15 research outputs found
Brazil nuts intake improves lipid profile, oxidative stress and microvascular function in obese adolescents: a randomized controlled trial
Desafios técnicos e barreiras sociais,econômicas e regulatórias na fitorremediação de solos contaminados
Acute Effects of Metformin and Vildagliptin after a Lipid-Rich Meal on Postprandial Microvascular Reactivity in Patients with Type 2 Diabetes and Obesity: A Randomized Trial
Background: Type 2 diabetes mellitus and obesity are both related to endothelial dysfunction. Postprandial lipemia is a cardiovascular risk. Notably, it is known that a high-fat diet may elicit microvascular dysfunction, even in healthy subjects. Since anti-diabetic drugs have different mechanisms of action and also distinct vascular benefits, we aimed to compare the results of two anti-diabetic drugs after the intake of a lipid-rich meal on microcirculation in patients with type 2 diabetes and obesity. In parallel, we also investigated the metabolic profile, oxidative stress, inflammation, plasma viscosity, and some gastrointestinal peptides. Subjects/Methods: We included 38 drug-naïve patients, all women aged between 19 and 50 years, with BMI ≥ 30 kg/m2. We performed endothelial measurements and collected samples before (fasting) and after the intake of a lipid-rich meal at 30, 60, 120, and 180 min. Patients were randomized to metformin or vildagliptin, given orally just before the meal. Endothelial function was assessed by videocapillaroscopy and laser-Doppler flowmetry to investigate microvascular reactivity. Besides, we also investigated plasma viscosity, inflammatory and oxidative stress biomarkers, gastrointestinal peptides, and metabolic profile in all time points. Results: No differences at baseline were noted between groups. Vildagliptin increased glucagon-like peptide-1 compared to metformin. Paired comparisons showed that, during the postprandial period, vildagliptin significantly changed levels of insulin and glucagon-like peptide-1, and also the dipeptidyl peptidase-4 activity, while metformin had effects on plasma glucose solely. Metformin use during the test meal promoted an increase in functional capillary density, while vildagliptin kept non-nutritive microvascular blood flow and vasomotion unchanged. Conclusions: After the intake of a lipid-rich meal, the use of vildagliptin preserved postprandial non-nutritive microflow and vasomotion, while metformin increased capillary recruitment, suggesting protective and different mechanisms of action on microcirculation
Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach
<p>Abstract</p> <p>Background</p> <p>We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements.</p> <p>Methods</p> <p>Data from 189 female subjects (34.0±15.5 years, 30.5±7.1 kg/m<sup>2</sup>), who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA). PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI), waist circumference, systolic and diastolic blood pressure (BP), fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), insulin, C-reactive protein (CRP), and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV) at rest and peak after 1 min of arterial occlusion (RBCV<sub>max</sub>), and the time taken to reach RBCV<sub>max</sub> (TRBCV<sub>max</sub>).</p> <p>Results</p> <p>A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCV<sub>max</sub> varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCV<sub>max</sub>, but in the opposite way. Principal component 3 was associated only with microvascular variables in the same way (functional capillary density, RBCV and RBCV<sub>max</sub>). Fasting plasma glucose appeared to be related to principal component 4 and did not show any association with microvascular reactivity.</p> <p>Conclusions</p> <p>In non-diabetic female subjects, a multivariate scenario of associations between classic clinical variables strictly related to obesity and metabolic syndrome suggests a significant relationship between these diseases and microvascular reactivity.</p
30-days effects of vildagliptin on vascular function, plasma viscosity, inflammation, oxidative stress, and intestinal peptides on drug-naïve women with diabetes and obesity: a randomized head-to-head metformin-controlled study
Genomic detection of a virus lineage replacement event of dengue virus serotype 2 in Brazil, 2019
Zika virus (ZIKV) has caused an explosive epidemic linked to severe clinical outcomes in the Americas. As of June 2018, 4,929 ZIKV suspected infections and 46 congenital syndrome cases had been reported in Manaus, Amazonas, Brazil. Although Manaus is a key demographic hub in the Amazon region, little is known about the ZIKV epidemic there, in terms of both transmission and viral genetic diversity. Using portable virus genome sequencing, we generated 59 ZIKV genomes in Manaus. Phylogenetic analyses indicated multiple introductions of ZIKV from northeastern Brazil to Manaus. Spatial genomic analysis of virus movement among six areas in Manaus suggested that populous northern neighborhoods acted as sources of virus transmission to other neighborhoods. Our study revealed how the ZIKV epidemic was ignited and maintained within the largest urban metropolis in the Amazon. These results might contribute to improving the public health response to outbreaks in Brazil
Chikungunya virus outbreak in the Amazon region: replacement of the Asian genotype by an ECSA lineage
Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.Universidade de São Paulo. Faculdade de Medicina. Instituto de Medicina Tropical. São Paulo, SP, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil / Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Patologia Experimental. Salvador, BA, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Patologia Experimental. Salvador, BA, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil / Fundação Ezequiel Dias. Instituto Octávio Magalhães. Laboratório Central de Saúde Pública. Belo Horizonte, MG, Brazil.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.Universidade Federal do Rio de Janeiro. Instituto de Biologia. Departamento de Genética Laboratório de Virologia Molecular. Rio de Janeiro, RJ, Brazil.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.Harvard Medical School. Department of Pediatrics. Boston, MA, USA / Boston Children’s Hospital. Computational Health Informatics Program. Boston, MA, USA.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom / Boston Children’s Hospital. Computational Epidemiology Lab. Boston, MA, USA.University of Birmingham. Institute of Microbiology and Infection. Birmingham, United Kingdom.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.Universidade de São Paulo. Faculdade de Medicina. Instituto de Medicina Tropical. São Paulo, SP, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Centro de Inovações Tecnológicas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Centro de Inovações Tecnológicas. Ananindeua, PA, Brasil.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.Laboratório Central de Saúde Pública. Boa Vista, RR, Brazil.Laboratório Central de Saúde Pública. Boa Vista, RR, Brazil.Laboratório Central de Saúde Pública. Boa Vista, RR, Brazil.Secretaria Municipal de Saúde de Boa Vista. Superintendência de Vigilância em Saúde. Boa Vista, RR, Brazil.Fundação de Medicina Tropical Doutor Heitor Vieira. Departamento de Virologia. Manaus, AM, Brazil.Secretaria Municipal de Saúde de Boa Vista. Superintendência de Vigilância em Saúde. Boa Vista, RR, Brazil.Laboratório Central de Saúde Pública do Amazonas. Manaus, AM, Brazil.Organização Pan - Americana da Saúde/Organização Mundial da Saúde. Brasília, DF, BrazilMinistério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Fundação Oswaldo Cruz. Instituto Leônidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.University of Birmingham. Institute of Microbiology and Infection. Birmingham, United Kingdom.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.Universidade de São Paulo. Faculdade de Medicina. Instituto de Medicina Tropical. São Paulo, SP, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Brasília, DF, Brazil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Flavivírus. Rio de Janeiro, RJ, Brazil / Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Laboratório de Genética Celular e Molecular. Belo Horizonte, MG, Brazil.University of Oxford. Department of Zoology. South Parks Road, Oxford, United Kingdom.Background Since its first detection in the Caribbean in late 2013, chikungunya virus (CHIKV) has affected 51 countries in the Americas. The CHIKV epidemic in the Americas was caused by the CHIKV-Asian genotype. In August 2014, local transmission of the CHIKV-Asian genotype was detected in the Brazilian Amazon region. However, a distinct lineage, the CHIKV-East-Central-South-America (ECSA)-genotype, was detected nearly simultaneously in Feira de Santana, Bahia state, northeast Brazil. The genomic diversity and the dynamics of CHIKV in the Brazilian Amazon region remains poorly understood despite its importance to better understand the epidemiological spread and public health impact of CHIKV in the country. Methodology/Principal Findings We report a large CHIKV outbreak (5,928 notified cases between August 2014 and August 2018) in Boa vista municipality, capital city of Roraima’s state, located in the Brazilian Amazon region. In just 48 hours, we generated 20 novel CHIKV-ECSA genomes from the Brazilian Amazon region using MinION portable genome sequencing. Phylogenetic analyses revealed that despite an early introduction of the Asian genotype in 2015 in Roraima, the large CHIKV outbreak in 2017 in Boa Vista was caused by an ECSA-lineage most likely introduced from northeastern Brazil. Epidemiological analyses suggest a basic reproductive number of R0 of 1.66, which translates in an estimated 39 (95% CI: 36 to 45) % of Roraima’s population infected with CHIKV-ECSA. Finally, we find a strong association between Google search activity and the local laboratory-confirmed CHIKV cases in Roraima. Conclusions/Significance This study highlights the potential of combining traditional surveillance with portable genome sequencing technologies and digital epidemiology to inform public health surveillance in the Amazon region. Our data reveal a large CHIKV-ECSA outbreak in Boa Vista, limited potential for future CHIKV outbreaks, and indicate a replacement of the Asian genotype by the ECSA genotype in the Amazon region
Genomic and epidemiological surveillance of Zika virus in the Amazon Region
Zika virus (ZIKV) has caused an explosive epidemic linked to severe clinical outcomes in the Americas. As of June 2018, 4,929 ZIKV suspected infections and 46 congenital syndrome cases had been reported in Manaus, Amazonas, Brazil. Although Manaus is a key demographic hub in the Amazon region, little is known about the ZIKV epidemic there, in terms of both transmission and viral genetic diversity. Using portable virus genome sequencing, we generated 59 ZIKV genomes in Manaus. Phylogenetic analyses indicated multiple introductions of ZIKV from northeastern Brazil to Manaus. Spatial genomic analysis of virus movement among six areas in Manaus suggested that populous northern neighborhoods acted as sources of virus transmission to other neighborhoods. Our study revealed how the ZIKV epidemic was ignited and maintained within the largest urban metropolis in the Amazon. These results might contribute to improving the public health response to outbreaks in Brazil.SCOPUS: ar.jinfo:eu-repo/semantics/publishe