Hao-Min Cheng1, Shao-Yuan Chuang2, Jiun-Jr Wang3, Yuan-Ta Shih4, Hsin-Ning Wang5, Chi-Jung Huang6, Jui-Tzu Huang5, Shih-Hsien Sung7, Edward G Lakatta8, Frank C P Yin9, Pesus Chou10, Chih-Jung Yeh11, Chyi-Huey Bai12, Wen-Harn Pan13, Chen-Huan Chen14. 1. Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan; Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan; Department of Public Health, National Yang-Ming University, Taipei, Taiwan. 2. Division of Preventive Medicine and Health Service, Research Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan. 3. School of Medicine, Fu Jen Catholic University, Xinzhuang District, New Taipei City, Taiwan. 4. Molecular Imaging Center, National Taiwan University, Taipei, Taiwan. 5. Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan. 6. Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan. 7. Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan. 8. The Laboratory of Cardiovascular Science in the National Institute on Aging Intramural Research Program in Baltimore, MD, USA. 9. Department of Biomedical Engineering, Washington University, St Louis, MO, USA. 10. Department of Public Health, National Yang-Ming University, Taipei, Taiwan. 11. Department of Public Health, Chung-Shan Medical University, Taichung, Taiwan. 12. School of Public Health, Taipei medical university. 13. Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan. 14. Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan; Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan; Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan; Department of Public Health, National Yang-Ming University, Taipei, Taiwan. Electronic address: chench@vghtpe.gov.tw.
Abstract
BACKGROUND: Numerous mechanical biomarkers derived from pulse wave analysis (PWA) have been proposed to predict cardiovascular outcomes. However, whether these biomarkers carry independent prognostic value and clinical utility beyond traditional cardiovascular risk factors hasn't been systematically evaluated. We aimed to investigate the additive utility of PWA-derived biomarkers in two independent population-based cohorts. METHODS: PWA on central arterial pressure waveforms obtained from subjects without a prior history of cardiovascular diseases of two studies was conducted based on the wave transmission and reservoir-wave theory: firstly in the Kinmen study (1272 individuals, a median follow-up of 19.8years); and then in the Cardiovascular Disease Risk Factors Two-Township Study (2221 individuals, median follow-up of 10years). The incremental value of the biomarkers was evaluated by net reclassification index (NRI). RESULTS: In multivariate Cox analyses accounting for age, gender, body mass index, systolic blood pressure, fasting glucose, high-density- and low-density-lipoprotein cholesterol, and smoking, only systolic (SC) and diastolic rate constant (DC) of reservoir pressure could independently and consistently predict cardiovascular mortality in both cohorts and the combined cohort (SC: hazard ratio 1.18 [95% confidence interval 1.08-1.28, p<0.001; DC: 1.18 [1.09-1.28], p<0.001]. Risk prediction estimates in traditional risk prediction models were significantly more accurate when incorporating peak of reservoir pressure (NRI=0.049, p=0.0361), SC (NRI=0.043, p=0.0236) and DC (NRI=0.054, p=0.047). CONCLUSIONS: Of all PWA-derived biomarkers, SC and DC were consistently identified as valuable parameters for incremental cardiovascular risk prediction in two large prospective cohorts.
BACKGROUND: Numerous mechanical biomarkers derived from pulse wave analysis (PWA) have been proposed to predict cardiovascular outcomes. However, whether these biomarkers carry independent prognostic value and clinical utility beyond traditional cardiovascular risk factors hasn't been systematically evaluated. We aimed to investigate the additive utility of PWA-derived biomarkers in two independent population-based cohorts. METHODS: PWA on central arterial pressure waveforms obtained from subjects without a prior history of cardiovascular diseases of two studies was conducted based on the wave transmission and reservoir-wave theory: firstly in the Kinmen study (1272 individuals, a median follow-up of 19.8years); and then in the Cardiovascular Disease Risk Factors Two-Township Study (2221 individuals, median follow-up of 10years). The incremental value of the biomarkers was evaluated by net reclassification index (NRI). RESULTS: In multivariate Cox analyses accounting for age, gender, body mass index, systolic blood pressure, fasting glucose, high-density- and low-density-lipoprotein cholesterol, and smoking, only systolic (SC) and diastolic rate constant (DC) of reservoir pressure could independently and consistently predict cardiovascular mortality in both cohorts and the combined cohort (SC: hazard ratio 1.18 [95% confidence interval 1.08-1.28, p<0.001; DC: 1.18 [1.09-1.28], p<0.001]. Risk prediction estimates in traditional risk prediction models were significantly more accurate when incorporating peak of reservoir pressure (NRI=0.049, p=0.0361), SC (NRI=0.043, p=0.0236) and DC (NRI=0.054, p=0.047). CONCLUSIONS: Of all PWA-derived biomarkers, SC and DC were consistently identified as valuable parameters for incremental cardiovascular risk prediction in two large prospective cohorts.
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