Predicting the prevalence of type 2 diabetes in Brazil: a modeling study

AimsWe adopted a modeling approach to predict the likely future prevalence of type 2 diabetes, taking into account demographic changes and trends in obesity and smoking in Brazil. We then used the model to estimate the likely future impact of different policy scenarios, such as policies to reduce obesity.MethodsThe IMPACT TYPE 2 DIABETES model uses a Markov approach to integrate population, obesity, and smoking trends to estimate future type 2 diabetes prevalence. We developed a model for the Brazilian population from 2006 to 2036. Data on the Brazilian population in relation to sex and age were collected from the Brazilian Institute of Geography and Statistics, and data on the prevalence of type 2 diabetes, obesity, and smoking were collected from the Surveillance of Risk and Protection Factors for Chronic Diseases by Telephone Survey (VIGITEL).ResultsThe observed prevalence of type 2 diabetes among Brazilians aged over 25 years was 10.8% (5.2–14.3%) in 2006, increasing to 13.7% (6.9–18.4%) in 2020. Between 2006 and 2020, the observed prevalence in men increased from 11.0 to 19.1% and women from 10.6 to 21.3%. The model forecasts a dramatic rise in prevalence by 2036 (27.0% overall, 17.1% in men and 35.9% in women). However, if obesity prevalence declines by 1% per year from 2020 to 2036 (Scenario 1), the prevalence of diabetes decreases from 26.3 to 23.7, which represents approximately a 10.0% drop in 16 years. If obesity declined by 5% per year in 16 years as an optimistic target (Scenario 2), the prevalence of diabetes decreased from 26.3 to 21.2, representing a 19.4% drop in diabetes prevalence.ConclusionThe model predicts an increase in the prevalence of type 2 diabetes in Brazil. Even with ambitious targets to reduce obesity prevalence, type 2 diabetes in Brazil will continue to have a large impact on Brazilian public health

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

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

Glutamine or whey-protein supplementation on alloxan-induced diabetic rats. Effects on CD4+ and CD8+ lymphocytes Efeitos da oferta de glutamina ou de proteína do soro de leite sobre os linfócitos CD4+ e CD8+ em ratos diabéticos aloxano induzidos

PURPOSE: To evaluate the effects of glutamine (L-Gln) or whey-protein supplementation on CD4+ and CD8+ lymphocytes in alloxan-induced diabetic rats. METHODS: Thirty-two healthy male Wistar rats were used in the experiment. Eight rats served as baseline controls (G-1). The remaining 24 animals received alloxan 150mg/Kg intraperitonially dissolved in buffer solution and were equally distributed in 3 subgroups, upon induction of diabetes mellitus, and treated as follows: (G2): saline, 2.0ml; (G3): glutamine solution (0.7g/kg), 2.0 ml; and (G4): whey-protein (WPS) solution (0.7g/kg), 2.0 ml. All solutions were administered by daily 7:00 AM gavages during 30 days. Next, arterial blood samples (3.0 ml) were collected from anesthetized rats for CD4+ and CD8+ lymphocyte count through flow cytometry technology. RESULTS: CD4+ and CD8+ counts decreased significantly in all groups compared with baseline values (G1). G2 rats CD4+/CD8+ ratio decreased significantly compared with G1. CD4+/CD8+ ratio increased significantly (>260%) in L-Gln treated group (G3) compared with saline-treated rats (G2). There were no statistical differences in lymphocyte counts (CD4+ and CD8+) between L-Gln (G3) and saline-treated (G2) groups. There was a significant reduction in CD8+ cell count compared with CD4+ cell count in L-Gln treated rats (G3). CONCLUSION: The offer of L-Gln to experimental diabetic rats enhances the immunologic response to infection.<br>OBJETIVO: Avaliar os efeitos da suplementação de glutamina ou proteína do soro de leite ( PSL) sobre os linfócitos CD4+ e CD8+ em ratos diabéticos aloxano induzidos. MÉTODOS: Trinta e dois ratos Wistar machos, saudáveis, foram utilizados no estudo. Oito ratos foram usados como controles basais (G1). Os 24 animais remanescentes foram equitativamente distribuídos em 3 subgrupos, após indução do diabetes mellitus por injeção intraperitonial de aloxano (150mg/Kg) e tratados como se segue: (G2): salina; (G3): 2,0 ml de solução de glutamina (0,75g/Kg);(G4): PSL, (0,7g/Kg), 2,0ml. Todas as soluções foram administradas por gavagem, diariamente, cada 7:00 h, durante 30 dias. Após esse período, foram coletadas amostradas de sangue arterial para contagem de linfócitos CD4+ e CD8+ por citometria de fluxo. RESULTADOS: A população de linfócitos CD4+ e CD8+ diminuiu significantemente em todos os grupos em comparação aos valores encontrados no grupo G1. A razão CD4+/CD8+ foi significantemente maior (>260%) nos ratos tratados com L-Gln que nos ratos tratados com salina (G2). Não se observaram diferenças significantes na população de linfócitos CD4+ e CD8+ entre os grupos G3 e G2. Houve redução significante do número de células CD8+ quando comparado ao número de células CD4+ nos ratos tratados com L-Gln (G3). CONCLUSÃO: A oferta de L-Gln em ratos diabéticos aloxano-induzidos melhora a resposta imunológica à infecção