Emergent Biomarkers of Residual Cardiovascular Risk in Patients with Low HDL-c and/or High Triglycerides and Average LDL-c Concentrations: Focus on HDL Subpopulations, Oxidized LDL, Adiponectin, and Uric Acid

This study intended to determine the impact of HDL-c and/or TGs levels on patients with average LDL-c concentration, focusing on lipidic, oxidative, inflammatory, and angiogenic profiles. Patients with cardiovascular risk factors (n = 169) were divided into 4 subgroups, combining normal and low HDL-c with normal and high TGs patients. The following data was analyzed: BP, BMI, waist circumference and serum glucose, Total-c, TGs, LDL-c, oxidized-LDL, total HDL-c and HDL subpopulations, paraoxonase-1 (PON1) activity, hsCRP, uric acid, TNF- α , adiponectin, VEGF, and iCAM1. The two populations with increased TGs levels, regardless of the normal or low HDL-c, presented obesity and higher waist circumference, Total-c, LDL-c, Ox-LDL, and uric acid. Adiponectin concentration was significantly lower and VEGF was higher in the population with cumulative low values of HDL-c and high values of TGs, while HDL quality was reduced in the populations with impaired values of HDL-c and/or TGs, viewed by reduced large and increased small HDL subfractions. In conclusion, in a population with cardiovascular risk factors, low HDL-c and/or high TGs concentrations seem to be associated with a poor cardiometabolic profile, despite average LDL-c levels. This condition, often called residual risk, is better evidenced by using both traditional and nontraditional CV biomarkers, including large and small HDL subfractions, Ox-LDL, adiponectin, VEGF, and uric acid.info:eu-repo/semantics/publishedVersio

Liraglutide effectiveness in type 2 diabetes: insights from a real-world cohort of Portuguese patients

Liraglutide is a long-acting glucagon-like peptide-1 receptor agonist prescribed to diabetic patients for glycaemic control. To understand the impact of liraglutide in the real-world setting, this study analysed its effects in a Portuguese cohort of Type 2 diabetes patients. This was an observational, multicentric, and retrospective study that included 191 liraglutide-treated patients with at least 12 months of treatment. Patients' data were collected and analysed during a 24-month follow-up period. Overall, liraglutide treatment effectively reduced HbA1c levels from 8.3% to around 7.5%, after 6, 12, and 24 months (p < 0.001). In fact, 38.2%, 37.2%, and 44.8% of patients at 6, 12, and 24 months, respectively, experienced an HbA1c reduction of at least 1%. Moreover, a persistent reduction in anthropometric features was also observed, with 44.0%, 47.6%, and 54.4% of patients achieving a weight reduction of at least 3% at 6, 12, and 24 months, respectively. Finally, significant improvements were observed in the HDL-c and LDL-c levels. Our results demonstrate that liraglutide effectively promoted the reduction of HbA1c values during routine clinical practice, which was sustained throughout the study. In addition, there were significant improvements in anthropometric parameters and other cardiovascular risk factors.info:eu-repo/semantics/publishedVersio

New Tool for Signal Patients at Risk

Introduction: Pancreas transplantation is currently the only treatment that can re-establish normal endocrine pancreatic function. Despite all efforts, pancreas allograft survival and rejection remain major clinical problems. The purpose of this study was to identify features that could signal patients at risk of pancreas allograft rejection. Methods: We collected 74 features from 79 patients who underwent simultaneous pancreas–kidney transplantation (SPK) and used two widely-applicable classification methods, the Naive Bayesian Classifier and Support Vector Machine, to build predictive models. We used the area under the receiver operating characteristic curve and classification accuracy to evaluate the predictive performance via leave-one-out cross-validation. Results: Rejection events were identified in 13 SPK patients (17.8%). In feature selection approach, it was possible to identify 10 features, namely: previous treatment for diabetes mellitus with long-term Insulin (U/I/day), type of dialysis (peritoneal dialysis, hemodialysis, or pre-emptive), de novo DSA, vPRA_Pre-Transplant (%), donor blood glucose, pancreas donor risk index (pDRI), recipient height, dialysis time (days), warm ischemia (minutes), recipient of intensive care (days). The results showed that the Naive Bayes and Support Vector Machine classifiers prediction performed very well, with an AUROC and classification accuracy of 0.97 and 0.87, respectively, in the first model and 0.96 and 0.94 in the second model. Conclusion: Our results indicated that it is feasible to develop successful classifiers for the prediction of graft rejection. The Naive Bayesian generated nomogram can be used for rejection probability prediction, thus supporting clinical decision making.publishersversionpublishe

