men and women aged 46 to 94 years (19% A frican A mericans, 81% whites) were pooled to derive predictive models for AF using clinical variables. Validation of the derived models was performed in 7672 participants from the Age, Gene and Environment—Reykjavik study ( AGES ) and the Rotterdam Study ( RS ). The analysis included 1186 incident AF cases in the derivation cohorts and 585 in the validation cohorts. A simple 5‐year predictive model including the variables age, race, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes, and history of myocardial infarction and heart failure had good discrimination (C‐statistic, 0.765; 95% CI , 0.748 to 0.781). Addition of variables from the electrocardiogram did not improve the overall model discrimination (C‐statistic, 0.767; 95% CI , 0.750 to 0.783; categorical net reclassification improvement, −0.0032; 95% CI , −0.0178 to 0.0113). In the validation cohorts, discrimination was acceptable ( AGES C‐statistic, 0.664; 95% CI , 0.632 to 0.697 and RS C‐statistic, 0.705; 95% CI , 0.664 to 0.747) and calibration was adequate. Conclusion A risk model including variables readily available in primary care settings adequately predicted AF in diverse populations from the U nited S tates and E urope.