The Association of Air Pollution Exposure With Glucose and Lipid Levels: The Role of an Extreme Air Pollution Event Alongside 2 Decades of Moderate Exposure.

Extreme air pollution events and moderate exposure to fine particulate matter (PM2.5) are associated with increased cardiometabolic risk. The World Trade Center (WTC) Health Program general responder cohort includes responders to the WTC disaster. We investigated whether their exposure to this extreme air pollution event (2001) was associated with long-term metabolic outcomes, independently from the associations of intermediate-term PM2.5 exposure later in life (2004-2019). We included 22,447 cohort members with cholesterol (n = 96,155) and glucose (n = 81,599) laboratory results. Self-reported WTC exposure was derived from a questionnaire. PM2.5 exposure was derived from a satellite-based model. We observed an increase of 0.78 mg/dL (95% confidence interval (CI): 0.30, 1.26) in glucose and 0.67 mg/dL (95% CI: 1.00, 2.35) in cholesterol levels associated with an interquartile range increase in PM2.5 averaged 6 months before the study visit. Higher WTC-exposure categories were also associated with higher cholesterol (0.99 mg/dL, 95% CI: 0.30, 1.67, for intermediate exposure) and glucose (0.82 mg/dL, 95% CI: 0.22, 1.43, for high exposure) levels. Most associations were larger among people with diabetes. Extreme air pollution events and intermediate PM2.5 exposure have independent metabolic consequences. These exposures contributed to higher glucose and lipids levels among WTC responders, which may be translated into increased cardiovascular risk.

Associations between neighborhood characteristics and child well-being before and during the COVID-19 pandemic: A repeated cross-sectional study in the Environmental influences on Child Health Outcomes (ECHO) program.

The corona virus disease (COVID-19) pandemic disrupted daily life worldwide, and its impact on child well-being remains a major concern. Neighborhood characteristics affect child well-being, but how these associations were affected by the pandemic is not well understood. We analyzed data from 1039 children enrolled in the Environmental influences on Child Health Outcomes Program whose well-being was assessed using the Patient-Reported Outcomes Measurement Information System Global Health questionnaire and linked these data to American Community Survey (ACS) data to evaluate the impacts of neighborhood characteristics on child well-being before and during the pandemic. We estimated the associations between more than 400 ACS variables and child well-being t-scores stratified by race/ethnicity (non-Hispanic white vs. all other races and ethnicities) and the timing of outcome data assessment (pre-vs. during the pandemic). Network graphs were used to visualize the associations between ACS variables and child well-being t-scores. The number of ACS variables associated with well-being t-scores decreased during the pandemic period. Comparing non-Hispanic white with other racial/ethnic groups during the pandemic, different ACS variables were associated with child well-being. Multiple ACS variables representing census tract-level housing conditions and neighborhood racial composition were associated with lower well-being t-scores among non-Hispanic white children during the pandemic, while higher percentage of Hispanic residents and higher percentage of adults working as essential workers in census tracts were associated with lower well-being t-scores among non-white children during the same study period. Our study provides insights into the associations between neighborhood characteristics and child well-being, and how the COVID-19 pandemic affected this relationship.

A pilot study to identify factors associated with frailty within the World Trade Center general responder cohort.