CONUM

Escola de Engenharia da Universidade do Minh

Implication of Low HDL-c Levels in Patients with Average LDL-c Levels: A Focus on Oxidized LDL, Large HDL Subpopulation, and Adiponectin

To evaluate the impact of low levels of high density lipoprotein cholesterol (HDL-c) on patients with LDL-c average levels, focusing on oxidative, lipidic, and inflammatory profiles. Patients with cardiovascular risk factors (n=169) and control subjects (n=73) were divided into 2 subgroups, one of normal HDL-c and the other of low HDL-c levels. The following data was analyzed: BP, BMI, waist circumference and serum glucose Total-c, TGs, LDL-c, oxidized LDL, total HDL-c and subpopulations (small, intermediate, and large), paraoxonase-1 (PON1) activity, hsCRP, uric acid, TNF-α, adiponectin, VEGF, and iCAM1. In the control subgroup with low HDL-c levels, significantly higher values of BP and TGs and lower values of PON1 activity and adiponectin were found, versus control normal HDL-c subgroup. However, differences in patients’ subgroups were clearly more pronounced. Indeed, low HDL-c subgroup presented increased HbA1c, TGs, non-HDL-c, Ox-LDL, hsCRP, VEGF, and small HDL-c and reduced adiponectin and large HDL. In addition, Ox-LDL, large-HDL-c, and adiponectin presented interesting correlations with classical and nonclassical markers, mainly in the normal HDL-c patients’ subgroup. In conclusion, despite LDL-c average levels, low HDL-c concentrations seem to be associated with a poor cardiometabolic profile in a population with cardiovascular risk factors, which is better evidenced by traditional and nontraditional CV biomarkers, including Ox-LDL, large HDL-c, and adiponectin

Diabetes abrogates sex differences and aggravates cardiometabolic risk in postmenopausal women

Background: The aim of this study is to evaluate the effect of gender and menopause in cardiometabolic risk in a type 2 diabetes mellitus (T2DM) population, based on classical and non-traditional markers. Methods: Seventy four volunteers and 110 T2DM patients were enrolled in the study. Anthropometric data, blood pressure, body mass index (BMI), waist circumference (WC) and the following serum markers were analyzed: glucose, Total-c, TGs, LDL-c, Oxidized-LDL, total HDL-c and large and small HDL-c subpopulations, paraoxonase 1 activity, hsCRP, uric acid, TNF-α, adiponectin and VEGF. Results: Non-diabetic women, compared to men, presented lower glycemia, WC, small HDL-c, uric acid, TNF-α and increased large HDL-c. Diabetes abrogates the protective effect of female gender, since diabetic women showed increased BMI, WC, small HDL-c, VEGF, uric acid, TNF-α and hsCRP, as well as reduced adiponectin, when compared with non-diabetic. In diabetic females, but not in males, WC is directly and significantly associated with TNF-α, VEGF, hsCRP and uric acid; TNF-α is directly associated with VEGF and hsCRP, and inversely with adiponectin. Postmenopausal females presented a worsen cardiometabolic profile, viewed by the increased WC, small HDL-c, VEGF, uric acid, TNF-α and hsCRP. In this population, WC is directly and significantly associated with TNF-α, VEGF, hsCRP; TNF-α is directly associated with VEGF; and uric acid is inversely associated with large HDL-c and hsCRP with adiponectin, also inversely. Conclusions: Diabetes abrogates the protective effect of gender on non-diabetic women, and postmenopausal diabetic females presented worsen cardiometabolic risk, including a more atherogenic lipid sketch and a proinflammatory and pro-angiogenic profile. The classical cardiovascular risk factors (CVRFs) fail to completely explain these differences, which are better clarified using “non-traditional” factors, such as HDL-c subpopulations, rather than total HDL-c content, and markers of inflammation and angiogenesis, namely TNF-α, hsCRP, uric acid and VEGF. Multi-therapeutic intervention, directed to obesity, atherogenic lipid particles and inflammatory mediators is advisory in order to efficiently prevent the serious diabetic cardiovascular complications

