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BACKGROUND: Maternal diet during gestation has been linked to infant sleep; whether associations persist through adolescence is unknown. OBJECTIVES: We explored associations between trimester-specific maternal diet patterns and measures of sleep health among adolescent offspring in a Mexico City birth cohort. METHODS: Data from 310 mother–adolescent dyads were analyzed. Maternal diet patterns were identified by principal component analysis derived from FFQs collected during each trimester of pregnancy. Sleep duration, midpoint, and fragmentation were obtained from 7-d actigraphy data when adolescents were between 12 and 20 y old. Unstratified and sex-stratified association analyses were conducted using linear regression models, adjusted for potential confounders. RESULTS: Mean ± SD age of offspring was 15.1 ± 1.9 y, and 52.3% of the sample was female. Three diet patterns were identified during each trimester of pregnancy: the Prudent Diet (PD), high in lean proteins and vegetables; the Transitioning Mexican Diet (TMD), high in westernized foods; and the High Meat & Fat Diet (HMFD), high in meats and fat products. Mean ± SD sleep duration was 8.5 ± 1.5 h/night. Most associations were found in the third trimester. Specifically, PD maternal adherence was associated with shorter sleep duration among offspring (−0.57 h; 95% CI: −0.98, −0.16 h, in the highest tertile compared with the lowest) and earlier sleep midpoint among females (−0.77 h; 95% CI: −1.3, −0.26 h). Adherence to the HMFD and TMD was nonlinearly associated with less fragmented sleep, with the latter only evident among females. CONCLUSIONS: Findings indicate that maternal dietary patterns, especially during the third trimester of pregnancy, may have long-term impacts on offspring sleep.

Rather than generating 1 transplant by directly donating to a candidate on the waitlist, deceased donors (DDs) could achieve additional transplants by donating to a candidate in a kidney paired donation (KPD) pool, thereby, initiating a chain that ends with a living donor (LD) donating to a candidate on the waitlist. We model outcomes arising from various strategies that allow DDs to initiate KPD chains.We base simulations on actual 2016 to 2017 US DD and waitlist data and use simulated KPD pools to model DD-initiated KPD chains. We also consider methods to assess and overcome the primary criticism of this approach, namely the potential to disadvantage blood type O-waitlisted candidates.Compared with shorter DD-initiated KPD chains, longer chains increase the number of KPD transplants by up to 5% and reduce the number of DDs allocated to the KPD pool by 25%. These strategies increase the overall number of blood type O transplants and make LDs available to candidates on the waitlist. Restricting allocation of blood type O DDs to require ending KPD chains with LD blood type O donations to the waitlist markedly reduces the number of KPD transplants achieved.Allocating fewer than 3% of DD to initiate KPD chains could increase the number of kidney transplants by up to 290 annually. Such use of DDs allows additional transplantation of highly sensitized and blood type O KPD candidates. Collectively, patients of each blood type, including blood type O, would benefit from the proposed strategies.

Background Post-transplantation diabetes mellitus (PTDM) might be preventable. Methods This open-label, multicenter randomized trial compared 133 kidney transplant recipients given intermediate-acting insulin isophane for postoperative afternoon glucose ≥140 mg/dl with 130 patients given short-acting insulin for fasting glucose ≥200 mg/dl (control). The primary end point was PTDM (antidiabetic treatment or oral glucose tolerance test-derived 2 hour glucose ≥200 mg/dl) at month 12 post-transplant. Results In the intention-to-treat population, PTDM rates at 12 months were 12.2% and 14.7% in treatment versus control groups, respectively (odds ratio [OR], 0.82; 95% confidence interval [95% CI], 0.39 to 1.76) and 13.4% versus 17.4%, respectively, at 24 months (OR, 0.71; 95% CI, 0.34 to 1.49). In the per-protocol population, treatment resulted in reduced odds for PTDM at 12 months (OR, 0.40; 95% CI, 0.16 to 1.01) and 24 months (OR, 0.54; 95% CI, 0.24 to 1.20). After adjustment for polycystic kidney disease, per-protocol ORs for PTDM (treatment versus controls) were 0.21 (95% CI, 0.07 to 0.62) at 12 months and 0.35 (95% CI, 0.14 to 0.87) at 24 months. Significantly more hypoglycemic events (mostly asymptomatic or mildly symptomatic) occurred in the treatment group versus the control group. Within the treatment group, nonadherence to the insulin initiation protocol was associated with significantly higher odds for PTDM at months 12 and 24. Conclusions At low overt PTDM incidence, the primary end point in the intention-to-treat population did not differ significantly between treatment and control groups. In the per-protocol analysis, early basal insulin therapy resulted in significantly higher hypoglycemia rates but reduced odds for overt PTDM-a significant reduction after adjustment for baseline differences-suggesting the intervention merits further study.Clinical Trial registration number: NCT03507829.

