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OBJECTIVES: The prevalence of overweight increases the risk of several non-communicable diseases (NCDs) and, consequently, the costs of health care systems. In this study, we aimed to project the economic burden of NCDs attributable to overweight in Brazil between 2021 and 2030. METHODS: A cohort simulation of adults (17-117 years) using multistate lifetable modeling was used to estimate the costs of NCDs attributable to overweight in Brazil. The projections of direct health care costs (outpatient and inpatient expenses in the Unified Health System) and indirect costs (years of productive life lost) considered different trajectories of the prevalence of overweight between 2021 and 2030. RESULTS: In 2019, the prevalence of overweight was 55.4% in the adult Brazilian population. We estimate that around 1.8 billion international dollars (Int$) would be spent on the direct health care cost of NCDs between 2021 and 2030, through the continued increase in overweight prevalence observed between 2006 and 2020. The indirect costs over the same time would be approximately 20.1 billion Int$. We estimate that halving the annual increase in body mass index slope from the beginning of 2021 until 2030 would save 20.2 million Int$ direct and indirect costs by 2030. In the scenario of keeping the prevalence of overweight observed in 2019 constant until 2030, the savings would be 40.8 million Int$. Finally, in the scenario of a 6.7% reduction in the prevalence of overweight observed in 2019 (to be achieved gradually until 2030), 74.1 million Int$ would be saved. CONCLUSIONS: These results highlight the high economic burden of overweight in the Brazilian adult population.

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OBJECTIVES: Lack of sufficient physical activity (PA) has been associated with an increased risk of several non-communicable diseases (NCDs) and all-cause mortality. This study aimed to estimate the number of preventable incidence cases of NCDs attributable to insufficient PA in the Chilean population. STUDY DESIGN: Comparative risk assessment modelling study. METHODS: This study examined data from 5834 participants aged ≥20 years from the Chilean National Survey (2016-2017). PA was assessed by the Global Physical Activity Questionnaire (GPAQ), and metabolic equivalent of tasks (METs) were assigned according to PA intensity. Estimated incidence cases of NCDs in Chile in 2019 were obtained from the Global Burden of Disease study. Relative risks for breast cancer, colon cancer, ischaemic heart disease, diabetes and stroke were obtained from a published meta-analysis and applied to the prevalence of insufficient PA estimates through the potential impact fraction equation. RESULTS: High levels of PA (≥8000 MET-min/week) could potentially avoid more than 22,000 (64.6 %) incidence NCD cases, ranging from 498 (10.1 %) preventable cases of breast cancer to 5629 (14.7 %) cases of diabetes. Other modelled scenarios also showed to reduce the incidence cases of all five NCDs but to a lesser extent; where at least PA recommendation was achieved, preventable NCDs were reduced by 6522 cases (18.7 %), and where a 10 % relative reduction in insufficient PA level in the population was achieved, preventable NCDs were reduced by 651 (1.8 %) cases. CONCLUSIONS: The study results provide estimates for the incidence cases of preventable NCDs attributable to insufficient PA, highlighting the important role of PA in NCD prevention in Chile.

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OBJECTIVES: Parallel to rising obesity prevalence in Brazil, there is expected to be increased direct health care costs related to non-communicable diseases (NCDs). In this study, we estimated the economic burden of NCDs attributable to overweight and obesity in the Brazilian Unified Health System (SUS). METHODS: We used self-reported body mass index of 85,715 adults from the 2019 Brazilian National Health Survey. Annual costs (1 US$ = 2.281 Reais) with inpatient and outpatient procedures were obtained from the Hospital and Ambulatory Information Systems of the Brazilian SUS. Relative risks for cardiovascular disease, chronic respiratory disease, neoplasm, digestive disease, musculoskeletal disorders, diabetes and kidney diseases, sense organ diseases, and neurological disorders were retrieved from the Global Burden of Disease study. RESULTS: Annually, US$ 654 million (95% uncertainty interval: US$ 418.4 to US$ 893.2) direct health care costs related to NCDs were attributable to overweight and obesity. Attributable costs were higher in women than men. Cardiovascular diseases had the highest attributable costs (US$ 289 million), followed by chronic respiratory diseases (US$ 110 million), neoplasms (US$ 96 million), digestive diseases (US$ 60 million), musculoskeletal disorders (US$ 44 million), diabetes and kidney disease (US$ 31 million), sense organ diseases (US$ 22 million) and neurological disorders (US$ 11 million). CONCLUSIONS: Overweight and obesity account for US$ 654 million direct costs of NCDs annually. Effective policies to promote healthy body weight may have economic benefits.

