High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

In order to investigate the applicability of routine 10s electrocardiogram (ECG) recordings for time-domain heart rate variability (HRV) calculation we explored to what extent these (ultra-)short recordings capture the “actual” HRV. The standard deviation of normal-to-normal intervals (SDNN) and the root mean square of successive differences (RMSSD) were measured in 3,387 adults. SDNN and RMSSD were assessed from (ultra)short recordings of 10s(3x), 30s, and 120s and compared to 240s–300s (gold standard) measurements. Pearson’s correlation coefficients (r), Bland-Altman 95% limits of agreement and Cohen’s d statistics were used as agreement analysis techniques. Agreement between the separate 10s recordings and the 240s-300s recording was already substantial (r = 0.758–0.764/Bias = 0.398–0.416/d = 0.855–0.894 for SDNN; r = 0.853–0.862/Bias = 0.079–0.096/d = 0.150–0.171 for RMSSD), and improved further when three 10s periods were averaged (r = 0.863/Bias = 0.406/d = 0.874 for SDNN; r = 0.941/Bias = 0.088/d = 0.167 for RMSSD). Agreement increased with recording length and reached near perfect agreement at 120s (r = 0.956/Bias = 0.064/d = 0.137 for SDNN; r = 0.986/Bias = 0.014/d = 0.027 for RMSSD). For all recording lengths and agreement measures, RMSSD outperformed SDNN. Our results confirm that it is unnecessary to use recordings longer than 120s to obtain accurate measures of RMSSD and SDNN in the time domain. Even a single 10s (standard ECG) recording yields a valid RMSSD measurement, although an average over multiple 10s ECGs is preferable. For SDNN we would recommend either 30s or multiple 10s ECGs. Future research projects using time-domain HRV parameters, e.g. genetic epidemiological studies, could calculate HRV from (ultra-)short ECGs enabling such projects to be performed at a large scale.

Recent literature has introduced (a) the network perspective to psychology and (b) collection of time series data to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intraindividual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time series data. We explain the importance of partial correlation networks and exemplify the network structures on time series data of a psychiatric patient.

Background Actigraphy may provide a more valid assessment of sleep, circadian rhythm (CR), and physical activity (PA) than self-reported questionnaires, but has not been used widely to study the association with depression/anxiety and their clinical characteristics. Methods Fourteen-day actigraphy data of 359 participants with current (n = 93), remitted (n = 176), or no (n = 90) composite international diagnostic interview depression/anxiety diagnoses were obtained from the Netherlands Study of Depression and Anxiety. Objective estimates included sleep duration (SD), sleep efficiency, relative amplitude (RA) between day-time and night-time activity, mid sleep on free days (MSF), gross motor activity (GMA), and moderate-to-vigorous PA (MVPA). Self-reported measures included insomnia rating scale, SD, MSF, metabolic equivalent total, and MVPA. Results Compared to controls, individuals with current depression/anxiety had a significantly different objective, but not self-reported, PA and CR: lower GMA (23.83 vs. 27.4 milli-gravity/day, p = .022), lower MVPA (35.32 vs. 47.64 min/day, p = .023), lower RA (0.82 vs. 0.83, p = .033). In contrast, self-reported, but not objective, sleep differed between people with current depression/anxiety compared to those without current disorders; people with current depression/anxiety reported both shorter and longer SD and more insomnia. More depressive/anxiety symptoms and number of depressive/anxiety diagnoses were associated with larger disturbances of the actigraphy measures. Conclusion Actigraphy provides ecologically valid information on sleep, CR, and PA that enhances data from self-reported questionnaires. As those with more severe or comorbid forms showed the lowest PA and most CR disruptions, the potential for adjunctive behavioral and chronotherapy interventions should be explored, as well as the potential of actigraphy to monitor treatment response to such interventions.