OBJECTIVE/BACKGROUND: Self-reported sleep difficulties, such as insomnia symptoms, have been reported among adolescents; yet, studies of their prevalence and correlates are scarce among Latin Americans. This study sought (1) to describe associations between sociodemographic and lifestyle factors with self-reported sleep and (2) to examine associations between self-reported sleep and actigraphy-based sleep. PARTICIPANTS: Participants included 477 Mexican adolescents from the ELEMENT cohort. METHODS: Over 7 days, self-reported sleep measures (hard time falling asleep, overall sleep difficulties, and specific types of sleep difficulties) were obtained from daily sleep diaries. Actigraphy-based sleep measures (duration i.e. sleep onset to morning wake, midpoint, and fragmentation) were concurrently assessed using a wrist actigraph. RESULTS: Mean (SD) age was 15.9 (2.2) years, and 53.5% were females. Mean (SD) sleep duration was 8.5 (1.2) h/night. Half reported a hard time falling asleep (at least 3 days), and 25% had sleep difficulties at least 3 days over 7 days. The 3 types of sleep difficulties commonly reported among the entire cohort were insomnia/restlessness (29%), environmental (27%), and mental/emotional (19%). Female sex, smoking behavior, and socioeconomic indicators were among the most consistent factors associated with sleep difficulties. Subjective sleep difficulties were associated with shorter sleep duration (β= −20.8 [−35.3, −6.2] min), while subjective hard time falling asleep was associated with longer sleep duration (β= 11.3 [4.6, 27.2] min). CONCLUSION: A high proportion of Mexican adolescents in the sample reported sleep difficulties. Findings demonstrate the importance of obtaining subjective and objective sleep measures for a more comprehensive assessment of adolescent sleep.

Sleep duration is a recognized determinant of mental health, obesity and cardiovascular disease, cognition, and memory across the lifespan. Due to convenience and cost, sleep duration is often measured through self-report; yet, self-reported sleep duration can be highly biased. Actigraphy is a viable alternative as an objective measure of sleep. To analyze this actigraphy data, various sleep evaluation algorithms have been developed using regression methods, with coefficients constructed on minute-by-minute data measured at a specific device placement (wrist or hip). Because activity counts per minute may be affected by various factors in the study (e.g., type of device, sampling frequencies), regression-based algorithms developed within specific populations may not be generalizable to wider use. To address these concerns, we propose a new learning method to obtain robust and consistent sleep duration estimates. First, we identify temporal segments via pruned dynamic programming; then, we develop a calling algorithm with individual-specific thresholds and capture sleep periods. Our proposed method is motivated by and demonstrated in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study and the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) study.

Amyotrophic lateral sclerosis (ALS) is a neurological disease that starts at a focal point and gradually spreads to other parts of the nervous system. One of the main clinical symptoms of ALS is muscle weakness. To study spreading patterns of muscle weakness, we analyze spatiotemporal binary muscle strength data, which indicates whether observed muscle strengths are impaired or healthy. We propose a hidden Markov model-based approach that assumes the observed disease status depends on two latent disease states. The model enables us to estimate the incidence rate of ALS disease and the probability of disease state transition. Specifically, the latter is modeled by a logistic autoregression in that the spatial network of susceptible muscles follows a Markov process. The proposed model is flexible to allow both historical muscle conditions and their spatial relationships to be included in the analysis. To estimate the model parameters, we provide an iterative algorithm to maximize sparse-penalized likelihood with bias correction, and use the Viterbi algorithm to label hidden disease states. We apply the proposed approach to analyze the ALS patients' data from EMPOWER Study.

