The inheritance of cardiovascular disease risk

Cardiovascular disease (CVD) is foremost among the non‐communicable diseases (NCDs) which account for 71% of deaths globally each year. CVD is also prominent among the pre‐existing conditions still accounting for nearly 25% of maternal deaths and is linked to gestational diabetes and pre‐eclampsia. Markers of CVD risk have been reported even in young children, related to prenatal factors such as mother's diet or body composition. The underlying mechanisms include epigenetic changes which can alter the trajectory of risk across the life course. Preventive interventions need to commence before conception, to reduce transmission of CVD risk by promoting healthy behaviours in prospective parents, as well as in pregnancy, and postpartum through breastfeeding and healthy complementary feeding. Surprisingly, these opportunities are not emphasised in the 2018 United Nations Political Declaration on NCDs. NCDs such as CVD have communicable risk components transmitted across generations by socio‐economic as well as biological factors, although the former can also become embodied in the offspring by epigenetic mechanisms. The inheritance of CVD risk, and social inequalities in such risk, thus raises wider questions about responsibility for the health of future generations at societal as well as individual levels

Determinants of black carbon, particle mass and number concentrations in London transport microenvironments

We investigated the determinants of personal exposure concentrations black carbon (BC), ultrafine particle number concentrations (PNC), and particulate matter (PM1, PM2.5 and PM10) in different travel modes. We quantified the contribution of key factors that explain the variation of the previous pollutants in four commuting routes in London, each covered by four transport modes (car, bus, walk and underground). Models were performed for each pollutant, separately to assess the effect of meteorology (wind speed) or ambient concentrations (with either high spatial or temporal resolution). Concentration variations were mainly explained by wind speed or ambient concentrations and to a lesser extent by route and period of the day. In multivariate models with wind speed, the wind speed was the common significant predictor for all the pollutants in the above-ground modes (i.e., car, bus, walk); and the only predictor variable for the PM fractions. Wind speed had the strongest effect on PM during the bus trips, with an increase in 1 m s-1 leading to a decrease in 2.25, 2.90 and 4.98 μg m-3 of PM1, PM2.5 and PM10, respectively. PM2.5 and PM10 concentrations in car trips were better explained by ambient concentrations with high temporal resolution although from a single monitoring station. On the other hand, ambient 32 concentrations with high spatial coverage although lower temporal resolution predicted better the concentrations in bus trips, due to bus routes passing through streets with a high variability of traffic intensity. In the underground models, wind speed was not significant and line and type of windows on the train explained 42% of the variation of PNC and 90% of all PM fractions. Trains in the district line with openable windows had an increase in concentrations of 1684 cm-3 for PNC and 40.69 μg m-3 for PM2.5 compared with trains that has non-openable windows. The results from this work can be used to target efforts to reduce personal exposures of London commuters