Search

PDF - 80K, Association between the timing of mother smoking (before and during pregnancy) and DNA methylation at 26 CpGs of interest. Each point represents the association for each CpG in each smoking category. *Significance threshold p

PDF - 16K, Association between the combined mother and grandmother smoking categories and DNA methylation at 26 CpGs of interest. Each point represents the association for each CpG in each smoking category. *Significance threshold p

PDF - 225K, Evaluation of the additional effect of grandmother smoking on DNA methylation at the 26 CpGs of interest, given the mother smoked in her pregnancy. Each point represents the association for each CpG in each category. *Significance threshold p

Background: Maternal smoking in pregnancy is associated with adverse health outcomes in children, including cancers; underlying mechanisms may include epigenetic modifications. Using Illumina's 450K array, we previously identified differential DNA methylation related to maternal smoking during pregnancy at 26 CpG sites (CpGs) in 10 genes in newborn cord bloods from the Norwegian Mother and Child Cohort Study (MoBa). Whether these methylation signals in newborns reflect in utero exposure only or possibly epigenetic inheritance of smoking-related modifications is unclear.Methods: We therefore evaluated the impact of the timing of mother's smoking (before or during pregnancy using cotinine measured at 18 weeks gestation), the father's smoking before conception, and the grandmother's smoking during her pregnancy with the mother on methylation at these 26 CpGs in 1,042 MoBa newborns. We used robust linear regression, adjusting for covariates, applying Bonferroni correction.Results: The strongest and only statistically significant associations were observed for sustained smoking by the mother during pregnancy through at least gestational week 18 (P < 1.6 × 10−5 for all 26 CpGs). We observed no statistically significant differential methylation due to smoking by the mother before pregnancy or that ceased by week 18, father's smoking before conception, or grandmother's smoking while pregnant with the mother.Conclusions: Differential methylation at these CpGs in newborns seems to reflect sustained in utero exposure rather than epigenetic inheritance.Impact: Smoking cessation in early pregnancy may negate effects on methylation. Analyses of maternal smoking during pregnancy and offspring health outcomes, including cancer, limited to ever smoking might miss true associations. Cancer Epidemiol Biomarkers Prev; 23(6); 1007–17. ©2014 AACR.

PDF - 2930K, Timeline describing the collection of smoking information and methylation measurements for the study participants.

PDF - 89K, Supplementary Table 1. Differential methylation in cord blood DNA in relation to the father's smoking prior to the mother's pregnancy. Supplementary Table 2. The combined effect of mother and grandmother smoking in pregnancy compared to only the mother smoking in pregnancy, on differential DNA methylation in cord blood. Legends for Supplementary Figures 1 through 4.

OBJECTIVE: The microbiome may be affected by trauma and critical illness. Many studies of the microbiome in critical illness are restricted to a single body site or timepoint and confounded by pre-existing conditions. We report temporal and spatial alterations in the microbiome of previously healthy children with severe traumatic brain injury (TBI). DESIGN: We collected oral, rectal, and skin swabs within 72 hours of admission and then twice weekly until ICU discharge. Samples were analyzed by 16S rRNA gene amplicon sequencing. Children undergoing elective outpatient surgery served as controls. Alpha and beta diversity comparisons were performed with Phyloseq and differentially abundant taxa were predicted using ANCOM. SETTING: Five quaternary care pediatric intensive care units PATIENTS: Patients < 18 years with severe TBI requiring placement of an intracranial pressure (ICP) monitor INTERVENTIONS: None MEASUREMENTS AND MAIN RESULTS: 327 samples were analyzed from 23 children with severe TBI and 35 controls. The community composition of initial oral (F=3.2756, R(2)=0.0535, p=0.012) and rectal (F=3.0702, R(2)=0.0649, p=0.007) samples differed between TBI and control patients. Rectal samples were depleted of commensal bacteria from Ruminococcaceae, Bacteroidaceae, and Lachnospiraceae families and enriched in Staphylococcaceae after TBI (p

Little is known about the association of specific nutrients, especially proteins, on the age-related gut dysbiosis.To determine the associations between the quantity and sources (vegetable and animal) of dietary protein intake and gut microbiome composition in community-dwelling older men.We performed a cross-sectional analysis on 775 older men from the Osteoporotic Fractures in Men (MrOS) Study (age 84.2 ± 4.0 years) with available dietary information and stool samples at visit 4 (2014-16). Protein intake was estimated from a brief food frequency questionnaire and adjusted to total energy intake. The gut microbiome composition was determined by 16S (v4) sequencing (processed by DADA2 and SILVA). 11,534 amplicon sequence variants (ASVs) were identified and assigned to 21 phyla with dominance of Firmicutes (45%) and Bacteroidetes (43%). We performed α-diversity, β-diversity, and taxa abundance (by ANCOM-BC) to determine the associations between protein intake and gut microbiome.Median protein intake was 0.7 g/(kg body weight · d). Participants with higher energy-adjusted protein intakes had higher Shannon and Chao1 α-diversity indices (P 0.05). For β-diversity analysis, participants with higher protein intakes had a different center in weighted and unweighted UniFrac PCoA vs. those with lower intake (P 0.05), adjusted for age, race, education, clinical center, batch number, fiber and energy intake, weight, height, and medications. Similarly, higher protein consumptions from either animal or vegetable sources were associated with higher gut microbiome diversity. Several genus-level ASVs, including Christensenellaceae, Veillonella, Haemophilus, and Klebsiella were more abundant in participants with higher protein intakes, whereas Clostridiales bacterium DTU089 and Desulfovibrio were more abundant in participants with lower protein intake (Bonferroni corrected P 0.05).We observed significant associations between protein intake and gut microbiome diversity in community-living older men. Further studies are needed to elucidate the mediation role of gut microbiome on the relationship between protein intake and health outcomes in older adults.

