Personal Care Products, Socioeconomic Status, and Endocrine-Disrupting Chemical Mixtures in Black Women.

Personal care products (PCPs) are sources of exposure to endocrine-disrupting chemicals (EDCs) among women, and socioeconomic status (SES) may influence these exposures. Black women have inequitable exposure to EDCs from PCP use, but no study has investigated how exposure to EDCs through PCPs may vary by SES, independent of race. Using data from the Study of Environment, Lifestyle, and Fibroids, a cohort of reproductive-aged Black women (n = 751), we quantified associations between PCPs and urinary biomarker concentrations of EDC mixtures (i.e., phthalates, phenols, parabens) within SES groups, defined using k-modes clustering based on education, income, marital status, and employment. Information about PCP use and SES was collected through questionnaires and interviews. We used principal component analysis to characterize the EDC mixture profiles. Stratified linear regression models were fit to assess associations between PCP use and EDC mixture profiles, quantified as mean differences in PC scores, by SES group. Associations between PCP use and EDC mixture profiles varied by SES group; e.g., vaginal powder use was associated with a mixture of phenols among lower SES women, whereas this association was null for higher SES women. Findings suggest that SES influences PCP EDC exposure in Black women, which has implications for public health interventions.

A prospective ultrasound study of cigarette smoking and uterine leiomyomata incidence and growth.

BACKGROUND: Uterine leiomyomata (fibroids) are common, benign neoplasms that contribute substantially to gynecologic morbidity. Some existing epidemiologic studies indicate that cigarette smoking is associated with lower uterine leiomyomata risk. However, no prospective studies have systematically screened an entire study population for uterine leiomyomata using transvaginal ultrasound or evaluated the association between cigarette smoking and uterine leiomyomata growth. OBJECTIVE: This study aimed to examine the association between cigarette smoking and uterine leiomyomata incidence and growth in a prospective ultrasound study. STUDY DESIGN: We enrolled 1693 residents from the Detroit metropolitan area into the Study of Environment, Lifestyle, and Fibroids during 2010 to 2012. Eligible participants were aged 23 to 34 years, had an intact uterus but no previous diagnosis of uterine leiomyomata, and self-identified as Black or African American. We invited participants to complete a baseline visit and 4 follow-up visits over approximately 10 years. At each visit, we used transvaginal ultrasound to assess uterine leiomyomata incidence and growth. Participants provided extensive self-reported data throughout follow-up including exposures to active and passive cigarette smoking in adulthood. We excluded participants who did not return for any follow-up visits (n=76; 4%). We fit Cox proportional hazards regression models to estimate hazard ratios and 95% confidence intervals for the association between time-varying smoking history and incidence rates of uterine leiomyomata. We fit linear mixed models to estimate the percentage difference and 95% confidence intervals for the association between smoking history and uterine leiomyomata growth. We adjusted for sociodemographic, lifestyle, and reproductive factors. We interpreted our results based on magnitude and precision rather than binary significance testing. RESULTS: Among 1252 participants without ultrasound evidence of uterine leiomyomata at baseline, uterine leiomyomata were detected in 394 participants (31%) during follow-up. Current cigarette smoking was associated with a lower uterine leiomyomata incidence rate (hazard ratio, 0.67; 95% confidence interval, 0.49-0.92). Associations were stronger among participants who had smoked for longer durations (≥15 years vs never: hazard ratio, 0.49; 95% confidence interval, 0.25-0.95). The hazard ratio for former smokers was 0.78 (95% confidence interval, 0.50-1.20). Among never smokers, the hazard ratio for current passive smoke exposure was 0.84 (95% confidence interval, 0.65-1.07). Uterine leiomyomata growth was not appreciably associated with current (percent difference, -3%; 95% confidence interval, -13% to 8%) or former (percent difference, -9%; 95% confidence interval, -22% to 6%) smoking. CONCLUSION: We provide evidence from a prospective ultrasound study that cigarette smoking is associated with lower uterine leiomyomata incidence.

Associations of an industry-relevant metal mixture with verbal learning and memory in Italian adolescents: The modifying role of iron status.

