SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe

Aims The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe.Methods and results We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low- risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries.Conclusion SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe.Cardiolog

Prospects for increasing the resolution of crop diversity for agroecosystem service delivery in a Dutch arable system

Finding a balance between the agroecological benefits of in-field crop diversification and the associated management demands, while maintaining expected production levels, is essential for making crop diversity work for farmers. The aim of this study was to find a workable resolution of diversity within the context of an on-station organic arable cropping systems experiment in the Netherlands. The experiment tested a gradient of crop diversity treatments from sole-crop references to strip cropping (3 m x 54 m strips sown in adjacent crop pairs of varying complexity) and pixel cropping (0.25 m2 plots each sown with one out of six total crops and arranged in 7.5 m x 12 m grids). In these treatments we assessed the performance of multiple agroecosystem service (AES) indicators (soil fertility, crop yield and quality, weed cover and diversity, and natural enemy activity density) for three focal crops (cabbage, wheat, and potato) using three years of field data and a three-part analysis. First, we used linear mixed models to assess the effects of each diversification treatment on the AES indicators. We found no clear indication that one treatment performed better than the rest across AES indicators. Second, we developed a novel method for quantifying the temporal, spatial, and genetic structural diversity of the tested treatments into compound diversity scores, and used these scores to analyze response relationships between increasing in-field diversity and AES delivery. Here we found that increasing compound diversity had a positive effect on the indicators weed species diversity and natural enemy activity density. For production indicators, we observed an inflection point between the most diverse strip cropping treatment and the pixel cropping treatment, with pixel cropping performing notably poorly. Third, we used a multivariate analysis approach to assess the contribution of temporal, spatial, and genetic diversity to AES delivery, but found no clear effects of individual diversity dimensions on AES delivery. These findings suggest that prospects for strip cropping are better than for pixel cropping when it comes to balancing production aims with increases in other AES while also maintaining management feasibility. Reconciling management shortcomings in highly diverse cropping systems (e.g. through the development of appropriate technologies) may be one way to mitigate trade-offs between ecological and production aims at resolutions of diversity higher than strip cropping