We develop a health informatics toolbox that enables timely analysis and evaluation of the time-course dynamics of a range of infectious disease epidemics As a case study, we examine the novel coronavirus (COVID-19) epidemic using the publicly available data from the China CDC This toolbox is built upon a hierarchical epidemiological model in which two observed time series of daily proportions of infected and removed cases are generated from the underlying infection dynamics governed by a Markov Susceptible-Infectious-Removed (SIR) infectious disease process We extend the SIR model to incorporate various types of time-varying quarantine protocols, including government-level 'macro' isolation policies and community-level 'micro' social distancing (e g self-isolation and self-quarantine) measures We develop a calibration procedure for underreported infected cases This toolbox provides forecasts, in both online and offline forms, as well as simulating the overall dynamics of the epidemic An R software package is made available for the public, and examples on the use of this software are illustrated Some possible extensions of our novel epidemiological models are discussed

Summary Multi‐compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community‐level micromodel that enables high‐resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper.

Major alterations in metabolism occur during pregnancy enabling the mother to provide adequate nutrients to support infant development, affecting birth weight (BW) and potentially long-term risk of obesity and cardiometabolic disease. We classified dynamic changes in the maternal lipidome during pregnancy and identified lipids associated with Fenton BW z-score and the umbilical cord blood (CB) lipidome. Lipidomics was performed on first trimester maternal plasma (M1), delivery maternal plasma (M3), and CB plasma in 106 mother-infant dyads. Shifts in the maternal and CB lipidome were consistent with the selective transport of long-chain polyunsaturated fatty acids (PUFA) as well as lysophosphatidylcholine (LysoPC) and lysophosphatidylethanolamine (LysoPE) species into CB. Partial correlation networks demonstrated fluctuations in correlations between lipid groups at M1, M3, and CB, signifying differences in lipid metabolism. Using linear models, LysoPC and LysoPE groups in CB were positively associated with BW. M1 PUFA containing triglycerides (TG) and phospholipids were correlated with CB LysoPC and LysoPE species and total CB polyunsaturated TGs. These results indicate that early gestational maternal lipid levels influence the CB lipidome and its relationship with BW, suggesting an opportunity to modulate maternal diet and improve long-term offspring cardiometabolic health.

Stratification is a very commonly used approach in biomedical studies to handle sample heterogeneity arising from, for examples, clinical units, patient subgroups, or missing-data. A key rationale behind such approach is to overcome potential sampling biases in statistical inference. Two issues of such stratification-based strategy are (i) whether individual strata are sufficiently distinctive to warrant stratification, and (ii) sample size attrition resulted from the stratification may potentially lead to loss of statistical power. To address these issues, we propose a penalized generalized estimating equations approach to reducing the complexity of parametric model structures due to excessive stratification. Specifically, we develop a data-driven fusion learning approach for longitudinal data that improves estimation efficiency by integrating information across similar strata, yet still allows necessary separation for stratum-specific conclusions. The proposed method is evaluated by simulation studies and applied to a motivating example of psychiatric study to demonstrate its usefulness in real world settings.