BACKGROUND: Biomarker concentrations of metals are associated with neurodevelopment, and these associations may be modified by nutritional status (e.g., iron deficiency). No prior study on associations of metal mixtures with neurodevelopment has assessed effect modification by iron status. OBJECTIVES: We aimed to quantify associations of an industry-relevant metal mixture with verbal learning and memory among adolescents, and to investigate the modifying role of iron status on those associations. METHODS: We used cross-sectional data from 383 Italian adolescents (10-14 years) living in proximity to ferroalloy industry. Verbal learning and memory was assessed using the California Verbal Learning Test for Children (CVLT-C), and metals were quantified in hair (manganese, copper, chromium) or blood (lead) using inductively coupled plasma mass spectrometry. Serum ferritin, a proxy for iron status, was measured using immunoassays. Covariate-adjusted associations of the metal mixture with CVLT subtests were estimated using Bayesian Kernel Machine Regression, and modification of the mixture associations by ferritin was examined. RESULTS: Compared to the 50th percentile of the metal mixture, the 90th percentile was associated with a 0.12 standard deviation [SD] (95% CI = -0.27, 0.50), 0.16 SD (95% CI = -0.11, 0.44), and 0.11 SD (95% CI = -0.20, 0.43) increase in the number of words recalled for trial 5, long delay free, and long delay cued recall, respectively. For an increase from its 25th to 75th percentiles, copper was beneficially associated the recall trials when other metals were fixed at their 50th percentiles (for example, trial 5 recall: ß = 0.31, 95% CI = 0.14, 0.48). The association between copper and trial 5 recall was stronger at the 75th percentile of ferritin, compared to the 25th or 50th percentiles. CONCLUSIONS: In this metal mixture, copper was beneficially associated with neurodevelopment, which was more apparent at higher ferritin concentrations. These findings suggest that metal associations with neurodevelopment may depend on iron status, which has important public health implications.

Bayesian kernel machine regression-causal mediation analysis.

Greater understanding of the pathways through which an environmental mixture operates is important to design effective interventions. We present new methodology to estimate natural direct and indirect effects and controlled direct effects of a complex mixture exposure on an outcome through a mediator variable. We implement Bayesian Kernel Machine Regression (BKMR) to allow for all possible interactions and nonlinear effects of (1) the co-exposures on the mediator, (2) the co-exposures and mediator on the outcome, and (3) selected covariates on the mediator and/or outcome. From the posterior predictive distributions of the mediator and outcome, we simulate counterfactuals to obtain posterior samples, estimates, and credible intervals of the mediation effects. Our simulation study demonstrates that when the exposure-mediator and exposure-mediator-outcome relationships are complex, BKMR-Causal Mediation Analysis performs better than current mediation methods. We applied our methodology to quantify the contribution of birth length as a mediator between in utero co-exposure to arsenic, manganese, and lead, and children's neurodevelopmental scores, in a prospective birth cohort in Bangladesh. Among younger children, we found a negative (adverse) association between the metal mixture and neurodevelopment. We also found evidence that birth length mediates the effect of exposure to the metal mixture on neurodevelopment for younger children. If birth length were fixed to its 75th percentile value, the harmful effect of the metal mixture on neurodevelopment is attenuated, suggesting nutritional interventions to help increase fetal growth, and thus birth length, could potentially block the harmful effect of the metal mixture on neurodevelopment.

Waxing activity as a potential source of exposure to per- and polyfluoroalkyl substances (PFAS) and other environmental contaminants among the US ski and snowboard community.

BACKGROUND: Skiers and snowboarders apply waxes and solvents to their equipment to enhance glide across the snow. Waxing results in exposures to per- and polyfluoroalkyl substances (PFAS) and particulate matter, which have been associated with adverse health effects among professional wax technicians in Scandinavia. However, little is known about exposure among people who participate at other levels of sport, including recreationally, in other regions. OBJECTIVE: We sought to characterize wax-related exposures among US skiers and snowboarders who participate across numerous levels of sport to expand scientific understanding of environmental health risks among this population. METHODS: We used an anonymous electronic survey to evaluate wax-related exposures among US cross-country and downhill skiers and snowboarders. Specifically, we assessed (Fang et al., 2020): duration of time involved with each sport in any role (Freberg et al., 2013), intensity of wax-related exposures based on time spent in waxing areas, wax use, and wax type (Rogowski et al., 2007), frequency of fluorinated wax application, and (Freberg et al., 2010) use of exposure interventions. RESULTS: Participants tended to be long-term winter sports enthusiasts (e.g., median downhill skiing duration: 31 years). Nearly all (92%) participants personally applied some wax to their skis/snowboards and most applied waxes containing PFAS (67%) and solvents (62%). Ski professionals waxed the most pairs of skis with fluorinated waxes annually (median (IQR): 20 (1, 100)), though individuals participating recreationally also applied fluorinated waxes regularly. Exposure interventions were not widely used. SIGNIFICANCE: Waxing activities may pose significant risk of exposure to PFAS and other environmental contaminants among the US ski and snowboard community. Efforts are needed to reduce these exposures through changes to wax use patterns and broader adoption of exposure reduction strategies.