Background Regional body compositions are differentially associated with cardiometabolic risk factors. Simultaneous inclusion of both upper and lower body composition predictors in models is not often done, and studies which do include both measures (1) tend to exclude some tissue(s) of potential metabolic relevance, and (2) have used study populations with underrepresentation of individuals with African ancestries. Further, most body composition analyses do not employ compositional data analytic approaches, which may result in spurious associations. Objective The objective of this analysis was to assess associations of abdominal and thigh adipose (AT) and muscle tissues with hypertension and type 2 diabetes using compositional data analytic methods. Research Design and Methods This cross‐sectional analysis included 610 African Caribbean men (median age: 62 years; mean BMI: 27.8 kg/m2). Abdominal (three components: subcutaneous [ASAT] and visceral [VAT] AT, ‘other’ abdominal tissue) and mid‐thigh (four components: subcutaneous and intermuscular AT, muscle, bone) compositions were measured by computed tomography; additive log ratio transformations were applied to each composition. Regression models were used to simultaneously assess associations of abdominal and thigh component ratios with continuous risk factors (blood pressures, fasting glucose and insulin, HOMA‐IR) and disease categories. Results A two‐fold increase in ASAT:‘Other’ ratio was associated with higher continuous risk factors and with odds of being in a higher hypertension (OR: 1.77, 95%CI: 1.10–2.84) or diabetes (OR: 1.81, 95%CI: 1.06–3.10) category. A two‐fold increased VAT ratio was only associated with higher log‐insulin and log‐HOMA‐IR (β = 0.10, p

BACKGROUND: Bacterial exposure from house dust has been associated with asthma and atopy in children but whether these relationships are present in adults remains unclear. OBJECTIVE: To examine associations of house dust microbiota with adult asthma, atopy, and hay fever. METHODS: Vacuumed bedroom dust samples from the homes of 879 participants (average age 62) in the Agricultural Lung Health Study, a case-control study of asthma nested within a farming cohort, were subjected to 16S rRNA amplicon sequencing to characterize bacterial communities. We defined current asthma and hay fever using questionnaires and current atopy by blood specific immunoglobulin E > 0.70 IU/ml to ≥ 1 of ten common allergens. We used linear regression to examine whether overall within-sample bacterial diversity differed by outcome, microbiome regression-based kernel association test (MiRKAT) to evaluate whether between-sample bacterial community compositions differed by outcome, and analysis of composition of microbiomes (ANCOM) to identify differentially abundant bacterial taxa. RESULTS: Overall diversity of bacterial communities in house dust was similar by asthma status but was lower (p-value < 0.05) with atopy or hay fever. Many individual bacterial taxa were differentially abundant (false discovery rate < 0.05) by asthma, atopy, or hay fever. Several taxa from Cyanobacteria, Bacteroidetes, and Fusobacteria were more abundant with asthma, atopy, or hay fever. In contrast, several taxa from Firmicutes were more abundant in homes of individuals with adequately controlled asthma (vs inadequately controlled asthma), non-atopics, or individuals without hay fever. CONCLUSION: Microbial composition of house dust may influence allergic outcomes in adults.

BACKGROUNDThe function of the gestational sac (GS) and the placenta in the closely related processes of embryogenesis and teratogenicity in the first trimester has been minimally described. The prevailing assumption is that direct teratogenic effects are mediated by the critical extraembryonic organ, the placenta, which either blocks or transfers exposures to the foetus. Placental transfer is a dominant mechanism, but there are other paradigms by which the placenta can mediate teratogenic effects. Knowledge of these paradigms and first trimester human developmental biology can be useful to the epidemiologist in the conduct of biomarker-based studies of both maternal and child health.OBJECTIVE AND RATIONALEOur aim is to provide a causal framework for modelling the teratogenic effects of first trimester exposures on child health outcomes mediated by the GS and placenta using biomarker data collected in the first trimester. We initially present first trimester human developmental biology for the sake of informing and strengthening epidemiologic approaches. We then propose analytic approaches of modelling placental mechanisms by way of causal diagrams using classical non-embryolethal teratogens (diethylstilboestrol [DES], folic acid deficiency and cytomegalovirus [CMV]) as illustrative examples. We extend this framework to two chronic exposures of particular current interest, phthalates and maternal adiposity.SEARCH METHODSInformation on teratogens was identified by a non-systematic, narrative review. For each teratogen, we included papers that answered the five following questions: (i) why were these exposures declared teratogens? (ii) is there a consensus on biologic mechanism? (iii) is there reported evidence of a placental mechanism? (iv) can we construct a theoretical model of a placental mechanism? and (v) can this knowledge inform future work on measurement and modelling of placental-foetal teratogenesis? We prioritized literature specific to human development, the organogenesis window in the first trimester and non-embryolethal mechanisms.OUTCOMESAs a result of our review of the literature on five exposures considered harmful in the first trimester, we developed four analytic strategies to address first trimester placental mechanisms in birth cohort studies: placental transfer and direct effects on the foetus (DES and maternal adiposity), indirect effects through targeted placental molecular pathways (DES and phthalates), pre-placental effects through disruptions in embryonic and extraembryonic tissue layer differentiation (folic acid deficiency), and multi-step mechanisms that involve maternal, placental and foetal immune function and inflammation (DES and CMV).WIDER IMPLICATIONSThe significance of this review is to offer a causal approach to classify the large number of potentially harmful exposures in pregnancy when the exposure occurs in the first trimester. Our review will facilitate future research by advancing knowledge of the first trimester mechanisms necessary for researchers to effectively associate environmental exposures with child health outcomes.