Context A person’s intrinsic metabolism, reflected in the metabolome, may describe the relationship between nutrient intake and metabolic health. Objectives Untargeted metabolomics was used to identify metabolites associated with metabolic health. Path analysis classified how habitual dietary intake influences body mass index z-score (BMIz) and insulin resistance (IR) through changes in the metabolome. Design Data on anthropometry, fasting metabolites, C-peptide, and dietary intake were collected from 108 girls and 98 boys aged 8 to 14 years. Sex-stratified linear regression identified metabolites associated with BMIz and homeostatic model assessment of IR using C-peptide (HOMA-CP), accounting for puberty, age, and muscle and fat area. Path analysis identified clusters of metabolites that underlie the relationship between energy-adjusted macronutrient intake with BMIz and HOMA-CP. Results Metabolites associated with BMIz include positive associations with diglycerides among girls and positive associations with branched chain and aromatic amino acids in boys. Intermediates in fatty acid metabolism, including medium-chain acylcarnitines (AC), were inversely associated with HOMA-CP. Carbohydrate intake is positively associated with HOMA-CP through decreases in levels of AC, products of β-oxidation. Approaching significance, fat intake is positively associated with HOMA-CP through increases in levels of dicarboxylic fatty acids, products of omega-oxidation. Conclusions This cross-sectional analysis suggests that IR in children is associated with reduced fatty acid oxidation capacity. When consuming more grams of fat, there is evidence for increased extramitochondrial fatty acid metabolism, while higher carbohydrate intake appears to lead to decreases in intermediates of β-oxidation. Thus, biomarkers of IR and mitochondrial oxidative capacity may depend on macronutrient intake.

Objective: We present a non-invasive wearable device for fertility monitoring and propose an effective and flexible statistical learning algorithm to detect and predict ovulation using data captured by this device. Methods: The system consists of an earpiece, which measures the ear canal temperature every 5 min during night sleep hours, and a base station that transmits data to a smartphone application for analysis. We establish a data-cleaning protocol for data preprocessing and then fit a Hidden Markov Model (HMM) with two hidden states of high and low temperature to identify the more probable state of each time point via the predicted probabilities. Finally, a post-processing procedure is developed to incorporate biorhythm information to form a time-course biphasic profile for each subject. Results: The performance of the proposed algorithms applied to data collected by the device are compared with traditional methods in terms of match rate with self-reported ovulation days confirmed with an ovulation test kit. Empirical study results from a group of 34 users yielded significant improvements over the traditional methods in terms of detection accuracy (with sensitivity 92.31%) and prediction power (23.07–31.55% higher). Conclusion: We demonstrated the feasibility for reliable ovulation detection and prediction with high-frequency temperature data collected by a non-invasive wearable device. Significance: Traditional fertility monitoring methods are often either inaccurate or inconvenient. The wearable device and learning algorithm presented in this paper provide a user friendly and reliable platform for tracking ovulation, which may have a broad impact on both fertility research and real-world family planning.

Alterations in body composition (BC) during adolescence relates to future metabolic risk, yet underlying mechanisms remain unclear.To assess the association between the metabolome with changes in adiposity (body mass index [BMI], waist circumference [WC], triceps skinfold [TS], fat percentage [BF%]) and muscle mass (MM).In Mexican adolescents (n = 352), untargeted serum metabolomics was profiled at baseline. and data were reduced by pairing hierarchical clustering with confirmatory factor analysis, yielding 30 clusters with 51 singleton metabolites. At the baseline and follow-up visits (1.6-3.5 years apart), anthropometry was collected to identify associations between baseline metabolite clusters and change in BC (∆) using seemingly unrelated and linear regression.Between visits, MM increased in boys and adiposity increased in girls. Sex differences were observed between metabolite clusters and changes in BC. In boys, aromatic amino acids (AAA), branched chain amino acids (BCAA) and fatty acid oxidation metabolites were associated with increases in ∆BMI, and ∆BF%. Phospholipids were associated with decreases in ∆TS and ∆MM. Negative associations were observed for ∆MM in boys with a cluster including AAA and BCAA, whereas positive associations were found for a cluster containing tryptophan metabolites. Few associations were observed between metabolites and BC change in girls, with one cluster comprising methionine, proline and lipids associated with decreases in ∆BMI, ∆WC and ∆MM.Sex-specific associations between the metabolome and change in BC were observed, highlighting metabolic pathways underlying adolescent physical growth.