New Markers of Early Cardiovascular Risk in Multiple Sclerosis Patients: Oxidized-LDL Correlates with Clinical Staging

OBJECTIVES: This study aimed to characterize a population of multiple sclerosis (MS) patients in terms of traditional and new cardiovascular risk factors and assess their putative correlation with clinical disease activity (evaluated by the Expanded Disability Status Scale [EDSS])

Machine Learning-Based Model Helps to Decide which Patients May Benefit from Pancreatoduodenectomy

Pancreatic ductal adenocarcinoma is an invasive tumor with similar incidence and mortality rates. Pancreaticoduodenectomy has morbidity and mortality rates of up to 60% and 5%, respectively. The purpose of our study was to assess preoperative features contributing to unfavorable 1-year survival prognosis. Study Design: Retrospective, single-center study evaluating the impact of preoperative features on short-term survival outcomes in head PDAC patients. Forty-four prior features of 172 patients were tested using different supervised machine learning models. Patient records were randomly divided into training and validation sets (80–20%, respectively), and model performance was assessed by area under curve (AUC) and classification accuracy (CA). Additionally, 33 patients were included as an independent revalidation or holdout dataset group. Results: Eleven relevant features were identified: age, sex, Ca-19-9, jaundice, ERCP with biliary stent, neutrophils, lymphocytes, lymphocyte/neutrophil ratio, neoadjuvant treatment, imaging tumor size, and ASA. Tree regression (tree model) and logistic regression (LR) performed better than the other tested models. The tree model had an AUC = 0.92 and CA = 0.85. LR had an AUC = 0.74 and CA = 0.78, allowing the development of a nomogram based on absolute feature significance. The best performance model was the tree model which allows us to have a decision tree to help clinical decisions. Discussion and conclusions: Based only on preoperative data, it was possible to predict 1-year survival (91.5% vs. 78.1% alive and 70.9% vs. 76.6% deceased for the tree model and LR, respectively). These results contribute to informed decision-making in the selection of which patients with PDAC can benefit from pancreatoduodenectomy. A machine learning algorithm was developed for the recognition of unfavorable 1-year survival prognosis in patients with pancreatic ductal adenocarcinoma. This will contribute to the identification of patients who would benefit from pancreatoduodenectomy. In our cohort, the tree regression model had an AUC = 0.92 and CA = 0.85, whereas the logistic regression had an AUC = 0.74 and CA = 0.78. To further inform decision-making, a decision tree based on tree regression was developed

Ciência, Crise e Mudança. 3.º Encontro Nacional de História das Ciências e da Tecnologia. ENHCT2012

III Encontro Nacional de História das Ciências e da Tecnologia. O Centro de Estudos de História e Filosofia da Ciência, organiza o 3.º Encontro Nacional de História da Ciência e da Técnica, sob o tema «Ciência, Crise e Mudança» que tem lugar na Universidade de Évora, nos dias 26, 27 e 28 de Setembro de 2012. O Primeiro Encontro Nacional de História da Ciência teve lugar em 21 e 22 Julho de 2009, no seguimento do programa de estímulo ao de¬senvolvimento da História da Ciência em Portugal e de valorização do património cultural e científico do País, lançado pelo Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) em 31 de Janeiro desse ano. A sua organização coube a investigadores do Instituto de História Contemporânea (IHC), da FCSH da UNL, e do Centro Científico e Cultural de Macau (CCCM), em cujas instalações se realizou. De en¬tre as conclusões do Encontro, destacou-se a de realizar periodicamen¬te novos Encontros Nacionais, a serem organizados de forma rotativa por diferentes centros e núcleos de investigadores. Na sequência deste Primeiro Encontro, o Centro Interuniversitário de História das Ciências e da Tecnologia (CIUHCT) organizou, entre 26 e 28 de Julho de 2010, o II Encontro, dedicado ao tema “Comunicação das Ciências e da Tecnologia em Portugal: Agentes, Meios e Audiências”. Cabe agora ao CEHFCi cumprir o que foi decidido no final deste Encontro. Na situação económica e política que hoje vivemos torna-se particularmente urgente aprofundar o estudo e o debate sobre a interação entre a Sociedade, a Ciência e a sua História. Coordenação Científica e Executiva do encontro estiveram a cargo de dois investigadores CEHFCi: Maria de Fátima Nunes, José Pedro Sousa Dia