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  • This study used publicly available data, that were anonymized and de-identified prior to analysis, and was exempt from human subjects review. Study population and data collection: We completed a cross-sectional study using the NHANES 2001–2002. NHANES is an annual repeated cross-sectional survey conducted and maintained by the National Center for Health Statistics of the Center for Disease Control (CDC). NHANES uses a stratified complex sampling scheme to select a nationally representative sample of 5,000 participants per year from 15 randomly selected counties across the US for each cycle to collect clinical, behavioral, demographic, dietary, social, and laboratory data through interviews, exercises, physical examinations, and serum and urine samples. For this study, we used the Continuous NHANES (2001–2002) to select all eligible subjects which included all adults over the age of 60 years with measures for urinary PAH metabolites with a concurrently assessed DSST score (n = 454; Fig 1). Figure data removed from full text. Figure identifier and caption: 10.1371/journal.pone.0147632.g001 Flow Diagram of the Total Eligible Population and Study Population. Cognitive function was assessed by the DSST score, a subtest of the WAIS-III that was administered to all eligible (literate, available writing surface, able to commit to the time frame required to complete the test) adults 60 years and older. This test consists of a coding exercise where individuals print symbols that are matched with numbers, identified in a key, for 133 situations over 120 seconds [46]. The score consists of the number of correct symbols printed during the allotted time frame (minimum: 0 and maximum: 133). DSST score was treated as a continuous variable consistent with previous studies [25, 47]. Urinary polycyclic aromatic hydrocarbon analysis: The urine samples were collected, stored, and shipped in accordance with the Laboratory/Medical Technologists Procedures Manual. Following collection, the samples were stored at a temperature of -20°C and then sent to National Center for Environmental Health for analysis [48]. Once at the laboratory the urine samples underwent enzymatic hydrolysis to remove PAH conjugates, solid-phase extraction to extract the mono-hydroxylated PAH metabolites, and derivatization to create more volatile metabolites [48]. Urinary metabolites were then quantified using capillary gas chromatography combined with high-resolution mass spectrometry (GC/MS). Method details are presented elsewhere [48]. When urinary PAH metabolite concentrations were non-detectable, a value of the detection limit divided by the square root of two was imputed into the dataset [48]. Levels of urinary PAH metabolites [1-OHN, 2-OHN, 3-OHFl, 2-OHFl, 3-OHPhe, 1-OHPhe, 2-OHPhe, and 1-OHPyr] were evaluated as predictors of the DSST score. Statistical analyses of all PAH metabolite measures were done after natural logarithmic transformation to satisfy normality assumption (S1 Appendix). Urinary PAHs were assessed in three separate measures: 1) sum of all urinary PAH metabolites; 2) sum of smaller molecular PAHs (NFP) consisting of naphthols (1-OHN, 2-OHN), fluorenols (2-OHFl, 3-OHFl), and phenanthrols; and 3) measures of 1-pyrenol (1-OHPyr). Each of these biomarker measures was evaluated as a predictor of DSST score in separate linear regression models. Potential confounders to the association between urinary PAH metabolites and DSST scores were evaluated based on findings from previous research. These included age, gender, SES [18], alcohol consumption [14], smoking status [25], vision and hearing problems [49], hypertension diagnosis [50], physical activity [51], history of thyroid disease [52], history of kidney disease [53], history of liver disease, history of stroke [54], current use of medications that potentially alter test taking ability such as medication to treat depression [55], anxiety [56], dementia/Alzheimer’s, and pain killers [57]. In this study, test taking impairment was defined as having poor self-reported hearing, vision, or both poor vision and hearing. Medication that could potentially alter test taking performance was investigated by combining the use (user/non-user) of the following FDA drug classes: anti-depressant, anti-anxiety, dementia, Alzheimer’s disease, and pain killers. Clinical data was collected through an examination and medical interview. Subjects were asked to self-report previous clinical diagnoses (yes/no) including diseases of the kidney, liver, thyroid, and stroke. Blood pressure was measured four times for each individual in a seated position, after 5 minutes of rest using a sphygmomanometer. Hypertension (yes/ no) was assigned to each individual if his/her average systolic blood pressure was higher than140 mmHg, average diastolic blood pressure was higher than 90 mmHg, or both. Urinary creatinine was measured via gas GC/MS for each subject. Biomarkers in spot urine samples are variable due to changes in urine dilution. To adjust for this variation, urinary creatinine measurements were evaluated as potential confounders in linear regression models [58]. Study subjects