Association between ADAMTS13 polymorphisms and risk of cardiovascular events in chronic coronary disease

Introduction: Association between ADAMTS13 levels and cardiovascular events has been described recently. However, no genetic study of ADAMTS13 in coronary patients has been described. Materials and Methods: Based on related populations frequencies and functional studies, we tested three ADAMTS13 polymorphisms: C1342G (Q448E), C1852G (P618A) and C2699T (A900V) in a group of 560 patients enrolled in the Medical, Angioplasty, or Surgery Study II (MASS II), a randomized trial comparing treatments for patients with coronary artery disease (CAD) and preserved left ventricular function. The incidence of the 5-year end-points of death and death from cardiac causes, myocardial infarction, refractory angina requiring revascularization and cerebrovascular accident was determined for each polymorphim`s allele, genotype and haplotype. Risk was assessed with the use of logistic regression and Cox proportional-hazards model and multivariable adjustment was employed for possible confounders. Results: Clinical characteristics and received treatment of each genotype group were similar at baseline. In an adjusted model for cardiovascular risk variables, we were able to observe a significant association between ADAMTS13 900V variant and an increased risk of death (OR: 1,92 CI: 1,14-3,23, p = 0,015) or death from cardiac cause (OR: 2,67, CI: 1,59-4,49, p = 0,0009). No association between events and ADAMTS13 Q448E or P618A was observed. Conclusions: This first report studying the association between ADAMTS13 genotypes and cardiovascular events provides evidence for the association between ADAMTS13 900V variant and an increased risk of death in a population with multi-vessel CAD. (C) 2009 Elsevier Ltd. All rights reserved.

Risk Factors for Visual Field Progression in Treated Glaucoma

Objective: To determine intraocular pressure (IOP)-dependent and IOP-independent variables associated with visual field (VF) progression in treated glaucoma. Design: Retrospective cohort of the Glaucoma Progression Study. Methods: Consecutive, treated glaucoma patients with repeatable VF loss who had 8 or more VF examinations of either eye, using the Swedish Interactive Threshold Algorithm (24-2 SITA-Standard, Humphrey Field Analyzer II; Carl Zeiss Meditec, Inc, Dublin, California), during the period between January 1999 and September 2009 were included. Visual field progression was evaluated using automated pointwise linear regression. Evaluated data included age, sex, race, central corneal thickness, baseline VF mean deviation, mean follow-up IOP, peak IOP, IOP fluctuation, a detected disc hemorrhage, and presence of beta-zone parapapillary atrophy. Results: We selected 587 eyes of 587 patients (mean [SD] age, 64.9 [13.0] years). The mean (SD) number of VFs was 11.1 (3.0), spanning a mean (SD) of 6.4 (1.7) years. In the univariable model, older age (odds ratio [OR], 1.19 per decade; P = .01), baseline diagnosis of exfoliation syndrome (OR, 1.79; P = .01), decreased central corneal thickness (OR, 1.38 per 40 mu m thinner; P < .01), a detected disc hemorrhage (OR, 2.31; P < .01), presence of beta-zone parapapillary atrophy (OR, 2.17; P < .01), and all IOP parameters (mean follow-up, peak, and fluctuation; P < .01) were associated with increased risk of VF progression. In the multivariable model, peak IOP (OR, 1.13; P < .01), thinner central corneal thickness (OR, 1.45 per 40 mu m thinner; P < .01), a detected disc hemorrhage (OR, 2.59; P < .01), and presence of beta-zone parapapillary atrophy (OR, 2.38; P < .01) were associated with VF progression. Conclusions: IOP-dependent and IOP-independent risk factors affect disease progression in treated glaucoma. Peak IOP is a better predictor of progression than is IOP mean or fluctuation.

