The hypolipidemic and pleiotropic effects of rosuvastatin are not enhanced by its association with zinc and selenium supplementation in coronary artery disease patients: a double blind randomized controlled study.

OBJECTIVE: Statins treatment may modify the levels of zinc and selenium, minerals that can improve vascular function and reduce oxidative damage and inflammation in atherosclerotic patients. This study aimed to evaluate the effects of rosuvastatin, alone or associated with zinc and selenium supplementation, on lipid profile, antioxidant enzymes and mineral status in coronary artery disease patients. MATERIAL AND METHODS: A double-blind randomized clinical trial was performed in which patients (n = 76) were treated with 10 mg rosuvastatin over 4 months associated or not with zinc (30 mg/d) and selenium (150 µg/d) supplementation. The following parameters were analyzed before and after the intervention: anthropometric measurements, lipid profile, high sensitivity C-reactive protein (hs-CRP), electronegative low density lipoprotein (LDL(-)) concentrations, activities of glutathione peroxidase (GPx), superoxide dismutase (SOD), zinc and selenium concentrations in blood plasma and erythocytes. Significance was determined using an α of 5% (two-tailed). RESULTS: We found that rosuvastatin therapy was efficient in reducing total cholesterol, LDL-cholesterol, non-HDL cholesterol, triglycerides, and hs-CRP independently of mineral supplementation. Neither treatment was associated with significant changes in LDL(-). Similarly, the antioxidant enzymes GPx and SOD activity were unchanged by treatments. Neither treatment was associated with significant differences in concentrations of zinc or selenium in blood plasma and erythocytes of studied groups. CONCLUSION: Rosuvastatin treatment did not affect zinc and selenium levels in coronary artery disease patients. The zinc and selenium supplementation at doses used in this study did not change lipid profile or SOD and GPx activity in patients receiving rosuvastatin. Further studies should be focused on testing alternative doses and supplements in different populations to contribute for a consensus on the ideal choice of antioxidants to be used as possible complementary therapies in atherosclerotic patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT01547377.

Diabetes subdiagnosticado e necrose miocárdica: preditores de hiperglicemia no infarto do miocárdio / Unrecognized diabetes and myocardial necrosis: predictors of hyperglycemia in myocardial infarction

FUNDAMENTO: Hiperglicemia na fase aguda do infarto do miocárdio é importante fator prognóstico. Entretanto, sua fisiopatologia não está completamente elucidada. OBJETIVO: Analisar simultaneamente correlação entre hiperglicemia e marcadores bioquímicos relacionados ao estresse,metabolismo glicídico e lipídico, coagulação, inflamação e necrose miocárdica. MÉTODOS: Oitenta pacientes com infarto agudo do miocárdio foram incluídos prospectivamente. Os parâmetros analisados foram: glicose, hormônios do estresse (cortisol e norepinefrina), fatores do metabolismo glicídico [hemoglobina glicada (HbA1c), insulina], lipoproteínas (colesterol total, LDL, HDL, LDL eletronegativa minimamente modificada e adiponectina), glicerídeos (triglicérides, VLDL e ácido graxo), fatores da coagulação (fator VII, fibrinogênio,inibidor do ativador do plasminogênio-1), inflamação (proteína C reativa ultrassensível) e necrose miocárdica (CK-MB e troponina). Variáveis contínuas foram convertidas em graus de pertinência por intermédio de lógica fuzzy. RESULTADOS: Houve correlação significativa entre hiperglicemia e metabolismo glicídico (p < 0,001), lipoproteínas (p = 0,03) e fatores de necrose (p = 0,03). Na análise multivariada, somente metabolismo glicídico (OR = 4,3; IC = 2,1-68,9 e p < 0,001) e necrose miocárdica (OR = 22,5; IC = 2-253 e p = 0,012) mantiveram correlação independente e significativa.Para análise da influência da história de diabetes mellitus , modelo de regressão, incluindo somente pacientes sem diabetes mellitus foi desenvolvido, e os resultados não alteraram. Finalmente, no modelo ajustado para idade, sexo e variáveis clínicas(história de diabetes mellitus, hipertensão arterial e dislipidemia), três variáveis mantiveram associação significativa e independente com hiperglicemia: metabolismo glicídico (OR = 24,1; IC = 4,8-122,1 e p < 0,001) necrose miocárdica (OR = 21,9; IC = 1,3-360,9 e p = 0,03) e história de DM (OR = 27, IC = 3,7-195,7 e p = 0,001). CONCLUSÃO: Marcadores do metabolismo glicídico e necrose miocárdica foram os melhores preditores de hiperglicemia em pacientes com infarto agudo do miocárdio.

