Abdominal obesity and insulin resistance after an episode of acute pancreatitis.

BACKGROUND: Emerging evidence indicates that individuals after an episode of acute pancreatitis (AP) are at an increased risk of developing metabolic derangements. While the link between general obesity and insulin resistance (IR) is well established, only a few studies have investigated the association between abdominal obesity and IR. The aim of this study was to investigate the associations between abdominal obesity and several indices of IR in individuals after an episode of AP. METHODS: Patients were eligible for this cross-sectional study if they were previously admitted with a primary diagnosis of AP based on the recent international guidelines. Fasting venous bloods were collected to measure glucose, insulin, free fatty acids, glycerol, adiponectin (AD), omentin (OM), and vaspin (VAS). The IR indices - HOMA-IR, Adipo-IR, insulin*glycerol (IG) index, HOMA-AD, HOMA-OM, and HOMA-VAS were calculated. Modified Poisson regression was conducted, with statistical model adjusting for patient-, metabolic-, and pancreatitis-related risk factors. Areas under ROC curve were calculated and Bland-Altman plots were created. RESULTS: Of the 92 individuals recruited, 41 had abdominal obesity. HOMA-IR, IG index, HOMA-OM, and HOMA-VAS were significantly associated with abdominal obesity, both in unadjusted and adjusted models. Area under ROC curves for HOMA-IR, IG index, HOMA-OM, and HOMA-VAS were 0.698, 0.695, 0.756, and 0.735, respectively. There was a good agreement between observed HOMA-IR values and values obtained from HOMA-OM (P = 0.733) and HOMA-VAS (P = 0.595). CONCLUSION: Individuals with abdominal obesity after AP have a significantly higher IR, independent of diabetes and other covariates. Visceral adipose tissue specific adipokines, omentin and vaspin, hold promise for future clinical investigation of tissue-specific IR.

Associations between circulating levels of adipocytokines and abdominal adiposity in patients after acute pancreatitis.

Adipocytokines are strongly associated with abdominal adiposity during the course of acute pancreatitis (AP). This study investigated associations between a panel of adipocytokines and abdominal adiposity in AP patients after hospital discharge, as well as the effect of several covariates. Fasting venous blood samples were collected to measure adiponectin, interleukin 6, leptin, monocyte chemoattractant protein 1, tumour necrosis factor α (TNFα), resistin, and retinol-binding protein 4. Waist circumference (WC), waist-hip ratio, and waist-height ratio (WheightR) were used as measures of abdominal adiposity. Generalised linear models were built, adjusting for age, sex, ethnicity, diabetes status, aetiology, duration since admission for AP, recurrence, and severity of AP. A total of 93 patients were studied, on average at 22 months after AP. Interleukin 6, TNFα, and leptin were significantly associated with WC in both the unadjusted and all the three adjusted models. Also, they were significantly associated with WheightR in both the unadjusted and the three adjusted models. Other studied adipocytokines did not show a consistent association or were not significantly associated with the abdominal adiposity indices. The results suggest that excess abdominal adiposity favours pro-inflammatory milieu in AP patients after hospital discharge, independent of diabetes and effect of other covariates.

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects.

Bayesian genomic-enabled prediction as an inverse problem.

Genomic-enabled prediction in plant and animal breeding has become an active area of research. Many prediction models address the collinearity that arises when the number (p) of molecular markers (e.g. single-nucleotide polymorphisms) is larger than the sample size (n). Here we propose four Bayesian approaches to the problem based on commonly used data reduction methods. Specifically, we use a Gaussian linear model for an orthogonal transformation of both the observed data and the matrix of molecular markers. Because shrinkage of estimates is affected by the prior variance of transformed effects, we propose four structures of the prior variance as a way of potentially increasing the prediction accuracy of the models fitted. To evaluate our methods, maize and wheat data previously used with standard Bayesian regression models were employed for measuring prediction accuracy using the proposed models. Results indicate that, for the maize and wheat data sets, our Bayesian models yielded, on average, a prediction accuracy that is 3% greater than that of standard Bayesian regression models, with less computational effort.

Effect of particulate matter less than 10µm (PM10) on mortality in Bogota, Colombia: a time-series analysis, 1998-2006 / Efecto del material particulado menor a 10µm (PM10) sobre la mortalidad en Bogotá, Colombia: un análisis de series de tiempo 1998-2006

Objective. To analyze the association between daily mortality from different causes and acute exposure to particulate matter less than 10 microns in aerodynamic diameter (PM10), in Bogota, Colombia. Materials and methods. A time-series ecological study was conducted from 1998 to 2006. The association between mortality (due to different causes) and exposure was analyzed using single and distributed lag models and adjusting for potential confounders. Results. For all ages, the cumulative effect of acute mortality from all causes and respiratory causes increased 0.71% (95%CI 0.46-0.96) and 1.43% (95%CI 0.85-2.00), respectively, per 10µg/m³ increment in daily average PM10 with a lag of three days before death. Cumulative effect of mortality from cardiovascular causes was -0.03% (95%CI -0.49-0.44%) with the same lag. Conclusions. The results suggest an association between an increase in PM10 concentrations and acute mortality from all causes and respiratory causes.