Antimony trioxide (AT) is used as a flame retardant in fabrics and plastics. Occupational exposure in miners and smelters is mainly through inhalation and dermal contact. Chronic inhalation exposure to AT particulates in B6C3F1/N mice and Wistar Han rats resulted in increased incidences and tumor multiplicities of alveolar/bronchiolar carcinomas (ABCs). In this study, we demonstrated Kras (43%) and Egfr (46%) hotspot mutations in mouse lung tumors (n = 80) and only Egfr (50%) mutations in rat lung tumors (n = 26). Interestingly, there were no differences in the incidences of these mutations in ABCs from rats and mice at exposure concentrations that did and did not exceed the pulmonary overload threshold. There was increased expression of p44/42 mitogen-activated protein kinase (MAPK) (Erk1/2) protein in ABCs harboring mutations in Kras and/or Egfr, confirming the activation of MAPK signaling. Transcriptomic analysis indicated significant alterations in MAPK signaling such as ephrin receptor signaling and signaling by Rho-family GTPases in AT-exposed ABCs. In addition, there was significant overlap between transcriptomic data from mouse ABCs due to AT exposure and human pulmonary adenocarcinoma data. Collectively, these data suggest chronic AT exposure exacerbates MAPK signaling in ABCs and, thus, may be translationally relevant to human lung cancers.

PURPOSE OF REVIEW: In this review, we provide essential background knowledge and an analytical framework for the application of placental-fetal molecular biomarkers in fetal origins chronic disease epidemiology. The widely available and highly quantitative placental hormone human chorionic gonadotropin (hCG) is used as an example. hCG is currently used for diagnosing fetal genetic disorders; yet it can and should be expanded to understanding the fetal origins of chronic diseases. We provide justification and methods to do this. RECENT FINDINGS: Ten papers published in the last 5 years were identified with supportive findings relevant to the application of biomarkers of hCG in epidemiologic studies on the developmental origins of health and disease (DOHaD). SUMMARY: There is increasing and consistent evidence that placental-fetal biomarkers may be highly informative in observational studies, as exemplified by hCG, with the correct approaches for measurement and data analysis.

Linear models are widely used in the field of epidemiology to model the relationship between placental-fetal hormone and fetal/infant outcome. When a nonlinear relationship is suspected, researchers explore nonparametric models such as regression splines, smoothing splines and penalized regression splines (Korevaar et al., Lancet: Diabetes Endocrinol. 4, 35–43 2016; Wu and Zhang 2006). By applying these nonparametric techniques, researchers can relax the linearity assumption and capture scientifically meaningful or appropriate shapes. In this paper, we focus on the regression spline technique and develop a method to help researchers select the most suitable shape to describe their data among increasing, decreasing, convex and concave shapes. Specifically, we develop a technique based on mixed effects regression spline to analyze hormonal data described in this paper. The proposed methodology is general enough to be applied to other similar problems. We illustrate the method using a prenatal screening program data set.

Differential abundance (DA) analysis of microbiome data continues to be a challenging problem due to the complexity of the data. In this article we define the notion of “sampling fraction” and demonstrate a major hurdle in performing DA analysis of microbiome data is the bias introduced by differences in the sampling fractions across samples. We introduce a methodology called Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The absolute abundance data are modeled using a linear regression framework. This formulation makes a fundamental advancement in the field because, unlike the existing methods, it (a) provides statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR), (d) maintains adequate power, and (e) is computationally simple to implement., Differential abundance analysis of microbiome data continues to be challenging due to data complexity. The authors propose a method which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples.