BACKGROUND: Given the significant exposures experienced by the World Trade Center (WTC) general responders, there is increasing interest in understanding the effect of these exposures on aging in this population. We aim to identify factors that may be associated with frailty, a clinical syndrome characterized by a decrease in one's reserve that has been linked to poor health outcomes. METHODS: WTC general responders enrolled in the WTC Health Program aged 50 and older provided informed consent. Validated frailty assessments, the Frailty Phenotype (with the Johns Hopkins Frailty Assessment Calculator) along with the FRAIL scale, categorized nonfrail from prefrail/frail. Fall risk, functional status, and cognition were also assessed. WTC variables, including an identified WTC-certified condition, were utilized. The risk of frailty was estimated using log binomial regression analysis. A 95% confidence interval (CI) was used to estimate the prevalence ratio (PR). RESULTS: One hundred and six participants were included; 38 (35.8%) were classified as pre-frail or frail. More of the pre-frail/frail group were obese (57.9% vs. 25%; p = 0.004) and had a WTC-certified condition (78.9% vs. 58.8%; p = 0.036). Obesity (PR = 2.43, 95% CI = 1.31, 4.53), a WTC-certified condition (PR = 1.77, 95% CI = 1.09, 2.89), and risk of falling (PR = 1.97, 95% CI = 1.01, 3.84) were independently associated with frailty. CONCLUSIONS: Obesity and having a WTC-certified condition were found to be risk factors for frailty in our pilot study. Future work may focus on further identifying risk factors for frailty in the larger WTC general responder population.

Machine Learning Assisted Discovery of Interactions between Pesticides, Phthalates, Phenols, and Trace Elements in Child Neurodevelopment.

A growing body of literature suggests that developmental exposure to individual or mixtures of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD). However, investigating the effect of interactions among these ECs can be challenging. We introduced a combination of the classical exposure-mixture Weighted Quantile Sum (WQS) regression and a machine-learning method termed Signed iterative Random Forest (SiRF) to discover synergistic interactions between ECs that are (1) associated with higher odds of ASD diagnosis, (2) mimic toxicological interactions, and (3) are present only in a subset of the sample whose chemical concentrations are higher than certain thresholds. In a case-control Childhood Autism Risks from Genetics and Environment (CHARGE) study, we evaluated multiordered synergistic interactions among 62 ECs measured in the urine samples of 479 children in association with increased odds for ASD diagnosis (yes vs no). WQS-SiRF identified two synergistic two-ordered interactions between (1) trace-element cadmium (Cd) and the organophosphate pesticide metabolite diethyl-phosphate (DEP); and (2) 2,4,6-trichlorophenol (TCP-246) and DEP. Both interactions were suggestively associated with increased odds of ASD diagnosis in the subset of children with urinary concentrations of Cd, DEP, and TCP-246 above the 75th percentile. This study demonstrates a novel method that combines the inferential power of WQS and the predictive accuracy of machine-learning algorithms to discover potentially biologically relevant chemical-chemical interactions associated with ASD.

Urinary parabens and breast cancer risk: Modification by LINE-1 and LUMA global DNA methylation, and associations with breast cancer defined by tumor promoter methylation status.

Parabens are a group of alkyl esters of p-hydroxybenzoic acid added to consumer products to prevent the growth of harmful bacteria and molds. Parabens are hypothesized to increase the risk of breast cancer (BC); however, no study has examined the interactions between parabens, global DNA methylation (DNAm), and BC risk. We examined the modifying effects of DNAm on the associations between parabens and BC, and whether parabens were associated with BC defined by tumor promoter methylation status. Participants included 708 cases and 598 controls from the Long Island Breast Cancer Study Project. Methylparaben (MPB), propylparaben, and butylparaben levels were measured in spot urine samples. Global DNAm was measured by analysis of long interspersed elementes-1 (LINE-1) and the luminometric methylation assay (LUMA). The promoter methylation status of 13 genes was measured in tumor samples from 509 cases. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between parabens and BC stratified by LINE-1/LUMA, and between parabens and gene-specific promoter methylation-defined BC. Outcome heterogeneity was evaluated using ratios of ORs (RORs). We assessed the joint effects of the multiple parabens using quantile g-computation. The highest versus lowest tertile of MPB and a one-quantile increase in all parabens were associated with ORs of 1.46 (95% CI = 0.96-2.23) and 1.32 (95% CI = 1.02-1.71), respectively, among women with hypomethylated LINE-1. A one-ln unit increase in MPB was associated with a 25% increase in the odds of hypomethylated (vs. hypermethylated) CCND2 promoter-defined BC (ROR = 1.25, 95% CI = 1.06-1.48), and a one-quantile increase in all parabens was associated with a 55% increase in the odds of hypomethylated (vs. hypermethylated) CCND2 promoter-defined BC (ROR = 1.55, 95% CI = 1.04-2.32). Exposure to parabens may increase the risk of BC among women with hypomethylated global DNAm and may increase the risk of tumors with gene-specific hypomethylated promoter regions.