were interviewed regarding demographic factors (e.g., age, gender, SES), lifestyle, and medical conditions. Demographic information including age, gender, and SES were collected. Age was recorded for each study subject as a continuous variable up to age 84. All individuals who were 85 and older were categorized into one age category defined as 85 and older. SES was estimated based on the family income poverty index ratio (ratio of the family income to poverty threshold as reported by the Department and Health and Human Services) and categorized into three groups low (0–1.85), middle (1.85–3.5), and high (3.5-above). Behavioral information was collected through a medical interview. Subjects were asked about current alcohol consumption, and were defined as drinkers (more than 12 alcoholic drinks a year) or non-drinkers (fewer than 12 alcoholic drinks per year) to assess the interaction between alcohol and urinary metabolite levels, as found in previous studies [59, 60]. Tobacco smoke exposure was defined as having smoked 100 cigarettes or more over one’s lifetime, smoked 20 cigars over one’s lifetime, or smoked 20 pipes over one’s lifetime (yes/no). Physical activity level (low, medium, high) was determined through a physical assessment algorithm derived from interview questions about duration, intensity, and type of exercise. Non-valid measures of variables included missing values or responses of “I don’t know” or “refuse to answer”. Non-valid measures were reported for SES, alcohol consumption, physical activity, thyroid issues, stroke, kidney disease and liver disease. All analyses were conducted using SAS Version 9.4 using survey procedures (PROC SURVEY REG) that account for the complex survey design. Missing data labeled as ‘non-valid response’ for alcohol consumption, physical activity level, thyroid disease, and stroke were imputed by assigning the value of the highest frequency from available valid responses. A two-step algorithm was used to impute the missing SES values. SES was imputed as low for individuals with individual family income less than $20,000 per year, Medicaid use, or marginally secure food security. The remaining SES values were imputed with the highest SES frequency by education status for each of the missing individuals. Residuals and outliers were evaluated for the outcome and primary exposure variable with residual plots (S2 Appendix) and a criterion of less than 5 percent presence was determined to be acceptable. Two values for 1-OHN and 2-OHN were high in our final study population. To evaluate their influence these observations were removed from the model and analyses were repeated. Results for both models were similar (with two high values in data: parameter estimate of 1-OHPyr = -1.81 (95 percent confidence interval of -3.41, -0.21), and model r-squared of 0.366; and without the two high values: parameter estimate of 1-OHPyr = -1.82 (95 percent confidence interval of -3.44, -0.20), and model r-squared of 0.365). A non-response analysis was performed to confirm that those who were eligible for the DSST but did not have a valid measure were similar to those who did complete the DSST. To test if there were significant differences in the three urinary PAH metabolite measures amongst covariate categories, means were compared with a t-test using PROC SURVEY REG with a LSMEANS statement. Additionally, the Pearson correlation was calculated for each of the three urinary PAH metabolite measures and the DSST score in univariate analyses (using PROC CORR). Univariate linear regression (using PROC SURVEY REG) investigating the association of urinary PAH metabolite concentrations and other covariates and DSST score were conducted to assess potential variables in a multiple regression model. Significance for the univariate analyses was defined as a p-value less than 0.1. Collinearity was assessed for all covariates identified during the univariate analyses. Multiple linear regression modeling (using PROC SURVEY REG) was used to assess the association between urinary PAH metabolite concentrations and DSST score. The general multiple linear regression model is: y = βo + β1log(X1) +β2log(X2) … + e; for our study y represents the subject specific DSST score, X1 represents the subject specific PAH concentrations, β1 is a regression coefficient which represents the change in the mean DSST score corresponding to a unit change in the log transformed subject specific PAH concentration(s), X2 denotes other covariates to adjust for in the model, with corresponding regression coefficient β2, and e is the error term. A backwards method was used to determine the factors of DSST score. During the backward selection process, all significant variables determined in the univariate analyses were entered into the model and the variable with the highest p-value was removed with each iteration until the p-values for all remaining variables were less than 0.05. Factors with a p-value of less than 0.05 were retained in final models. A sensitivity analysis was conducted to determine if similar results were obtained with and without imputation of non-valid or missing covariates.
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