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Approved research

Association between arterial stiffness index and risk of cardiovascular disease

Principal Investigator: Dr Anne-Claire Vergnaud
Approved Research ID: 3139
Approval date: September 16th 2013

Lay summary

Arterial stiffness is an established predictor of adverse cardiovascular outcome but is not currently measured in clinical practice. New devices are developed to facilitate its assessment. For example, the Pulse Trace (Micro Medical) estimates the Stiffness Index (SI) using the volume pulse obtained from an infrared light-transmitting unit placed in the index finger of the subjects. However, the prediction accuracy of the SI has never been evaluated. The goal of the present study is to investigate whether the SI could positively improve our current assessment of patient?s risk of cardiovascular disease. This study requires access to data from the full UK Biobank cohort and will have three stages. No sample is required. First, we will evaluate the participant?s characteristics and cardio-metabolic markers associated with the SI. Socio-demographic characteristics, anthropometric status, physical activity, diet, biomarkers from blood sample and blood pressure will be analysed. Second, we will investigate whether increased levels of SI can predict the incidence of cardiovascular events, independently of traditional risk factors. We will evaluate whether the prediction accuracy of the SI differs according to the type of cardiovascular disease or according to participant?s characteristics. Finally, we will investigate whether increased levels of SI can predict mortality from cardiovascular disease. Sensitivity analyses will be carried out using the repeat assessment of SI to try correcting the regression dilution bias caused by the measurement error in the baseline SI values. Cardiovascular disease remains a leading cause of death worldwide. Successful prevention relies on the accurate identification of individuals at risk of developing cardiovascular disease. All the risk prediction models including the traditional risk factors (age, gender, hypertension, dyslipidaemia, smoking, and diabetes) still cannot explain a large proportion of heart disease cases. This study could therefore potentially help to significantly improve the prevention and diagnosis of cardiovascular disease.