Correlates of plasma concentrations of per- and poly-fluoroalkyl substances among reproductive-aged Black women.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals used in commercial and consumer goods. Black women are underrepresented in studies of PFAS exposure. METHODS: We performed a cross-sectional analysis of correlates of plasma PFAS concentrations among 1499 Black women aged 23-35 participating in the Study of Environment, Lifestyle, and Fibroids (SELF), a Detroit-based cohort study. At baseline (2010-2012), participants provided questionnaire data on socio-demographics; behaviors; diet; and menstrual, contraceptive, and reproductive histories. Using mass spectrometry in non-fasting plasma samples collected at enrollment, we quantified several PFAS, including perfluorohexane sulfonate (PFHxS), perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), perfluorononanoate (PFNA), perfluorodecanoate (PFDA), perfluoroundecanoate (PFUnDA), and 2-N-methyl-perfluorooctane sulfonamido acetate (MeFOSAA). We used linear regression to calculate percentage differences (%D) and 95 % confidence intervals (CIs) for associations between selected correlates and PFAS concentrations, adjusting for all other correlates. RESULTS: PFHxS, PFOS, PFOA, and PFNA were detected in ≥97 % of women; PFDA in 86 %; MeFOSAA in 70 %; and PFUnDA in 52 %. Age, income, education, and intakes of water, alcohol, and seafood were positively associated with several PFAS. Current smoking was positively associated with MeFOSAA. Body mass index was inversely associated with most PFAS, except PFHxS. Strong inverse associations (%D; 95 % CI) were observed between parity (≥3 vs. 0 births) and PFHxS (-34.7; -43.0, -25.1) and PFOA (-33.1; -39.2, -26.3); breastfeeding duration (≥6 months vs. nulliparous) and PFOA (-31.1; -37.8, -23.7), PFHxS (-24.2; -34.5, -12.3), and PFOS (-18.4; -28.3, -7.1); recent birth (<2 years ago vs. nulliparous) and PFOA (-33.1; -39.6, -25.8), PFHxS (-29.3; -39.0, -18.1), PFNA (-25.2; -32.7, -16.8), and PFOS (-18.3; -28.3, -6.9); and intensity of menstrual bleed (heavy vs. light) and PFHxS (-18.8; -28.3, -8.2), PFOS (-16.4; -24.9, -7.1), PFNA (-10.5; -17.8, -2.6), and PFOA (-10.0; -17.2, -2.1). Current use of depot medroxyprogesterone acetate (DMPA) was positively associated with PFOS (20.2; 1.4, 42.5), PFOA (16.2; 1.5, 33.0), and PFNA (15.3; 0.4, 32.4). CONCLUSIONS: Reproductive factors that influence PFAS elimination showed strong associations with several PFAS (reduced concentrations with parity, recent birth, lactation, heavy menstrual bleeding; increased concentrations with DMPA use).

Associations of a metal mixture with iron status in U.S. adolescents: Evidence from the National Health and Nutrition Examination Survey.

Iron is needed for normal development in adolescence. Exposure to individual environmental metals (e.g., lead) has been associated with altered iron status in adolescence, but little is known about the cumulative associations of multiple metals with Fe status. We used data from the 2017-2018 National Health and Nutrition Examination Survey (NHANES) to examine associations between a metal mixture (lead, manganese, cadmium, selenium) and iron status in 588 U.S. adolescents (12-17 years). We estimated cumulative and interactive associations of the metal mixture with five iron status metrics using Bayesian Kernel Machine Regression (BKMR). Higher concentrations of manganese and cadmium were associated with lower log-transformed ferritin concentrations. Interactions were observed between manganese, cadmium, and lead for ferritin and the transferrin receptor, where iron status tended to be worse at higher concentrations of all metals. These results may reflect competition between environmental metals and iron for cellular uptake. Mixed metal exposures may alter normal iron function, which has implications for adolescent development.

Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach.

The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 healthy participants in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (1985-2006), we explored the association between 12 dietary factors and 10-year predicted risk of atherosclerotic cardiovascular disease (ASCVD) using an innovative approach, Bayesian kernel machine regression (BKMR). Employing BKMR, we found that among women, unprocessed red meat was most strongly related to the outcome: An interquartile range increase in unprocessed red meat consumption was associated with a 0.07-unit (95% credible interval: 0.01, 0.13) increase in ASCVD risk when intakes of other dietary components were fixed at their median values (similar results were obtained when other components were fixed at their 25th and 75th percentile values). Among men, fruits had the strongest association: An interquartile range increase in fruit consumption was associated with -0.09-unit (95% credible interval (CrI): -0.16, -0.02), -0.10-unit (95% CrI: -0.16, -0.03), and -0.11-unit (95% CrI: -0.18, -0.04) lower ASCVD risk when other dietary components were fixed at their 25th, 50th (median), and 75th percentile values, respectively. Using BKMR to explore the complex structure of the total diet, we found distinct sex-specific diet-ASCVD relationships and synergistic interaction between whole grain and fruit consumption.

A Prospective Ultrasound Study of Plasma Polychlorinated Biphenyl Concentrations and Incidence of Uterine Leiomyomata.

BACKGROUND: Uterine leiomyomata, or fibroids, are hormone-dependent neoplasms of the myometrium that can cause severe gynecologic morbidity. In previous studies, incidence of these lesions has been positively associated with exposure to polychlorinated biphenyls (PCBs), a class of persistent endocrine-disrupting chemicals. However, previous studies have been retrospective in design and none has used ultrasound to reduce disease misclassification. METHODS: The Study of Environment, Lifestyle, and Fibroids is a prospective cohort of 1,693 reproductive-aged Black women residing in Detroit, Michigan (enrolled during 2010-2012). At baseline and every 20 months for 5 years, women completed questionnaires, provided blood samples, and underwent transvaginal ultrasound to detect incident fibroids. We analyzed 754 baseline plasma samples for concentrations of 24 PCB congeners using a case-cohort study design. We used multivariable Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals for the association between plasma PCB concentrations and ultrasound-detected fibroid incidence over a 5-year period. RESULTS: We observed little association between PCB congener concentrations and fibroid incidence. The HR for a one-standard deviation increase in log-transformed total PCBs was 0.94 (95% CI = 0.78, 1.1). The PCB congener with the largest effect estimate was PCB 187 (HR for a one-standard deviation increase in log-transformed exposure = 0.88, 95% CI = 0.73, 1.1). Associations did not seem to vary strongly across PCB groupings based on hormonal activity. CONCLUSIONS: In this cohort of reproductive-aged Black women, plasma PCB concentrations typical of the contemporary general population were not appreciably associated with higher risk of fibroids.

Identification of novel loci associated with infant cognitive ability.

It is believed that genetic factors play a large role in the development of many cognitive and neurological processes; however, epidemiological evidence for the genetic basis of childhood neurodevelopment is very limited. Identification of the genetic polymorphisms associated with early-stage neurodevelopment will help elucidate biological mechanisms involved in neuro-behavior and provide a better understanding of the developing brain. To search for such variants, we performed a genome-wide association study (GWAS) for infant mental and motor ability at two years of age with mothers and children recruited from cohorts in Bangladesh and Mexico. Infant ability was assessed using mental and motor composite scores calculated with country-specific versions of the Bayley Scales of Infant Development. A missense variant (rs1055153) located in the gene WWTR1 reached genome-wide significance in association with mental composite score (meta-analysis effect size of minor allele ßmeta = -6.04; 95% CI: -8.13 to -3.94; P = 1.56×10-8). Infants carrying the minor allele reported substantially lower cognitive scores in both cohorts, and this variant is predicted to be in the top 0.3% of most deleterious substitutions in the human genome. Fine mapping and region-based association testing provided additional suggestive evidence that both WWTR1 and a second gene, LRP1B, were associated with infant cognitive ability. Comparisons with recently conducted GWAS in intelligence and educational attainment indicate that our phenotypes do not possess a high genetic correlation with either adolescent or adult cognitive traits, suggesting that infant neurological assessments should be treated as an independent outcome of interest. Additional functional studies and replication efforts in other cohorts may help uncover new biological pathways and genetic architectures that are crucial to the developing brain.