Molecular Gatekeeper Discovery: Workflow for Linking Multiple Exposure Biomarkers to Metabolomics.

The exposome reflects multiple exposures across the life-course that can affect health. Metabolomics can reveal the underlying molecular basis linking exposures to health conditions. Here, we explore the concept and general data analysis framework of "molecular gatekeepers"─key metabolites that link single or multiple exposure biomarkers with correlated clusters of endogenous metabolites─to inform health-relevant biological targets. We performed untargeted metabolomics on plasma from 152 adolescent girls participating in the Growing Up Healthy Study in New York City. We then performed network analysis to link metabolites to exposure biomarkers including five trace elements (Cd, Mn, Pb, Se, and Hg) and five perfluorinated chemicals (PFCs; n-PFOS, Sm-PFOS, n-PFOA, PFHxS, and PFNA). We found 144 molecular gatekeepers and annotated 22 of them. Lysophosphatidylcholine (16:0) and taurodeoxycholate were correlated with both n-PFOA and n-PFOS, suggesting a shared dysregulation from multiple xenobiotic exposures. Sphingomyelin (d18:2/14:0) was significantly associated with age at menarche; yet, no direct association was detected between any exposure biomarkers and age at menarche. Thus, molecular gatekeepers can also discover molecular linkages between exposure biomarkers and health outcomes that may otherwise be obscured by complex interactions in direct measurements.

Autoimmune conditions in the World Trade Center general responder cohort: A nested case-control and standardized incidence ratio analysis.

BACKGROUND: The World Trade Center (WTC) general responder cohort (GRC) was exposed to environmental toxins possibly associated with increased risk of developing autoimmune conditions. OBJECTIVES: Two study designs were used to assess incidence and risks of autoimmune conditions in the GRC. METHODS: Three clinically trained professionals established the status of possible GRC cases of autoimmune disorders adhering to diagnostic criteria, supplemented, as needed, by specialists' review of consenting responders' medical records. Nested case-control analyses using conditional logistic regression estimated the risk associated with high WTC exposure (being in the 9/11/2001 dust cloud or ≥median days' response worked) compared with low WTC exposure (all other GRC members'). Four controls were matched to each case on age at case diagnosis (±2 years), sex, race/ethnicity, and year of program enrollment. Sex-specific and sensitivity analyses were performed. GRC age- and sex-adjusted standardized incidence ratios (SIRs) were compared with the Rochester Epidemiology Project (REP). Complete REP inpatient and outpatient medical records were reviewed by specialists. Conditions meeting standardized criteria on ≥2 visits were classified as REP confirmed cases. RESULTS: Six hundred and twenty-eight responders were diagnosed with autoimmune conditions between 2002 and 2017. In the nested case-control analyses, high WTC exposure was not associated with autoimmune domains and conditions (rheumatologic domain odds ratio [OR] = 1.03, 95% confidence interval [CI] = 0.77, 1.37; rheumatoid arthritis OR = 1.12, 95% CI = 0.70, 1.77). GRC members had lower SIR than REP. Women's risks were generally greater than men's. CONCLUSIONS: The study found no statistically significant increased risk of autoimmune conditions with WTC exposures.

Identifying environmental exposure profiles associated with timing of menarche: A two-step machine learning approach to examine multiple environmental exposures.

