María Xosé Rodríguez-Álvarez

Insulin resistance index (HOMA-IR) levels in a general adult population: Curves percentile by gender and age. The EPIRCE study

AIMS To describe the distribution of HOMA-IR levels in a general nondiabetic population and its relationships with metabolic and lifestyles characteristics. METHODS Cross-sectional study. Data from 2246 nondiabetic adults in a random Spanish population sample, stratified by age and gender, were analyzed. Assessments included a structured interview, physical examination, and blood sampling. Generalized additive models (GAMs) were used to assess the effect of lifestyle habits and clinical and demographic measurements on HOMA-IR. Multivariate GAMs and quantile regression analyses of HOMA-IR were carried out separately in men and women. RESULTS This study shows refined estimations of HOMA-IR levels by age, body mass index, and waist circumference in men and women. HOMA-IR levels were higher in men (2.06) than women (1.95) (P=0.047). In women, but not men, HOMA-IR and age showed a significant nonlinear association (P=0.006), with increased levels above fifty years of age. We estimated HOMA-IR curves percentile in men and women. CONCLUSIONS Age- and gender-adjusted HOMA-IR levels are reported in a representative Spanish adult non-diabetic population. There are gender-specific differences, with increased levels in women over fifty years of age that may be related with changes in body fat distribution after menopause.

Clinical diagnosis of toenail onychomycosis is possible in some patients: cross-sectional diagnostic study and development of a diagnostic rule

Background  Suspected toenail onychomycosis is a frequent problem. Clinical diagnosis has been considered inadequate.

Admission and fasting plasma glucose for estimating risk of death of diabetic and nondiabetic patients with acute coronary syndrome: nonlinearity of hazard ratios and time-dependent comparison.

BACKGROUND In patients with acute coronary syndrome (ACS), increased plasma glucose levels are associated with worse outcome. Our aim is to ascertain the values of admission and fasting glucose for prediction of death among patients with ACS; and to compare their predictive capacities. METHODS The relationships of mortality to plasma glucose levels among 811 consecutive patients hospitalized with ACS were estimated using spline Cox models. Blood samples were obtained upon admission and after overnight fast. The predictive capacities of fasting and admission glucose were compared using time-dependent receiver operating characteristic curves. RESULTS Fasting and admission glucose levels were higher among the 151 patients who died (18.6%) than among survivors (P < .001). Among the 558 patients with no history of diabetes (68.8%) there was a J-shaped dependence of the all-time mortality hazard ratio on fasting glucose that persisted when adjusted for covariates: hazard was lowest at 110 mg/dL (6.1 mmol/L), and significantly greater at levels <90 mg/dL (5.0 mmol/L) or >117 mg/dL (6.5 mmol/L). Likewise among non-diabetic patients, the predictive capacities of admission and fasting glucose were similar for forecast times of up to about 1 year, but for later times the area under the receiver operating characteristic curve was larger for fasting glucose than admission glucose (P < .05). Neither admission nor fasting glucose levels discriminated among diabetic patients in regard to risk of death. CONCLUSIONS Both admission and fasting glucose may be used for triage of nondiabetic ACS patients; fasting glucose may additionally be useful for long-term management, for which the relationship with the all-time mortality hazard ratio is J-shaped.

Serum DJ-1/PARK 7 is a potential biomarker of choroidal nevi transformation.

PURPOSE There is substantial evidence that intraocular melanomas arise from benign nevi in the uveal tract. Previous studies performed by the authors revealed that uveal melanoma cells secrete the oncoprotein DJ-1/PARK7 into the extracellular environment and circulation. The aim of this study was to determine whether circulating DJ-1 serum levels correlate with known clinical risk factors of nevi growth. METHODS Standardized ultrasonography, optical coherence tomography, and eye fundus examinations were used to evaluate the clinical risk factors of nevi growth. These clinical risk factors (including nevi size, distance of margins to the optic disc, detection of acoustic hollowness, presence of ocular symptoms, orange pigment, subretinal fluid, and absence of drusen) were examined in 53 consecutive patients from January 2009 to February 2011. Serum levels of DJ-1/PARK7 in these patients and in healthy age- and sex-matched controls (n = 32) were analyzed using ELISA. RESULTS Within the choroidal nevi group, DJ-1 serum levels were higher in those with symptoms (P < 0.033), with a nevus thickness greater than 1.5 mm (P < 0.001), a large basal diameter greater than 8 mm (P < 0.001), and the presence of acoustic hollowness (P < 0.001), compared to those patients without these risk factors. Similar significant differences were found when these at risk nevi subgroups were compared to healthy persons. CONCLUSIONS Elevated serum levels of DJ-1 are associated with choroidal nevi transformation risk factors. Therefore, DJ-1 appears to be a promising factor for predicting the growth of choroidal nevi and may be a potential biomarker of malignancy.

Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm

A new computational algorithm for estimating the smoothing parameters of a multidimensional penalized spline generalized linear model with anisotropic penalty is presented. This new proposal is based on the mixed model representation of a multidimensional P-spline, in which the smoothing parameter for each covariate is expressed in terms of variance components. On the basis of penalized quasi-likelihood methods, closed-form expressions for the estimates of the variance components are obtained. This formulation leads to an efficient implementation that considerably reduces the computational burden. The proposed algorithm can be seen as a generalization of the algorithm by Schall (1991)—for variance components estimation—to deal with non-standard structures of the covariance matrix of the random effects. The practical performance of the proposed algorithm is evaluated by means of simulations, and comparisons with alternative methods are made on the basis of the mean square error criterion and the computing time. Finally, we illustrate our proposal with the analysis of two real datasets: a two dimensional example of historical records of monthly precipitation data in USA and a three dimensional one of mortality data from respiratory disease according to the age at death, the year of death and the month of death.

ROC curve and covariates: extending induced methodology to the non-parametric framework

Continuous diagnostic tests are often used to discriminate between diseased and healthy populations. The receiver operating characteristic (ROC) curve is a widely used tool that provides a graphical visualisation of the effectiveness of such tests. The potential performance of the tests in terms of distinguishing diseased from healthy people may be strongly influenced by covariates, and a variety of regression methods for adjusting ROC curves has been developed. Until now, these methodologies have assumed that covariate effects have parametric forms, but in this paper we extend the induced methodology by allowing for arbitrary non-parametric effects of a continuous covariate. To this end, local polynomial kernel smoothers are used in the estimation procedure. Our method allows for covariate effect not only on the mean, but also on the variance of the diagnostic test. We also present a bootstrap-based method for testing for a significant covariate effect on the ROC curve. To illustrate the method, endocrine data were analysed with the aim of assessing the performance of anthropometry for predicting clusters of cardiovascular risk factors in an adult population in Galicia (NW Spain), duly adjusted for age. The proposed methodology has proved useful for providing age-specific thresholds for anthropometric measures in the Galician community.

A new approach to categorising continuous variables in prediction models: Proposal and validation.

When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from a statistical point of view, due to loss of information and power, it is a common practice in medical research. Consequently, providing researchers with a useful and valid categorisation method could be a relevant issue when developing prediction models. Without recommending categorisation of continuous predictors, our aim is to propose a valid way to do it whenever it is considered necessary by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest area under the receiver operating characteristic curve (AUC). The proposed methodology is validated when the optimal cut points’ location is known in theory or in practice. In addition, the proposed method is applied to a real data-set of patients with an exacerbation of chronic obstructive pulmonary disease, in the context of the IRYSS-COPD study where a clinical prediction rule for severe evolution was being developed. The clinical variable PCO2 was categorised in a univariable and a multivariable setting.

Prevalence and genetic diversity of Salmonella spp. in a river in a tropical environment in Mexico

The capability of Salmonella to survive outside a host is especially relevant in tropical regions, where the environmental conditions could be more suitable for its long-term persistence. This study investigated the prevalence and genetic diversity of salmonellae within rivers of the Culiacan Valley in the northwestern region of Mexico. From July 2008 to June 2009, a total of 138 water samples were evaluated for the presence of Salmonella spp.; additionally, its association with environmental parameters was determined using Generalized Additive Models (GAMs). Salmonella spp. were isolated from 111 (80.4%) samples without any statistical influence on the environmental parameters investigated, according to the GAM analysis. Twenty-four serotypes were identified; the most frequently isolated serotypes were Salmonella Oranienburg (25%), Salmonella Saintpaul (9%) and Salmonella Minnesota (6%). Diverse genetic variants of Salmonella Oranienburg were found distributed across the valley with no distinctive geographical or temporal patterns. The high persistence of Salmonella spp. and the lack of differentiation of types found along the river basins suggest the existence of non-point source contamination. Furthermore, the discrepancy between the prevailing serotypes in human infections and those identified in this study denotes a limited influence of these aquatic environments in bacterial dissemination and disease transmission.

Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model

Key messageA flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials.AbstractAdjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. Furthermore, we used a flexible model to adequately adjust for field trends. This strategy reduces potential parameter identification problems and simplifies the model selection process. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials.

Nonparametric estimation of time‐dependent ROC curves conditional on a continuous covariate

The receiver-operating characteristic (ROC) curve is the most widely used measure for evaluating the performance of a diagnostic biomarker when predicting a binary disease outcome. The ROC curve displays the true positive rate (or sensitivity) and the false positive rate (or 1-specificity) for different cut-off values used to classify an individual as healthy or diseased. In time-to-event studies, however, the disease status (e.g. death or alive) of an individual is not a fixed characteristic, and it varies along the study. In such cases, when evaluating the performance of the biomarker, several issues should be taken into account: first, the time-dependent nature of the disease status; and second, the presence of incomplete data (e.g. censored data typically present in survival studies). Accordingly, to assess the discrimination power of continuous biomarkers for time-dependent disease outcomes, time-dependent extensions of true positive rate, false positive rate, and ROC curve have been recently proposed. In this work, we present new nonparametric estimators of the cumulative/dynamic time-dependent ROC curve that allow accounting for the possible modifying effect of current or past covariate measures on the discriminatory power of the biomarker. The proposed estimators can accommodate right-censored data, as well as covariate-dependent censoring. The behavior of the estimators proposed in this study will be explored through simulations and illustrated using data from a cohort of patients who suffered from acute coronary syndrome.