Validation of Electrochemical Sensor for Determination of Manganese in Drinking Water.

Manganese (Mn) is an essential nutrient for metabolic functions, yet excessive exposure can lead to neurological disease in adults and neurodevelopmental deficits in children. Drinking water represents one of the routes of excessive Mn exposure. Both natural enrichment from rocks and soil, and man-made contamination can pollute groundwater that supplies drinking water for a substantial fraction of the U.S. population. Conventional methods for Mn monitoring in drinking water are costly and involve a long turn-around time. Recent advancements in electrochemical sensing, however, have led to the development of miniature sensors for Mn determination. These sensors rely on a cathodic stripping voltammetry electroanalytical technique on a miniaturized platinum working electrode. In this study, we validate these electrochemical sensors for the determination of Mn concentrations in drinking water against the standard method using inductively coupled plasma mass spectrometry (ICP-MS). Drinking water samples (n = 78) in the 0.03 ppb to 5.3 ppm range were analyzed. Comparisons with ICP-MS yielded 100% agreement, ∼70% accuracy, and ∼91% precision. We envision the use of our system for rapid and inexpensive point-of-use identification of Mn levels in drinking water, which is especially valuable for frequent monitoring where contamination is present.

Correlates of Persistent Endocrine-Disrupting Chemical Mixtures among Reproductive-Aged Black Women.

Black women are exposed to multiple endocrine-disrupting chemicals (EDCs), but few studies have examined their profiles of exposure to EDC mixtures. We identified biomarker profiles and correlates of exposure to EDC mixtures in a cross-sectional analysis of data from a prospective cohort study of 749 Black women aged 23-35 years. We quantified plasma concentrations of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), organochlorine pesticides (OCPs), and per- and polyfluoroalkyl substances (PFAS) in nonfasting samples collected at baseline. Demographic, behavioral, dietary, and reproductive covariates were also collected at baseline. We used k-means clustering and principal component analysis (PCA) to describe concentration profiles of EDC mixtures (17 PCBs, 6 PBDEs, 4 OCPs, 6 PFAS), followed by multinomial logistic and multivariable linear regression to estimate mean differences in PCA scores (ß) and odds ratios (ORs) of cluster membership with their respective 95% confidence intervals (CIs). Older age (per 1 year increase: ß = 0.47, CI = 0.39, 0.54; OR = 1.27, CI = 1.20, 1.35), lower body mass index (per 1 kg/m2 increase: ß = -0.14, CI = -0.17, -0.12; OR = 0.91, CI = 0.89, 0.94), and current smoking (≥10 cigarettes/day vs never smokers: ß = 1.37, CI = 0.20, 2.55; OR = 2.63, CI = 1.07, 6.50) were associated with profiles characterized by higher concentrations of all EDCs. Other behaviors and traits, including dietary factors and years since last birth, were also associated with EDC mixtures.

Prenatal exposure to a mixture of elements and neurobehavioral outcomes in mid-childhood: Results from Project Viva.