BACKGROUND: Variation in the timing of menarche has been linked with adverse health outcomes in later life. There is evidence that exposure to hormonally active agents (or endocrine disrupting chemicals; EDCs) during childhood may play a role in accelerating or delaying menarche. The goal of this study was to generate hypotheses on the relationship between exposure to multiple EDCs and timing of menarche by applying a two-stage machine learning approach. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) for years 2005-2008. Data were analyzed for 229 female participants 12-16 years of age who had blood and urine biomarker measures of 41 environmental exposures, all with >70% above limit of detection, in seven classes of chemicals. We modeled risk for earlier menarche (<12 years of age vs older) with exposure biomarkers. We applied a two-stage approach consisting of a random forest (RF) to identify important exposure combinations associated with timing of menarche followed by multivariable modified Poisson regression to quantify associations between exposure profiles ("combinations") and timing of menarche. RESULTS: RF identified urinary concentrations of monoethylhexyl phthalate (MEHP) as the most important feature in partitioning girls into homogenous subgroups followed by bisphenol A (BPA) and 2,4-dichlorophenol (2,4-DCP). In this first stage, we identified 11 distinct exposure biomarker profiles, containing five different classes of EDCs associated with earlier menarche. MEHP appeared in all 11 exposure biomarker profiles and phenols appeared in five. Using these profiles in the second-stage of analysis, we found a relationship between lower MEHP and earlier menarche (MEHP ≤ 2.36 ng/mL vs >2.36 ng/mL: adjusted PR = 1.36, 95% CI: 1.02, 1.80). Combinations of lower MEHP with benzophenone-3, 2,4-DCP, and BPA had similar associations with earlier menarche, though slightly weaker in those smaller subgroups. For girls not having lower MEHP, exposure profiles included other biomarkers (BPA, enterodiol, monobenzyl phthalate, triclosan, and 1-hydroxypyrene); these showed largely null associations in the second-stage analysis. Adjustment for covariates did not materially change the estimates or CIs of these models. We observed weak or null effect estimates for some exposure biomarker profiles and relevant profiles consisted of no more than two EDCs, possibly due to small sample sizes in subgroups. CONCLUSION: A two-stage approach incorporating machine learning was able to identify interpretable combinations of biomarkers in relation to timing of menarche; these should be further explored in prospective studies. Machine learning methods can serve as a valuable tool to identify patterns within data and generate hypotheses that can be investigated within future, targeted analyses.

Cardiovascular disease in the World Trade Center Health Program General Responder Cohort.

BACKGROUND: Over 90,000 rescue and recovery responders to the September 2001 World Trade Center (WTC) attacks were exposed to toxic materials that can impair cardiac function and increase cardiovascular disease (CVD) risk. We examined WTC-related exposures association with annual and cumulative CVD incidence and risk over 17 years in the WTC Health Program (HP) General Responder Cohort (GRC). METHODS: Post 9/11 first occurrence of CVD was assessed in 37,725 responders from self-reported physician diagnosis of, or current treatment for, coronary artery disease, myocardial infarction, stroke and/or congestive heart failure from WTCHP GRC monitoring visits. Kaplan-Meier estimates of CVD incidence used the generalized Wilcoxon test statistic to account for censored data. Cox proportional hazards regression analyses estimated the CVD hazard ratio associated with 9/11/2001 arrival in responders with and without dust cloud exposure, compared with arrival on or after 9/12/2001. Additional analyses adjusted for comorbidities. RESULTS: To date, 6.3% reported new CVD. In covariate-adjusted analyses, men's CVD 9/11/2001 arrival risks were 1.40 (95% confidence interval [CI] = 1.26, 1.56) and 1.43 (95% CI = 1.29, 1.58) and women's were 2.16 (95% CI = 1.49, 3.11) and 1.59 (95% CI = 1.11, 2.27) with and without dust cloud exposure, respectively. Protective service employment on 9/11 had higher CVD risk. CONCLUSIONS: WTCHP GRC members with 9/11/2001 exposures had substantially higher CVD risk than those initiating work afterward, consistent with observations among WTC-exposed New York City firefighters. Women's risk was greater than that of men's. GRC-elevated CVD risk may also be occurring at a younger age than in the general population.

