7 resultados para weighting triangles

em Université de Lausanne, Switzerland


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AIMS/HYPOTHESIS: Several susceptibility genes for type 2 diabetes have been discovered recently. Individually, these genes increase the disease risk only minimally. The goals of the present study were to determine, at the population level, the risk of diabetes in individuals who carry risk alleles within several susceptibility genes for the disease and the added value of this genetic information over the clinical predictors. METHODS: We constructed an additive genetic score using the most replicated single-nucleotide polymorphisms (SNPs) within 15 type 2 diabetes-susceptibility genes, weighting each SNP with its reported effect. We tested this score in the extensively phenotyped population-based cross-sectional CoLaus Study in Lausanne, Switzerland (n = 5,360), involving 356 diabetic individuals. RESULTS: The clinical predictors of prevalent diabetes were age, BMI, family history of diabetes, WHR, and triacylglycerol/HDL-cholesterol ratio. After adjustment for these variables, the risk of diabetes was 2.7 (95% CI 1.8-4.0, p = 0.000006) for individuals with a genetic score within the top quintile, compared with the bottom quintile. Adding the genetic score to the clinical covariates improved the area under the receiver operating characteristic curve slightly (from 0.86 to 0.87), yet significantly (p = 0.002). BMI was similar in these two extreme quintiles. CONCLUSIONS/INTERPRETATION: In this population, a simple weighted 15 SNP-based genetic score provides additional information over clinical predictors of prevalent diabetes. At this stage, however, the clinical benefit of this genetic information is limited.

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[Contents] Introduction. Objectives. Methodology. Results. Characteristics of the sample. Substance use (Psychoactive substances, Performance-enhancing substances). Profile of sportive adolescents using substances. Mixed substance use. Other factors related to substance use. Inactivity. Conclusions. References. Annexes. Annex 1. Questionnaire. Annex 2. Sample weighting procedure. Annex 3. Sports type.

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Therapeutic drug monitoring (TDM) aims to optimize treatments by individualizing dosage regimens based on the measurement of blood concentrations. Dosage individualization to maintain concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculations currently represent the gold standard TDM approach but require computation assistance. In recent decades computer programs have been developed to assist clinicians in this assignment. The aim of this survey was to assess and compare computer tools designed to support TDM clinical activities. The literature and the Internet were searched to identify software. All programs were tested on personal computers. Each program was scored against a standardized grid covering pharmacokinetic relevance, user friendliness, computing aspects, interfacing and storage. A weighting factor was applied to each criterion of the grid to account for its relative importance. To assess the robustness of the software, six representative clinical vignettes were processed through each of them. Altogether, 12 software tools were identified, tested and ranked, representing a comprehensive review of the available software. Numbers of drugs handled by the software vary widely (from two to 180), and eight programs offer users the possibility of adding new drug models based on population pharmacokinetic analyses. Bayesian computation to predict dosage adaptation from blood concentration (a posteriori adjustment) is performed by ten tools, while nine are also able to propose a priori dosage regimens, based only on individual patient covariates such as age, sex and bodyweight. Among those applying Bayesian calculation, MM-USC*PACK© uses the non-parametric approach. The top two programs emerging from this benchmark were MwPharm© and TCIWorks. Most other programs evaluated had good potential while being less sophisticated or less user friendly. Programs vary in complexity and might not fit all healthcare settings. Each software tool must therefore be regarded with respect to the individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Computer-assisted TDM is gaining growing interest and should further improve, especially in terms of information system interfacing, user friendliness, data storage capability and report generation.

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The World Health Organization (WHO) criteria for the diagnosis of osteoporosis are mainly applicable for dual X-ray absorptiometry (DXA) measurements at the spine and hip levels. There is a growing demand for cheaper devices, free of ionizing radiation such as promising quantitative ultrasound (QUS). In common with many other countries, QUS measurements are increasingly used in Switzerland without adequate clinical guidelines. The T-score approach developed for DXA cannot be applied to QUS, although well-conducted prospective studies have shown that ultrasound could be a valuable predictor of fracture risk. As a consequence, an expert committee named the Swiss Quality Assurance Project (SQAP, for which the main mission is the establishment of quality assurance procedures for DXA and QUS in Switzerland) was mandated by the Swiss Association Against Osteoporosis (ASCO) in 2000 to propose operational clinical recommendations for the use of QUS in the management of osteoporosis for two QUS devices sold in Switzerland. Device-specific weighted "T-score" based on the risk of osteoporotic hip fractures as well as on the prediction of DXA osteoporosis at the hip, according to the WHO definition of osteoporosis, were calculated for the Achilles (Lunar, General Electric, Madison, Wis.) and Sahara (Hologic, Waltham, Mass.) ultrasound devices. Several studies (totaling a few thousand subjects) were used to calculate age-adjusted odd ratios (OR) and area under the receiver operating curve (AUC) for the prediction of osteoporotic fracture (taking into account a weighting score depending on the design of the study involved in the calculation). The ORs were 2.4 (1.9-3.2) and AUC 0.72 (0.66-0.77), respectively, for the Achilles, and 2.3 (1.7-3.1) and 0.75 (0.68-0.82), respectively, for the Sahara device. To translate risk estimates into thresholds for clinical application, 90% sensitivity was used to define low fracture and low osteoporosis risk, and a specificity of 80% was used to define subjects as being at high risk of fracture or having osteoporosis at the hip. From the combination of the fracture model with the hip DXA osteoporotic model, we found a T-score threshold of -1.2 and -2.5 for the stiffness (Achilles) determining, respectively, the low- and high-risk subjects. Similarly, we found a T-score at -1.0 and -2.2 for the QUI index (Sahara). Then a screening strategy combining QUS, DXA, and clinical factors for the identification of women needing treatment was proposed. The application of this approach will help to minimize the inappropriate use of QUS from which the whole field currently suffers.