BACKGROUND: Hyperglycemia in the acute phase of myocardial infarction is an important prognostic factor. However, its pathophysiology is not fully understood. OBJECTIVE: To analyze simultaneously the correlation between hyperglycemia and biochemical markers related to stress, glucose and lipid metabolism, coagulation, inflammation, and myocardial necrosis. METHODS Eighty patients with acute myocardial infarction were prospectively included. The following parameters were analyzed: blood glucose; stress hormones (cortisol and norepinephrine); glucose metabolism factors [glycated hemoglobin (HbA1c); insulin]; lipoproteins (total cholesterol, LDL, HDL, minimally modified electronegative LDL, and adiponectin); glycerides (triglycerides, VLDL and fatty acids); coagulation factors (factor VII, fibrinogen, plasminogen activator inhibitor-1); inflammation (high-sensitivity C reactive protein); and myocardial necrosis (CK-MB and troponin). Continuous variables were converted into degrees of relevance using fuzzy logic. RESULTS: Significant correlation was observed between hyperglycemia and glucose metabolism (p < 0.001), lipoproteins (p = 0.03), and necrosis factors (p = 0.03). In the multivariate analysis, only glucose metabolism (OR = 4.3; CI = 2.1-68.9; and p < 0.001) and myocardial necrosis (OR = 22.5; CI = 2-253; and p = 0.012) showed independent and significant correlation. For the analysis of the influence of history of diabetes mellitus, a regression model including only patients without diabetes mellitus was developed, and the results did not change. Finally, in the model adjusted for age, gender, and clinical variables (history of diabetes mellitus, hypertension and dyslipidemia), three variables maintained a significant and independent association with hyperglycemia: glucose metabolism (OR = 24.1; CI = 4.8-122.1; and p < 0.001), myocardial necrosis (OR = 21.9; CI = 1.3-360.9; and p = 0.03), and history of DM (OR = 27; CI = 3.7-195.7; and p = 0.001). CONCLUSION: Glucose metabolism and myocardial necrosis markers were the best predictors of hyperglycemia in patients with acute myocardial infarction.

Unrecognized diabetes and myocardial necrosis: predictors of hyperglycemia in myocardial infarction.

BACKGROUND: Hyperglycemia in the acute phase of myocardial infarction is an important prognostic factor. However, its pathophysiology is not fully understood. OBJECTIVE: To analyze simultaneously the correlation between hyperglycemia and biochemical markers related to stress, glucose and lipid metabolism, coagulation, inflammation, and myocardial necrosis. METHODS Eighty patients with acute myocardial infarction were prospectively included. The following parameters were analyzed: blood glucose; stress hormones (cortisol and norepinephrine); glucose metabolism factors [glycated hemoglobin (HbA1c); insulin]; lipoproteins (total cholesterol, LDL, HDL, minimally modified electronegative LDL, and adiponectin); glycerides (triglycerides, VLDL and fatty acids); coagulation factors (factor VII, fibrinogen, plasminogen activator inhibitor-1); inflammation (high-sensitivity C reactive protein); and myocardial necrosis (CK-MB and troponin). Continuous variables were converted into degrees of relevance using fuzzy logic. RESULTS: Significant correlation was observed between hyperglycemia and glucose metabolism (p < 0.001), lipoproteins (p = 0.03), and necrosis factors (p = 0.03). In the multivariate analysis, only glucose metabolism (OR = 4.3; CI = 2.1-68.9; and p < 0.001) and myocardial necrosis (OR = 22.5; CI = 2-253; and p = 0.012) showed independent and significant correlation. For the analysis of the influence of history of diabetes mellitus, a regression model including only patients without diabetes mellitus was developed, and the results did not change. Finally, in the model adjusted for age, gender, and clinical variables (history of diabetes mellitus, hypertension and dyslipidemia), three variables maintained a significant and independent association with hyperglycemia: glucose metabolism (OR = 24.1; CI = 4.8-122.1; and p < 0.001), myocardial necrosis (OR = 21.9; CI = 1.3-360.9; and p = 0.03), and history of DM (OR = 27; CI = 3.7-195.7; and p = 0.001). CONCLUSION: Glucose metabolism and myocardial necrosis markers were the best predictors of hyperglycemia in patients with acute myocardial infarction.