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Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

Resumo A região semiárida do nordeste do Brasil, a Caatinga, é extremamente importante devido à sua biodiversidade e endemismo. Medidas de fisiologia vegetal são cruciais para a calibração de Modelos de Vegetação Globais Dinâmicos (DGVMs) que são atualmente usados para simular as respostas da vegetação diante das mudanças globais. Em um trabalho de campo realizado em uma área de floresta preservada na Caatinga localizada em Petrolina, Pernambuco, medidas de assimilação de carbono (em resposta à luz e ao CO2) foram realizadas em 11 indivíduos de Poincianella microphylla, uma espécie nativa que é abundante nesta região. Estes dados foram utilizados para calibrar a velocidade máxima de carboxilação (Vcmax) usada no modelo INLAND. As técnicas de calibração utilizadas foram Regressão Linear Múltipla (MLR) e técnicas de mineração de dados como Classification And Regression Tree (CART) e K-MEANS. Os resultados foram comparados com o modelo INLAND não calibrado. Verificou-se que a Produtividade Primária Bruta (PPB) simulada atingiu 72% da PPB observada ao usar os valores de Vcmax calibrado, enquanto que o modelo não calibrado obteve-se 42% da PPB observada. Assim, este trabalho mostra os benefícios de calibrar DGVMs usando medidas ecofisiológicas de campo, especialmente em áreas onde os dados de campo são escassos ou inexistentes, como na Caatinga.

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Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.(AU)

Resumo A região semiárida do nordeste do Brasil, a Caatinga, é extremamente importante devido à sua biodiversidade e endemismo. Medidas de fisiologia vegetal são cruciais para a calibração de Modelos de Vegetação Globais Dinâmicos (DGVMs) que são atualmente usados para simular as respostas da vegetação diante das mudanças globais. Em um trabalho de campo realizado em uma área de floresta preservada na Caatinga localizada em Petrolina, Pernambuco, medidas de assimilação de carbono (em resposta à luz e ao CO2) foram realizadas em 11 indivíduos de Poincianella microphylla, uma espécie nativa que é abundante nesta região. Estes dados foram utilizados para calibrar a velocidade máxima de carboxilação (Vcmax) usada no modelo INLAND. As técnicas de calibração utilizadas foram Regressão Linear Múltipla (MLR) e técnicas de mineração de dados como Classification And Regression Tree (CART) e K-MEANS. Os resultados foram comparados com o modelo INLAND não calibrado. Verificou-se que a Produtividade Primária Bruta (PPB) simulada atingiu 72% da PPB observada ao usar os valores de Vcmax calibrado, enquanto que o modelo não calibrado obteve-se 42% da PPB observada. Assim, este trabalho mostra os benefícios de calibrar DGVMs usando medidas ecofisiológicas de campo, especialmente em áreas onde os dados de campo são escassos ou inexistentes, como na Caatinga.(AU)

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The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

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Dynamic global vegetation models (DGVMs) simulate surface processes such as the transfer of energy, water, CO2, and momentum between the terrestrial surface and the atmosphere, biogeochemical cycles, carbon assimilation by vegetation, phenology, and land use change in scenarios of varying atmospheric CO2 concentrations. DGVMs increase the complexity and the Earth system representation when they are coupled with atmospheric global circulation models (AGCMs) or climate models. However, plant physiological processes are still a major source of uncertainty in DGVMs. The maximum velocity of carboxylation (Vcmax), for example, has a direct impact over productivity in the models. This parameter is often underestimated or imprecisely defined for the various plant functional types (PFTs) and ecosystems. Vcmax is directly related to photosynthesis acclimation (loss of response to elevated CO2), a widely known phenomenon that usually occurs when plants are subjected to elevated atmospheric CO2 and might affect productivity estimation in DGVMs. Despite this, current models have improved substantially, compared to earlier models which had a rudimentary and very simple representation of vegetation-atmosphere interactions. In this paper, we describe this evolution through generations of models and the main events that contributed to their improvements until the current state-of-the-art class of models. Also, we describe some main challenges for further improvements to DGVMs.