Objetivo. Analizar la asociación entre la mortalidad diaria debida a distintas causas y la exposición aguda a partículas menores de 10 micras de diámetro aerodinámico (PM10), en Bogotá, Colombia. Material y métodos. Se realizó un estudio ecológico de series de tiempo (1998-2006). La asociación entre mortalidad y exposición se analizó ajustando modelos de retraso simple y retraso distribuido para diferentes causas de mortalidad. Resultados. En todas las edades, el riesgo acumulado en la mortalidad aguda por todas las causas y causa respiratoria aumentó 0.71% (IC95% 0.46-0.96) y 1.43% (IC95% 0.85-2.00), respectivamente, por incremento de 10µg/m³ en el promedio diario de PM10, tomando un retraso de tres días anteriores al deceso, mientras el riesgo acumulado en la mortalidad por causa cardiovascular fue de -0.03% (IC95% -0.49-0.44), para el mismo retraso. Conclusiones. Los resultados sugieren asociación entre el incremento de las concentraciones de PM10 y la mortalidad aguda por todas las causas y causa respiratoria.

Effect of socioeconomic status on the association between air pollution and mortality in Bogota, Colombia / Efecto del nivel socioeconómico sobre la asociación entre contaminantes atmosféricos y mortalidad en Bogotá, Colombia

Objective. To evaluate the modification effect of socioeconomic status (SES) on the association between acute exposure to particulate matter less than 10 microns in aerodynamic diameter (PM10) and mortality in Bogota, Colombia. Materials and methods. A time-series ecological study was conducted (1998-2006). The localities of the cities were stratified using principal components analysis, creating three levels of aggregation that allowed for the evaluation of the impact of SES on the relationship between mortality and air pollution. Results. For all ages, the change in the mortality risk for all causes was 0.76% (95%CI 0.27-1.26) for SES I (low), 0.58% (95%CI 0.16-1.00) for SES II (mid) and -0.29% (95%CI -1.16-0.57) for SES III (high) per 10µg/m³ increment in the daily average of PM10 on day of death. Conclusions. The results suggest that SES significantly modifies the effect of environmental exposure to PM10 on mortality from all causes and respiratory causes.

Objetivo. Evaluar el efecto modificador del nivel socioeconómico (NSE) sobre la asociación entre la exposición aguda a partículas menores de 10 micras de diámetro aerodinámico (PM10) y la mortalidad en Bogotá, Colombia. Material y métodos. Se realizó un estudio ecológico de series de tiempo (1998-2006). Mediante análisis de componentes principales se estableció una estratificación de las localidades de la ciudad, de lo que se generaron tres niveles de agregación que permitieron evaluar el impacto de la variable NSE en la relación mortalidad-contaminación atmosférica. Resultados. En todas las edades, para la mortalidad por todas las causas, el porcentaje de cambio en el riesgo fue 0.76% (IC95% 0.27-1.26) en el NSE I (bajo), 0.58% (IC95% 0.16-1.00) en el NSE II (medio) y -0.29% (IC95% -1.16-0.57) en el NSE III (alto), por incremento de 10µg/m³ en el promedio diario de PM10 en el día del deceso. Conclusiones. Los resultados sugieren que el NSE modifica de manera significativa el efecto de la exposición ambiental a PM10 sobre la mortalidad por todas las causas y causas respiratorias.

Effect of particulate matter less than 10µm (PM10) on mortality in Bogota, Colombia: a time-series analysis, 1998-2006.

OBJECTIVE: To analyze the association between daily mortality from different causes and acute exposure to particulate matter less than 10 microns in aerodynamic diameter (PM10), in Bogota, Colombia. MATERIALS AND METHODS: A time-series ecological study was conducted from 1998 to 2006. The association between mortality (due to different causes) and exposure was analyzed using single and distributed lag models and adjusting for potential confounders. RESULTS: For all ages, the cumulative effect of acute mortality from all causes and respiratory causes increased 0.71% (95%CI 0.46-0.96) and 1.43% (95%CI 0.85-2.00), respectively, per 10µg/m³ increment in daily average PM10 with a lag of three days before death. Cumulative effect of mortality from cardiovascular causes was -0.03% (95%CI -0.49-0.44%) with the same lag. CONCLUSIONS: The results suggest an association between an increase in PM10 concentrations and acute mortality from all causes and respiratory causes.