OBJECTIVES Although inadequate health literacy has been shown to impact health outcomes in other cancers, little is known about its impact in head and neck cancer (HNC). This study aimed to determine the prevalence and predictors of inadequate health literacy and evaluate the association between health literacy and quality of life (QOL) in HNC survivors. METHODS We conducted a retrospective analysis of HNC survivors evaluated in a multidisciplinary HNC survivorship clinic. Survivors had to be ≥1-year postcompletion of treatment to be included in the analysis. Health literacy was assessed via self-report with the Brief Health Literacy Screen (score

Motivated by applications in physical and biological sciences, we developed a Frequency Modulated Möbius (FMM) model to describe rhythmic patterns in oscillatory systems. Unlike standard symmetric sinusoidal models, FMM is a flexible parametric model that allows deformations to sinusoidal shape to accommodate commonly seen asymmetries in applications. FMM model parameters are easy to estimate and the model is easy to interpret complex rhythmic data. We illustrate FMM model in three disparate applications, namely, circadian clock gene expression, corticoptropin levels in depressed patients and the temporal light intensity patterns of distant stars. In each case, FMM model is demonstrated to be flexible, scientifically plausible and easy to interpret. Analysis of synthetic data derived from patterns of real data, suggest that FMM model fits the data very well both visually as well as in terms of the goodness of fit measure total mean squared error. An R language based software for implementing FMM model is available.

Background The epithelium is increasingly recognized as a pathologic contributor to asthma and its phenotypes. Although delayed wound closure by asthmatic epithelial cells is consistently observed, underlying mechanisms remain poorly understood, partly due to difficulties in studying dynamic physiologic processes involving polarized multilayered cell systems. Although type-2 immunity has been suggested to play a role, the mechanisms by which repair is diminished are unclear. Objectives This study sought to develop and utilize primary multilayered polarized epithelial cell systems, derived from patients with asthma, to evaluate cell migration in response to wounding under type-2 and untreated conditions. Methods A novel wounding device for multilayered polarized cells, along with time-lapse live cell/real-time confocal imaging were evaluated under IL-13 and untreated conditions. The influence of inhibition of 15 lipoxygenase (15LO1), a type-2 enzyme, on the process was also addressed. Cell migration patterns were analyzed by high-dimensional frequency modulated Mobius for statistical comparisons. Results IL-13 stimulation negatively impacts wound healing by altering the total speed, directionality, and acceleration of individual cells. Inhibition 15LO1 partially improved the wound repair through improving total speed. Conclusions Migration abnormalities contributed to markedly slower wound closure of IL-13 treated cells, which was modestly reversed by 15LO1 inhibition, suggesting its potential as an asthma therapeutic target. These novel methodologies offer new ways to dynamically study cell movements and identify contributing pathologic processes.

OBJECTIVE. Although neck impairment has been described following surgical resection, limited studies have investigated its prevalence in nonsurgical treatment. The purpose of this study is to determine the prevalence and predictors of neck disability following head and neck cancer (HNC) treatment and to explore its association with quality of life (QOL). STUDY DESIGN. Cross-sectional study. SETTING. HNC survivorship clinic. SUBJECTS AND METHODS. We identified 214 survivors who completed treatment ≥1 year prior to evaluation in the clinic. Self-reported neck impairment was measured using the Neck Disability Index. QOL was measured using the University of Washington QOL Questionnaire, with physical and social subscale scores calculated. Regression analysis and trend tests were employed to explore associations. RESULTS. Over half of survivors (54.2%) reported neck disability. The odds of neck disability in survivors who received nonsurgical treatment and those who received surgery plus adjuvant treatment were 3.46 and 4.98 times higher compared to surgery alone (P = .008, P = .004). Survivors who underwent surgery only had higher physical and social QOL than those who received nonsurgical treatment (physical QOL: P < .001, social QOL: P = .023) and those who received surgery plus adjuvant treatment (physical QOL: P

Large spatial datasets are typically modelled through a small set of knot locations; often these locations are specified by the investigator by arbitrary criteria. Existing methods of estimating th...

Background Early disruption of the microbial community may influence life-long health. Environmental toxicants can contaminate breast milk and the developing infant gut microbiome is directly exposed. We investigated whether environmental toxicants in breastmilk affect the composition and function of the infant gut microbiome at 1 month. We measured environmental toxicants in breastmilk, fecal short-chain fatty acids (SCFAs), and gut microbial composition from 16S rRNA gene amplicon sequencing using samples from 267 mother-child pairs in the Norwegian Microbiota Cohort (NoMIC). We tested 28 chemical exposures: polychlorinated biphenyls (PCBs), polybrominated flame retardants (PBDEs), per- and polyfluoroalkyl substances (PFASs), and organochlorine pesticides. We assessed chemical exposure and alpha diversity/SCFAs using elastic net regression modeling and generalized linear models, adjusting for confounders, and variation in beta diversity (UniFrac), taxa abundance (ANCOM), and predicted metagenomes (PiCRUSt) in low, medium, and high exposed groups. Results PBDE-28 and the surfactant perfluorooctanesulfonic acid (PFOS) were associated with less microbiome diversity. Some sub-OTUs of Lactobacillus, an important genus in early life, were lower in abundance in samples from infants with relative “high” (> 80th percentile) vs. “low” (

