Aims Growing evidence suggests that poor sleep health is associated with cardiovascular risk. However, research in this area often relies upon recollection dependent questionnaires or diaries. Accelerometers provide an alternative tool for measuring sleep parameters objectively. This study examines the association between wrist-worn accelerometer-derived sleep onset timing and cardiovascular disease (CVD). Methods and results We derived sleep onset and waking up time from accelerometer data collected from 103 712 UK Biobank participants over a period of 7 days. From this, we examined the association between sleep onset timing and CVD incidence using a series of Cox proportional hazards models. A total of 3172 cases of CVD were reported during a mean follow-up period of 5.7 (±0.49) years. An age- and sex-controlled base analysis found that sleep onset time of 10:00 p.m.–10:59 p.m. was associated with the lowest CVD incidence. An additional model, controlling for sleep duration, sleep irregularity, and established CVD risk factors, did not attenuate this association, producing hazard ratios of 1.24 (95% confidence interval, 1.10–1.39; P < 0.005), 1.12 (1.01–1.25; P= 0.04), and 1.25 (1.02–1.52; P= 0.03) for sleep onset <10:00 p.m., 11:00 p.m.–11:59 p.m., and ≥12:00 a.m., respectively, compared to 10:00 p.m.–10:59 p.m. Importantly, sensitivity analyses revealed this association with increased CVD risk was stronger in females, with only sleep onset <10:00 p.m. significant for males. Conclusions Our findings suggest the possibility of a relationship between sleep onset timing and risk of developing CVD, particularly for women. We also demonstrate the potential utility of collecting information about sleep parameters via accelerometry-capable wearable devices, which may serve as novel cardiovascular risk indicators.

Background Cardiovascular diseases (CVDs) are among the leading causes of death worldwide. Predictive scores providing personalised risk of developing CVD are increasingly used in clinical practice. Most scores, however, utilise a homogenous set of features and require the presence of a physician. Objective The aim was to develop a new risk model (DiCAVA) using statistical and machine learning techniques that could be applied in a remote setting. A secondary goal was to identify new patient-centric variables that could be incorporated into CVD risk assessments. Methods Across 466,052 participants, Cox proportional hazards (CPH) and DeepSurv models were trained using 608 variables derived from the UK Biobank to investigate the 10-year risk of developing a CVD. Data-driven feature selection reduced the number of features to 47, after which reduced models were trained. Both models were compared to the Framingham score. Results The reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in determining the CVD risk compared to Framingham score. Minimal difference was observed when cholesterol and blood pressure were excluded from the models (CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and discrimination on the test data. Conclusion We developed a cardiovascular risk model that has very good predictive capacity and encompasses new variables. The score could be incorporated into clinical practice and utilised in a remote setting, without the need of including cholesterol. Future studies will focus on external validation across heterogeneous samples.

Accelerated coronary artery disease seen following radiation exposure is termed 'radiation-induced coronary artery disease' (RICAD) and results from both the direct and indirect effects of radiation exposure. Long-term data are available from survivors of nuclear explosions and accidents, nuclear workers as well as from radiotherapy patients. The last group is, by far, the biggest cause of RICAD presentation.The incidence of RICAD continues to increase as cancer survival rates improve and it is now the second most common cause of morbidity and mortality in patients treated with radiotherapy for breast cancer, Hodgkin's lymphoma and other mediastinal malignancies. RICAD will frequently present atypically or even asymptomatically with a latency period of at least 10 years after radiotherapy treatment. An awareness of RICAD, as a long-term complication of radiotherapy, is therefore essential for the cardiologist, oncologist and general medical physician alike.Prior cardiac risk factors, a higher radiation dose and a younger age at exposure seem to increase a patient's risk ratio of developing RICAD. Significant radiation exposure, therefore, requires a low threshold for screening for early diagnosis and timely intervention.

Aims Growing evidence suggests that sleep quality is associated with cardiovascular risk. However, research in this area often relies upon recollection dependant questionnaires or diaries. Accelerometers provide an alternative tool for deriving sleep parameters measuring sleep patterns objectively. This study examines the associations between accelerometer derived sleep onset timing and cardiovascular disease (CVD). Methods and Results We derived sleep onset and waking up time from accelerometer data collected from 103,712 UK Biobank participants over a period of seven days. From this, we examined the association between sleep onset timing and CVD incidence using a series of Cox proportional hazards models. 3172 cases of CVD were reported during a mean follow-up period of 5.7 (±0.49) years. An age- and sex-controlled base analysis found that sleep onset time of 10:00pm-10:59pm was associated with the lowest CVD incidence. A fully adjusted model, additionally controlling for sleep duration, sleep irregularity, and established CVD risk factors, was unable to eliminate this association, producing hazard ratios of 1.24 (95% CI, 1.10-1.39; p<0.005), 1.12 (1.01-1.25; p=0.04), and 1.25 (1.02-1.52; p=0.03) for sleep onset <10:00pm, 11:00pm-11:59pm, and & ≥12:00am, respectively, compared to 10:00pm-10:59pm. Importantly, sensitivity analyses revealed this association was stronger in females, with only sleep onset <10:00pm significant for males. Conclusions Our findings suggest an independent relationship between sleep onset timing and risk of developing CVD, particularly for women. We also demonstrate the potential utility of collecting information about sleep parameters via accelerometry-capable wearable devices, which may serve as novel cardiovascular risk indicators.

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