Effect of socioeconomic status on the association between air pollution and mortality in Bogota, Colombia.

OBJECTIVE: To evaluate the modification effect of socioeconomic status (SES) on the association between acute exposure to particulate matter less than 10 microns in aerodynamic diameter (PM10) and mortality in Bogota, Colombia. MATERIALS AND METHODS: A time-series ecological study was conducted (1998-2006). The localities of the cities were stratified using principal components analysis, creating three levels of aggregation that allowed for the evaluation of the impact of SES on the relationship between mortality and air pollution. RESULTS: For all ages, the change in the mortality risk for all causes was 0.76% (95%CI 0.27-1.26) for SES I (low), 0.58% (95%CI 0.16-1.00) for SES II (mid) and -0.29% (95%CI -1.16-0.57) for SES III (high) per 10µg/m³ increment in the daily average of PM10 on day of death. CONCLUSIONS: The results suggest that SES significantly modifies the effect of environmental exposure to PM10 on mortality from all causes and respiratory causes.

Multicity study of air pollution and mortality in Latin America (the ESCALA study).

INTRODUCTION: The ESCALA* project (Estudio de Salud y Contaminación del Aire en Latinoamérica) is an HEI-funded study that aims to examine the association between exposure to outdoor air pollution and mortality in nine Latin American cities, using a common analytic framework to obtain comparable and updated information on the effects of air pollution on several causes of death in different age groups. This report summarizes the work conducted between 2006 and 2009, describes the methodologic issues addressed during project development, and presents city-specific results of meta-analyses and meta-regression analyses. METHODS: The ESCALA project involved three teams of investigators responsible for collection and analysis of city-specific air pollution and mortality data from three different countries. The teams designed five different protocols to standardize the methods of data collection and analysis that would be used to evaluate the effects of air pollution on mortality (see Appendices B-F). By following the same protocols, the investigators could directly compare the results among cities. The analysis was conducted in two stages. The first stage included analyses of all-natural-cause and cause-specific mortality related to particulate matter < or = 10 pm in aerodynamic diameter (PM10) and to ozone (O3) in cities of Brazil, Chile, and México. Analyses for PM10 and O3 were also stratified by age group and O3 analyses were stratified by season. Generalized linear models (GLM) in Poisson regression were used to fit the time-series data. Time trends and seasonality were modeled using natural splines with 3, 6, 9, or 12 degrees of freedom (df) per year. Temperature and humidity were also modeled using natural splines, initially with 3 or 6 df, and then with degrees of freedom chosen on the basis of residual diagnostics (i.e., partial autocorrelation function [PACF], periodograms, and a Q-Q plot) (Appendix H, available on the HEI Web site). Indicator variables for day-of-week and holidays were used to account for short-term cyclic fluctuations. To assess the association between exposure to air pollution and risk of death, the PM10 and O3 data were fit using distributed lag models (DLMs). These models are based on findings indicating that the health effects associated with air pollutant concentrations on a given day may accumulate over several subsequent days. Each DLM measured the cumulative effect of a pollutant concentration on a given day (day 0) and that day's contribution to the effect of that pollutant on multiple subsequent (lagged) days. For this study, exposure lags of up to 3, 5, and 10 days were explored. However, only the results of the DLMs using a 3-day lag (DLM 0-3) are presented in this report because we found a decreasing association with mortality in various age-cause groups for increasing lag effects from 3 to 5 days for both PM10 and O3. The potential modifying effect of socioeconomic status (SES) on the association of PM10 or O3 concentration and mortality was also explored in four cities: Mexico City, Rio de Janeiro, São Paulo, and Santiago. The methodology for developing a common SES index is presented in the report. The second stage included meta-analyses and metaregression. During this stage, the associations between mortality and air pollution were compared among cities to evaluate the presence of heterogeneity and to explore city-level variables that might explain this heterogeneity. Meta-analyses were conducted to combine mortality effect estimates across cities and to evaluate the presence of heterogeneity among city results, whereas meta-regression models were used to explore variables that might explain the heterogeneity among cities in mortality risks associated with exposures to PM10 (but not to O3). RESULTS: The results of the mortality analyses are presented as risk percent changes (RPC) with a 95% confidence interval (CI). RPC is the increase in mortality risk associated with an increase of 10 microg/m3 in the 24-hour average concentration of PM10 or in the daily maximum 8-hour moving average concentration of O3. Most of the results for PM10 were positive and statistically significant, showing an increased risk of mortality with increased ambient concentrations. Results for O3 also showed a statistically significant increase in mortality