Exposome-wide association study of semen quality: Systematic discovery of endocrine disrupting chemical biomarkers in fertility require large sample sizes.
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ABSTRACT:
SUBMITTER: Chung MK
PROVIDER: S-EPMC6400484 | biostudies-literature | 2019 Apr
REPOSITORIES: biostudies-literature
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Exposome-wide association study of semen quality: Systematic discovery of endocrine disrupting chemical biomarkers in fertility require large sample sizes.
Chung Ming Kei MK Buck Louis Germaine M GM Kannan Kurunthachalam K Patel Chirag J CJ
Environment international 20181222
<h4>Objectives</h4>Exposome-wide association studies (EWAS) are a systematic and unbiased way to investigate multiple environmental factors associated with phenotype. We applied EWAS to study semen quality and queried the sample size requirements to detect modest associations in a reproductive cohort.<h4>Study design and setting</h4>We conducted 1) a multivariate EWAS of 128 endocrine disrupting chemicals (EDCs) from 15 chemical classes measured in urine/serum relative to 7 semen quality endpoin ...[more]
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Project description: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 (β) and odds ratios (ORs) of cluster membership with their respective 95% confidence intervals (CIs). Older age (per 1 year increase: β = 0.47, CI = 0.39, 0.54; OR = 1.27, CI = 1.20, 1.35), lower body mass index (per 1 kg/m2 increase: β = -0.14, CI = -0.17, -0.12; OR = 0.91, CI = 0.89, 0.94), and current smoking (≥10 cigarettes/day vs never smokers: β = 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.
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Project description:BackgroundDevelopment and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and tissue-combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset.ResultsMulti-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their targeted chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain.ConclusionsThe discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher's discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. Classifier discovery based on individual TF networks could yield more mechanistically-oriented biomarkers. GSEA proved to be a flexible and effective tool for application of gene classifiers but a similar and more refined algorithm, connectivity mapping, should also be explored. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions.
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2012-08-22 | E-GEOD-38070 | biostudies-arrayexpress