Emerging research has shed light on the potential impact of environmental toxicants on sleep health, however, it remains unclear if these associations exist during adolescence and whether associations differ by sex. This study aimed to examine associations between phthalates, parabens, and phenols on adolescent sleep health using cross-sectional data from 470 participants from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) study.In 2015, spot urine samples were analyzed for exposure biomarkers of 13 phthalate metabolites, ten phenol, and four paraben analytes. Over seven consecutive days, sleep duration, midpoint, and fragmentation were assessed with wrist-actigraphy. We examined associations between summary phthalates, individual phthalate metabolites, and phenol and paraben analytes with mean weekday sleep duration, midpoint, and fragmentation using linear regression models adjusted for specific-gravity and sex, age, pubertal status, smoking and alcohol behavior, physical activity, and screen time.Mean (SD) age was 13.8 (2.1) years; 53.5 % were female. Σ Plastic - summary measure for toxicants from plastic sources - and Σ DEHP and its metabolites, were associated with longer sleep duration in the unstratified sample. To illustrate, every 1-unit log increase in Σ DEHP was associated with 7.7 min (95 % CI: 0.32, 15.1; p 0.05) longer duration. Summary measures of toxicants from plastic sources, personal care products, anti-androgenic toxicants, and multiple individual phthalates, phenols, and parabens were associated with later midpoint. The midpoint associations were largely female-specific. There were no associations with sleep fragmentation.Higher EDC exposure may be related to longer sleep duration and later sleep timing during adolescence, and associations may vary by toxicant and according to sex.

It is of importance to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible outliers in real-world applications such as imaging data analyses. We propose a new robust high-dimensional regression with coefficient thresholding, in which an efficient nonconvex estimation procedure is proposed through a thresholding function and the robust Huber loss. The proposed regularization method accounts for complex dependence structures in predictors and is robust against outliers in outcomes. Theoretically, we analyze rigorously the landscape of the population and empirical risk functions for the proposed method. The fine landscape enables us to establish both {statistical consistency and computational convergence} under the high-dimensional setting. The finite-sample properties of the proposed method are examined by extensive simulation studies. An illustration of real-world application concerns a scalar-on-image regression analysis for an association of psychiatric disorder measured by the general factor of psychopathology with features extracted from the task functional magnetic resonance imaging data in the Adolescent Brain Cognitive Development study., 38 pages

AIMS: The purpose of this study was to examine whether pandemic exposure impacted unmet social and diabetes needs, self-care behaviors, and diabetes outcomes in a sample with diabetes and poor glycemic control. METHODS: This was a cross-sectional analysis of participants with diabetes and poor glycemic control in an ongoing trial (n = 353). We compared the prevalence of unmet needs, self-care behaviors, and diabetes outcomes in successive cohorts of enrollees surveyed pre-pandemic (prior to March 11, 2020, n = 182), in the early stages of the pandemic (May-September, 2020, n = 75), and later (September 2020-January 2021, n = 96) stratified by income and gender. Adjusted multivariable regression models were used to examine trends. RESULTS: More participants with low income reported food insecurity (70% vs. 83%, p < 0.05) and needs related to access to blood glucose supplies (19% vs. 67%, p < 0.05) during the pandemic compared to pre-pandemic levels. In adjusted models among people with low incomes, the odds of housing insecurity increased among participants during the early pandemic months compared with participants pre-pandemic (OR 20.2 [95% CI 2.8-145.2], p < 0.01). A1c levels were better among participants later in the pandemic than those pre-pandemic (s = -1.1 [95% CI -1.8 to -0.4], p < 0.01), but systolic blood pressure control was substantially worse (s = 11.5 [95% CI 4.2-18.8, p < 0.001). CONCLUSION: Adults with low-incomes and diabetes were most impacted by the pandemic. A1c may not fully capture challenges that people with diabetes are facing to manage their condition; systolic blood pressures may have worsened and problems with self-care may forebode longer-term challenges in diabetes control.

