A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes

Evaluating associations of bisphenol and phthalate exposure with time to pregnancy and subfecundity in a New York City pregnancy cohort

It is important to understand the impact of consumer chemical exposure and fecundity, a couple's measure of probability of successful conception, given approximately 15% of couples experience infertility. Prior research has generally found null associations between bisphenol and phthalate exposure and fecundability, measured via time to pregnancy (TTP). However, this research has not been updated with current chemical exposures and have often lacked diversity in their study populations. We evaluated the associations between common bisphenol and phthalate chemical exposure groups and TTP as well as subfecundity (TTP>12 months) in the New York University Children's Health Study, a diverse pregnancy cohort from 2016 onward. Using first-trimester spot-urine samples to measure chemical exposure and self-reported TTP from first-trimester questionnaires, we observed a significant adverse association between total bisphenol exposure and certain phthalate groups on TTP and odds of subfecundity. Furthermore, in a mixtures analysis to explore the joint effects of the chemical groups on the outcomes, we found evidence of a potential interaction between total bisphenol exposure and low-molecular weight phthalates on TTP. Future research should continue to update our knowledge regarding the complex and potentially interacting effects of these chemicals on reproductive health.

Exposure to Cadmium, Lead, Mercury, and Arsenic and Uric Acid Levels: Results from NHANES 2007-2016

Mechanisms underlying abnormal uric acid (UA) levels from exposure to toxic metals/metalloids have not been not fully elucidated, especially in the context of mixtures. The aim was to identify major toxic metals/metalloids that affected UA levels with a mixture exposure concept in the association model. From 2007-2016 National Health and Nutrition Examination Survey (NHANES), 4794 adults were involved. Serum UA (SUA) and SUA to serum creatinine ratio (SUA/SCr) were used to estimate the UA levels, and cadmium (Cd), lead (Pb), mercury (Hg), and arsenic (As) in the blood and/or urine were evaluated in the study. We assessed the associations between toxic metals and UA levels using linear regression and Bayesian kernel machine regression (BKMR). The median [P₂₅, P₇₅] SUA/SCr and SUA level were 6.22 [5.27, 7.32] and 0.83 [0.72, 0.98], respectively. There was no difference for SUA/SCr by gender (men, 6.25 [5.39, 7.29]; women, 6.17 [5.17, 7.36], P = 0.162), but men had higher SUA than women (men, 0.95 [0.85, 1.05]; women, 0.72 [0.64, 0.82], P < 0.001). Blood Pb (β_men = 0.651 and β_women = 1.014) and urinary Cd (β_men = 0.252 and β_women = 0.613) were positively associated with SUA/SCr, and urinary Pb (β_men =  - 0.462 and β_women =  - 0.838) was inversely associated with SUA/SCr in multivariate linear regression analysis. However, urinary As (β_men = 0.351) was positively associated with SUA/SCr only in men. BKMR showed that higher concentrations of exposure to a mixture of toxic metals were positively associated with higher UA levels, where Cd, Pb, and urinary As contributed most to the overall mixture effect in men, while Pb and urinary Cd in women. Our study provided the first evidence that mixtures of metals are associated with the UA levels. Increased concentrations of metals, mainly blood Pb, urinary Cd, and As (only in men) may increase the level of UA.

Bayesian multiple index models for environmental mixtures

An important goal of environmental health research is to assess the risk posed by mixtures of environmental exposures. Two popular classes of models for mixtures analyses are response-surface methods and exposure-index methods. Response-surface methods estimate high-dimensional surfaces and are thus highly flexible but difficult to interpret. In contrast, exposure-index methods decompose coefficients from a linear model into an overall mixture effect and individual index weights; these models yield easily interpretable effect estimates and efficient inferences when model assumptions hold, but, like most parsimonious models, incur bias when these assumptions do not hold. In this paper, we propose a Bayesian multiple index model framework that combines the strengths of each, allowing for non-linear and non-additive relationships between exposure indices and a health outcome, while reducing the dimensionality of the exposure vector and estimating index weights with variable selection. This framework contains response-surface and exposure-index models as special cases, thereby unifying the two analysis strategies. This unification increases the range of models possible for analysing environmental mixtures and health, allowing one to select an appropriate analysis from a spectrum of models varying in flexibility and interpretability. In an analysis of the association between telomere length and 18 organic pollutants in the National Health and Nutrition Examination Survey (NHANES), the proposed approach fits the data as well as more complex response-surface methods and yields more interpretable results.

Variability and correlations of synthetic chemicals in urine from a New York City-based cohort of pregnant women

Fetal exposure to environmental chemicals has been associated with adverse health outcomes in children and later into adulthood. While several studies have examined correlations and variability of non-persistent chemical exposures throughout pregnancy, many do not capture more recent exposures, particularly in New York City. Our goal was to characterize exposure to phthalates, bisphenols, polycyclic aromatic hydrocarbons, and organophosphate pesticides among pregnant women residing in New York City who enrolled in the New York University Children's Health and Environment Study (NYU CHES) between 2016 and 2018. We measured urinary chemical metabolite concentrations in 671 women at early, mid, and late pregnancy (median 10.8, 20.8, and 29.3 weeks, respectively). We calculated Spearman correlation coefficients among chemical concentrations at each measurement time point. We compared changes in population-level urinary metabolites at each stage using paired Wilcoxon signed-rank tests and calculated intraclass correlation coefficients (ICCs) to quantify intra-individual variability of metabolites across pregnancy. Geometric means and ICCs were compared to nine other pregnancy cohorts that recruited women in 2011 or later as well as nationally reported levels from women of child-bearing age. Compared with existing cohorts, women in NYU CHES had higher geometric means of organophosphate pesticides (Σdiethylphosphates = 28.7 nmol/g cr, Σdimethylphosphates = 57.3 nmol/g cr, Σdialkyl phosphates = 95.9 nmol/g cr), bisphenol S (0.56 μg/g cr), and 2-naphthalene (8.98 μg/g cr). Five PAH metabolites and two phthalate metabolites increased between early to mid and early to late pregnancy at the population level. Spearman correlation coefficients for chemical metabolites were generally below 0.50. Intra-individual exposures varied over time, as indicated by low ICCs (0.22-0.88, median = 0.38). However, these ICCs were often higher than those observed in other pregnancy cohorts. These results provide a general overview of the chemical metabolites measured in NYU CHES in comparison to other contemporary pregnancy cohorts and highlight directions for future studies.

