Assessing urinary phenol and paraben mixtures in pregnant women with and without gestational diabetes mellitus: a case-control study

Prior studies have identified the associations between environmental phenol and paraben exposures and increased risk of gestational diabetes mellitus (GDM), but no study addressed these exposures as mixtures. As methods have emerged to better assess exposures to multiple chemicals, our study aimed to apply Bayesian kernel machine regression (BKMR) to evaluate the association between phenol and paraben mixtures and GDM. This study included 64 GDM cases and 237 obstetric patient controls from the University of Oklahoma Medical Center. Mid-pregnancy spot urine samples were collected to quantify concentrations of bisphenol A (BPA), benzophenone-3, triclosan, 2,4-dichlorophenol, 2,5-dichlorophenol, butylparaben, methylparaben, and propylparaben. Multivariable logistic regression was used to evaluate the associations between individual chemical biomarkers and GDM while controlling for confounding. We used probit implementation of BKMR with hierarchical variable selection to estimate the mean difference in GDM probability for each component of the phenol and paraben mixtures while controlling for the correlation among the chemical biomarkers. When analyzing individual chemicals using logistic regression, benzophenone-3 was positively associated with GDM [adjusted odds ratio (aOR) per interquartile range (IQR) = 1.54, 95% confidence interval (CI) 1.15, 2.08], while BPA was negatively associated with GDM (aOR 0.61, 95% CI 0.37, 0.99). In probit-BKMR analysis, an increase in z-score transformed log urinary concentrations of benzophenone-3 from the 10th to 90th percentile was associated with an increase in the estimated difference in the probability of GDM (0.67, 95% Credible Interval 0.04, 1.30), holding other chemicals fixed at their medians. No associations were identified between other chemical biomarkers and GDM in the BKMR analyses. We observed that the association of BPA and GDM was attenuated when accounting for correlated phenols and parabens, suggesting the importance of addressing chemical mixtures in perinatal environmental exposure studies. Additional prospective investigations will increase the understanding of the relationship between benzophenone-3 exposure and GDM development

Urinary total arsenic and arsenic methylation capacity in pregnancy and gestational diabetes mellitus: A case-control study

Previous studies suggest arsenic exposure may increase the risk of gestational diabetes mellitus (GDM). However, prior assessments of total arsenic concentrations have not distinguished between toxic and nontoxic species. Our study aimed to investigate the relationships between inorganic arsenic exposure, arsenic methylation capacity, and GDM. Sixty-four cases of GDM and 237 controls were analyzed for urinary concentrations of inorganic arsenic species and their metabolites (arsenite (As3), arsenate (As5), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA)), and organic forms of arsenic. Inorganic arsenic exposure was defined as the sum of inorganic and methylated arsenic species (iSumAs). Methylation capacity indices were calculated as the percentage of inorganic arsenic species [iAs% = (As3 + As5)/iSumAs, MMA% = MMA/iSumAs, and DMA% = DMA/iSumAs]. Multivariable logistic regression was performed to evaluate the association between inorganic arsenic exposure, methylation capacity indices, and GDM. We did not observe evidence of a positive association between iSumAs and GDM. However, women with GDM had an increased odds of inefficient methylation capacity when comparing the highest and lowest tertiles of iAs% (adjusted odds ratio (aOR) = 1.48, 95% CI 0.58–3.77) and MMA% (aOR = 1.95 (95% CI 0.81–4.70) and a reduced odds of efficient methylation capacity as indicated by DMA% (aOR = 0.62 (95% CI 0.25–1.52), though the confidence intervals included the null value. While the observed associations with arsenic methylation indices were imprecise and warrant cautious interpretation, the direction and magnitude of the relative measures reflected a pattern of lower detoxification of inorganic arsenic exposures among women with GDM

Are we validly assessing major depression disorder risk and associated factors among mothers of young children? A cross-sectional study involving home visitation programs.