As a safe exposure level for fluoride in pregnancy has not been established, we used data from two prospective studies for benchmark dose modeling. We included mother-child pairs from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) cohort in Mexico and the Maternal-Infant Research on Environmental Chemicals (MIREC) cohort in Canada. Children were assessed for IQ at age 4 (n=211) and between 6 and 12 years (n=287) in the ELEMENT cohort and between ages 3 and 4 years (n=512) in the MIREC cohort. We calculated covariate-adjusted regression coefficients and their standard errors to explore the concentration-effect function for maternal urinary fluoride with children’s IQ, including possible sex-dependence. Assuming a benchmark response of 1 IQ point, we derived benchmark concentrations (BMCs) of maternal urinary fluoride and benchmark concentration levels (BMCLs). No deviation from linearity was detected from the results of the two studies. Using a linear slope, the BMC for maternal urinary fluoride associated with a 1-point decrease in IQ scores of preschool-aged boys and girls was 0.29 mg/L (BMCL, 0.18 mg/L). The BMC was 0.30 mg/L (BMCL, 0.19 mg/L) when pooling the IQ scores from the older ELEMENT children and the MIREC cohort. Boys showed slightly lower BMC values compared with girls. Relying on two prospective studies, maternal urine-fluoride exposure at levels commonly occurring in the general population, the joint data showed BMCL results about 0.2 mg/L. These results can be used to guide decisions on preventing excess fluoride exposure in vulnerable populations.

Purpose: Spiritual and/or existential (S/E) distress is the experience of strain or conflict within a person’s religious, spiritual, or existential framework and is associated with worse psychological and health outcomes among individuals with acute illness. Little is known about S/E distress among individuals with a chronic condition, such as diabetes mellitus (DM). We explored the prevalence of S/E distress among adults with DM and examined patient factors associated with S/E distress. Methods: Data came from the 2019-2020 baseline assessment of participants in an ongoing trial of an intervention addressing social determinants of health among people with DM (n=286). Participants had HbA1cs ≥ 7.5 (18-70 years) or ≥ 8.0 for (71-75 years). We measured S/E distress in two ways: 1) with an abbreviated version of the Religious and Spiritual Struggle Scale (RSSS-14); and 2) with three screening questions adapted from the Rush Religious Struggle Screening Protocol (3Q). Bivariate analyses examined the association between patient factors, including health behaviors, DM distress, social support, and S/E distress. Results: Mean age of the sample was 53 years (SD:13.3). Prevalence of S/E distress was 20% (3Q) and 22% (RSSS-14). Both screeners found that compared to those without S/E distress, those with S/E distress reported a poorer general diet, lower social support, higher levels of DM distress, and higher psychological distress (all p Conclusions: One-fifth of adults with poor glycemic control have potential S/E distress, and such distress is associated with worse mental health and functional status. Future interventions should seek to address S/E distress as part of efforts to improve DM patient-centered and clinical outcomes. Disclosure G. Rajaee: Consultant; Self; UnitedHealth Group. M. Heisler: None. J. Piette: None. K. Resnicow: None. X. Shi: None. A. N. Smith: None. P. X. Song: None. M. R. Patel: None. Funding National Institutes of Health (R01DK116715)

Advancing our ability to predict who is likely to develop depression in response to stress holds great potential in reducing the burden of the disorder. Large-scale genome-wide association studies (GWAS) of depression have, for the first time, provided a basis for meaningful depression polygenic risk score construction (MDD-PRS). The Intern Health Study utilizes the predictable and large increase in depression with physician training stress to identify predictors of depression. Applying the MDD-PRS derived from the PGC2/23andMe GWAS to 5,227 training physicians, we found that MDD-PRS predicted depression under training stress (beta=0.082, p=2.1×10−12) and that MDD-PRS was significantly more strongly associated with depression under stress than at baseline (MDD-PRS × stress interaction - beta=0.029, p=0.02). While known risk factors accounted for 85.6% of the association between MDD-PRS and depression at baseline, they only accounted for 55.4% of the association between MDD-PRS and depression under stress, suggesting that MDD-PRS can add unique predictive power to existing models of depression under stress. Further, we found that low MDD-PRS may have particular utility in identifying individuals with high resilience. Together, these findings suggest that polygenic risk score holds promise in furthering our ability to predict vulnerability and resilience under stress.

