New indices in predicting cardiometabolic risk and its relation to endothelial dysfunction in adolescents: The HELENA study

[Background and aims]: Blood pressure (BP) changes and insulin resistance (IR) are important cardiometabolic risk (CMR) factors; their early identification can contribute to the reduction of cardiovascular events in adulthood. This necessitates the search for more accessible and easily applied indicators for their prediction. Therefore, this study aimed to evaluate the predictive power of the indices, TyG, TG/HDL-c, height-corrected lipid accumulation product (HLAP), and visceral adiposity index (VAI), in identifying the CMR obtained by high BP and IR and to verify their relationship with biomarkers of endothelial dysfunction (ED) in European adolescents.[Methods and results]: The anthropometric data and blood biomarkers of 744 adolescents (343 boys and 401 girls) from the Healthy Lifestyle in Europe by Nutrition in Adolescence Cross-Sectional Study (HELENA-CSS), with a mean age of 14.67 (SD 1.15) years, were assessed. The adolescents were then classified according to the presence or absence of high BP and IR. The cut-off points of the indices evaluated for the identification of CMR were determined. The relationship between CMR diagnosed using these indices and ED biomarkers was tested. The HLAP and TG/HDL-c were fair predictors of CMR obtained by IR in male adolescents. These indices showed association with hsCRP in sVCAM-1 in boys, but it lost significance after adjusting for age and body mass index.[Conclusion]: TG/HDL-c and HLAP indices showed a fair performance in predicting CMR, obtained by IR, in male adolescents. ED showed no association with the CMR identified by the indices.This work was supported by the European Community's Sixth RTD Framework Program (Contract FOOD-CT-2005-007034).Peer reviewe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio