Prevalence of symptoms of neurotoxicity and potential occupational pesticide and solvent exposures among high school students in Starr County, Texas: A pilot study

Numerous harmful occupational exposures affect working teens in the United States. Teens working in agriculture and other heavy-labor industries may be at risk for occupational exposures to pesticides and solvents. The neurotoxicity of pesticides and solvents at high doses is well-known; however, the long term effects of these substances at low doses on occupationally exposed adolescents have not been well-studied. To address this research gap, a secondary analysis of cross-sectional data was completed in order to estimate the prevalence of self-reported symptoms of neurotoxicity among a cohort of high school students from Starr County, Texas, a rural area along the Texas-Mexico border. Multivariable linear regression was used to estimate the association between work status (i.e., no work, farm work, and non-farm work) and symptoms of neurotoxicity, while controlling for age, gender, Spanish speaking preference, inhalant use, tobacco use, and alcohol use. The sample included 1,208 students. Of these, the majority (85.84%) did not report having worked during the prior nine months compared to 4.80% who did only farm work, 6.21% who did only non-farm work, and 3.15% who did both types of work. On average, students reported 3.26 symptoms with a range from 0-16. The most commonly endorsed items across work status were those related to memory impairment. Adolescents employed in non-farm work jobs reported more neurotoxicity symptoms than those who reported that they did not work (Mean 4.31; SD 3.97). In the adjusted multivariable regression model, adolescents reporting non-farm work status reported an average of 0.77 more neurotoxicity symptoms on the Q16 than those who did not work (P = 0.031). The confounding variables included in the final model were all found to be factors significantly associated with report of neurotoxicity symptoms. Future research should examine the relationship between these variables and self-report of symptoms of neurotoxicity.^

Detecting genetic and nutritional lung cancer risk factors related to folate metabolism using Bayesian generalized linear models

Complex diseases, such as cancer, are caused by various genetic and environmental factors, and their interactions. Joint analysis of these factors and their interactions would increase the power to detect risk factors but is statistically. Bayesian generalized linear models using student-t prior distributions on coefficients, is a novel method to simultaneously analyze genetic factors, environmental factors, and interactions. I performed simulation studies using three different disease models and demonstrated that the variable selection performance of Bayesian generalized linear models is comparable to that of Bayesian stochastic search variable selection, an improved method for variable selection when compared to standard methods. I further evaluated the variable selection performance of Bayesian generalized linear models using different numbers of candidate covariates and different sample sizes, and provided a guideline for required sample size to achieve a high power of variable selection using Bayesian generalize linear models, considering different scales of number of candidate covariates. ^ Polymorphisms in folate metabolism genes and nutritional factors have been previously associated with lung cancer risk. In this study, I simultaneously analyzed 115 tag SNPs in folate metabolism genes, 14 nutritional factors, and all possible genetic-nutritional interactions from 1239 lung cancer cases and 1692 controls using Bayesian generalized linear models stratified by never, former, and current smoking status. SNPs in MTRR were significantly associated with lung cancer risk across never, former, and current smokers. In never smokers, three SNPs in TYMS and three gene-nutrient interactions, including an interaction between SHMT1 and vitamin B12, an interaction between MTRR and total fat intake, and an interaction between MTR and alcohol use, were also identified as associated with lung cancer risk. These lung cancer risk factors are worthy of further investigation.^

Performance of HbA1c for classifying diabetes and diagnostic threshold for diabetes among the Mexican American border community

Objective. In 2009, the International Expert Committee recommended the use of HbA1c test for diagnosis of diabetes. Although it has been recommended for the diagnosis of diabetes, its precise test performance among Mexican Americans is uncertain. A strong “gold standard” would rely on repeated blood glucose measurement on different days, which is the recommended method for diagnosing diabetes in clinical practice. Our objective was to assess test performance of HbA1c in detecting diabetes and pre-diabetes against repeated fasting blood glucose measurement for the Mexican American population living in United States-Mexico border. Moreover, we wanted to find out a specific and precise threshold value of HbA1c for Diabetes Mellitus (DM) and pre-diabetes for this high-risk population which might assist in better diagnosis and better management of patient diabetes. ^ Research design and methods. We used CCHC dataset for our study. In 2004, the Cameron County Hispanic Cohort (CCHC), now numbering 2,574, was established drawn from randomly selected households on the basis of 2000 Census tract data. The CCHC study randomly selected a subset of people (aged 18-64 years) in CCHC cohort households to determine the influence of SES on diabetes and obesity. Among the participants in Cohort-2000, 67.15% are female; all are Hispanic. ^ Individuals were defined as having diabetes mellitus (Fasting plasma glucose [FPG] ≥ 126 mg/dL or pre-diabetes (100 ≤ FPG < 126 mg/dL). HbA1c test performance was evaluated using receiver operator characteristic (ROC) curves. Moreover, change-point models were used to determine HbA1c thresholds compatible with FPG thresholds for diabetes and pre-diabetes. ^ Results. When assessing Fasting Plasma Glucose (FPG) is used to detect diabetes, the sensitivity and specificity of HbA1c≥ 6.5% was 75% and 87% respectively (area under the curve 0.895). Additionally, when assessing FPG to detect pre-diabetes, the sensitivity and specificity of HbA1c≥ 6.0% (ADA recommended threshold) was 18% and 90% respectively. The sensitivity and specificity of HbA1c≥ 5.7% (International Expert Committee recommended threshold) for detecting pre-diabetes was 31% and 78% respectively. ROC analyses suggest HbA1c as a sound predictor of diabetes mellitus (area under the curve 0.895) but a poorer predictor for pre-diabetes (area under the curve 0.632). ^ Conclusions. Our data support the current recommendations for use of HbA1c in the diagnosis of diabetes for the Mexican American population as it has shown reasonable sensitivity, specificity and accuracy against repeated FPG measures. However, use of HbA1c may be premature for detecting pre-diabetes in this specific population because of the poor sensitivity with FPG. It might be the case that HbA1c is differentiating the cases more effectively who are at risk of developing diabetes. Following these pre-diabetic individuals for a longer-term for the detection of incident diabetes may lead to more confirmatory result.^

Social integration, health behaviors and health status among Mexican-American women in the *southwestern United States

Using the Hispanic Health and Nutrition Examination Survey (HHANES), this research examined several health behaviors and the health status of Mexican American women. This study focused on determining the relative impact of social contextual factors: age, socioeconomic status, quality of life indicators, and urban/rural residence on (a) health behaviors (smoking, obesity and alcohol use) and (b) health status (physician's assessment of health status, subject's assessment of health status and blood pressure levels). In addition, social integration was analyzed. The social integration indicators relate to an individual's degree of integration within his/her social group: marital status, level of acculturation (a continuum of traditional Mexican ways to dominant U.S. cultural ways), status congruency, and employment status. Lastly, the social contextual factors and social integration indicators were examined to identify those factors that contribute most to understanding health behaviors and health status among Mexican American women.^ The study found that the social contextual factors and social integration indicators proved to be important concepts in understanding the health behaviors. Social integration, however, did not predict health status except in the case of the subject's assessment of health status. Age and obesity were the strongest predictors of blood pressure. The social contextual factors and obesity were significant predictors of the physician's assessment of health status while acculturation, education, alcohol use and obesity were significant predictors of the subject's assessment of health status. ^