Menopausal hormone therapy use and long-term all-cause and cause-specific mortality in the Long Island Breast Cancer Study Project.

Previous studies have observed a reduced mortality risk associated with menopausal hormone therapy (MHT) use among breast cancer survivors. We sought to clarify whether such association could be explained by tumor heterogeneity, specific causes of death, confounding from comorbidities or health behaviors, and a comparison group of women without breast cancer. We interviewed 1508 women newly diagnosed with first primary breast cancer in 1996 to 1997 (~3 months after diagnosis), and 1556 age-matched women without breast cancer, about MHT use history. The National Death Index was used to ascertain vital status after a median of 17.6 years of follow-up (N = 597 deaths for breast cancer subjects). Multivariable-adjusted Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (95%CIs) for all-cause mortality, and cause-specific HR (cHR) for breast cancer and cardiovascular disease (CVD). The Fine-Gray model was used to account for competing causes of death. Among women with breast cancer, ever vs never MHT use was inversely associated with all-cause (HR = 0.77, 95%CI = 0.62-0.95), breast cancer-specific (cHR = 0.69, 95%CI = 0.48-0.98), and CVD-specific mortality (cHR = 0.57, 95%CI = 0.38-0.85). Difference of the association was observed in breast cancer-specific mortality according to hormone receptor status (negative tumors: cHR = 0.44, 95%CI = 0.19-1.01; positive tumors: cHR = 0.96, 95%CI = 0.60-1.53). Among the comparison group, we observed similar, but more modest inverse associations for all-cause and CVD-specific mortality. MHT use was inversely associated with mortality after breast cancer, even after accounting for competing causes of death and multiple confounders, and was evident among women without breast cancer. Potential heterogeneity by hormone receptor status requires more study.

Diabetes and cardiovascular disease mortality among a population-based cohort of women with and without breast cancer.

PURPOSE: We investigated whether the relationship between diabetes and all-cause and CVD-related mortality differed between women with and without breast cancer among a cohort drawn from the same source population. METHODS: We interviewed 1,363 women newly diagnosed with breast cancer in 1996-1997, and 1,358 age-matched women without breast cancer, to assess history of physician-diagnosed diabetes. All-cause (n = 631) and CVD-specific mortality (n = 234) was determined by the National Death Index through 2009. We estimated multivariable-adjusted hazard ratios (HRs) for the rates of all-cause and CVD-specific mortality and, to account for competing causes of death, and subdistribution HRs (sHRs) for risk of CVD-related death. RESULTS: Among women with and without breast cancer, respectively, diabetes was associated with: all-cause mortality [HR (95% CI) 1.52 (1.13, 2.05) and 2.17 (1.46, 3.22)]; CVD-specific deaths [1.74 (1.06, 2.84) and 2.06 (1.11, 3.84)]; and risk of CVD-related death [sHR 1.36 (0.81, 2.27) and 1.79 (0.94, 3.40)]. Differences in effect estimates between women with and without breast cancer did not reach statistical significance (p-interaction > 0.10). CONCLUSION: We found that the positive association between a history of physician-diagnosed diabetes and risk of all-cause and CVD-related mortality is of similar magnitude among a population-based cohort of women with or without breast cancer.

Prediagnosis aspirin use, DNA methylation, and mortality after breast cancer: A population-based study.

