Glucose and triglyceride excursions following a standardized meal in individuals with diabetes: ELSA-Brasil study

Objective: To assess glucose and triglyceride excursions 2 hours after the ingestion of a standardized meal and their associations with clinical characteristics and cardiovascular complications in individuals with diabetes. Research design and methods: Blood samples of 898 subjects with diabetes were collected at fasting and 2 hours after a meal containing 455 kcal, 14 g of saturated fat and 47 g of carbohydrates. Self-reported morbidity, socio-demographic characteristics and clinical measures were obtained by interview and exams performed at the baseline visit of the ELSA-Brasil cohort study. Results: Median (interquartile range, IQR) for fasting glucose was 150.5 (123–198) mg/dL and for fasting triglycerides 140 (103–199) mg/dL. The median excursion for glucose was 45 (15–76) mg/dL and for triglycerides 26 (11–45) mg/dL. In multiple linear regression, a greater glucose excursion was associated with higher glycated hemoglobin (10.7, 95% CI 9.1–12.3 mg/dL), duration of diabetes (4.5; 2.6–6.4 mg/dL, per 5 year increase), insulin use (44.4; 31.7–57.1 mg/dL), and age (6.1; 2.5–9.6 mg/dL, per 10 year increase); and with lower body mass index (−5.6; −8.4– -2.8 mg/dL, per 5 kg/m2 increase). In adjusted logistic regression models, a greater glucose excursion was marginally associated with the presence of cardiovascular comorbidities (coronary heart disease, myocardial infarction and angina) in those with obesity. Conclusions: A greater postprandial glycemic response to a small meal was positively associated with indicators of a decreased capacity for insulin secretion and negatively associated with obesity. No pattern of response was observed with a greater postprandial triglyceride excursion

Brazilian dietary patterns and the dietary approaches to stop hypertension (DASH) diet-relationship with metabolic syndrome and newly diagnosed diabetes in the ELSA-Brasil study

Background: Studies evaluating dietary patterns, including the DASH diet, and their relationship with the metabolic syndrome and diabetes may help to understand the role of dairy products (low fat or full fat) in these conditions. Our aim is to identify dietary patterns in Brazilian adults and compare them with the (DASH) diet quality score in terms of their associations with metabolic syndrome and newly diagnosed diabetes in the Brazilian Longitudinal Study of Adult Health-the ELSA-Brasil study. Methods: The ELSA-Brasil is a multicenter cohort study comprising 15,105 civil servants, aged 35–74 years at baseline (2008–2010). Standardized interviews and exams were carried out, including an OGTT. We analyzed baseline data for 10,010 subjects. Dietary patterns were derived by principal component analysis. Multivariable logistic regression investigated associations of dietary patterns with metabolic syndrome and newly diagnosed diabetes and multivariable linear regression with components of metabolic syndrome. Results: After controlling for potential confounders, we observed that greater adherence to the Common Brazilian meal pattern (white rice, beans, beer, processed and fresh meats), was associated with higher frequencies of newly diagnosed diabetes, metabolic syndrome and all of its components, except HDL-C. Participants with greater intake of a Common Brazilian fast foods/full fat dairy/milk based desserts pattern presented less newly diagnosed diabetes. An inverse association was also seen between the DASH Diet pattern and the metabolic syndrome, blood pressure and waist circumference. Diet, light foods and beverages/low fat dairy pattern was associated with more prevalence of both outcomes, and higher fasting glucose, HDL-C, waist circumference (among men) and lower blood pressure. Vegetables/fruit dietary pattern did not protect against metabolic syndrome and newly diagnosed diabetes but was associated with lower waist circumference. Conclusions: The inverse associations found for the dietary pattern characterizing Brazilian fast foods and desserts, typically containing dairy products, with newly diagnosed diabetes, and for the DASH diet with metabolic syndrome, support previously demonstrated beneficial effects of dairy products in metabolism. The positive association with metabolic syndrome and newly diagnosed diabetes found for the pattern characterizing a typical Brazilian meal deserves further investigation, particularly since it is frequently accompanied by processed meat

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

How much leaf area do insects eat? A data set of insect herbivory sampled globally with a standardized protocol

Herbivory is ubiquitous. Despite being a potential driver of plant distribution and performance, herbivory remains largely undocumented. Some early attempts have been made to review, globally, how much leaf area is removed through insect feeding. Kozlov et al., in one of the most comprehensive reviews regarding global patterns of herbivory, have compiled published studies regarding foliar removal and sampled data on global herbivory levels using a standardized protocol. However, in the review by Kozlov et al., only 15 sampling sites, comprising 33 plant species, were evaluated in tropical areas around the globe. In Brazil, which ranks first in terms of plant biodiversity, with a total of 46,097 species, almost half (43%) being endemic, a single data point was sampled, covering only two plant species. In an attempt to increase knowledge regarding herbivory in tropical plant species and to provide the raw data needed to test general hypotheses related to plant–herbivore interactions across large spatial scales, we proposed a joint, collaborative network to evaluate tropical herbivory. This network allowed us to update and expand the data on insect herbivory in tropical and temperate plant species. Our data set, collected with a standardized protocol, covers 45 sampling sites from nine countries and includes leaf herbivory measurements of 57,239 leaves from 209 species of vascular plants belonging to 65 families from tropical and temperate regions. They expand previous data sets by including a total of 32 sampling sites from tropical areas around the globe, comprising 152 species, 146 of them being sampled in Brazil. For temperate areas, it includes 13 sampling sites, comprising 59 species

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others