There is an important, unmet need to improve current cardiovascular (CV) disease risk prediction algorithms, allowing better stratification models for disease prevention and, ultimately, a personalised medicine approach. This is particularly important for ‘moderate risk’ individuals, where relatively few people will suffer a CV event, but where a large number of individuals are likely to be offered drug therapy. On the other hand, individuals below the treatment threshold will not be treated and many go on to have CV events. Indeed, the need for improved risk prediction has been highlighted by the initiation of the lower treatment threshold for hypertension from a systolic blood pressure (BP) of 140mmHg to 130mmHg in line with the US guidelines as recommended by the AHA/ACC. This would dramatically increase the number of adults requiring drug therapy.

Aortic stiffness is an attractive novel biomarker, particularly for those with borderline hypertension, because it can be measured simply and non-invasively in large numbers of individuals, and shares a close association with BP. Stiffening of the aorta with ageing and disease occurs in almost all societies worldwide and indicates a deterioration of the ability of the large elastic arteries to ‘buffer’ the cyclical changes in BP resulting from intermittent ventricular ejection. Indeed, aortic stiffening appears to drive the development of systolic hypertension – by far the most common form of hypertension in older individuals and may, itself, provide a measure of end organ damage. However, the potential added value of aortic stiffness has not yet been examined in middle risk individuals or in those with borderline hypertension.

It is also unclear whether BP is the main driver of arterial stiffness or whether other factors such as heart rate have an important role. This is an important question because ultimately aortic stiffness may provide a better measure of long term average BP than single clinic SBP readings or isolated 24-hr ambulatory measurements, which could improve CV risk prediction.

In this thesis, an updated meta-analyses has been performed using data from 11 population based cohort studies consisting of 15,987 individuals at moderate CV risk, to assess the prognostic value of cfPWV beyond traditional CV risk factors. Novel risk scores including cfPWV measurements were derived and validated and compared to established CV risk scores used in clinical practice. These data showed that cfPWV was independently associated with CV risk after adjustment for established CV risk factors (age, sex, HDL, total cholesterol, smoking status, diabetes, antihypertensive medications) and that the addition of cfPWV to traditional CV risk factors significantly improved the ability of the model to discriminate between individuals who have an event and those who do not. Integrating the novel cfPWV risk scores into clinical practice in combination with the currently established risk models in a 2-stage screening programme and therefore additional screening with cfPWV measurements could reduce the risk of CV events by approximately 3% in the US and Europe, compared to the current guidelines.

A subset of the studies with longitudinal data available contributed to longitudinal analyses allowing BP and heart rate trajectories to be modelled. When predicting CV events, the prognostic value of the SBP trajectory and cfPWV both acted independently, although the SBP trajectory was not an independent predictor of CV events. The results in this thesis suggest that a one off cfPWV measurement does not provide a good surrogate measure for long-term SBP measurements when predicting CV events. These analyses also suggested that current SBP, heart rate and age were the key predictors of current cfPWV, but that preceding trajectories of SBP and heart rate added a small amount of predictive value.

Due to the COVID-19 pandemic occurring part way through this research project, an additional, unplanned project was performed looking at the effect of hypertension, absolute SBP and antihypertensive medications on the risk of severe COVID-19.