BACKGROUND: The authors hypothesized that epigenetic changes may help to clarify the underlying biologic mechanism linking aspirin use to breast cancer prognosis. To the authors' knowledge, this is the first epidemiologic study to examine whether global methylation and/or tumor promoter methylation of breast cancer-related genes interact with aspirin use to impact mortality after breast cancer. METHODS: Prediagnosis aspirin use was assessed through in-person interviews within a population-based cohort of 1508 women diagnosed with a first primary breast cancer in 1996 and 1997. Global methylation in peripheral blood was assessed by long interspersed elements-1 (LINE-1) and the luminometric methylation assay. Promoter methylation of 13 breast cancer-related genes was measured in tumor by methylation-specific polymerase chain reaction and the MethyLight assay. Vital status was determined by the National Death Index through December 31, 2014 (N = 202/476 breast cancer-specific/all-cause deaths identified among 1266 women with any methylation assessment and complete aspirin data). Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs, and the likelihood ratio test was used to evaluate multiplicative interactions. RESULTS: All-cause mortality was elevated among aspirin users who had methylated promotor of BRCA1 (HR, 1.67; 95% CI, 1.26-2.22), but not among those with unmethylated promoter of BRCA1 (HR, 0.99; 95% CI, 0.67-1.45; P for interaction ≤.05). Decreased breast cancer-specific mortality was observed among aspirin users who had unmethylated promotor of BRCA1 and PR and global hypermethylation of LINE-1 (HR, 0.60, 0.78, and 0.63, respectively; P for interaction ≤.05), although the 95% CIs included the null. CONCLUSIONS: The current study suggests that the LINE-1 global methylation and promoter methylation of BRCA1 and PR in tumor may interact with aspirin use to influence mortality after breast cancer.

Genetic polymorphisms of diabetes-related genes, their interaction with diabetes status, and breast cancer incidence and mortality: The Long Island Breast Cancer Study Project.

To examine 143 diabetes risk single nucleotide polymorphisms (SNPs), identified from genome-wide association studies, in association with breast cancer (BC) incidence and subsequent mortality. A population-based sample of Caucasian women with first primary invasive BC (n = 817) and controls (n = 1021) were interviewed to assess diabetes status. Using the National Death Index, women with BC were followed for >18 years during which 340 deaths occurred (139 BC deaths). Genotyping was done using DNA extracted from blood samples. We used unconditional logistic regression to estimate age-adjusted odds ratios and 95% confidence intervals (CIs) for BC incidence, and Cox regression to estimate age-adjusted hazard ratios and CIs for all-cause and BC-specific mortality. Twelve SNPs were associated with BC risk in additive genotype models, at α = 0.05. The top three significant SNPs included SLC30A8-rs4876369 (P = 0.0034), HHEX-rs11187146 (P = 0.0086), and CDKN2A/CDKN2B-rs1333049 (P = 0.0094). Diabetes status modified the associations between rs4876369 and rs2241745 and BC incidence, on the multiplicative interaction scale. Six SNPs were associated with all-cause (CDKAL1-rs981042, P = 0.0032; HHEX-rs1111875, P = 0.0361; and INSR-rs919275, P = 0.0488) or BC-specific (CDKN2A/CDKN2B-rs3218020, P = 0.0225; CDKAL1-rs981042, P = 0.0246; and TCF2/HNF1B-rs3094508, P = 0.0344) mortality in additive genotype models, at α = 0.05. Genetic polymorphisms that increase the risk of developing diabetes may also increase the risk of developing and dying from BC.

Reproductive characteristics are associated with gene-specific promoter methylation status in breast cancer.

BACKGROUND: Reproductive characteristics are well-established risk factors for breast cancer, but the underlying mechanisms are not fully resolved. We hypothesized that altered DNA methylation, measured in tumor tissue, could act in concert with reproductive factors to impact breast carcinogenesis. METHODS: Among a population-based sample of women newly diagnosed with first primary breast cancer, reproductive history was assessed using a life-course calendar approach in an interviewer-administered questionnaire. Methylation-specific polymerase chain reaction and Methyl Light assays were used to assess gene promotor methylation status (methylated vs. unmethylated) for 13 breast cancer-related genes in archived breast tumor tissue. We used case-case unconditional logistic regression to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for associations with age at menarche and parity (among 855 women), and age at first birth and lactation (among a subset of 736 parous women) in association with methylation status. RESULTS: Age at first birth > 27 years, compared with < 23 years, was associated with lower odds of methylation of CDH1 (OR = 0.44, 95% CI = 0.20-0.99) and TWIST1 (OR = 0.48, 95% CI = 0.28-0.82), and higher odds of methylation of BRCA1 (OR = 1.63, 95% CI = 1.14-2.35). Any vs. no lactation was associated with higher odds of methylation of the PGR gene promoter (OR = 1.59, 95% CI = 1.01-2.49). No associations were noted for parity and methylation in any of the genes assayed. CONCLUSIONS: Our findings indicate that age at first birth, lactation and, perhaps age at menarche, are associated with gene promoter methylation in breast cancer, and should be confirmed in larger studies with robust gene coverage.

