Small intestine submucosa as a growth factor attractor promotes peripheral nerve regeneration by enhancing syndecan-3/glial cell line-derived neurotrophic factor (GDNF) signalling:in vivostudy.

Peripheral nerve regeneration (PNR) following trauma requires the reconstruction of the extracellular matrix (ECM) and the proper stimulation of growth factors. Decellularised small intestine submucosa (SIS) has been extensively used as an ECM scaffold for tissue repair, but its potential to enhance the effects of exogenous growth factors on PNR is not well understood. In this study, we evaluated the effects of SIS implantation combined with glial cell-derived growth factor (GDNF) treatment on PNR in a rat neurorrhaphy model. We found that both SIS and regenerating nerve tissue expressed syndecan-3 (SDC3), one of major heparan sulphate proteoglycans in nerve tissue, and that SDC3 interacted with GDNF in the regenerating nerve tissue. Importantly, the SIS-GDNF combined treatment enhanced the recovery of neuromuscular function andß3-tubulin-positive axonal outgrowth, indicating an increase in the number of functioning motor axons connecting to the muscle after neurorrhaphy. Our findings suggest that the SIS membrane offers a new microenvironment for neural tissue and promotes neural regeneration based on SDC3-GDNF signalling, providing a potential therapeutic approach for PNR.

A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures.

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose-response relationship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Existing penalized regression methods that account for exposure interactions either cannot accommodate nonlinear interactions while maintaining strong heredity or are computationally unstable in applications with limited sample size. In this paper, we propose a general shrinkage and selection framework to identify noteworthy nonlinear main and interaction effects among a set of exposures. We design hierarchical integrative group least absolute shrinkage and selection operator (HiGLASSO) to (a) impose strong heredity constraints on two-way interaction effects (hierarchical), (b) incorporate adaptive weights without necessitating initial coefficient estimates (integrative), and (c) induce sparsity for variable selection while respecting group structure (group LASSO). We prove sparsistency of the proposed method and apply HiGLASSO to an environmental toxicants dataset from the LIFECODES birth cohort, where the investigators are interested in understanding the joint effects of 21 urinary toxicant biomarkers on urinary 8-isoprostane, a measure of oxidative stress. An implementation of HiGLASSO is available in the higlasso R package, accessible through the Comprehensive R Archive Network.

Training in clinical genetics and genetic counseling in Asia.

The status of training in clinical genetics and genetic counseling in Asia is at diverse stages of development and maturity. Most of the training programs are in academic training centers where exposure to patients in the clinics or in the hospital is a major component. This setting provides trainees with knowledge and skills to be competent geneticists and genetic counselors in a variety of patient care interactions. Majority of the training programs combine clinical and research training which provide trainees a broad and integrated approach in the diagnosis and management of patients while providing opportunities for research discoveries that can be translated to better patient care. The background on how the training programs in clinical genetics and genetic counseling in Asia evolved to their current status are described. Each of these countries can learn from each other through sharing of best practices and resources.

Distributed Lag Interaction Models with Two Pollutants.

Distributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on a health outcome of interest such as mortality and morbidity. Most previous DLM approaches only consider one pollutant at a time. In this article, we propose distributed lag interaction model (DLIM) to characterize the joint lagged effect of two pollutants. One natural way to model the interaction surface is by assuming that the underlying basis functions are tensor products of the basis functions that generate the main-effect distributed lag functions. We extend Tukey's one-degree-of-freedom interaction structure to the two-dimensional DLM context. We also consider shrinkage versions of the two to allow departure from the specified Tukey's interaction structure and achieve bias-variance tradeoff. We derive the marginal lag effects of one pollutant when the other pollutant is fixed at certain quantiles. In a simulation study, we show that the shrinkage methods have better average performance in terms of mean squared error (MSE) across different scenarios. We illustrate the proposed methods by using the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) data to model the joint effects of PM10 and O3 on mortality count in Chicago, Illinois, from 1987 to 2000.

Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes.

In this paper, we propose a stepwise forward selection algorithm for detecting the effects of a set of correlated exposures and their interactions on a health outcome of interest when the underlying relationship could potentially be nonlinear. Though the proposed method is very general, our application in this paper remains to be on analysis of multiple pollutants and their interactions. Simultaneous exposure to multiple environmental pollutants could affect human health in a multitude of complex ways. For understanding the health effects of multiple environmental exposures, it is often important to identify and estimate complex interactions among exposures. However, this issue becomes analytically challenging in the presence of potential nonlinearity in the outcome-exposure response surface and a set of correlated exposures. Through simulation studies and analyses of test datasets that were simulated as a part of a data challenge in multipollutant modeling organized by the National Institute of Environmental Health Sciences (http://www.niehs.nih.gov/about/events/pastmtg/2015/statistical/), we illustrate the advantages of our proposed method in comparison with existing alternative approaches. A particular strength of our method is that it demonstrates very low false positives across empirical studies. Our method is also used to analyze a dataset that was released from the Health Outcomes and Measurement of the Environment Study as a benchmark beta-tester dataset as a part of the same workshop.

Robust distributed lag models using data adaptive shrinkage.

Distributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on an outcome of interest such as mortality or cardiovascular events. Generally speaking, DLMs can be applied to time-series data where the current measure of an independent variable and its lagged measures collectively affect the current measure of a dependent variable. The corresponding distributed lag (DL) function represents the relationship between the lags and the coefficients of the lagged exposure variables. Common choices include polynomials and splines. On one hand, such a constrained DLM specifies the coefficients as a function of lags and reduces the number of parameters to be estimated; hence, higher efficiency can be achieved. On the other hand, under violation of the assumption about the DL function, effect estimates can be severely biased. In this article, we propose a general framework for shrinking coefficient estimates from an unconstrained DLM, that are unbiased but potentially inefficient, toward the coefficient estimates from a constrained DLM to achieve a bias-variance trade-off. The amount of shrinkage can be determined in various ways, and we explore several such methods: empirical Bayes-type shrinkage, a hierarchical Bayes approach, and generalized ridge regression. We also consider a two-stage shrinkage approach that enforces the effect estimates to approach zero as lags increase. We contrast the various methods via an extensive simulation study and show that the shrinkage methods have better average performance across different scenarios in terms of mean squared error (MSE).We illustrate the methods by using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) to explore the association between PM$_{10}$, O$_3$, and SO$_2$ on three types of disease event counts in Chicago, IL, from 1987 to 2000.

Incretin-based therapy in type 2 diabetes: An evidence based systematic review and meta-analysis.

Incretin based therapies such as dipeptidyl peptidase-4 inhibitors (DPP-4i) and glucagon-like peptide-1 receptor agonists (GLP-1Ra) are increasingly used for the treatment of Type 2 diabetes mellitus. In clinical practice and in previously performed clinical trials, these agents are often used in combination with other oral anti-diabetic agents (OADs) and Insulin. Prior meta-analytic reviews however do not adequately address the impact of background therapy and active comparator arms. Accordingly, we aimed to further investigate the efficacy of incretin based therapies by updating existing reviews by including clinical trial evidence after 2008; estimating the pooled effect of incretin therapies on glycemic efficacy and weight-loss, stratified by comparator therapy (placebo, mono-therapy, etc.), estimating the impact of background OADs and within class (GLP-1Ra or DPP-4i) comparative efficacy, on glycemia control. 82 randomized controlled trials after 2008 with glycemic control and weight loss as primary end-points were included. Both DPP-4i and GLP-1Ra reduced HbA1c, but only GLP-1Ra caused weight loss when compared to either active comparator drugs or placebo. GLP-1Ra were more effective than DPP-4i in glycemia lowering. Long acting GLP-1Ra were more effective in HbA1c lowering than short-acting agents but with similar weight loss effect. The effect of DPP-4i incretin glycemic efficacy was not modified by background therapy used in the study.

