Abstract-Investigation of arterial stiffness, especially of the large arteries, has gathered pace in recent years with the development of readily available noninvasive assessment techniques. These include the measurement of pulse wave velocity, the use of ultrasound to relate the change in diameter or area of an artery to distending pressure, and analysis of arterial waveforms obtained by applanation tonometry. Here, we describe each of these techniques and their limitations and discuss how the measured parameters relate to established cardiovascular risk factors and clinical outcome. We also consider which techniques might be most appropriate for wider clinical application. Finally, the effects of current and future cardiovascular drugs on arterial stiffness are also discussed, as is the relationship between arterial elasticity and endothelial function. Arterial StiffnessData from the Framingham Heart Study have determined how systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP; the difference between SBP and DBP) change with advancing age. 1 DBP, largely determined by peripheral arterial resistance, increases until middle age and then tends to fall. In contrast, SBP and PP, influenced more by the stiffness of large arteries, as well as peripheral pulse wave reflection and the pattern of left ventricular ejection, increase continuously with age. Changes in the stiffness of the large arteries, such as the aorta and its major branches, largely account for the changes in SBP, DBP, and PP that occur from 50 years of age onward. Although DBP has traditionally been the major focus in the treatment of hypertension, over recent years SBP has become recognized as a stronger cardiovascular risk factor in older people. Thus, SBP has greater predictive value than DBP for coronary heart disease (CHD) in older people (Ͼ60 years). 2,3 Isolated systolic hypertension (ISH; SBP Ն140 mm Hg and DBP Ͻ90 mm Hg), is the most common subtype of hypertension in the middle aged and is overwhelmingly so in the elderly. 4 It is a major risk factor for stroke, 5 CHD, 2,3 and cardiovascular and total mortality. 6,7 Furthermore, measurement of SBP alone identifies Ͼ90% of hypertensives according to the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure VI criteria, whereas DBP alone identifies only Ϸ20%. 8 The treatment of ISH with conventional antihypertensive drugs is of proven clinical benefit. 9,10 However, although it is recognized that few hypertensives are controlled to target pressures, 11 it is much more commonly

Dysfunction of the vascular endothelium is a hallmark of most conditions that are associated with atherosclerosis and is therefore held to be an early feature in atherogenesis. However, the mechanisms by which endothelial dysfunction occurs in smoking, dyslipidaemia, hyperhomocysteinaemia, diabetes mellitus, arterial hypertension, cerebrovascular diseases, coronary artery disease and heart failure are complex and heterogeneous. Recent data indicate that endothelial dysfunction is often associated with erectile dysfunction, which can precede and predict cardiovascular disease in men. This paper will provide a concise overview of the mechanisms causing endothelial dysfunction in the different cardiovascular risk factors and disease conditions, and of the impact of the intervention measures and treatments.

Wrist-worn accelerometers are increasingly being used for the assessment of physical activity in population studies, but little is known about their value for sleep assessment. We developed a novel method of assessing sleep duration using data from 4,094 Whitehall II Study (United Kingdom, 2012–2013) participants aged 60–83 who wore the accelerometer for 9 consecutive days, filled in a sleep log and reported sleep duration via questionnaire. Our sleep detection algorithm defined (nocturnal) sleep as a period of sustained inactivity, itself detected as the absence of change in arm angle greater than 5 degrees for 5 minutes or more, during a period recorded as sleep by the participant in their sleep log. The resulting estimate of sleep duration had a moderate (but similar to previous findings) agreement with questionnaire based measures for time in bed, defined as the difference between sleep onset and waking time (kappa = 0.32, 95%CI:0.29,0.34) and total sleep duration (kappa = 0.39, 0.36,0.42). This estimate was lower for time in bed for women, depressed participants, those reporting more insomnia symptoms, and on weekend days. No such group differences were found for total sleep duration. Our algorithm was validated against data from a polysomnography study on 28 persons which found a longer time window and lower angle threshold to have better sensitivity to wakefulness, while the reverse was true for sensitivity to sleep. The novelty of our method is the use of a generic algorithm that will allow comparison between studies rather than a “count” based, device specific method.