Multi-media biomarkers: Integrating information to improve lead exposure assessment.

Exposure assessment traditionally relies on biomarkers that measure chemical concentrations in individual biological media (i.e., blood, urine, etc.). However, chemicals distribute unevenly among different biological media; thus, each medium provides incomplete information about body burden. We propose that machine learning and statistical approaches can create integrated exposure estimates from multiple biomarker matrices that better represent the overall body burden, which we term multi-media biomarkers (MMBs). We measured lead (Pb) in blood, urine, hair and nails from 251 Italian adolescents aged 11-14 years from the Public Health Impact of Metals Exposure (PHIME) cohort. We derived aggregated MMBs from the four biomarkers and then tested their association with Wechsler Intelligence Scale for Children (WISC) IQ scores. We used three approaches to derive the Pb MMB: one supervised learning technique, weighted quantile sum regression (WQS), and two unsupervised learning techniques, independent component analysis (ICA) and non-negative matrix factorization (NMF). Overall, the Pb MMB derived using WQS was most consistently associated with IQ scores and was the only method to be statistically significant for Verbal IQ, Performance IQ and Total IQ. A one standard deviation increase in the WQS MMB was associated with lower Verbal IQ (ß [95% CI] = -2.2 points [-3.7, -0.6]), Performance IQ (-1.9 points [-3.5, -0.4]) and Total IQ (-2.1 points [-3.8, -0.5]). Blood Pb was negatively associated with only Verbal IQ, with a one standard deviation increase in blood Pb being associated with a -1.7 point (95% CI: [-3.3, -0.1]) decrease in Verbal IQ. Increases of one standard deviation in the ICA MMB were associated with lower Verbal IQ (-1.7 points [-3.3, -0.1]) and lower Total IQ (-1.7 points [-3.3, -0.1]). Similarly, an increase of one standard deviation in the NMF MMB was associated with lower Verbal IQ (-1.8 points [-3.4, -0.2]) and lower Total IQ (-1.8 points [-3.4, -0.2]). Weights highlighting the contributions of each medium to the MMB revealed that blood Pb was the largest contributor to most MMBs, although the weights varied from more than 80% for the ICA and NMF MMBs to between 30% and 54% for the WQS-derived MMBs. Our results suggest that MMBs better reflect the total body burden of a chemical that may be acting on target organs than individual biomarkers. Estimating MMBs improved our ability to estimate the full impact of Pb on IQ. Compared with individual Pb biomarkers, including blood, a Pb MMB derived using WQS was more strongly associated with IQ scores. MMBs may increase statistical power when the choice of exposure medium is unclear or when the sample size is small. Future work will need to validate these methods in other cohorts and for other chemicals.

A Novel Approach to Chemical Mixture Risk Assessment-Linking Data from Population-Based Epidemiology and Experimental Animal Tests.

Humans are continuously exposed to chemicals with suspected or proven endocrine disrupting chemicals (EDCs). Risk management of EDCs presents a major unmet challenge because the available data for adverse health effects are generated by examining one compound at a time, whereas real-life exposures are to mixtures of chemicals. In this work, we integrate epidemiological and experimental evidence toward a whole mixture strategy for risk assessment. To illustrate, we conduct the following four steps in a case study: (1) identification of single EDCs ("bad actors")-measured in prenatal blood/urine in the SELMA study-that are associated with a shorter anogenital distance (AGD) in baby boys; (2) definition and construction of a "typical" mixture consisting of the "bad actors" identified in Step 1; (3) experimentally testing this mixture in an in vivo animal model to estimate a dose-response relationship and determine a point of departure (i.e., reference dose [RfD]) associated with an adverse health outcome; and (4) use a statistical measure of "sufficient similarity" to compare the experimental RfD (from Step 3) to the exposure measured in the human population and generate a "similar mixture risk indicator" (SMRI). The objective of this exercise is to generate a proof of concept for the systematic integration of epidemiological and experimental evidence with mixture risk assessment strategies. Using a whole mixture approach, we could find a higher rate of pregnant women under risk (13%) when comparing with the data from more traditional models of additivity (3%), or a compound-by-compound strategy (1.6%).

Bayesian varying coefficient kernel machine regression to assess neurodevelopmental trajectories associated with exposure to complex mixtures.