Common mental disorders during pregnancy and adverse obstetric outcomes

Methods. A prospective cohort study was conducted with 831 pregnant women from antenatal clinics in primary healthcare in Sao Paulo, Brazil. The clinical interview schedule-revised and demographic questionnaires were administered between the 20th and 30th weeks of gestation. Information on infant weight and gestational age at birth were obtained from hospital records. Univariate analyses were used to examine the association between the main exposure and main outcomes. Statistical associations were examined with chimultivariable logistic regression model. Results. The prevalence of CMD during gestation was 33.6 (95% CI: 30.4-36.9). The follow-up rate was 99.5%. Sixty three (7.6%) newborns were classified as LBW and 56 (6.9%) were classified as PTB. CMD during pregnancy was not associated with risk of PTB (adjusted OR:1.03, 95% CI: 0.57-1.88) or LBW (adjusted OR:1.09, 95% CI: 0.62-1.91). Conclusions. CMD prevalence is high among low-income and low-risk pregnant women attended by public health services in a middle-income country, but not confer an increased risk for adverse obstetric outcome.

Predictive Factors for Positive Surgical Margins and Their Locations After Robot-Assisted Laparoscopic Radical Prostatectomy

Background: Positive surgical margin (PSM) after radical prostatectomy (RP) has been shown to be an independent predictive factor for cancer recurrence. Several investigations have correlated clinical and histopathologic findings with surgical margin status after open RP. However, few studies have addressed the predictive factors for PSM after robot-assisted laparoscopic RP (RARP). Objective: We sought to identify predictive factors for PSMs and their locations after RARP. Design, setting, and participants: We prospectively analyzed 876 consecutive patients who underwent RARP from January 2008 to May 2009. Intervention: All patients underwent RARP performed by a single surgeon with previous experience of > 1500 cases. Measurements: Stepwise logistic regression was used to identify potential predictive factors for PSM. Three logistic regression models were built: (1) one using preoperative variables only, (2) another using all variables (preoperative, intraoperative, and postoperative) combined, and (3) one created to identify potential predictive factors for PSM location. Preoperative variables entered into the models included age, body mass index (BMI), prostate-specific antigen, clinical stage, number of positive cores, percentage of positive cores, and American Urological Association symptom score. Intra-and postoperative variables analyzed were type of nerve sparing, presence of median lobe, percentage of tumor in the surgical specimen, gland size, histopathologic findings, pathologic stage, and pathologic Gleason grade. Results and limitations: In the multivariable analysis including preoperative variables, clinical stage was the only independent predictive factor for PSM, with a higher PSM rate for T3 versus T1c (odds ratio [OR]: 10.7; 95% confidence interval [CI], 2.6-43.8) and for T2 versus T1c (OR: 2.9; 95% CI, 1.9-4.6). Considering pre-, intra-, and postoperative variables combined, percentage of tumor, pathologic stage, and pathologic Gleason score were associated with increased risk of PSM in the univariable analysis (p < 0.001 for all variables). However, in the multivariable analysis, pathologic stage (pT2 vs pT1; OR: 2.9; 95% CI, 1.9-4.6) and percentage of tumor in the surgical specimen (OR: 8.7; 95% CI, 2.2-34.5; p = 0.0022) were the only independent predictive factors for PSM. Finally, BMI was shown to be an independent predictive factor(OR: 1.1; 95% CI, 1.0-1.3; p = 0.0119) for apical PSMs, with increasing BMI predicting higher incidence of apex location. Because most of our patients were referred from other centers, the biopsy technique and the number of cores were not standardized in our series. Conclusions: Clinical stage was the only preoperative variable independently associated with PSM after RARP. Pathologic stage and percentage of tumor in the surgical specimen were identified as independent predictive factors for PSMs when analyzing pre-, intra-, and postoperative variables combined. BMI was shown to be an independent predictive factor for apical PSMs. (C) 2010 European Association of Urology. Published by Elsevier B. V. All rights reserved.