Desenvolvimento de imunoensaios e biossensores para determinação de LDL eletronegativa / Development of immunoassays and biosensors for electronegative LDL quantification

A lipoproteína de baixa densidade eletronegativa (LDL-) é um importante antígeno envolvido na patogênese da aterosclerose. A LDL(-) provoca resposta inflamatória e imunológica, levando à produção de autoanticorpos anti-LDL(-) e a formação subsequente de imunocomplexos (IC-LDL-), os quais também contribuem com o processo aterosclerótico. Diante disso, este trabalho teve como objetivo o desenvolvimento e validação de imunoensaios para quantificação plasmática de LDL(-), anti-LDL(-) e IC-LDL(-), assim como o desenvolvimento de ferramentas aplicáveis em um biossensor para LDL(-). Após a padronização de cada ELISA, foram avaliadas as características de desempenho dos métodos: limites de detecção (LD) e quantificação (LQ), precisão intra e inter-ensaios, exatidão, linearidade de diluição e interferentes. Os LD e LQ do ELISA para LDL(-) foram 0,423 mg/L e 0,517 mg/L de LDL(-), respectivamente. As concentrações plasmáticas de LDL(-) apresentaram linearidade quando os plasmas foram diluídos 1:1000, 1:2000, 1:4000 e 1:8000. Os LD e LQ do ELISA para anti-LDL(-) foram 0,0028 mg/L e 0,0032 mg/L de anti-LDL(-), respectivamente. Os plasmas apresentaram linearidade na diluição quando diluídos 1:100, 1:200, 1:400 e 1:800. Os LD e LQ do ELISA para IC-LDL(-) foram 0,023 g/L e 0,034 g/L de IC-LDL(-), respectivamente. Os plasmas apresentaram linearidade quando diluídos 1:12,5, 1:25, 1:50 e 1:100. Os três ELISAs apresentaram precisão intra e inter-ensaios e recuperação dentro dos limites requeridos para imunoensaios. Para o desenvolvimento de um biossensor para LDL(-), uma proteína recombinante, denominada GFP5-scFv, foi expressa em bactérias Escherichia coli da linhagem BL21(DE3). Para obtenção dessa proteína foi realizada a inserção da sequência de DNA de um fragmento variável de cadeia única (scFv) anti-LDL(-) em um vetor bacteriano com a sequência de DNA da proteína verde fluorescente (GFP5). Dessa forma, a GFP5-scFv é fluorescente e tem afinidade pela LDL(-)...

The electronegative low-density lipoprotein (LDL) is an important antigen involved in the pathogenesis of atherosclerosis. The LDL(-) causes inflammatory and immune response, leading to production of autoantibodies anti-LDL(-) and the subsequent formation of immune complexes (IC-LDL), which also contribute to the atherosclerotic process. Thus, this study aimed to develop and validate immunoassays for quantification of plasma LDL(-), anti-LDL(-) and LDL-IC(-) as well as developing tools applicable to a biosensor for LDL(-). After the standardization of each ELISA, were evaluated the performance characteristics of methods: limits of detection (LD) and quantification (LQ), intra- and inter-assays precision, accuracy, linearity of dilution and interferences. The LD and LQ of LDL(-) ELISA were 0.423 mg/L and 0.517 mg/L of LDL(-), respectively. Plasmas showed linearity when diluted 1:1000, 1:2000, 1:4000 and 1:8000. The LD and LQ of anti-LDL(-) ELISA were 0.0028 mg/L and 0.0032 mg/L of anti-LDL(-), respectively. Plasmas showed linearity when diluted 1:100, 1:200, 1:400 and 1:800. The LD and LQ of IC-LDL(-) ELISA were 0.023 g/L and 0.034 g/L of LDL-IC(-), respectively. Plasma showed linearity when diluted 1:12.5, 1:25, 1:50 and 1:100. The three ELISAs showed good intra- and inter-assays precision and recovery within the limits required for immunoassays. To develop a biosensor for LDL(-), a recombinant protein, called GFP5-scFv was expressed in Escherichia coli BL21(DE3) strain. The obtainment of this protein was performed inserting the DNA sequence of an anti-LDL(-) single chain variable fragment (scFv) in a bacterial vector with a DNA sequence of green fluorescent protein (GFP5). Thus, the scFv-GFP5 is fluorescent and has affinity for LDL(-). Were also synthesized gold nanoparticles, which can be efficiently used for quenching the fluorescence emission of GFP5-scFv. Therefore, immunoassays validated and developed applications for the biosensor are tools that have...