Adequate maternal thyroid hormone (TH) is necessary for fetal brain development. The role of placental human chorionic gonadotropin (hCG) in ensuring the production of TH is less well understood. The objective of the study was to evaluate 1) associations of placental hCG and its subunits, and maternal TH in the second trimester, and 2) the single and joint effects of TH and placental hormones on cognitive development and communication at ages 1 and 3 years. Fifty individuals (5%) were selected from the CANDLE (Conditions Affecting Neurocognitive Development and Early Learning) pregnancy cohort in Memphis, Tennessee, with recruitment from 2006 to 2011, to equally represent male and female fetuses. Participants were 68% Black and 32% White. Hormones measured were maternal thyroid (thyrotropin [TSH] and free thyroxine [FT4]) and placental hormones (hCG, its hyperglycosylated form [hCG-h], and free α- [hCGα] and β-subunits [hCGβ]) in maternal serum (17-28 weeks). The primary outcome measurement was the Bayley Scales of Infant and Toddler Development. All forms of hCG were negatively associated with FT4 and not associated with TSH. hCGα was associated with cognitive development at age 1 year and jointly interacted with TSH to predict cognitive development at age 3 years. This pilot study added insight into the thyrotropic actions of hCG in the second trimester, and into the significance of this mechanism for brain development. More research is warranted to elucidate differences between hCGα, hCGβ, and hCG-h in relation to TH regulation and child brain function.

Ginkgo biloba extract (GBE) is used in traditional Chinese medicine as an herbal supplement for improving memory. Exposure of B6C3F1/N mice to GBE in a 2-year National Toxicology Program (NTP) bioassay resulted in a dose-dependent increase in hepatocellular carcinomas (HCC). To identify key microRNAs that modulate GBE-induced hepatocarcinogenesis, we compared the global miRNA expression profiles in GBE-exposed HCC (GBE-HCC) and spontaneous HCC (SPNT-HCC) with age-matched vehicle control normal livers (CNTL) from B6C3F1/N mice. The number of differentially altered miRNAs in GBE-HCC and SPNT-HCC were 74 (52 up and 22 down) and 33 (15 up and 18 down), respectively. Among the uniquely differentially altered miRNAs in GBE-HCC, miR-31 and one of its predicted targets, Cdk1 were selected for functional validation. A potential miRNA response element (MRE) in the 3’-untranslated regions (3’-UTR) of Cdk1 mRNA was revealed by in silico analysis and confirmed by luciferase assays. In mouse hepatoma cell line HEPA-1 cells, we demonstrated an inverse correlation between miR-31 and CDK1 protein levels, but no change in Cdk1 mRNA levels, suggesting a post-transcriptional effect. Additionally, a set of miRNAs (miRs-411, 300, 127, 134, 409–3p, and 433–3p) that were altered in the GBE-HCCs were also altered in non-tumor liver samples from the 90-day GBE-exposed group compared to the vehicle control group, suggesting that some of these miRNAs could serve as potential biomarkers for GBE exposure or hepatocellular carcinogenesis. These data increase our understanding of miRNA-mediated epigenetic regulation of GBE-mediated hepatocellular carcinogenesis in B6C3F1/N mice.

Background In the context of COVID‐19, cancer survivors represent a particularly vulnerable population that may be “doubly hit” by both costs of cancer treatment and financial strain imposed by the pandemic. Methods We performed a review of the literature pertaining to cancer, financial toxicity, and economic challenges. Results Multiple societies have put forth recommendations to modify delivery of cancer care in order to minimize patient exposure to the virus. Cancer survivors, especially patients with head and neck cancer, have been disproportionately affected by rising unemployment levels and economic recessions in the past, both of which are linked to higher cancer mortality. Patients who rely on employer‐provided insurance and do not qualify for Medicaid may lose access to life‐saving treatments. Conclusions It is essential to implement interventions and policy changes in order to mitigate the effects of this pandemic but also to ensure this becomes a nonissue during the next one.

We consider Bayesian inference about collections of unknown distributions subject to a partial stochastic ordering. To address problems in testing of equalities between groups and estimation of group-specific distributions, we propose classes of restricted dependent Dirichlet process priors. These priors have full support in the space of stochastically ordered distributions, and can be used for collections of unknown mixture distributions to obtain a flexible class of mixture models. Theoretical properties are discussed, efficient methods are developed for posterior computation using Markov chain Monte Carlo simulation and the methods are illustrated using data from a study of DNA damage and repair. Copyright 2008, Oxford University Press.

Large spatial datasets are typically modeled through a small set of knot locations; often these locations are specified by the investigator by arbitrary criteria. Existing methods of estimating the locations of knots assume their number is known a priori, or are otherwise computationally intensive. We develop a computationally efficient method of estimating both the location and number of knots for spatial mixed effects models. Our proposed algorithm, Threshold Knot Selection (TKS), estimates knot locations by identifying clusters of large residuals and placing a knot in the centroid of those clusters. We conduct a simulation study showing TKS in relation to several comparable methods of estimating knot locations. Our case study utilizes data of particulate matter concentrations collected during the course of the response and clean-up effort from the 2010 Deepwater Horizon oil spill in the Gulf of Mexico.

Background: Adipose tissue (AT) distribution, which differs by race/ethnicity, can differentially impact risk for cardiometabolic disease. Traditional analyses using AT volumes tend to place all ATs in a regression model, estimating an increase in one AT volume while “holding other volumes constant”; however, the compositional nature of the data suggests the other ATs cannot remain constant. Compositional data analysis (CODA) methods eliminate this concern by comparing logratios of components. CODA was designed for analyses of data like those of body composition , but has yet to be used in this field. We demonstrate the use of CODA for the comparison of visceral AT (VAT) and subcutaneous AT of the abdomen (ASAT) and thigh (TSAT) with prevalence of cardiometabolic diseases. Methods: This cross-sectional analysis used 615 African Caribbean men from Tobago aged 50-91 years (mean age 63.6 years, mean BMI 27.7 kg/m 2 ). AT volumes were obtained from CT scans of the abdomen (VAT and ASAT) and of the mid-thigh (TSAT). An additive log2 ratio transformation was applied to the ATs to generate the VAT, ASAT, and TSAT component effects relative to the other tissues captured in the respective CT scan. Ordinal logistic regression analyses were performed for increasing hypertension or glucose impairment categories (defined in Table) after adjustment for age, disease status, and lifestyle factors. Results: After full adjustment (Table), a 2-fold higher ASAT volume (relative to other abdominal tissues) was associated with higher odds, and a 2-fold higher TSAT volume (relative to other thigh tissues) was associated with lower odds, of being in a higher hypertension category. After BMI adjustment, no AT was associated with glucose impairment category. VAT was not a significant predictor for either outcome in African Ancestry men. Conclusion: CODA methods, which avoid problematic assumptions made in traditional analyses, found significant and opposing associations of TSAT and ASAT with hypertension categories in African Ancestry men.

Increasingly, researchers are discovering associations between microbiome and a wide range of human diseases such as obesity, inflammatory bowel diseases, HIV, and so on. The first step towards microbiome wide association studies is the characterization of the composition of human microbiome under different conditions. Determination of differentially abundant microbes between two or more environments, known as differential abundance (DA) analysis, is a challenging and an important problem that has received considerable interest during the past decade. It is well documented in the literature that the observed microbiome data (OTU/SV table) are relative abundances with an excess of zeros. Since relative abundances sum to a constant, these data are necessarily compositional. In this article we review some recent methods for DA analysis and describe their strengths and weaknesses.

There was a significant increase in the incidence of retinal degeneration in F344/N rats chronically exposed to Kava kava extract (KKE) in National Toxicology Program (NTP) bioassay. A retrospective evaluation of these rat retinas indicated a similar spatial and morphological alteration as seen in light-induced retinal degeneration in albino rats. Therefore, it was hypothesized that KKE has a potential to exacerbate the light-induced retinal degeneration. To investigate the early mechanism of retinal degeneration, we conducted a 90-day F344/N rat KKE gavage study at doses of 0 and 1.0 g/kg (dose which induced retinal degeneration in the 2-year NTP rat KKE bioassay). The morphological evaluation indicated reduced number of phagosomes in the retinal pigment epithelium (RPE) of the superior retina. Transcriptomic alterations related to retinal epithelial homeostasis and melatoninergic signaling were observed in microarray analysis. Phagocytosis of photoreceptor outer segment by the underlying RPE is essential to maintain the homeostasis of the photoreceptor layer and is regulated by melatonin signaling. Therefore, reduced photoreceptor outer segment disc shedding and subsequent lower number of phagosomes in the RPE and alterations in the melatonin pathway may have contributed to the increased incidences of retinal degeneration observed in F344/N rats in the 2-year KKE bioassay.

Background There is mounting evidence for a connection between the gut and Parkinson's disease (PD). Dysbiosis of gut microbiota could explain several features of PD. Objective The objective of this study was to determine if PD involves dysbiosis of gut microbiome, disentangle effects of confounders, and identify candidate taxa and functional pathways to guide research. Methods A total of 197 PD cases and 130 controls were studied. Microbial composition was determined by 16S rRNA gene sequencing of DNA extracted from stool. Metadata were collected on 39 potential confounders including medications, diet, gastrointestinal symptoms, and demographics. Statistical analyses were conducted while controlling for potential confounders and correcting for multiple testing. We tested differences in the overall microbial composition, taxa abundance, and functional pathways. Results Independent microbial signatures were detected for PD (P = 4E-5), participants' region of residence within the United States (P = 3E-3), age (P = 0.03), sex (P = 1E-3), and dietary fruits/vegetables (P = 0.01). Among patients, independent signals were detected for catechol-O-methyltransferase-inhibitors (P = 4E-4), anticholinergics (P = 5E-3), and possibly carbidopa/levodopa (P = 0.05). We found significantly altered abundances of the Bifidobacteriaceae, Christensenellaceae, [Tissierellaceae], Lachnospiraceae, Lactobacillaceae, Pasteurellaceae, and Verrucomicrobiaceae families. Functional predictions revealed changes in numerous pathways, including the metabolism of plant-derived compounds and xenobiotics degradation. Conclusion PD is accompanied by dysbiosis of gut microbiome. Results coalesce divergent findings of prior studies, reveal altered abundance of several taxa, nominate functional pathways, and demonstrate independent effects of PD medications on the microbiome. The findings provide new leads and testable hypotheses on the pathophysiology and treatment of PD. © 2017 International Parkinson and Movement Disorder Society

