Hábitos alimentares e risco de doenças cardiovasculares em universitários

Modelo de estudo: Pesquisa descritiva, observacional, transversal. Objetivo: Descrever qualitativamente a frequência de ingestão de determinados alimentos, considerados de risco e de proteção para doenças cardiovasculares (DCV), além de determinar o Escore de Risco de Framingham (ERF) em indivíduos supostamente saudáveis, estudantes de graduação de uma universidade pública brasileira. Metodologia: Participaram 97 estudantes, 45 homens e 52 mulheres, na faixa etária de 18 a 25 anos. Após assinatura do Termo de Consentimento Livre e Esclarecido (TCLE), aprovado pelo Comitê de Ética em Pesquisa, os estudantes preencheram um questionário com os dados da pesquisa. Foram estudados alimentos classificados como de risco e de proteção, conforme sua composição química avaliada por tabelas de alimentos. O teste Qui-quadrado fui utilizado quando as frequências esperadas foram iguais ou superiores a 5. Para os demais parâmetros foi utilizado o teste exato de Fischer. Resultados: Entre os alimentos protetores destacou-se a ingestão diária de legumes (33%), verduras (22%) e frutas (17%) e entre os de risco estão a ingestão diária de café/chás com açúcar (39%), maionese/ margarina/manteiga (34%) e doces (14%). Houve variação de consumo conforme o sexo, para as frequências de 0,1, 2 e 4 vezes por semana para os alimentos: farinha de milho/mandioca, biscoito maisena/caseiro/água e sal, aveia, frango com pele. Houve variação significativa de consumo diário entre os sexos para os alimentos: frutas, doces, maionese/margarina/manteiga, biscoito maisena/caseiro/ água e sal. Conclusões: Este estudo demonstrou que os estudantes universitários apresentaram uma maior frequência diária de ingestão de alimentos considerados de proteção para DCV do que alimentos de risco. Em adição, o ERF calculado demonstrou baixo risco de desenvolvimento de DCV nos indivíduos avaliados.Study Design: Descriptive, observational, cross-sectional. Aims: The aims of this study were to qualitatively describe the frequency of eating certain foods, as risk and protective for cardiovascular disease (CVD), and determine the Framingham Risk Score (FRS) in supposedly healthy individuals, graduate students from a Brazilian public university. Methodology: Participants were 97 students, 45 males and 52 females, 18-25 years. After signing the consent form, approved by the Ethics Committee on Research, students completed a questionnaire survey data. We studied food classified as risk and protection, as assessed by its chemical composition tables. The Chi-square test was used when the expected frequencies greater than or equal to 5. For other parameters we used the Fisher exact test. Results: Among the foods considered protective stood out the daily intake of vegetables (33%), greens (22%) and fruit (17%). Among the foods considered at risk are the daily intake of coffee / tea with sugar (39%), mayonnaise / margarine / butter (34%) and sweets (14%). There was variation in consumption according to sex, for the frequencies of 0, 1, 2 and 4 times a week for food: corn flour / cassava, cornstarch cookie /homemade cookie / water and salt cookie, oats, chicken with skin. There was significant variation in daily consumption between the sexes for food: fruit, sweets, mayonnaise / margarine / butter, cornstarch cookie / homemade cookie / water and salt cookie. Conclusion: This study showed that college students presented higher frequency of daily intake of foods considered protective for CVD than risk food. In addition, the calculated FRS demonstrated low risk of developing CVD in studied individuals

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