Histology and Transcriptome Profiles of the Mammary Gland across Critical Windows of Development in Sprague Dawley Rats.

Breast development occurs through well-defined stages representing 'windows of susceptibility' to adverse environmental exposures that potentially modify breast cancer risk. Systematic characterization of morphology and transcriptome during normal breast development lays the foundation of our understanding of cancer etiology. We examined mammary glands in female Sprague Dawley rats across six developmental stages - pre-pubertal, peri-pubertal, pubertal, lactation, adult parous and adult nulliparous. We investigated histology by Hematoxylin and Eosin and Mallory's Trichrome stain, proliferative and apoptotic rate by immunohistochemistry and whole-transcriptome by microarrays. We identified differentially expressed genes between adjacent developmental stages by linear models, underlying pathways by gene ontology analysis and gene networks and hubs active across developmental stages by coexpression network analysis. Mammary gland development was associated with large-scale changes in the transcriptome; particularly from pre-pubertal to peri-pubertal period and the lactation period were characterized by distinct patterns of gene expression with unique biological functions such as immune processes during pre-pubertal development and cholesterol biosynthesis during lactation. These changes were reflective of the shift in mammary gland histology, from a rudimentary organ during early stages to a secretory organ during lactation followed by regression with age. Hub genes within mammary gene networks included metabolic genes such as Pparg during the pre-pubertal stage and tight junction-related genes claudins and occludins in lactating mammary glands. Transcriptome profile paired with histology enhanced our understanding of mammary development, which is fundamental in understanding the etiologic mechanism of breast cancer, especially pertaining to windows of susceptibility to environmental exposures that may alter breast cancer risk.

Pre-diagnostic aspirin use and mortality after breast cancer.

BACKGROUND: Whether aspirin or other nonsteroidal anti-inflammation drug (NSAID) use is associated with mortality following breast cancer remains unclear. Consideration of use patterns and interaction with obesity may help to clarify the inconsistent results. METHODS: Pre-diagnosis NSAID use, weight, and height were assessed ~ 3 months after diagnosis through in-person interviews with a population-based cohort of 1,442 women with first primary breast cancer. Vital status was determined through the national death index after ~ 18 years of follow-up (N = 237/597 breast cancer-specific/all-cause deaths). We used Cox proportional hazards regression to estimate multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Multiplicative interaction by body mass index (BMI) was evaluated using the likelihood ratio test. RESULTS: Ever aspirin use was inversely associated with breast cancer-specific mortality (HR 0.87, 95% CI 0.59-1.29), but positively associated with all-cause mortality (HR 1.21, 95% CI 0.99-1.48); the CIs included the null values. The HRs, however, were more pronounced for the highest level of duration, frequency, regularity, and timing for all-cause, but not breast cancer-specific mortality. Interactions with BMI revealed no significant heterogeneity (pinteraction = 0.37 and pinteraction = 0.36, respectively). CONCLUSION: Pre-diagnosis aspirin use was not strongly associated with mortality following breast cancer. The all-cause mortality associations, however, were slightly stronger when we considered patterns of use.

A new method to study the change of miRNA-mRNA interactions due to environmental exposures.