A New Variance Component Score Test for Testing Distributed Lag Functions with Applications in Time Series Analysis.

We propose to test a given constrained distributed lag model (DLM) of the form ß = Cθ against an unconstrained alternative using a variance component score test (VCST) and show that VCST is more powerful than the standard likelihood ratio test in a simulation study.

Mediation of the Relationship between Maternal Phthalate Exposure and Preterm Birth by Oxidative Stress with Repeated Measurements across Pregnancy.

BACKGROUND: Mediation analysis is useful for understanding mechanisms and has been used minimally in the study of the environment and disease. OBJECTIVE: We examined mediation of the association between phthalate exposure during pregnancy and preterm birth by oxidative stress. METHODS: This nested case-control study of preterm birth (n = 130 cases, 352 controls) included women who delivered in Boston, Massachusestts, from 2006 through 2008. Phthalate metabolites and 8-isoprostane, an oxidative stress biomarker, were measured in urine from three visits in pregnancy. We applied four counterfactual mediation methods: method 1, utilizing exposure and mediator averages; method 2, using averages but allowing for an exposure-mediator interaction; method 3, incorporating longitudinal measurements of the exposure and mediator; and method 4, using longitudinal measurements and allowing for an exposure-mediator interaction. RESULTS: We observed mediation of the associations between phthalate metabolites and all preterm birth by 8-isoprostane, with the greatest estimated proportion mediated observed for spontaneous preterm births specifically. Fully utilizing repeated measures of the exposure and mediator improved precision of indirect (i.e., mediated) effect estimates, and including an exposure-mediator interaction increased the estimated proportion mediated. For example, for mono(2-ethyl-carboxy-propyl) phthalate (MECPP), a metabolite of di(2-ethylhexyl) phthalate (DEHP), the percent of the total effect mediated by 8-isoprostane increased from 47% to 60% with inclusion of an exposure-mediator interaction term, in reference to a total adjusted odds ratio of 1.67 or 1.48, respectively. CONCLUSIONS: This demonstrates mediation of the phthalate-preterm birth relationship by oxidative stress, and the utility of complex regression models in capturing mediated associations when repeated measures of exposure and mediator are available and an exposure-mediator interaction may exist. Citation: Ferguson KK, Chen YH, VanderWeele TJ, McElrath TF, Meeker JD, Mukherjee B. 2017. Mediation of the relationship between maternal phthalate exposure and preterm birth by oxidative stress with repeated measurements across pregnancy. Environ Health Perspect 125:488-494; http://dx.doi.org/10.1289/EHP282.

Urinary phthalate metabolite and bisphenol A associations with ultrasound and delivery indices of fetal growth.

Growth of the fetus is highly sensitive to environmental perturbations, and disruption can lead to problems in pregnancy as well as later in life. This study investigates the relationship between maternal exposure to common plasticizers in pregnancy and fetal growth. Participants from a longitudinal birth cohort in Boston were recruited early in gestation and followed until delivery. Urine samples were collected at up to four time points and analyzed for concentrations of phthalate metabolites and bisphenol A (BPA). Ultrasound scans were performed at four time points during pregnancy for estimation of growth parameters, and birthweight was recorded at delivery. Growth measures were standardized to a larger population. For the present analysis we examined cross-sectional and repeated measures associations between exposure biomarkers and growth estimates in 482 non-anomalous singleton pregnancies. Cross-sectional associations between urinary phthalate metabolites or BPA and growth indices were imprecise. However, in repeated measures models, we observed significant inverse associations between di-2-ethylhexyl phthalate (DEHP) metabolites and estimated or actual fetal weight. An interquartile range increase in summed DEHP metabolites was associated with a 0.13 standard deviation decrease in estimated or actual fetal weight (95% confidence interval=-0.23, -0.03). Associations were consistent across different growth parameters (e.g., head circumference, femur length), and by fetal sex. No consistent associations were observed for other phthalate metabolites or BPA. Maternal exposure to DEHP during pregnancy was associated with decreased fetal growth, which could have repercussive effects.

