Faculty Bibliography

BACKGROUND:Exposure to endocrine-disrupting chemicals such as bisphenols and phthalates during pregnancy may disrupt fetal developmental programming and influence early-life growth. We hypothesized that prenatal bisphenol and phthalate exposure was associated with alterations in adiposity through 4 years. This associations might change over time. METHODS:Among 1091 mother-child pairs in a New York City birth cohort study, we measured maternal urinary concentrations of bisphenols and phthalates at three time points in pregnancy and child weight, height, and triceps and subscapular skinfold thickness at ages 1, 2, 3, and 4 years. We used linear mixed models to assess associations of prenatal individual and grouped bisphenols and phthalates with overall and time-point-specific adiposity outcomes from birth to 4 years. RESULTS:We observed associations of higher maternal urinary second trimester total bisphenol and bisphenol A concentrations in pregnancy and overall child weight between birth and 4 years only (Beta 0.10 (95 % confidence interval 0.04, 0.16) and 0.07 (0.02, 0.12) standard deviation score (SDS) change in weight per natural log increase in exposure), We reported an interaction of the exposures with time, and analysis showed associations of higher pregnancy-averaged mono-(2-carboxymethyl) phthalate with higher child weight at 3 years (0.14 (0.06, 0.22)), and of higher high-molecular-weight phthalate, di-2-ethylhexyl phthalate, mono-(2-ethyl-5-carboxypentyl) phthalate, mono-(2-carboxymethyl) phthalate, and mono-(2-ethylhexyl) phthalate with higher child weight at 4 years (0.16 (0.04, 0.28), 0.15 (0.03, 0.27), 0.19 (0.07, 0.31), 0.16 (0.07, 0.24), 0.11 (0.03, 0.19)). Higher pregnancy-averaged high-molecular-weight phthalate, di-2-ethylhexyl phthalate, mono-(2-ethyl-5-carboxypentyl) phthalate, mono-(2-ethyl-5-hydroxyhexyl) phthalate, and mono-2(ethyl-5-oxohexyl) phthalate concentrations were associated with higher child BMI at 4 years (0.20 (0.05, 0.35), 0.20 (0.05, 0.35), 0.22 (0.06, 0.37), 0.20 (0.05, 0.34), 0.20 (0.05, 0.34)). For skinfold thicknesses, we observed no associations. DISCUSSION/CONCLUSIONS:This study contributes to the evidence suggesting associations of prenatal exposure to bisphenols and high-molecular-weight phthalates on childhood weight and BMI.

BACKGROUND:Particulate matter exposure (PM) is a cause of aerodigestive disease globally. The destruction of the World Trade Center (WTC) exposed first responders and inhabitants of New York City to WTC-PM and caused obstructive airways disease (OAD), gastroesophageal reflux disease (GERD) and Barrett's Esophagus (BE). GERD not only diminishes health-related quality of life but also gives rise to complications that extend beyond the scope of BE. GERD can incite or exacerbate allergies, sinusitis, bronchitis, and asthma. Disease features of the aerodigestive axis can overlap, often necessitating more invasive diagnostic testing and treatment modalities. This presents a need to develop novel non-invasive biomarkers of GERD, BE, airway hyperreactivity (AHR), treatment efficacy, and severity of symptoms. METHODS:No GERD or AHR, from the sub-cohort control group. We will then phenotype and examine non-invasive biomarkers of these subgroups to identify under-diagnosis and/or treatment efficacy. The findings may further contribute to the development of future biologically plausible therapies, ultimately enhance patient care and quality of life. DISCUSSION/CONCLUSIONS:Although many studies have suggested interdependence between airway and digestive diseases, the causative factors and specific mechanisms remain unclear. The detection of the disease is further complicated by the invasiveness of conventional GERD diagnosis procedures and the limited availability of disease-specific biomarkers. The management of reflux is important, as it directly increases risk of cancer and negatively impacts quality of life. Therefore, it is vital to develop novel noninvasive disease markers that can effectively phenotype, facilitate early diagnosis of premalignant disease and identify potential therapeutic targets to improve patient care. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov Identifier: NCT05216133; January 18, 2022.

BACKGROUND/UNASSIGNED:Isolated small airway abnormalities may be demonstrable at rest in patients with normal spirometry; however, the relationship of these abnormalities to exertional symptoms remains uncertain. This study uses an augmented cardiopulmonary exercise test (CPET) to include evaluation of small airway function during and following exercise to unmask abnormalities not evident with standard testing in individuals with dyspnoea and normal spirometry. METHODS/UNASSIGNED:volume curves during exercise to assess for dynamic hyperinflation and expiratory flow limitation; and 2) post-exercise spirometry and oscillometry to evaluate for airway hyperreactivity. RESULTS/UNASSIGNED:0.05). CONCLUSIONS/UNASSIGNED:We uncovered mechanisms for exertional dyspnoea in subject with normal spirometry that was attributable to either small airway dysfunction during exercise and/or small airway hyperreactivity following exercise. The similarity of findings in WTC environmentally exposed and clinically referred cohorts suggests broad relevance for these evaluations.

In the original publication [...].

BACKGROUND:Living in neighborhoods with higher levels of walkability has been associated with a reduced risk of obesity and higher levels of physical activity. Obesity has been linked to increased risk of 13 cancers in women. However, long-term prospective studies of neighborhood walkability and risk for obesity-related cancer are scarce. OBJECTIVES:We evaluated the association between long-term average neighborhood walkability and obesity-related cancer risk in women. METHODS:The New York University Women's Health Study (NYUWHS) is a prospective cohort with 14,274 women recruited between 1985 and 1991 in New York City and followed over nearly three decades. We geocoded residential addresses for each participant throughout follow-up and calculated an average annual measure of neighborhood walkability across years of follow-up using data on population density and accessibility to destinations associated with geocoded residential addresses. We used ICD-9 codes to characterize first primary obesity-related cancers and employed Cox proportional hazards models to assess the association between average neighborhood walkability and risk of overall and site-specific obesity-related cancers. RESULTS: DISCUSSION:Our study highlights a potential protective role of neighborhood walkability in preventing obesity-related cancers in women. https://doi.org/10.1289/EHP11538.