Desenvolvimento de imunoensaios e biossensores para determinação de LDL eletronegativa / Development of immunoassays and biosensors for electronegative LDL quantification

A lipoproteína de baixa densidade eletronegativa (LDL-) é um importante antígeno envolvido na patogênese da aterosclerose. A LDL(-) provoca resposta inflamatória e imunológica, levando à produção de autoanticorpos anti-LDL(-) e a formação subsequente de imunocomplexos (IC-LDL-), os quais também contribuem com o processo aterosclerótico. Diante disso, este trabalho teve como objetivo o desenvolvimento e validação de imunoensaios para quantificação plasmática de LDL(-), anti-LDL(-) e IC-LDL(-), assim como o desenvolvimento de ferramentas aplicáveis em um biossensor para LDL(-). Após a padronização de cada ELISA, foram avaliadas as características de desempenho dos métodos: limites de detecção (LD) e quantificação (LQ), precisão intra e inter-ensaios, exatidão, linearidade de diluição e interferentes. Os LD e LQ do ELISA para LDL(-) foram 0,423 mg/L e 0,517 mg/L de LDL(-), respectivamente. As concentrações plasmáticas de LDL(-) apresentaram linearidade quando os plasmas foram diluídos 1:1000, 1:2000, 1:4000 e 1:8000. Os LD e LQ do ELISA para anti-LDL(-) foram 0,0028 mg/L e 0,0032 mg/L de anti-LDL(-), respectivamente. Os plasmas apresentaram linearidade na diluição quando diluídos 1:100, 1:200, 1:400 e 1:800. Os LD e LQ do ELISA para IC-LDL(-) foram 0,023 g/L e 0,034 g/L de IC-LDL(-), respectivamente. Os plasmas apresentaram linearidade quando diluídos 1:12,5, 1:25, 1:50 e 1:100. Os três ELISAs apresentaram precisão intra e inter-ensaios e recuperação dentro dos limites requeridos para imunoensaios. Para o desenvolvimento de um biossensor para LDL(-), uma proteína recombinante, denominada GFP5-scFv, foi expressa em bactérias Escherichia coli da linhagem BL21(DE3). Para obtenção dessa proteína foi realizada a inserção da sequência de DNA de um fragmento variável de cadeia única (scFv) anti-LDL(-) em um vetor bacteriano com a sequência de DNA da proteína verde fluorescente (GFP5). Dessa forma, a GFP5-scFv é fluorescente e tem afinidade pela LDL(-). Também foram sintetizadas nanopartículas de ouro, as quais podem ser eficientemente utilizadas na supressão da emissão da fluorescência de GFP5-scFv. Portanto, os imunoensaios validados e os aplicativos desenvolvidos para o biossensor são ferramentas que tem potencial para serem utilizadas na avaliação da patogênese da aterosclerose

The electronegative low-density lipoprotein (LDL) is an important antigen involved in the pathogenesis of atherosclerosis. The LDL(-) causes inflammatory and immune response, leading to production of autoantibodies anti-LDL(-) and the subsequent formation of immune complexes (IC-LDL), which also contribute to the atherosclerotic process. Thus, this study aimed to develop and validate immunoassays for quantification of plasma LDL(-), anti-LDL(-) and LDL-IC(-) as well as developing tools applicable to a biosensor for LDL(-). After the standardization of each ELISA, were evaluated the performance characteristics of methods: limits of detection (LD) and quantification (LQ), intra- and inter-assays precision, accuracy, linearity of dilution and interferences. The LD and LQ of LDL(-) ELISA were 0.423 mg/L and 0.517 mg/L of LDL(-), respectively. Plasmas showed linearity when diluted 1:1000, 1:2000, 1:4000 and 1:8000. The LD and LQ of anti-LDL(-) ELISA were 0.0028 mg/L and 0.0032 mg/L of anti-LDL(-), respectively. Plasmas showed linearity when diluted 1:100, 1:200, 1:400 and 1:800. The LD and LQ of IC-LDL(-) ELISA were 0.023 g/L and 0.034 g/L of LDL-IC(-), respectively. Plasma showed linearity when diluted 1:12.5, 1:25, 1:50 and 1:100. The three ELISAs showed good intra- and inter-assays precision and recovery within the limits required for immunoassays. To develop a biosensor for LDL(-), a recombinant protein, called GFP5-scFv was expressed in Escherichia coli BL21(DE3) strain. The obtainment of this protein was performed inserting the DNA sequence of an anti-LDL(-) single chain variable fragment (scFv) in a bacterial vector with a DNA sequence of green fluorescent protein (GFP5). Thus, the scFv-GFP5 is fluorescent and has affinity for LDL(-). Were also synthesized gold nanoparticles, which can be efficiently used for quenching the fluorescence emission of GFP5-scFv. Therefore, immunoassays validated and developed applications for the biosensor are tools that have potential to be used in evaluating the pathogenesis of atherosclerosis