Utilizing Longitudinal Measures of Fetal Growth to Create a Standard Method to Assess the Impacts of Maternal Disease and Environmental Exposure.

Impaired or suboptimal fetal growth is associated with an increased risk of perinatal morbidity and mortality. By utilizing readily available clinical data on the relative size of the fetus at multiple points in pregnancy, including delivery, future epidemiological research can improve our understanding of the impacts of maternal, fetal, and environmental factors on fetal growth at different windows during pregnancy. This study presents mean and standard deviation ultrasound measurements from a clinically representative US population that can be utilized for creating Z-scores to this end. Between 2006 and 2012, 18, 904 non-anomalous pregnancies that received prenatal care, first and second trimester ultrasound evaluations, and ultimately delivered singleton newborns at Brigham and Women's hospital in Boston were used to create the standard population. To illustrate the utility of this standard, we created Z-scores for ultrasound and delivery measurements for a cohort study population and examined associations with factors known to be associated with fetal growth. In addition to cross-sectional regression models, we created linear mixed models and generalized additive mixed models to illustrate how these scores can be utilized longitudinally and for the identification of windows of susceptibility. After adjustment for a priori confounders, maternal BMI was positively associated with increased fetal size beginning in the second trimester in cross-sectional models. Female infants and maternal smoking were associated with consistently reduced fetal size in the longitudinal models. Maternal age had a non-significant association with increased size in the first trimester that was attenuated as gestation progressed. As the growth measurements examined here are widely available in contemporary obstetrical practice, these data may be abstracted from medical records by investigators and standardized with the population means presented here. This will enable easy extension of clinical data to epidemiologic studies investigating novel maternal, fetal, and environmental factors that may impact fetal growth.

Microsatellite Alterations With Allelic Loss at 9p24.2 Signify Less-Aggressive Colorectal Cancer Metastasis.

BACKGROUND & AIMS: Molecular events that lead to recurrence and/or metastasis after curative treatment of patients with colorectal cancers (CRCs) are poorly understood. Patients with stage II or III primary CRC with elevated microsatellite alterations at selected tetranucleotide repeats and low levels of microsatellite instability (E/L) are more likely to have disease recurrence after treatment. Hypoxia and/or inflammation not only promote metastasis, but also induce elevated microsatellite alterations at selected tetranucleotide repeats by causing deficiency of MSH3 in the cancer cell nucleus. We aimed to identify genetic alterations associated with metastasis of primary colorectal tumors to liver and to determine their effects on survival. METHODS: We obtained 4 sets of primary colorectal tumors and matched liver metastases from hospitals in Korea and Japan. Intragenic microsatellites with large repeats at 141 loci were examined for frame-shift mutations and/or loss of heterozygosity (LOH) as possible consequences of MSH3 deficiency. Highly altered loci were examined for association with E/L in liver metastases. We analyzed data from 156 of the patients with stage II or III primary colorectal tumors to determine outcomes and whether altered loci were associated with E/L. RESULTS: LOH at several loci at chromosome 9p24.2 (9p24.2-LOH) was associated with E/L in liver metastases (odds ratio = 10.5; 95% confidence interval: 2.69-40.80; P = .0007). We found no significant difference in the frequency of E/L, 9p24.2-LOH, mutations in KRAS or BRAF, or the combination of E/L and 9p24.2-LOH, between primary colorectal tumors and their matched metastases. Patients with stage II or III colorectal tumors with E/L and 9p24.2-LOH had increased survival after CRC recurrence (hazard ratio = 0.25; 95% CI: 0.12-0.50; P = .0001), compared with patients without with E/L and 9p24.2-LOH. E/L with 9p24.2-LOH appeared to be an independent prognostic factor for overall survival of patients with stage III CRC (hazard ratio = 0.06; 95% CI: 0.01-0.57; P = .01). CONCLUSIONS: E/L with 9p24-LOH appears to be a biomarker for less aggressive metastasis from stage III primary colorectal tumors.