Determinants of phthalate exposures in pregnant women in New York City

Previous studies have provided data on determinants of phthalates in pregnant women, but results were disparate across regions. We aimed to identify the food groups and demographic factors that predict phthalate exposure in an urban contemporary pregnancy cohort in the US. The study included 450 pregnant women from the New York University Children's Health and Environment Study in New York City. Urinary concentrations of 22 phthalate metabolites, including metabolites of di-2-ethylhexylphthalate (DEHP), were determined at three time points across pregnancy by liquid chromatography coupled with tandem mass spectrometry. The Diet History Questionnaire II was completed by pregnant women at mid-pregnancy to assess dietary information. Linear mixed models were fitted to examine determinants of urinary phthalate metabolite concentrations. Using partial-linear single-index (PLSI) models, we assessed the major contributors, among ten food groups, to phthalate exposure. Metabolites of DEHP and its ortho-phthalate replacement, diisononyl phthalate (DiNP), were found in >90% of the samples. The sum of creatinine-adjusted DiNP metabolite concentrations was higher in older and single women and in samples collected in summer. Hispanic and non-Hispanic Black women had lower urinary concentrations of summed metabolites of di-n-octyl phthalate (DnOP), but higher concentrations of low molecular weight phthalates compared with non-Hispanic White women. Each doubling of grain products consumed was associated with a 20.9% increase in ∑DiNP concentrations (95%CI: 4.5, 39.9). PLSI models revealed that intake of dried beans and peas was the main dietary factor contributing to urinary ∑DEHP, ∑DiNP, and ∑DnOP levels, with contribution proportions of 76.3%, 35.8%, and 27.4%, respectively. Urinary metabolite levels of phthalates in pregnant women in NYC varied by age, marital status, seasonality, race/ethnicity, and diet. These results lend insight into the major determinants of phthalates levels, and may be used to identify exposure sources and guide interventions to reduce exposures in susceptible populations.

Partially linear single-index generalized mean residual life models

Mean residual life (MRL) function defines the remaining life expectancy of a subject who has survived to a time point and is an important alternative to the hazard function for characterizing the distribution of a time-to-event variable. Existing MRL models primarily focus on studying the association between risk factors and disease risks using linear model specifications in multiplicative or additive scale. When risk factors have complex correlation structures, nonlinear effects, or interactions, the prefixed linearity assumption may be insufficient to capture the relationship. Single-index modeling framework offers flexibility in reducing dimensionality and modeling nonlinear effects. In this article, we propose a class of partially linear single-index generalized MRL models, the regression component of which consists of both a semiparametric single-index part and a linear regression part. Regression spline technique is employed to approximate the nonparametric single-index function, and parameters are estimated using an iterative algorithm. Double-robust estimators are also proposed to protect against the misspecification of censoring distribution or MRL models. A further contribution of this article is a nonparametric test proposed to formally evaluate the linearity of the single-index function. Asymptotic properties of the estimators are established, and the finite-sample performance is evaluated through extensive numerical simulations. The proposed models and inference approaches are demonstrated by a New York University Langone Health (NYULH) COVID-19 dataset.

Organophosphate pesticide exposure: Demographic and dietary predictors in an urban pregnancy cohort

Pregnant women are widely exposed to organophosphate (OP) pesticides, which are potentially neurotoxicant for the developing fetus. We aimed to identify principal demographic and dietary predictors of OP pesticide exposure among 450 pregnant women participating in the New York University Children's Health and Environment Study (enrolled 2016-19). Urinary concentrations of six dialkyl phosphate (DAP) metabolites (3 dimethyl (DM) metabolites and 3 diethyl (DE) metabolites) of OP pesticides were determined at three time points across pregnancy. At mid-gestation, the Diet History Questionnaire II was used to assess women's dietary intake over the past year. Demographic characteristics were obtained using questionnaires and/or electronic health records. We used linear mixed models to evaluate the associations of demographic and food groups with DAP metabolite levels, and partial-linear single-index (PLSI) models to analyze the contribution proportions of food groups to DAP metabolite concentrations and the dose-response relationships between them. We observed that pregnant women in NYC had lower levels of OP pesticide metabolites than pregnant populations in Europe, Asia, and other regions in the U.S. Having lower pre-pregnancy body mass index and being Asian, employed, and single were associated with higher DAP metabolite concentrations. Fruit and grain intakes were associated with higher ∑DM, ∑DE, and ∑DAP levels. ∑DE concentrations increased 9.0% (95% confidence interval (CI) = 1.2%, 17.4%) per two-fold increase in dairy consumption, whereas ∑DE concentrations decreased 1.8% (95%CI = -3.1%, -0.4%) per two-fold increase in seafood consumption. The PLSI model indicated that among the food mixture, fruit and grains were the main food groups contributed to higher levels of ∑DAP, while meat contributed to lower levels of ∑DAP. The contribution proportions of fruit, grains, and meat were 18.7%, 17.9%, and 39.3%, respectively. Our results suggest that fruit, grains, and meat are major dietary components associated with OP pesticide exposure in urban pregnant women.