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Raised blood pressure (BP) is a major risk factor for cardiovascular disease. Previous studies have identified 47 distinct genetic variants robustly associated with BP, but collectively these explain only a few percent of the heritability for BP phenotypes. To find additional BP loci, we used a bespoke gene-centric array to genotype an independent discovery sample of 25,118 individuals that combined hypertensive case-control and general population samples. We followed up four SNPs associated with BP at our p < 8.56 × 10(-7) study-specific significance threshold and six suggestively associated SNPs in a further 59,349 individuals. We identified and replicated a SNP at LSP1/TNNT3, a SNP at MTHFR-NPPB independent (r(2) = 0.33) of previous reports, and replicated SNPs at AGT and ATP2B1 reported previously. An analysis of combined discovery and follow-up data identified SNPs significantly associated with BP at p < 8.56 × 10(-7) at four further loci (NPR3, HFE, NOS3, and SOX6). The high number of discoveries made with modest genotyping effort can be attributed to using a large-scale yet targeted genotyping array and to the development of a weighting scheme that maximized power when meta-analyzing results from samples ascertained with extreme phenotypes, in combination with results from nonascertained or population samples. Chromatin immunoprecipitation and transcript expression data highlight potential gene regulatory mechanisms at the MTHFR and NOS3 loci. These results provide candidates for further study to help dissect mechanisms affecting BP and highlight the utility of studying SNPs and samples that are independent of those studied previously even when the sample size is smaller than that in previous studies.

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In coronary magnetic resonance angiography, a magnetization-preparation scheme for T2 -weighting (T2 Prep) is widely used to enhance contrast between the coronary blood-pool and the myocardium. This prepulse is commonly applied without spatial selection to minimize flow sensitivity, but the nonselective implementation results in a reduced magnetization of the in-flowing blood and a related penalty in signal-to-noise ratio. It is hypothesized that a spatially selective T2 Prep would leave the magnetization of blood outside the T2 Prep volume unaffected and thereby lower the signal-to-noise ratio penalty. To test this hypothesis, a spatially selective T2 Prep was implemented where the user could freely adjust angulation and position of the T2 Prep slab to avoid covering the ventricular blood-pool and saturating the in-flowing spins. A time gap of 150 ms was further added between the T2 Prep and other prepulses to allow for in-flow of a larger volume of unsaturated spins. Consistent with numerical simulation, the spatially selective T2 Prep increased in vivo human coronary artery signal-to-noise ratio (42.3 ± 2.9 vs. 31.4 ± 2.2, n = 22, P < 0.0001) and contrast-to-noise-ratio (18.6 ± 1.5 vs. 13.9 ± 1.2, P = 0.009) as compared to those of the nonselective T2 Prep. Additionally, a segmental analysis demonstrated that the spatially selective T2 Prep was most beneficial in proximal and mid segments where the in-flowing blood volume was largest compared to the distal segments. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

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BACKGROUND: The escalating prevalence of obesity might prompt obese subjects to consider themselves as normal, as this condition is gradually becoming as frequent as normal weight. In this study, we aimed to assess the trends in the associations between obesity and self-rated health in two countries. METHODS: Data from the Portuguese (years 1995-6, 1998-6 and 2005-6) and Swiss (1992-3, 1997, 2002 and 2007) National Health Surveys were used, corresponding to more than 130,000 adults (64,793 for Portugal and 65,829 for Switzerland). Body mass index and self-rated health were derived from self-reported data. RESULTS: Obesity levels were higher in Portugal (17.5% in 2005-6 vs. 8.9% in 2007 in Switzerland, p < 0.001) and increased in both countries. The prevalence of participants rating their health as "bad" or "very bad" was higher in Portugal than in Switzerland (21.8% in 2005-6 vs 3.9% in 2007, p < 0.001). In both countries, obese participants rated more frequently their health as "bad" or "very bad" than participants with regular weight. In Switzerland, the prevalence of "bad" or "very bad" rates among obese participants, increased from 6.5% in 1992-3 to 9.8% in 2007, while in Portugal it decreased from 41.3% to 32.3%. After multivariate adjustment, the odds ratio (OR) of stating one self's health as "bad" or "very bad" among obese relative to normal weight participants, almost doubled in Switzerland: from 1.38 (95% confidence interval, CI: 1.01-1.87) in 1992-3 to 2.64 (95% CI: 2.14-3.26) in 2007, and similar findings were obtained after sample weighting. Conversely, no such trend was found in Portugal: 1.35 (95% CI: 1.23-1.48) in 1995-6 and 1.52 (95% CI: 1.37-1.70) in 2005-6. CONCLUSION: Obesity is increasing in Switzerland and Portugal. Obesity is increasingly associated with poorer self-health ratings in Switzerland but not in Portugal.