MOTIVATION: Integrative approaches characterizing the interactions among different types of biological molecules have been demonstrated to be useful for revealing informative biological mechanisms. One such example is the interaction between microRNA (miRNA) and messenger RNA (mRNA), whose deregulation may be sensitive to environmental insult leading to altered phenotypes. The goal of this work is to develop an effective data integration method to characterize deregulation between miRNA and mRNA due to environmental toxicant exposures. We will use data from an animal experiment designed to investigate the effect of low-dose environmental chemical exposure on normal mammary gland development in rats to motivate and evaluate the proposed method. RESULTS: We propose a new network approach-integrative Joint Random Forest (iJRF), which characterizes the regulatory system between miRNAs and mRNAs using a network model. iJRF is designed to work under the high-dimension low-sample-size regime, and can borrow information across different treatment conditions to achieve more accurate network inference. It also effectively takes into account prior information of miRNA-mRNA regulatory relationships from existing databases. When iJRF is applied to the data from the environmental chemical exposure study, we detected a few important miRNAs that regulated a large number of mRNAs in the control group but not in the exposed groups, suggesting the disruption of miRNA activity due to chemical exposure. Effects of chemical exposure on two affected miRNAs were further validated using breast cancer human cell lines. AVAILABILITY AND IMPLEMENTATION: R package iJRF is available at CRAN. CONTACTS: pei.wang@mssm.edu or susan.teitelbaum@mssm.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Assessment of cumulative health risk in the World Trade Center general responder cohort.

BACKGROUND: Multiple comorbidities have been reported among rescue/recovery workers responding to the 9/11/2001 WTC disaster. In this study, we developed an index that quantifies the cumulative physiological burden of comorbidities and predicts life expectancy in this cohort. METHODS: A machine learning approach (gradient boosting) was used to model the relationship between mortality and several clinical parameters (laboratory test results, blood pressure, pulmonary function measures). This model was used to construct a risk index, which was validated by assessing its association with a number of health outcomes within the WTC general responder cohort. RESULTS: The risk index showed significant associations with mortality, self-assessed physical health, and onset of multiple chronic conditions, particularly COPD, hypertension, asthma, and sleep apnea. CONCLUSION: As an aggregate of several clinical parameters, this index serves as a cumulative measure of physiological dysregulation and could be utilized as a prognostic indicator of life expectancy and morbidity risk.

Modification of the association between recreational physical activity and survival after breast cancer by promoter methylation in breast cancer-related genes.

BACKGROUND: Mechanisms underlying the inverse association between physical activity and survival after breast cancer are unresolved, but DNA methylation may play a role. We hypothesized that promoter methylation of breast cancer-related genes, as well as global methylation, may modify the association between prediagnostic recreational physical activity (RPA) and breast cancer mortality. METHODS: Using a population-based sample of 1254 women diagnosed with first primary breast cancer, we examined modification of the RPA-mortality association by gene-specific promoter methylation and global methylation. Average lifetime RPA was assessed from menarche to diagnosis through structured in-home interviews. Promoter methylation of 13 breast cancer-related genes was evaluated in archived tumor by methylation-specific polymerase chain reaction and MethyLight assay. Global methylation in white blood cell DNA was determined at long interspersed nucleotide element 1 and by the luminometric methylation assay. After approximately 15 years of follow-up, 486 patients had died, and 186 of the deaths were breast cancer-related. We used Cox proportional hazards regression to estimate HRs and 95% CIs as well as likelihood ratio tests to assess multiplicative interactions. RESULTS: All-cause mortality was lower only among physically active women with methylated promoter of APC (HR 0.60, 95% CI 0.40-0.80), CCND2 (HR 0.56, 95% CI 0.32-0.99), HIN (HR 0.55, 95% CI 0.38-0.80), and TWIST1 (HR 0.28, 95% CI 0.14-0.56) in tumors, but not among those with unmethylated tumors (significant interaction p < 0.05). We found no interaction between RPA and global methylation. CONCLUSIONS: The improved survival after breast cancer that is associated with RPA may be more pronounced in women with promoter tumor methylation in biologically plausible genes.