BACKGROUND: Lead (Pb), manganese (Mn), selenium (Se) and methylmercury (MeHg) can be neurotoxic individually, despite Mn and Se also being essential elements. Little is known about the joint effects of essential and non-essential elements on neurobehavior, particularly for prenatal exposures. OBJECTIVES: To evaluate associations of prenatal exposure to multiple elements with executive function and neurobehavior in children. METHODS: Participants included 1009 mother-child pairs from the Project Viva pre-birth cohort. We estimated maternal erythrocyte Pb, Mn, Se, and Hg concentrations prenatally. In 6-11-year old children (median 7.6 years), parents and teachers rated children's executive function-related behaviors using the Behavior Rating Inventory of Executive Function (BRIEF) Global Executive Composite score and behavioral difficulties using the Strengths and Difficulties Questionnaire (SDQ) total difficulties score. We evaluated associations of element mixtures with neurobehavior using Bayesian kernel machine regression (BKMR), multivariable linear regression, and quantile g-computation. RESULTS: Median erythrocyte Pb, Mn, Se, and Hg concentrations were 1.1 µg/dL, 33.1 µg/L, 204.5 ng/mL, and 3.1 ng/g, respectively. Findings from BKMR and quantile g-computation models both showed worse (higher) parent-rated BRIEF and SDQ z-scores with higher concentrations of the mixture, although estimates were imprecise. When remaining elements were set at their median within BKMR models, increases in Pb and Se from the 25th to 75th percentile of exposure distributions were associated with 0.08 (95% CI: 0.02, 0.19) and 0.07 (95% CI: 0.03, 0.16) standard deviation increases in parent-rated BRIEF scores, and 0.08 (95% CI: 0.02, 0.17) and 0.05 (95% CI: 0.03, 0.13) standard deviation increases in SDQ scores, respectively. There was no evidence of element interactions. DISCUSSION: Although associations were small in magnitude, we found a trend of worsening neurobehavioral ratings with increasing prenatal exposure to an element mixture. However, we may be observing a limited range of dose-dependent impacts given the levels of exposure within our population.

Prenatal metal mixtures and fetal size in mid-pregnancy in the MADRES study.

BACKGROUND: Fetal growth is predictive of health later in life. Both toxic and essential metals influence fetal growth, but most studies have focused on these elements individually and used birth weight as an indicator of fetal growth. The objective of the current study was to investigate the impact of a mixture of metals on fetal size in mid-pregnancy in a predominately lower income Hispanic pregnancy cohort in Los Angeles. METHODS: For our primary analysis, we focused on six elements that have previously been associated individually with fetal size, including arsenic (As), barium (Ba), cadmium (Cd), mercury (Hg), molybdenum (Mo), and tin (Sn), measured in maternal urine samples collected in early pregnancy (median: 12.4 weeks gestation). In an exploratory analysis, we additionally included cobalt (Co), nickel (Ni), antimony (Sb), and thallium (Tl). Using covariate-adjusted Bayesian Kernel Machine Regression (BKMR) as our main mixture modeling approach, we examined the impact of these metals on fetal biometry measures obtained between 18 and 22 weeks gestation, with a focus on estimated fetal weight (EFW). RESULTS: BKMR identified Mo and Ba as the mixture components that contributed most to associations with EFW. Linear associations were observed for both metals. An increase in Mo from the 25th to 75th percentile was associated with a 0.114 (95% credible interval (CI): 0.019, 0.247) SD higher EFW, equivalent to a 7.4 g difference. Similar associations were observed between Mo and the other fetal measures evaluated. In contrast, an increase in Ba from the 25th to 75th percentile was associated with a -0.076 (95% CI: 0.217, 0.066) SD lower EFW, equivalent to a 4.9 g difference. Similar inverse associations were observed for Ba in relation to abdominal circumference and biparietal diameter. BKMR also identified a possible interaction between Ba and Mo in relation to head circumference, suggesting that the positive associations between Mo and this outcome may be attenuated at high levels of Ba, which was consistent with findings from linear regression (Pinteraction = 0.03). In an exploratory analysis accounting for a larger mixture of metals, Mo and Ba consistently contributed most to associations with EFW. An inverse association was also identified between Sb and EFW. CONCLUSIONS: Our results suggest that Mo may promote fetal growth, while Ba and Sb may reduce fetal growth, in this population.

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.

Correlates of organochlorine pesticide plasma concentrations among reproductive-aged black women.