Pentafecta: A New Concept for Reporting Outcomes of Robot-Assisted Laparoscopic Radical Prostatectomy

Background: Widespread use of prostate-specific antigen screening has resulted in younger and healthier men being diagnosed with prostate cancer. Their demands and expectations of surgical intervention are much higher and cannot be adequately addressed with the classic trifecta outcome measures. Objective: A new and more comprehensive method for reporting outcomes after radical prostatectomy, the pentafecta, is proposed. Design, setting, and participants: From January 2008 through September 2009, details of 1111 consecutive patients who underwent robot-assisted radical prostatectomy performed by a single surgeon were retrospectively analyzed. Of 626 potent men, 332 who underwent bilateral nerve sparing and who had 1 yr of follow-up were included in the study group. Measurements: In addition to the traditional trifecta outcomes, two perioperative variables were included in the pentafecta: no postoperative complications and negative surgical margins. Patients who attained the trifecta and concurrently the two additional outcomes were considered as having achieved the pentafecta. A logistic regression model was created to evaluate independent factors for achieving the pentafecta. Results and limitations: Continence, potency, biochemical recurrence-free survival, and trifecta rates at 12 mo were 96.4%, 89.8%, 96.4%, and 83.1%, respectively. With regard to the perioperative outcomes, 93.4% had no postoperative complication and 90.7% had negative surgical margins. The pentafecta rate at 12 mo was 70.8%. On multivariable analysis, patient age (p = 0.001) was confirmed as the only factor independently associated with the pentafecta. Conclusions: A more comprehensive approach for reporting prostate surgery outcomes, the pentafecta, is being proposed. We believe that pentafecta outcomes more accurately represent patients` expectations after minimally invasive surgery for prostate cancer. This approach may be beneficial and may be used when counseling patients with clinically localized disease. (C) 2011 European Association of Urology. Published by Elsevier B. V. All rights reserved.

Regression of glomerular injury by losartan in experimental diabetic nephropathy

Many features of chronic kidney disease may be reversed, but it is unclear whether advanced lesions, such as adhesions of sclerotic glomerular tufts to Bowman`s capsule (synechiae), can resolve during treatment. We previously showed, using a renal ablation model, that the renoprotective effect of the AT-1 receptor blocker, losartan, is dose-dependent. Here we determined if moderate and advanced glomerular lesions, associated with streptozotocin-induced diabetes, regress with conventional or high-dose losartan treatment. Using daily insulin injection for 10 months, we maintained diabetic adult male Munich-Wistar rats in a state of moderate hyperglycemia. Following this period, some rats continued to receive insulin with or without conventional or high-dose losartan for an additional 2 months. Diabetic rats pretreated with insulin for 10 months and age-matched non-diabetic rats served as controls. Mesangial expansion was found in the control diabetic rats and was exacerbated in those rats maintained on only insulin for an additional 2 months. Conventional and high-dose losartan treatments reduced this mesangial expansion and the severity of synechiae lesions below that found prior to treatment; however, the frequency of the latter was unchanged. There was no dose-response effect of losartan. Our results show that regression of mesangial expansion and contraction of sclerotic lesions is feasible in the treatment of diabetes, but complete resolution of advanced glomerulosclerosis may be hard to achieve.

Differential effects of coffee on the risk of type 2 diabetes according to meal consumption in a French cohort of women: the E3N/EPIC cohort study

Background: Coffee consumption has been associated with a lower risk of diabetes, but little is known about the mechanisms responsible for this association, especially related to the time when coffee is consumed. Objective: We examined the long-term effect of coffee, globally and according to the accompanying meal, and of tea, chicory, and caffeine on type 2 diabetes risk. Design: This was a prospective cohort study including 69,532 French women, aged 41-72 y from the E3N/EPIC (Etude Epidemiologique aupres de Femmes de la Mutuelle Generale de l`Education Nationale/European Prospective Investigation into Cancer and Nutrition) cohort study, without diabetes at baseline. Food and drink intakes per meal were assessed by using a validated diet-history questionnaire in 1993-1995. Results: During a mean follow-up of 11 y, 1415 new cases of diabetes were identified. In multivariable Cox regression models, the hazard ratio in the highest category of coffee consumption [>= 3 cups (375 mL)/d] was 0.73 (95% CI: 0.61, 0.87; P for trend < 0.001), in comparison with no coffee consumption. This inverse association was restricted to coffee consumed at lunchtime (hazard ratio: 0.66; 95% CI: 0.57, 0.76) when comparing >1.1 cup (125 mL)/meal with no intake. At lunchtime, this inverse association was observed for both regular and decaffeinated coffee and for filtered and black coffee, with no effect of sweetening. Total caffeine intake was also associated with a statistically significantly lower risk of diabetes. Neither tea nor chicory consumption was associated with diabetes risk. Conclusions: Our data support an inverse association between coffee consumption and diabetes and suggest that the time of drinking coffee plays a distinct role in glucose metabolism. Am J Clin Nutr 2010; 91: 1002-12.

