Although a large sample size is useful for the elucidation of what is called ‘missing heritability’, which is an unexplained part of a total phenotypic variance in a quantitative trait or familial disease aggregation, in prospective genome cohort studies, additional strategies may be required.¹ Historically, many patient-based cohort studies have been conducted with limited environmental information for the period preceding the onset of diseases.^2–6 Population-based prospective cohort designs have since been improved, and many risk factors for diseases have been identified.^7–10 Once the importance of in utero exposures was recognized, birth cohorts were also developed,^11–18 and were used to examine the lifelong effects of in utero exposures.¹⁹ Because a study assessing disease risk throughout a person’s lifespan requires exposure and outcome measurements in multiple generations with long-term follow-up, a three-generation prospective cohort design has emerged.^20,21 In accordance with the development of genome cohort designs, birth and three-generation cohort studies combined with genome and omics information appear to be necessary. However, because of the difficulty of this study design and the actual operation of recruiting and following-up pregnant women, babies, husbands (or partners) and grandparents of babies, no group in the world has ever tried this type of cohort study from an initial stage of cohort construction (Figure 1).

BackgroundWe established a community-based cohort study to assess the long-term impact of the Great East Japan Earthquake on disaster victims and gene-environmental interactions on the incidence of major diseases such as cancer and cardiovascular diseases.MethodsWe asked participants to join our cohort in the health check-up settings and assessment center based settings. Inclusion criteria was aged 20 years or over and living in Miyagi or Iwate Prefecture. We obtained information on lifestyle, effect of disaster, blood, and urine information (Type 1 survey), and some detailed measurements (Type 2 survey), for example, carotid echography, calcaneal ultrasound bone mineral density, and so on. All participants agreed to measure genome information and to distribute their information widely.ResultsAs a result, 87,865 gave their informed consent to join our study. Participation rate at health check-up site was about 70%. The participants with Type 1 survey were more likely to have psychological distress than those of Type 2 survey, and women were more likely to have psychological distress than men. Additionally, coastal residents were more likely to have higher degrees of psychological distress than inland residents regardless of sex.ConclusionThis cohort comprised large sample size and it contains information on disaster, genome information, and metabolome information. This cohort also had several detailed measurements. Using this cohort enabled us to clarify the long-term effect of disaster and also to establish personalized prevention based on genome, metabolome, and other omics information.

Screen time is the amount of time that individuals spend watching television, playing video games, and using mobile phones, tablets, and other electronic devices. To ensure that children engage in physical activity and obtain adequate sleep for healthy growth and well-being, the World Health Organization¹ and the American Academy of Pediatrics² have issued guidelines that recommend limiting screen time for children, including a limit of 1 hour per day for children aged 2 to 5 years.² However, a recent meta-analysis reported that only a minority of children meet these guidelines.³ In addition, children’s screen time has increased in recent years because of the rapid proliferation of digital devices and the COVID-19 pandemic.^4-6 Therefore, it is essential to consider how screen time affects child development.

Blood pressure variability based on office measurement predicts outcome in selected patients. We explored whether novel indices of blood pressure variability derived from the self-measured home blood pressure predicted outcome in a general population. We monitored mortality and stroke in 2421 Ohasama residents (Iwate Prefecture, Japan). At enrollment (1988–1995), participants (mean age, 58.6 years; 60.9% women; 27.1% treated) measured their blood pressure at home, using an oscillometric device. In multivariable-adjusted Cox models, we assessed the independent predictive value of the within-subject mean systolic blood pressure (SBP) and corresponding variability as estimated by variability independent of the mean, difference between maximum and minimum blood pressure, and average real variability. Over 12.0 years (median), 412 participants died, 139 of cardiovascular causes, and 223 had a stroke. In models including morning SBP, variability independent of the mean and average real variability (median, 26 readings) predicted total and cardiovascular mortality in all of the participants ( P ≤0.044); variability independent of the mean predicted cardiovascular mortality in treated ( P =0.014) but not in untreated ( P =0.23) participants; and morning maximum and minimum blood pressure did not predict any end point ( P ≥0.085). In models already including evening SBP, only variability independent of the mean predicted cardiovascular mortality in all and in untreated participants ( P ≤0.046). The R ² statistics, a measure for the incremental risk explained by adding blood pressure variability to models already including SBP and covariables, ranged from <0.01% to 0.88%. In a general population, new indices of blood pressure variability derived from home blood pressure did not incrementally predict outcome over and beyond mean SBP.