BACKGROUND: Organochlorine pesticides (OCPs) are lipophilic persistent organic pollutants associated with adverse health outcomes. Black women have higher body burdens compared with other U.S. populations and research on their correlates is limited. METHODS: Using baseline data from a prospective cohort study of Black women aged 23-35 years from the Detroit, Michigan metropolitan area (enrolled 2010-2012), we examined correlates of plasma concentrations of the following OCPs: dichlorodiphenyltrichloroethane (p,p'-DDE), hexachlorobenzene (HCB), oxychlordane, and trans-nonachlor. At enrollment, we collected non-fasting blood samples from 742 participants. We also collected data on demographic, behavioral, dietary, occupational, and medical history factors via self-administered questionnaires, telephone interviews, and in-person clinic visits. We fit linear regression models to calculate percent (%) differences across categories of each correlate and 95% confidence intervals (CIs). RESULTS: In models adjusted for all other correlates, a 5-year increase in age was associated with 24% higher oxychlordane (95% CI: 12%, 38%) and 26% higher trans-nonachlor (95% CI: 12%, 42%) plasma concentrations. Heavy alcohol use was associated with 7-9% higher plasma concentrations of p,p'-DDE, oxychlordane, and trans-nonachlor. Current smoking was associated with 10-19% higher plasma concentrations of all four OCPs, and was highest for current smokers of ≥10 cigarettes/day (% differences ranged from 22 to 29%). Compared with having never been breastfed during infancy, having been breastfed for ≥3 months was associated with 15% higher concentrations of p,p'-DDE (95% CI: 6%, 25%), 14% higher oxychlordane (95% CI: 5%, 24%), and 15% higher trans-nonachlor (95% CI: 5%, 27%). Consumption of ≥5 vs. ≤2 glasses/day of tap or bottled water was associated with 8-15% higher plasma concentrations of all four OCPs, and was highest for trans-nonachlor (% difference: 15%; 95% CI: 6%, 26%). No other dietary predictors were appreciably associated with plasma OCP concentrations. Obesity, parity, higher birth order, and longer lactation duration were inversely associated with plasma OCP concentrations. CONCLUSIONS: In Black U.S. women of reproductive age, older age was an important correlate of plasma OCP concentrations. Exposure to OCPs earlier in life appears to contribute to current blood concentrations. In addition, tobacco, alcohol, and drinking water may be important sources of exposure.

Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.

The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.

Maternal blood arsenic levels and associations with birth weight-for-gestational age.

BACKGROUND: Among highly exposed populations, arsenic exposure in utero may be associated with decreased birth weight, however less is known about potential effects of arsenic exposure in urban communities without contaminated sources such as drinking water. OBJECTIVE: Investigate the association of blood arsenic levels with birth weight-for-gestational age categories within a prospective birth cohort study. DESIGN/METHODS: We analyzed 730 mother-infant dyads within the Programming Research in Obesity, GRowth, Environment and Social Stressors (PROGRESS) cohort in Mexico City. Total arsenic was measured in maternal blood samples from the 2nd and 3rd trimesters, at delivery, as well as from infant umbilical cord blood samples. Multivariable, multinomial logistic regression models adjusting for maternal age at enrollment, pre-pregnancy body mass index, parity, infant sex, socioeconomic position, and prenatal environmental tobacco smoke exposure were used to calculate odds ratios of small-for-gestational age (<10th percentile, SGA) and large-for-gestational age (>90th percentile, LGA) compared to appropriate-for-gestational age (AGA) per unit increase of log-transformed arsenic. RESULTS: Median (IQR) blood arsenic levels for maternal second trimester were 0.72 (0.33) µg/L, maternal third trimester 0.75 (0.41) µg/L, maternal at delivery 0.85 (0.70) µg/L, and infant cord 0.78 (0.65) µg/L. Maternal delivery and infant cord blood samples were most strongly correlated (spearman r = 0.65, p < 0.0001). Maternal arsenic levels at delivery were associated with significantly higher odds of both SGA (adj. OR = 1.44, 95% CI: 1.08-1.93) and LGA (adj. OR = 2.03, 95% CI: 1.12-3.67) compared to AGA. Results were similar for cord blood. There were 130 SGA infants and 22 LGA infants. Earlier in pregnancy, there were no significant associations of arsenic and birth weight-for-gestational age. However, we observed non-significantly higher odds of LGA among women with higher arsenic levels in the 3rd trimester (adj. OR = 1.46, 95% CI: 0.67-3.12). CONCLUSION: We found that in a Mexico City birth cohort, higher maternal blood arsenic levels at delivery were associated with higher odds of both SGA and LGA. However, sources and species of arsenic were not known and the number of LGA infants was small, limiting the interpretation of this finding and highlighting the importance of future large studies to incorporate arsenic speciation. If our findings were confirmed in studies that addressed these limitations, determining modifiable factors that could be mitigated, such as sources of arsenic exposure, may be important for optimizing fetal growth to improve long-term health of children.

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.