Statistical methods for modeling repeated measures of maternal environmental exposure biomarkers during pregnancy in association with preterm birth.

BACKGROUND: It is of critical importance to evaluate the role of environmental chemical exposures in premature birth. While a number of studies investigate this relationship, most utilize single exposure measurements during pregnancy in association with the outcome. The studies with repeated measures of exposure during pregnancy employ primarily cross-sectional analyses that may not be fully leveraging the power and additional information that the data provide. METHODS: We examine 9 statistical methods that may be utilized to estimate the relationship between a longitudinal exposure and a binary, non-time-varying outcome. To exemplify these methods we utilized data from a nested case-control study examining repeated measures of urinary phthalate metabolites during pregnancy in association with preterm birth. RESULTS: The methods summarized may be useful for: 1) Examining sensitive windows of exposure in association with an outcome; 2) Summarizing repeated measures to estimate the relationship between average exposure and an outcome; 3) Identifying acute exposures that may be relevant to the outcome; and 4) Understanding the contribution of temporal patterns in exposure levels to the outcome of interest. In the study of phthalates, changes in urinary metabolites over pregnancy did not appear to contribute significantly to preterm birth, making summary of average exposure across gestation optimal given the current design. CONCLUSIONS: The methods exemplified may be of great use in future epidemiologic research projects intended to: 1) Elucidate the complex relationships between environmental chemical exposures and preterm birth; 2) Investigate biological mechanisms in prematurity using repeated measures of maternal factors throughout pregnancy; and 3) More generally, address the relationship between a longitudinal predictor and a binary, non-time-varying outcome.

Repeated measures of urinary oxidative stress biomarkers during pregnancy and preterm birth.

OBJECTIVE: The purpose of this study was to investigate oxidative stress as a mechanism of preterm birth in human subjects; we examined associations between urinary biomarkers of oxidative stress that were measured at multiple time points during pregnancy and preterm birth. STUDY DESIGN: This nested case-control study included 130 mothers who delivered preterm and 352 mothers who delivered term who were originally recruited as part of an ongoing prospective birth cohort at Brigham and Women's Hospital. Two biomarkers that included 8-hydroxydeoxyguanosine (8-OHdG) and 8-isoprostane were measured in urine samples that were collected at up to 4 time points (median 10, 18, 26, and 35 weeks) during gestation. RESULTS: Urinary concentrations of 8-isoprostane and 8-OHdG decreased and increased, respectively, as pregnancy progressed. Average levels of 8-isoprostane across pregnancy were associated with increased odds of spontaneous preterm birth (adjusted odds ratio, 6.25; 95% confidence interval, 2.86-13.7), and associations were strongest with levels measured later in pregnancy. Average levels of 8-OHdG were protective against overall preterm birth (adjusted odds ratio, 0.19; 95% confidence interval, 0.10-0.34), and there were no apparent differences in the protective effect in cases of spontaneous preterm birth compared with cases of placental origin. Odds ratios for overall preterm birth were more protective in association with urinary 8-OHdG concentrations that were measured early in pregnancy. CONCLUSION: Maternal oxidative stress may be an important contributor to preterm birth, regardless of subtype and timing of exposure during pregnancy. The 2 biomarkers that were measured in the present study had opposite associations with preterm birth; an improved understanding of what each represents may help to identify more precisely important mechanisms in the pathway to preterm birth.

Urinary phthalate metabolites and biomarkers of oxidative stress in pregnant women: a repeated measures analysis.

