40 research outputs found
Associations of urinary BPA levels and dietary fat intake with prior breast cancer diagnosis among women: Results from the National Health and Nutrition Examination Survey
Introduction: The prevalence of breast cancer, high saturated fat intake and environmental toxicant ingestion continues to be an issue within the United States. The aim of this study was to evaluate the association between urinary Bisphenol A (BPA) levels and dietary fat intake with breast cancer diagnosis in U.S. women 30 years and older using data from the National Health and Nutrition Examination Survey (NHANES).
Methods: This study examined dietary data for 200 women who participated in NHANES study cycles between 2003-2014. Inclusion criteria were women aged 30 years or older with a diagnosis of breast cancer, complete urinary BPA data, and complete dietary data for Healthy Eating Index calculation and ability to normalize BPA for body mass index (BMI). The sample population was matched with a randomly selected group of women of similar age from the same NHANES study cycles but no diagnosis of breast cancer. Non-parametric statistical analyses were performed. After descriptive and correlations were performed for preliminary statistics, the study population was divided into three groups for analyses: 1) never diagnosed with breast cancer (n=100), 2) less than 5 years since diagnosis (n=39), and 3) greater than 5 years since diagnosis (n=61) for regression and ANOVA analyses.
Results: Out of a scale of 100, average HEI scores for each group were found to be 55.96 (13.62) in never diagnosed, 56.58 (12.98) in women diagnosed with breast cancer less than 5 years ago, and 58.81 (13.41) in women more than 5 years from diagnosis. In all groups, urinary BPA was significantly correlated with HEI fatty acid ratio and saturated fat intake (r= 0.20, n=200 p= 0. 007, =0.05; r=-0.16, n=200, p=0.03, =0.05). Urinary BPA levels, however, did not correlate with breast cancer diagnosis (r=0.143, n=200, p=0.157, =0.05). There were no statistically significant interactions between saturated fat, added sugars in the diet, a healthy diet overall (HEI score), or urinary BPA levels and a prior diagnosis of breast cancer (p\u3e0.05 and =0.05 for all dietary components and urinary BPA). Urinary BPA levels varied with time since diagnosis. Women who had been diagnosed with cancer more than 5 years prior to the survey had significantly lower urinary total BPA levels and lower BPA levels normalized for body mass index (BMI) than the women who had never been diagnosed or women who had been diagnosed within the past five years, (p
Conclusions: The study found evidence that urinary BPA levels are associated with saturated fat intake. The study also showed a trend for a reduction in BPA levels and an increase in dietary polyunsaturated fat intake in cancer survivors more than 5 years post-diagnosis. It could be inferred from these trends that women modify their diet and fat intake levels post-cancer diagnosis. The study did not, however, find links between overall diet quality, saturated fat, or urinary BPA levels and a prior cancer diagnosis. This finding is consistent with previous research that suggests neither BPA levels in the blood nor saturated fat can be statistically linked to the incidence of breast cancer. That does not mean endocrine disruptors have no role in the development of cancer. Diet, lifestyle, and environmental factors all have the potential to alter DNA methylation, gene expression patterns, and inflammatory responses resulting in adverse health outcomes. Future examinations of the relationships between plastic packaging, canned foods, and processed foods on urinary BPA levels and the prevalence of breast cancer should be taken into consideration
A Multi-Center, Two-Arm, Single-Blind Randomized Trial of Two Different Intravenous Fluids During Labor (Poster).
Valutazione sperimentale dei cicli di isteresi e dei fattori di smorzamento di conglomerati bituminosi ordinari e modificati
Parallel computational issues of an interior point method for solving large bound-constrained quadratic programming problems
Modulation quality measurements in Bluetooth systems through time frequency representations
A Multi-Center, Two-Arm, Single-Blind Randomized Trial of IV Fluids During Labor (Presentation).
Approccio energetico per la valutazione delle caratteristiche elasto-viscose dei conglomerati bituminosi con prove dinamiche a flessione
Influence of thickness and interface condition variability on backcalculated layer moduli from surface deflection
Evaluation of pavement structural performance by Non Destructive Test (NDT) has been growing since the introduction of the Benkelman Beam at the WASHO Road Test in the early 1950's. The deflection basin data have to be analyzed d to obtain the structural properties of pavement layers and subgrade, that are typically used for pavement evaluation and rehabilitation design. One of the more common methods for analyzing deflection data is the back-calculation, in which theoretical deflections are compared with measured deflections and the assumed moduli are then adjusted in an iterative procedure until theoretical and measured deflection basins match acceptably well. The iterative process assume the pavement model as an input, and the accuracy and the precision of the backcalculation process can be reduced by errors due to the inaccurate input evaluation. Particularly pavement layer thickness and interface condition between layers are affected by a construction and measurement related variability. As matter of fact layer thickness information are measured accurately from core, in a limited number of test points because of their destructive nature, and are rough estimated from Ground Penetrating Radar equipment along the entire profile section. Layer interface properties depends on many factors (such as temperature, coat binder, normal pressure etc.) and are very difficult to be estimated.
In this study a sensitive analysis to evaluate the combined impact of the layer
thickness and interface condition variability was performed. A back-calculation program to accurately evaluate the layer moduli, based on a linear elastic multilayer model, was developed and a simulation procedure, based on Monte Carlo method, was applied to represent the thickness variability. The study showed that layer thickness and
interface condition variability have a strong impact on the back-calculated results, therefore it was proposed to perform the design of maintenance treatment according to
suitable percentile value of their probability distribution. This former could be evaluated by the simulation procedure developed, as a case study reported shows