AimsCohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research.Methods and resultsWe sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources for higher resolution clinical epidemiology and public health.ConclusionHigh volumes of inherently diverse (‘big’) EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare.

Objective. The aim of this study was to assess mortality amongst participants in long-distance ski races during the Vasaloppet week. We considered the 90 km races for men and 90 or 30 km for women. The vast majority of the participants in these races are not competing on the elite level. It is assumed, however, that they have to undergo regular physical training during a long period of time in order to successfully finish the race. Design. The cohort study consisted of 49 219 men and 24 403 women, who participated in any of the races during 1989-1998. All subjects were followed up in the National-Cause-of-Death-Register until 31 December 1999. We computed the standardized mortality ratios (SMRs) adjusting for age and calendar year.Results. Overall, 410 deaths occurred, compared with 850.6 expected, yielding an SMR of 0.48 [95% confidence interval (CI) 0.44-0.53]. Low SMRs were found in all age groups in both men and women and in all groups after categorization by finishing time and number of races. The lowest SMRs were found amongst older participants and in those who participated in several races. A decreased mortality was observed in all major diagnostic groups, namely cancers (SMR ¼ 0.61; 95% CI 0.52-0.71), diseases of the circulatory system (SMR ¼ 0.43; 95% CI 0.35-0.51), and injuries and poisoning (SMR ¼ 0.73; 95% CI 0.60-0.89). For lung cancer the SMR was 0.22, but even after exclusion of lung cancer the all-cancer mortality was low (SMR ¼ 0.72; 95% CI 0.59-0.86). Conclusions. We conclude that participants in longdistance skiing races, which demand prolonged regular physical training, have low mortality. The extent to which this is due to physical activity, related lifestyle factors, genetics or selection bias is yet to be assessed.

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Both height and BMI in early adulthood are strongly and inversely associated with risk of schizophrenia. We discuss these findings in relation to possible genetic and nutritional causal mechanisms.