in the cities with available data. With the distributed lag model, DLM 0-3, PM10 ambient concentrations were associated with an increased risk of mortality in all cities except Concepci6n and Temuco. In Mexico City and Santiago the RPC and 95% CIs were 1.02% (0.87 to 1.17) and 0.48% (0.35 to 0.61), respectively. PM10 was also significantly associated with increased mortality from cardiopulmonary, respiratory, cardiovascular, cerebrovascular-stroke, and chronic obstructive lung diseases (COPD) in most cities. The few nonsignificant effects generally were observed in the smallest cities (Concepción, Temuco, and Toluca). The percentage increases in mortality associated with ambient O3 concentrations were smaller than for those associated with PM10. All-natural-cause mortality was significantly related to O3 in Mexico City, Monterrey, São Paulo and Rio de Janeiro. Increased mortality risks for some specific causes were also observed in these cities and in Santiago. In the analyses stratified by season, different patterns in mortality and O3 were observed for cold and warm seasons. Risk estimates for the warm season were larger and significant for several causes of death in São Paulo and Rio de Janeiro. Risk estimates for the cold season were larger and significant for some causes of death in Mexico City, Monterrey, and Toluca. In an analysis stratified by SES, the all-natural-cause mortality risk in Mexico City was larger for people with a medium SES; however we observed that the risk of mortality related to respiratory causes was larger among people with a low SES, while the risk of mortality related to cardiovascular and cerebrovascular-stroke causes was larger among people with medium or high SES. In São Paulo, the all-natural-cause mortality risk was larger in people with a high SES, while in Rio de Janeiro the all-natural-cause mortality risk was larger in people with a low SES. In both Brazilian cities, the risks of mortality were larger for respiratory causes, especially for the low- and high-SES groups. In Santiago, all-natural-cause mortality risk did not vary with level of SES; however, people with a low SES had a higher respiratory mortality risk, particularly for COPD. People with a medium SES had larger risks of mortality from cardiovascular and cerebrovascular-stroke disease. The effect of ambient PM10 concentrations on infant and child mortality from respiratory causes and lower respiratory infection (LRI) was studied only for Mexico City, Santiago, and São Paulo. Significant increased mortality risk from these causes was observed in both Santiago (in infants and older children) and Mexico City (only in infants). For O3, an increased mortality risk was observed in Mexico City (in infants and older children) and in São Paulo (only in infants during the warm season). The results of the meta-analyses confirmed the positive and statistically significant association between PM10 and all-natural-cause mortality (RPC = 0.77% [95% CI: 0.60 to 1.00]) using the random-effects model. For mortality from specific causes, the percentage increase in mortality ranged from 0.72% (0.54 to 0.89) for cardiovascular disease to 2.44% (1.36 to 3.59) for COPD, also using the random-effects model. For O3, significant positive associations were observed using the random-effects model for some causes, but not for all natural causes or for respiratory diseases in people 65 years or older (> or = 65 years), and not for COPD and cerebrovascular-stroke in the all-age and the > or = 65 age groups. The percentage increase in all-natural-cause mortality was 0.16% (-0.02 to 0.33). In the meta-regression analyses, variables that best explained heterogeneity in mortality risks among cities were the mean average of temperature in the warm season, population percentage of infants (< 1 year), population percentage of children at least 1 year old but < 5 years (i.e., 1-4 years), population percentage of people > or = 65 years, geographic density of PM10 monitors, annual average concentrations of PM10, and mortality rates for lung cancer. CONCLUSIONS: The ESCALA project was undertaken to obtain information for assessing the effects of air pollutants on mortality in Latin America, where large populations are exposed to relatively high levels of ambient air pollution. An important goal was to provide evidence that could inform policies for controlling air pollution in Latin America. This project included the development of standardized protocols for data collection and for statistical analyses as well as statistical analytic programs (routines developed in R by the ESCALA team) to insure comparability of results. The analytic approach and statistical programming developed within this project should be of value for researchers carrying out single-city analyses and should facilitate the inclusion of additional Latin American cities within the ESCALA multicity project. Our analyses confirm what has been observed in other parts of the world regarding the effects of ambient PM10 and 03 concentrations on daily mortality. They also suggest that SES plays a role in the susceptibility of a population to air pollution; people with a lower SES appeared to have an increased risk of death from respiratory causes, particularly COPD. Compared with the general population, infants and young children appeared to be more susceptible to both PM10 and O3, although an increased risk of mortality was not observed in these age groups in all cities. (ABSTRACT TRUNCATED)