Exposure to environmental mixtures can exert wide-ranging effects on child neurodevelopment. However, there is a lack of statistical methods that can accommodate the complex exposure-response relationship between mixtures and neurodevelopment while simultaneously estimating neurodevelopmental trajectories. We introduce Bayesian varying coefficient kernel machine regression (BVCKMR), a hierarchical model that estimates how mixture exposures at a given time point are associated with health outcome trajectories. The BVCKMR flexibly captures the exposure-response relationship, incorporates prior knowledge, and accounts for potentially nonlinear and nonadditive effects of individual exposures. This model assesses the directionality and relative importance of a mixture component on health outcome trajectories and predicts health effects for unobserved exposure profiles. Using contour plots and cross-sectional plots, BVCKMR also provides information about interactions between complex mixture components. The BVCKMR is applied to a subset of data from PROGRESS, a prospective birth cohort study in Mexico city on exposure to metal mixtures and temporal changes in neurodevelopment. The mixture include metals such as manganese, arsenic, cobalt, chromium, cesium, copper, lead, cadmium, and antimony. Results from a subset of Programming Research in Obesity, Growth, Environment and Social Stressors data provide evidence of significant positive associations between second trimester exposure to copper and Bayley Scales of Infant and Toddler Development cognition score at 24 months, and cognitive trajectories across 6-24 months. We also detect an interaction effect between second trimester copper and lead exposures for cognition at 24 months. In summary, BVCKMR provides a framework for estimating neurodevelopmental trajectories associated with exposure to complex mixtures.

Modeling Pediatric Body Mass Index and Neighborhood Environment at Different Spatial Scales.

In public health research, it has been well established that geographic location plays an important role in influencing health outcomes. In recent years, there has been an increased emphasis on the impact of neighborhood or contextual factors as potential risk factors for childhood obesity. Some neighborhood factors relevant to childhood obesity include access to food sources, access to recreational facilities, neighborhood safety, and socioeconomic status (SES) variables. It is common for neighborhood or area-level variables to be available at multiple spatial scales (SS) or geographic units, such as the census block group and census tract, and selection of the spatial scale for area-level variables can be considered as a model selection problem. In this paper, we model the variation in body mass index (BMI) in a study of pediatric patients of the Virginia Commonwealth University (VCU) Medical Center, while considering the selection of spatial scale for a set of neighborhood-level variables available at multiple spatial scales using four recently proposed spatial scale selection algorithms: SS forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso. For pediatric BMI, we found evidence of significant positive associations with visit age and black race at the individual level, percent Hispanic white at the census block group level, percent Hispanic black at the census tract level, and percent vacant housing at the census tract level. We also found significant negative associations with population density at the census tract level, median household income at the census tract level, percent renter at the census tract level, and exercise equipment expenditures at the census block group level. The SS algorithms selected covariates at different spatial scales, producing better goodness-of-fit in comparison to traditional models, where all area-level covariates were modeled at the same scale. These findings underscore the importance of considering spatial scale when performing model selection.

Development and Validation of a Clinical Risk-Assessment Tool Predictive of All-Cause Mortality.

In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient's health and overall function of multiple organ systems. We have developed a statistical procedure that condenses the information from a variety of health biomarkers into a composite index, which could be used as a risk score for predicting all-cause mortality. It could also be viewed as a holistic measure of overall physiological health status. This health status metric is computed as a function of standardized values of each biomarker measurement, weighted according to their empirically determined relative strength of association with mortality. The underlying risk model was developed using the biomonitoring and mortality data of a large sample of US residents obtained from the National Health and Nutrition Examination Survey (NHANES) and the National Death Index (NDI). Biomarker concentration levels were standardized using spline-based Cox regression models, and optimization algorithms were used to estimate the weights. The predictive accuracy of the tool was optimized by bootstrap aggregation. We also demonstrate how stacked generalization, a machine learning technique, can be used for further enhancement of the prediction power. The index was shown to be highly predictive of all-cause mortality and long-term outcomes for specific health conditions. It also exhibited a robust association with concurrent chronic conditions, recent hospital utilization, and current health status as assessed by self-rated health.

Assessment of weighted quantile sum regression for modeling chemical mixtures and cancer risk.

In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case-control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome.

Assessment of phthalates/phthalate alternatives in children's toys and childcare articles: Review of the report including conclusions and recommendation of the Chronic Hazard Advisory Panel of the Consumer Product Safety Commission.