Failure to account for misclassification error accruing from imperfect case-finding instruments can produce biased estimates of suspected major depression disorder (MDD) risk factor associations. The objective of this study was to estimate the impact of misclassification error on the magnitude of measures of association between suspected risk factors and MDD assessed using the Center of Epidemiological Studies on Depression-Short Form during the prenatal and postnatal periods. Baseline data were collected from 520 mothers participating in two home visitation studies in Oklahoma City between 2010 and 2014. A Bayesian binomial latent class model was used to compare the prevalence proportion ratio (PPR) between suspected risk factors and MDD with and without adjustment for misclassification error and confounding by period of MDD symptom on-set. Adjustment for misclassification error and confounding by period of MDD on-set (prenatal vs postnatal) showed that the association between suspected risk factors and MDD is underestimated (-) and overestimated (+) differentially in different source populations of low-income mothers. The median bias in the magnitude of PPR estimates ranged between -.47 (95% Bayesian Credible Intervals [BCI]: -10.67, 1.90) for intimate partner violence to +.06 (95%BCI: -0.37, 0.47) for race/ethnicity among native-born US residents. Among recent Hispanic immigrants, bias ranged from -.77 (95%BCI: -15.31, 0.96) for history of childhood maltreatment to +.10 (95%BCI: -0.17, 0.39) for adequacy of family resources. Overall, the extent of bias on measures of association between maternal MDD and suspected risk factors is considerable without adjustment for misclassification error and is even higher for confounding by period of MDD assessment. Consideration of these biases in MDD prevention research is warranted

Retrospective Cohort Analysis of the Effect of Age on Lymph Node Harvest, Positivity, and Ratio in Colorectal Cancer

Introduction: Colon cancer among young patients has increased in incidence and mortality over the past decade. Our objective was to determine if age-related differences exist for total positive nodes (TPN), total lymph node harvest (TLH), and lymph node ratio (LNR). Material and Methods: A retrospective review of stage III surgically resected colorectal cancer patient data in the National Cancer Database (2004–2016) was performed, reviewing TPN, TLH, and LNR (TPN/TLH). Results: Unadjusted analyses suggested significantly higher levels of TLH and TPN (p < 0.0001) in younger patients, while LNR did not differ by age group. On adjusted analysis, TLH remained higher in younger patients (<35 years 1.56 (CI 95 1.54, 1.59)). The age-related effect was less pronounced for LNR (<35 years 1.16 (CI 95 1.13, 1.2)). Conclusion: Younger patients have increased TLH, even after adjusting for known confounders, while age does not have a strong independent impact on LNR

Dynamic 2-deoxy-D-glucose-enhanced multispectral optoacoustic tomography for assessing metabolism and vascular hemodynamics of breast cancer

Clinical tools for measuring tumor vascular hemodynamics, such as dynamic contrast-enhanced MRI, are clinically important to assess tumor properties. Here we explored the use of multispectral optoacoustic tomography (MSOT), which has a high spatial and temporal resolution, to measure the intratumoral pharmacokinetics of a near-infrared-dye-labeled 2-Deoxyglucose, 2-DG-800, in orthotropic 2-LMP breast tumors in mice. As uptake of 2-DG-800 is dependent on both vascular properties, and glucose transporter activity – a widely-used surrogate for metabolism, we evaluate hemodynamics of 2-DG-MP by fitting the dynamic MSOT signal of 2-DG-800 into two-compartment models including the extended Tofts model (ETM) and reference region model (RRM). We showed that dynamic 2-DG-enhanced MSOT (DGE-MSOT) is powerful in acquiring hemodynamic rate constants, including Ktrans and Kep, via systemically injecting a low dose of 2-DG-800 (0.5 µmol/kg b.w.). In our study, both ETM and RRM are efficient in deriving hemodynamic parameters in the tumor. Area-under-curve (AUC) values (which correlate to metabolism), and Ktrans and Kep values, can effectively distinguish tumor from muscle. Hemodynamic parameters also demonstrated correlations to hemoglobin, oxyhemoglobin, and blood oxygen level (SO2) measurements by spectral unmixing of the MSOT data. Together, our study for the first time demonstrated the capability of DGE-MSOT in assessing vascular hemodynamics of tumors