(1) Describe financial toxicity (FT) in head and neck cancer (HNC) survivors and assess its association with personal/health characteristics and health-related quality of life (HRQOL); (2) examine financial coping mechanisms (savings/loans); (3) assess relationship between COmprehensive Score for financial Toxicity (COST) and Financial Distress Questionnaire (FDQ).Cross-sectional survey from January - April 2018 of insured patients at a tertiary multidisciplinary HNC survivorship clinic who completed primary treatment for squamous cell carcinoma of the oral cavity, oropharynx, or larynx/hypopharynx.Of 104 survivors, 30 (40.5%) demonstrated high FT. Patients with worse FT were more likely (1) not married (COST, 25.33 ± 1.87 vs. 30.61 ± 1.34, p = 0.008); (2) of lower education levels (COST, 26.12 ± 1.47 vs. 34.14 ± 1.47, p 0.001); and (3) with larynx/hypopharynx primaries (COST, 22.86 ± 2.28 vs. 30.27 ± 1.50 vs. 32.72 ± 1.98, p = 0.005). Younger age (4.23, 95%CI 2.20 to 6.26, p 0.001), lower earnings at diagnosis (1.17, 95%CI 0.76 to 1.58, p 0.001), and loss in earnings (-1.80, 95%CI -2.43 to -1.16, p 0.001) were associated with worse FT. COST was associated with HRQOL (0.08, p = 0.03). Most survivors (63/102, 60%) reported using savings and/or loans. Worse FT was associated with increased likelihood of using more mechanisms (COST, OR1.06, 95%CI 1.02 to 1.10, p = 0.004). Similar results were found with FDQ.We found differences in FT by primary site, with worst FT in larynx/hypopharynx patients. This finding illuminates potential site-specific factors, e.g. workplace discrimination or inability to return to work, that may contribute to increased risk. FDQ correlates strongly with COST, encouraging further exploration as a clinically-meaningful screening tool.

Data derived from microarray technologies are generally subject to various sources of noise and accordingly the raw data are pre-processed before formally analysed. Data normalization is a key pre-processing step when dealing with microarray experiments, such as circadian gene-expressions, since it removes systematic variations across arrays. A wide variety of normalization methods are available in the literature. However, from our experience in the study of rhythmic expression patterns in oscillatory systems (e.g. cell-cycle, circadian clock), the choice of the normalization method may substantially impair the identification of rhythmic genes. Hence, the identification of a gene as rhythmic could be just as an artefact of how the data were normalized. Yet, gene rhythmicity detection is crucial in modern toxicological and pharmacological studies, thus a procedure to truly identify rhythmic genes that are robust to the choice of a normalization method is required.To perform the task of detecting rhythmic features, we propose a rhythmicity measure based on bootstrap methodology to robustly identify rhythmic genes in oscillatory systems. Although our methodology can be extended to any high-throughput experiment, in this chapter, we illustrate how to apply it to a publicly available circadian clock microarray gene-expression data and give full details (both statistical and computational) so that the methodology can be used in an easy way. We will show that the choice of normalization method has very little effect on the proposed methodology since the results derived from the bootstrap-based rhythmicity measure are highly rank correlated for any pair of normalization methods considered. This suggests, on the one hand, that the rhythmicity measure proposed is robust to the choice of the normalization method, and on the other hand, that gene rhythmicity detected using this measure is potentially not a mere artefact of the normalization method used. In this way the researcher using this methodology will be protected against the possible effect of different normalizations, as the conclusions obtained will not depend so strongly on them. Additionally, the described bootstrap methodology can also be employed as a tool to simulate gene-expression participating in an oscillatory system from a reference data set.

Quantitative high throughput screening (qHTS) experiments can generate 1000s of concentration-response profiles to screen compounds for potentially adverse effects. However, potency estimates for a single compound can vary considerably in study designs incorporating multiple concentration-response profiles for each compound. We introduce an automated quality control procedure based on analysis of variance (ANOVA) to identify and filter out compounds with multiple cluster response patterns and improve potency estimation in qHTS assays. Our approach, called Cluster Analysis by Subgroups using ANOVA (CASANOVA), clusters compound-specific response patterns into statistically supported subgroups. Applying CASANOVA to 43 publicly available qHTS data sets, we found that only about 20% of compounds with response values outside of the noise band have single cluster responses. The error rates for incorrectly separating true clusters and incorrectly clumping disparate clusters were both less than 5% in extensive simulation studies. Simulation studies also showed that the bias and variance of concentration at half-maximal response (AC50 ) estimates were usually within 10-fold when using a weighted average approach for potency estimation. In short, CASANOVA effectively sorts out compounds with "inconsistent" response patterns and produces trustworthy AC50 values.