Presence of MRI-detected joint effusion and synovitis increases the risk of cartilage loss in knees without osteoarthritis at 30-month follow-up: the MOST study

Objective To evaluate if two different measures of synovial activation, baseline Hoffa synovitis and effusion synovitis, assessed by MRI, predict cartilage loss in the tibiofemoral joint at 30 months follow-up in subjects with neither cartilage damage nor tibiofemoral radiographic osteoarthritis of the knee. Methods Non-contrast-enhanced MRI was performed using proton density-weighted fat-suppressed sequences in the axial and sagittal planes and a short tau inversion recovery sequence in the coronal plane. Hoffa synovitis, effusion synovitis and cartilage status were assessed semiquantitatively according to the WORMS scoring system. Included were knees that had neither radiographic osteoarthritis nor MRI-detected tibiofemoral cartilage damage at the baseline visit. The presence of Hoffa synovitis was defined as any grade = 2 (range 0-3) and effusion synovitis as any grade = 2 (range 0-3). Logistic regression was performed to examine the relation of the presence of either measure to the risk of cartilage loss at 30 months adjusting for other potential confounders. Results Of 514 knees included in the analysis, the prevalence of Hoffa synovitis and effusion synovitis at the baseline visit was 8.4% and 10.3%, respectively. In the multivariable analysis, baseline effusion synovitis was associated with an increased risk of cartilage loss. No such association was observed for baseline Hoffa synovitis. Conclusions Baseline effusion synovitis, but not Hoffa synovitis, predicted cartilage loss. The findings suggest that effusion synovitis, a reflection of inflammatory activity including joint effusion and synovitic thickening, may play a role in the future development of cartilage lesions in knees without osteoarthritis.

Expression of fibroblast growth factor receptors during development and regression of the bovine corpus luteum

There is evidence that fibroblast growth factors (FGFs) are involved in the regulation of growth and regression of the corpus luteum (CL). However, the expression pattern of most FGF receptors (FGFRs) during CL lifespan is still unknown. The objective of the present study was to determine the pattern of expression of `B` and `C` splice variants of FGFRs in the bovine CL. Bovine CL were collected from an abattoir and classed as corpora hemorrhagica (Stage I), developing (Stage II), developed (Stage III) or regressed (Stage IV) CL. Expression of FGFR mRNA was measured by semiquantitative reverse transcription-polymerase chain reaction and FGFR protein was localised by immunohistochemistry. Expression of mRNA encoding the `B` and `C` spliced forms of FGFR1 and FGFR2 was readily detectable in the bovine CL and was accompanied by protein localisation. FGFR1C and FGFR2C mRNA expression did not vary throughout CL lifespan, whereas FGFR1B was upregulated in the developed (Stage III) CL. FGFR3B, FGFR3C and FGFR4 expression was inconsistent in the bovine CL. The present data indicate that FGFR1 and FGFR2 splice variants are the main receptors for FGF action in the bovine CL.

Heteroscedastic Nonlinear Regression Models

In this article, we present a generalization of the Bayesian methodology introduced by Cepeda and Gamerman (2001) for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm, we use MCMC methods to simulate samples for the joint posterior distribution. We illustrate this algorithm considering a simulated data set and also considering a real data set related to school attendance rate for children in Colombia. Finally, we present some extensions of the proposed MCMC algorithm.

Regression Models with Heteroscedasticity using Bayesian Approach

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.

Methods of Estimation in Multiple Linear Regression: Application to Clinical Data

Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.