Environmental health research aims to assess the impact of environmental exposures, making it crucial to understand their effects due to their broad impacts on the general population. However, a common issue with measuring exposures using bio-samples in laboratory is that values below the limit of detection (LOD) are either left unreported or inaccurately read by machines, which subsequently influences the analysis and assessment of exposure effects on health outcomes. We address the challenge of handling exposure variables subject to LOD when they are treated as either covariates or an outcome. We evaluate the performance of commonly-used methods including complete-case analysis and fill-in method, and advanced techniques such as multiple imputation, missing-indicator model, two-part model, Tobit model, and several others. We compare these methods through simulations and a dataset from NHANES 2013"“2014. Our numerical studies show that the missing-indicator model generally yields reasonable estimates when considering exposure variables as covariates under various settings, while other methods tend to be sensitive to the LOD-missing proportions and/or distributional skewness of exposures. When modeling an exposure variable as the outcome, Tobit model performs well under Gaussian distribution and quantile regression generally provides robust estimates across various shapes of the outcome"™s distribution. In the presence of missing data due to LOD, different statistical models should be considered for being aligned with scientific questions, model assumptions, requirements of data distributions, as well as their interpretations. Sensitivity analysis to handle LOD-missing exposures can improve the robustness of model conclusions.

BACKGROUND AND AIMS/OBJECTIVE:Gastroesophageal reflux disease (GERD) is a prevalent gastrointestinal disorder that may complicate conditions such as obstructive airway disease. Our group has identified predictive biomarkers of GERD in particulate exposed first responders with obstructive airway disease. In addition, GERD diagnosis and treatment is costly and invasive. In light of these clinical concerns, we aimed to systematically review studies identifying noninvasive, multiOmic, and multicompartmental biomarkers of GERD. METHODS:A systematic review of PubMed and Embase was performed using keywords focusing on reflux disease and biomarkers and registered with PROSPERO. We included original human studies in English, articles focusing on noninvasive biomarkers of GERD published after December 31, 2009. GERD subtypes (non-erosive reflux disease and erosive esophagitis) and related conditions (Barrett's Esophagus [BE] and Esophageal Adenocarcinoma). Predictive measures were synthesized and risk of bias assessed (Newcastle-Ottawa Scale). RESULTS:0.94 (95% confidence interval; 0.85-1.00). CONCLUSION/CONCLUSIONS:Prior studies identified significant multiOmic, multicompartmental noninvasive biomarker risks for GERD and BE. However, studies have a high risk of bias and the reliability and accuracy of the biomarkers identified are greatly limited, which further highlights the need to discover and validate clinically relevant noninvasive biomarkers of GERD.

Reproducible translation of transcriptomics data has been hampered by the ubiquitous presence of batch effects. Statistical methods for managing batch effects were initially developed in the setting of sample group comparison and later borrowed for other settings such as survival outcome prediction. The most notable such method is ComBat, which adjusts for batches by including it as a covariate alongside sample groups in a linear regression. In survival prediction, however, ComBat is used without definable groups for survival outcome and is done sequentially with survival regression for a potentially batch-confounded outcome. To address these issues, we propose a new method called BATch MitigAtion via stratificatioN (BatMan). It adjusts batches as strata in survival regression and uses variable selection methods such as the regularized regression to handle high dimensionality. We assess the performance of BatMan in comparison with ComBat, each used either alone or in conjunction with data normalization, in a resampling-based simulation study under various levels of predictive signal strength and patterns of batch-outcome association. Our simulations show that (1) BatMan outperforms ComBat in nearly all scenarios when there are batch effects in the data and (2) their performance can be worsened by the addition of data normalization. We further evaluate them using microRNA data for ovarian cancer from the Cancer Genome Atlas and find that BatMan outforms ComBat while the addition of data normalization worsens the prediction. Our study thus shows the advantage of BatMan and raises caution about the use of data normalization in the context of developing survival prediction models. The BatMan method and the simulation tool for performance assessment are implemented in R and publicly available at LXQin/PRECISION.survival-GitHub.

STUDY OBJECTIVE:To study associations between nighttime sleep characteristics and time to pregnancy. METHODS:Pregnant people age ≥18 years and<18 weeks' gestation were recruited from 3 New York University Grossman School of Medicine affiliated hospitals in Manhattan and Brooklyn (n = 1428) and enrolled into the New York University Children's Health and Environment Study. Participants in the first trimester of pregnancy were asked to recall their time to pregnancy and their sleep characteristics in the 3 months before conception. RESULTS:Participants who reported sleeping<7 hours per night tended to have shorter time to pregnancy than those who slept 7-9 hours per night (adjusted fecundability odds ratio = 1.16, 95% confidence interval: 0.94, 1.41). Participants with a sleep midpoint of 4 AM or later tended to have longer time to pregnancy compared with those with earlier sleep midpoints (before 4 AM) (adjusted fecundability odds ratio = 0.88, 95% confidence interval: 0.74, 1.04). When stratified by sleep midpoint, sleeping<7 hours was significantly associated with shorter time to pregnancy only among those whose sleep midpoint was before 4 AM (adjusted fecundability odds ratio = 1.33, 95% confidence interval: 1.07, 1.67). CONCLUSIONS:The association of sleep duration with time to pregnancy was modified by chronotype, suggesting that both biological and behavioral aspects of sleep may influence fecundability.