The Consumer Product Safety Commission (CPSC) convened a Chronic Hazard Advisory Panel (CHAP) on Phthalates found in children's toys, and childcare products, and in products used by women of childbearing age. The CHAP conducted a risk assessment on phthalates and phthalate substitutes, and made recommendations to either ban, impose an interim ban, or allow the continued use of phthalates and phthalate substitutes in the above products. After a review of the literature, the evaluation included toxic end points of primary concern, biomonitoring results, extant exposure reconstruction, and epidemiological results. The health end points chosen were associated with the rat phthalate syndrome, which is characterized by malformations of the epididymis, vas deferens, seminal vesicles, prostate, external genitalia (hypospadias), and by cryptorchidism (undescended testes), retention of nipples/areolae, and demasculinization (~incomplete masculinization) of the perineum, resulting in reduced anogenital distance. Risk assessment demonstrated that some phthalates should be permanently banned, removed from the banned list, or remain interim banned. Biomonitoring and toxicology data provided the strongest basis for a mixture risk assessment. In contrast, external exposure data were the weakest and need to be upgraded for epidemiological studies and risk assessments. Such studies would focus on routes and sources. The review presents recommendations and uncertainties.

Why do we pay? A national survey of investigators and IRB chairpersons.

The principle that payment to participants should not be undue or coercive is the consensus of international and national guidelines and ethical debates; however, what this means in practice is unclear. This study determined the attitudes and practices of IRB chairpersons and investigators regarding participant payment. One thousand six hundred investigators and 1900 IRB chairpersons received an invitation to participate in a web-based survey. Four hundred and fifty-five investigators (28.3%) and 395 IRB chairpersons (18.6%) responded. The survey was designed to gather considerations that govern payment determination and practical application of these considerations in hypothetical case studies. The survey asked best answer, multiple choice, and open text questions. Short hypothetical case scenarios where presented, and participants were asked to rate factors in the study that might impact payment and then determine their recommended payment. A predictive model was developed for each case to determine factors which affected payment. Although compensation was the primary reason given to justify payment by both investigators and IRB chairpersons, the cases suggested that, in practice, payment is often guided by incentive, as shown by the impact of anticipated difficulty recruiting, inconvenience, and risk in determining payment. Payment models varied by type of study. Ranges for recommended payments by both groups for different types of procedures and studies are presented.

Who's doing the math? Are we really compensating research participants?

Although compensation for expenses to participants in research projects is considered important and the primary reason for paying, there is no evidence to support that investigators and IRB members actually calculate participant cost. Payment recommendations for six hypothetical studies were obtained from a national survey of IRB chairpersons (N = 353) and investigators (N = 495). Survey respondents also recommended payment for specific study procedures. We calculated participant cost for the six hypothetical cases both by procedures and by time involvement. A large percentage recommended only token payments for survey, registry, and medical record review studies. Most chose payment for pharmaceutical studies but the recommended payment did not compensate for calculated costs. Results suggest that compensation and reimbursement as the primary reasons for paying research participants may not match actual practice.

Further considerations on the evaluation of potential reduced-risk tobacco products. Part II: Re-assessment of a heuristic using the CPS-II database.

In a previous analysis (see Part I) we proposed a heuristic for assessing the efficacy of potential reduced-risk tobacco products (PRRPs) on lung cancer (LC) rates, using smoking cessation data published in a report from the Iowa Women's Health Study (IWHS) as a basis for sample size estimates. In this study, an additional analysis was performed using cessation data from the much larger Cancer Prevention Study II (CPS-II), which also provides data on different durations of cessation. Statistical methods were used to assess whether smokers switching to a PRRP would reduce their risk of LC. Furthermore, non-inferiority tests compared the LC risk in switchers to that in smokers who had quit smoking. The present work shows that similar sample size estimates were obtained whether the analysis was based on the IWHS or the CPS-II data sets, suggesting that the heuristic may be generally applicable to prospective real-life studies to evaluate PRRPs. Non-inferiority testing of switchers compared with quitters required approximately 10-fold more subjects than did superiority testing of switchers compared with smokers. Altogether, these estimates indicate that it is feasible, in terms of study duration and sample size, to clinically assess the LC risk-reducing potential of a PRRP.

Alcohol use disorders increase the risk for mechanical ventilation in medical patients.