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Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

Resumo A região semiárida do nordeste do Brasil, a Caatinga, é extremamente importante devido à sua biodiversidade e endemismo. Medidas de fisiologia vegetal são cruciais para a calibração de Modelos de Vegetação Globais Dinâmicos (DGVMs) que são atualmente usados para simular as respostas da vegetação diante das mudanças globais. Em um trabalho de campo realizado em uma área de floresta preservada na Caatinga localizada em Petrolina, Pernambuco, medidas de assimilação de carbono (em resposta à luz e ao CO2) foram realizadas em 11 indivíduos de Poincianella microphylla, uma espécie nativa que é abundante nesta região. Estes dados foram utilizados para calibrar a velocidade máxima de carboxilação (Vcmax) usada no modelo INLAND. As técnicas de calibração utilizadas foram Regressão Linear Múltipla (MLR) e técnicas de mineração de dados como Classification And Regression Tree (CART) e K-MEANS. Os resultados foram comparados com o modelo INLAND não calibrado. Verificou-se que a Produtividade Primária Bruta (PPB) simulada atingiu 72% da PPB observada ao usar os valores de Vcmax calibrado, enquanto que o modelo não calibrado obteve-se 42% da PPB observada. Assim, este trabalho mostra os benefícios de calibrar DGVMs usando medidas ecofisiológicas de campo, especialmente em áreas onde os dados de campo são escassos ou inexistentes, como na Caatinga.

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We performed a systematic review of the prevalence of metabolically healthy obesity (MHO). Medline, Web of Science and EMBASE were searched for original articles from inception to November 2013. Only prospective and cross-sectional studies were included. After screening 478 titles, we selected 55 publications, of which 27 were population-based studies and were used in the narrative synthesis. From the 27 studies, we identified 30 definitions of metabolic health, mainly based on four criteria: blood pressure, high-density lipoprotein cholesterol, triglycerides and plasma glucose. Body mass index ≥30 kg m(-2) was the main indicator used to define obesity (74% of the studies). Overall, MHO prevalence ranged between 6% and 75%. In the studies that stratified the analysis by sex, prevalence was higher in women (seven out of nine studies) and in younger ages (all four studies). One-third of the studies (n = 9) reported the response rate. Of these, four reported a response rate of ≥70% and they showed MHO prevalence estimates between 10% and 51%. The heterogeneity of MHO prevalence estimates described in this paper strengthens calls for the urgent need for a commonly established metabolic health definition.

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Insulin clearance plays a major role in glucose homeostasis and insulin sensitivity in physiological and/or pathological conditions, such as obesity-induced type 2 diabetes as well as diet-induced obesity. The aim of the present work was to evaluate cafeteria diet-induced obesity-induced changes in insulin clearance and to explain the mechanisms underlying these possible changes. Female Swiss mice were fed either a standard chow diet (CTL) or a cafeteria diet (CAF) for 8 weeks, after which we performed glucose tolerance tests, insulin tolerance tests, insulin dynamics, and insulin clearance tests. We then isolated pancreatic islets for ex vivo glucose-stimulated insulin secretion as well as liver, gastrocnemius, visceral adipose tissue, and hypothalamus for subsequent protein analysis by western blot and determination of mRNA levels by real-time RT-PCR. The cafeteria diet induced insulin resistance, glucose intolerance, and increased insulin secretion and total insulin content. More importantly, mice that were fed a cafeteria diet demonstrated reduced insulin clearance and decay rate as well as reduced insulin-degrading enzyme (IDE) protein and mRNA levels in liver and skeletal muscle compared with the control animals. Furthermore, the cafeteria diet reduced IDE expression and alternative splicing in the liver and skeletal muscle of mice. In conclusion, a cafeteria diet impairs glucose homeostasis by reducing insulin sensitivity, but it also reduces insulin clearance by reducing IDE expression and alternative splicing in mouse liver; however, whether this mechanism contributes to the glucose intolerance or helps to ameliorate it remains unclear.

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