BACKGROUND: Phthalate exposure occurs readily in the environment and has been associated with an array of health end points, including adverse birth outcomes. Some of these may be mediated by oxidative stress, a proposed mechanism for phthalate action. OBJECTIVES: In the present study, we explored the associations between phthalate metabolites and biomarkers of oxidative stress measured in urine samples from multiple time points during pregnancy. METHODS: Women were participants in a nested case-control study of preterm birth (n = 130 cases, n = 352 controls). Each was recruited early in pregnancy and followed until delivery, providing urine samples at up to four visits. Nine phthalate metabolites were measured to assess exposure, and 8-hydroxydeoxyguanosine and 8-isoprostane were also measured in urine as markers of oxidative stress. Associations were assessed using linear mixed models to account for intraindividual correlation, with inverse selection probability weightings based on case status to allow for greater generalizability. RESULTS: Interquartile range increases in phthalate metabolites were associated with significantly higher concentrations of both biomarkers. Estimated differences were greater in association with monobenzyl phthalate (MBzP), mono-n-butyl phthalate (MBP), and monoisobutyl phthalate (MiBP), compared with di(2-ethylhexyl) phthalate (DEHP) metabolites. CONCLUSIONS: Urinary phthalate metabolites were associated with increased oxidative stress biomarkers in our study population of pregnant women. These relationships may be particularly relevant to the study of birth outcomes linked to phthalate exposure. Although replication is necessary in other populations, these results may also be of great importance for a range of other health outcomes associated with phthalates.

Longitudinal profiling of inflammatory cytokines and C-reactive protein during uncomplicated and preterm pregnancy.

PROBLEM: Previous studies have investigated the utility of inflammation markers as predictors of preterm birth, but none have compared trends in levels between uncomplicated and preterm pregnancy. METHOD OF STUDY: We explored longitudinal changes in plasma cytokines, including IL-1ß, IL-6, IL-10, and TNF-α, as well as C-reactive protein in pregnant women from a nested case-control study. RESULTS: IL-6 was associated with increased odds of spontaneous preterm birth, defined by presentation of spontaneous preterm labor and/or preterm premature rupture of the membranes. Associations were strongest later in pregnancy. IL-10 was associated with increased odds of placentally mediated preterm birth, defined by presentation with preeclampsia or intrauterine growth restriction, and odds ratios were also highest near the end of pregnancy. CONCLUSION: Maternal inflammation markers were associated with increased risk of preterm birth, and relationships differed by etiology of preterm delivery and gestational age at sample collection.

Massively parallel nonparametric regression, with an application to developmental brain mapping.

We propose a penalized spline approach to performing large numbers of parallel non-parametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naïvely performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70000 brain locations. Supplementary materials, including an appendix and an R package, are available online.

Joint rare variant association test of the average and individual effects for sequencing studies.

For many complex traits, single nucleotide polymorphisms (SNPs) identified from genome-wide association studies (GWAS) only explain a small percentage of heritability. Next generation sequencing technology makes it possible to explore unexplained heritability by identifying rare variants (RVs). Existing tests designed for RVs look for optimal strategies to combine information across multiple variants. Many of the tests have good power when the true underlying associations are either in the same direction or in opposite directions. We propose three tests for examining the association between a phenotype and RVs, where two of them jointly consider the common association across RVs and the individual deviations from the common effect. On one hand, similar to some of the best existing methods, the individual deviations are modeled as random effects to borrow information across multiple RVs. On the other hand, unlike the existing methods which pool individual effects towards zero, we pool them towards a possibly non-zero common effect by adding a pooled variant into the model. The common effect and the individual effects are jointly tested. We show through extensive simulations that at least one of the three tests proposed here is the most powerful or very close to being the most powerful in various settings of true models. This is appealing in practice because the direction and size of the true effects of the associated RVs are unknown. Researchers can apply the developed tests to improve power under a wide range of true models.