BACKGROUND: Annually, more than 300,000 patients receive mechanical ventilation in an intensive care unit in the United States. The hospital mortality for ventilated patients may approach 50%, depending on the primary diagnosis. In trauma and surgical patients, a diagnosis of alcohol use disorder (AUD) is common and is associated with a prolonged duration of mechanical ventilation. The objective of this study is to determine whether the presence of AUD and the development of alcohol withdrawal are associated with an increased use and duration of mechanical ventilation in patients with medical disorders that commonly require intensive care unit admission. METHODS: We performed a retrospective cohort study using the Nationwide Inpatient Sample, a large all-payer inpatient database representing approximately 1,000 hospitals. For the years 2002 to 2003, adult patients with 1 of the 6 most common diagnoses associated with medical intensive care unit admission were included in the study. Both univariate analysis and multivariable logistic regression were performed to determine whether AUD and alcohol withdrawal were independently associated with the use and duration of mechanical ventilation in these patients. RESULTS: There were a total 785,602 patients who fulfilled 1 of the 6 diagnoses, 26,577 (3.4%) had AUD, 3,967 (0.5%) had alcohol withdrawal, and 65,071 (8.3%) underwent mechanical ventilation (53% <96 hours, 47%> or =96 hours). Independent of the medical diagnosis, AUD was associated with an increased risk of requiring mechanical ventilation (13.7 vs 8.1%, odds ratio=1.49, 95% confidence interval [1.414; 1.574], p<0.0001) but was not associated with a prolonged duration of mechanical ventilation. However, the presence of alcohol withdrawal was associated with a longer duration of mechanical ventilation (57 vs 47%> or =96 hours, odds ratio=1.48, 95% confidence interval [1.266; 1.724], p<0.0001). CONCLUSIONS: In patients with medical diagnoses associated with intensive care unit admission, AUD increases the risk for mechanical ventilation while the development of alcohol withdrawal is associated with a longer duration of mechanical ventilation.

The utility of microvascular perfusion assessment in heart failure: a pilot study.

BACKGROUND: The evaluation of heart failure is routinely based on subjective patient symptoms and physician examination. We propose the noninvasive evaluation of microvascular and global perfusion can objectify heart failure severity and provide additional prognostic information. METHODS: A prospective, observational pilot study of patients previously stratified into New York Heart Association (NYHA) heart failure classes and who after a routine cardiology clinic evaluation were felt to be at their stable baseline state. Measurements included: thoracic impedance (Zo), hypothenar tissue hemoglobin oxygen saturation (StO2), and Zo-derived cardiac index (CI). To determine if adverse outcomes (hospitalization or death) occurred, patients or their families were contacted 6 months after enrollment and their charts reviewed. Monitor values between the NYHA classes were compared using analysis of variance. Values of those who later developed adverse outcomes were compared to patients who remained stable using a Student t-test (P < .05 considered significant). A Kaplan-Meier survival curve was used to describe the adverse outcome rate over time, and a Cox's proportional hazards model was used to relate perfusion values to adverse outcomes. RESULTS: There were no differences in CI (P = .08), Zo (P = .38), or StO2 (P = .14) found between NYHA classes (n = 46). After 6 months, 6 patients required hospitalization for heart failure and 1 died. This group had lower StO2 values compared with the stable group (P = .015). The time course of the adverse events was found not to be due to chance alone when evaluated using a Kaplan-Meier curve and the StO2 was significantly associated with time to adverse outcome (P < .05). CONCLUSIONS: Outpatient heart failure patients who later develop adverse outcomes have significantly lower StO2 values than those who remain stable. This suggests cardiac performance in stable heart failure patients may be better reflected at the microvascular level using measures such as StO2 as opposed to a global level using the physical exam or impedance cardiography. StO2 may serve as a predictor for future adverse events and as an adjunct to current evaluation techniques.

D-optimal experimental designs to test for departure from additivity in a fixed-ratio mixture ray.

Traditional factorial designs for evaluating interactions among chemicals in a mixture may be prohibitive when the number of chemicals is large. Using a mixture of chemicals with a fixed ratio (mixture ray) results in an economical design that allows estimation of additivity or nonadditive interaction for a mixture of interest. This methodology is extended easily to a mixture with a large number of chemicals. Optimal experimental conditions can be chosen that result in increased power to detect departures from additivity. Although these designs are used widely for linear models, optimal designs for nonlinear threshold models are less well known. In the present work, the use of D-optimal designs is demonstrated for nonlinear threshold models applied to a fixed-ratio mixture ray. For a fixed sample size, this design criterion selects the experimental doses and number of subjects per dose level that result in minimum variance of the model parameters and thus increased power to detect departures from additivity. An optimal design is illustrated for a 2:1 ratio (chlorpyrifos:carbaryl) mixture experiment. For this example, and in general, the optimal designs for the nonlinear threshold model depend on prior specification of the slope and dose threshold parameters. Use of a D-optimal criterion produces experimental designs with increased power, whereas standard nonoptimal designs with equally spaced dose groups may result in low power if the active range or threshold is missed.