It is widely agreed that adequate maternal thyroid hormone is necessary for proper brain development. The role of the fetal placenta in ensuring the production and delivery of thyroid hormone during fetal brain development is less well understood. We hypothesized that the placenta ‘hijacks’ the maternal thyroid in early pregnancy to ensure adequate thyroid hormone, and either or both endocrine systems are vulnerable to perturbation by phthalate exposure. We did a pilot study to examine this theory. 50 subjects were recruited in Memphis, TN into the CANDLE birth cohort study, and were were: 68% African-American, 32% White, 50% female babies, mean maternal age of 27.5 years, 28% nulliparous, 50% had greater than a high school education. Blood and urine samples were collected between 17-28 wks gestation. Infant cognition was measured by the Bayley’s Scales at 1 and 3 years of age, and combined into one measure. hCG-alpha, hCG-beta, hyperglycosylated hCG, intact hCG, thyroid stimulating hormone and free thyroxine were analyzed in maternal serum by immunofluorometric assays. Phthalate metabolites were measured in maternal urine by HPLC tandem mass spectrometry. Linear regression models, with quadratic terms, were used to estimate sex-specific associations, and then decomposed to evaluate direct vs. indirect effects of phthalates on infant cognition: phthalates > placental hormone > thyroid hormone > infant cognition. Consistent with previous knowledge, TSH was negatively associated with FT4 (-0.23 pmol/l per log TSH, 95% CI -0.4, -0.1) and both hormones were related to infant cognition, but without statistical significance. hCG-alpha was significantly associated with infant cognition (u-shaped), positively associated with TSH (0.38 log units TSH, 95% CI -0.1, 0.8), negatively associated with FT4 in females (-0.84 pmol/l FT4, 95% CI -1.8, 0.1). Of the phthalates, only mono-n-butyl phthalate (MnBP) was associated with lower hCG-alpha (-0.14 log units, 95% CI -0.3, 0.1), higher FT4 (1.11 pmol/l, 95% CI 0.5, 1.8) in females, and infant cognition to year 3 (-0.11 log units in females, 95% CI -0.2, 0.0). The MnBP indirect effect on cognition, through the hormones, was null. Pilot data were underpowered to generate robust findings, but provided insights: 1) hCG-alpha has a distinct correlation structure, different from the other forms of hCG, in relation to MnBP, thyroid hormone, and infant cognition; 2) these data offer support for the theory that hCG-alpha supplants TSH during this critical period of brain development. Endocrine pathways in pregnancy are related to fetal brain development and vulnerable to phthalate exposure. These types of effects can be studied by the measurement of relevant hormones. Funding was provided by The Department of Epidemiology at the University of Pittsburgh, NIEHS, Vanderbuilt University CTSA, and the Urban Child Institute.

Data derived from microarray technologies are generally subject to various sources of noise and accordingly the raw data are pre-processed before formally analysed. Data normalization is a key pre-processing step when dealing with microarray experiments, such as circadian gene-expressions, since it removes systematic variations across arrays. A wide variety of normalization methods are available in the literature. However, from our experience in the study of rhythmic expression patterns in oscillatory systems (e.g. cell-cycle, circadian clock), the choice of the normalization method may substantially impair the identification of rhythmic genes. Hence, the identification of a gene as rhythmic could be just as an artefact of how the data were normalized. Yet, gene rhythmicity detection is crucial in modern toxicological and pharmacological studies, thus a procedure to truly identify rhythmic genes that are robust to the choice of a normalization method is required.To perform the task of detecting rhythmic features, we propose a rhythmicity measure based on bootstrap methodology to robustly identify rhythmic genes in oscillatory systems. Although our methodology can be extended to any high-throughput experiment, in this chapter, we illustrate how to apply it to a publicly available circadian clock microarray gene-expression data and give full details (both statistical and computational) so that the methodology can be used in an easy way. We will show that the choice of normalization method has very little effect on the proposed methodology since the results derived from the bootstrap-based rhythmicity measure are highly rank correlated for any pair of normalization methods considered. This suggests, on the one hand, that the rhythmicity measure proposed is robust to the choice of the normalization method, and on the other hand, that gene rhythmicity detected using this measure is potentially not a mere artefact of the normalization method used. In this way the researcher using this methodology will be protected against the possible effect of different normalizations, as the conclusions obtained will not depend so strongly on them. Additionally, the described bootstrap methodology can also be employed as a tool to simulate gene-expression participating in an oscillatory system from a reference data set.

This paper is motivated by applications in oscillatory systems where researchers are typically interested in discovering components of those systems that display rhythmic temporal patterns. The contributions of this paper are twofold. First, a methodology is developed based on a circular signal plus error model that is defined using order restrictions. This mathematical formulation of rhythmicity is simple, easily interpretable and very flexible, with the latter property derived from the nonparametric formulation of the signal. Second, we address various commonly encountered problems in the analysis of oscillatory systems data. Specifically, we propose a methodology for (a) detecting rhythmic signals in an oscillatory system and (b) estimating the unknown sampling time that occurs when tissues are obtained from subjects whose time of death is unknown. The proposed methodology is computationally efficient, outperforms the existing methods, and is broadly applicable to address a wide range of questions related to oscillatory systems.

In many applications researchers are typically interested in testing for inequality constraints in the context of linear fixed effects and mixed effects models. Although there exists a large body of literature for performing statistical inference under inequality constraints, user friendly statistical software implementing such methods is lacking, especially in the context of linear fixed and mixed effects models. In this article we introduce CLME, a package in the R language that can be used for testing a broad collection of inequality constraints. It uses residual bootstrap based methodology which is reasonably robust to non-normality as well as heteroscedasticity. The package is illustrated using two data sets. The package also contains a graphical user interface built using the shiny package.