Context Early pregnancy exposure to endocrine disrupting chemicals (EDCs) may contribute to poor birth outcomes through oxidative stress (OS)-mediated disruption of the maternal and fetal milieu. Most studies have investigated the effect of single EDC exposures on OS. Objective Assess the association of uniquely weighted mixtures of early pregnancy exposures with the maternal and neonatal OS markers. Design Prospective analysis of mother–infant dyads Setting University hospital. Participants 56 mother–infant dyads. Main Outcome Measures The association of OS markers (nitrotyrosine, dityrosine, chlorotyrosine) in maternal first trimester and term, and cord blood plasma with maternal first trimester exposure levels of each of 41 toxicants (trace elements, metals, phenols, and phthalates) from 56 subjects was analyzed using Spearman correlations and linear regression. The association of OS markers with inflammatory cytokines and birth outcomes were analyzed by Spearman correlation and linear regression analysis, respectively. Weighted mixtures of early pregnancy exposures were created by principal component analysis and offspring sex-dependent and independent associations with oxidative stress markers were assessed. Results (1) An inverse relationship between levels of maternal/cord OS markers and individual EDCs was evident. In contrast, when assessed as EDC mixtures, both direct and inverse associations were evident in a sex-specific manner; (2) the maternal term OS marker, nitrotyrosine, was inversely associated with gestational age, and (3) both direct and inverse associations were evident between the 3 OS markers and individual cytokines. Conclusions Provides proof of concept that effects of exposures on OS varies when assessed as EDC mixtures versus individually.

This paper focuses on analysis of spatiotemporal binary data with absorbing states. The research was motivated by a clinical study on amyotrophic lateral sclerosis (ALS), a neurological disease marked by gradual loss of muscle strength over time in multiple body regions. We propose an autologistic regression model to capture complex spatial and temporal dependencies in muscle strength among different muscles. As it is not clear how the disease spreads from one muscle to another, it may not be reasonable to define a neighborhood structure based on spatial proximity. Relaxing the requirement for prespecification of spatial neighborhoods as in existing models, our method identifies an underlying network structure empirically to describe the pattern of spreading disease. The model also allows the network autoregressive effects to vary depending on the muscles’ previous status. Based on the joint distribution derived from this autologistic model, the joint transition probabilities of responses among locations can be estimated and the disease status can be predicted in the next time interval. Model parameters are estimated through maximization of penalized pseudo-likelihood. Postmodel selection inference was conducted via a bias-correction method, for which the asymptotic distributions were derived. Simulation studies were conducted to evaluate the performance of the proposed method. The method was applied to the analysis of muscle strength loss from the ALS clinical study.

Endocrine disrupting chemicals (EDCs) are ubiquitous, and pregnancy is a sensitive window for toxicant exposure. EDCs may disrupt the maternal immune system, which may lead to poor pregnancy outcomes. Most studies investigate single EDCs, even though “real life” exposures do not occur in isolation. We tested the hypothesis that uniquely weighted mixtures of early pregnancy exposures are associated with distinct changes in the maternal and neonatal inflammasome. First trimester urine samples were tested for 12 phthalates, 12 phenols, and 17 metals in 56 women. Twelve cytokines were measured in first trimester and term maternal plasma, and in cord blood after delivery. Spearman correlations and linear regression were used to relate individual exposures with inflammatory cytokines. Linear regression was used to relate cytokine levels with gestational age and birth weight. Principal component analysis was used to assess the effect of weighted EDC mixtures on maternal and neonatal inflammation. Our results demonstrated that maternal and cord blood cytokines were differentially associated with (1) individual EDCs and (2) EDC mixtures. Several individual cytokines were positively associated with gestational age and birth weight. These observed associations between EDC mixtures and the pregnancy inflammasome may have clinical and public health implications for women of childbearing age.

Kidney paired donation is a partial solution to overcoming biological incompatibility preventing kidney transplants. A kidney paired donation (KPD) program consists of altruistic or non-directed donors (NDDs) and pairs, each of which comprises a candidate in need of a kidney transplant and her/his willing but incompatible donor. Potential transplants from NDDs or donors in pairs to compatible candidates in other pairs are determined by computer assessment, though various situations involving either the donor, candidate, or proposed transplant may lead to a potential transplant failing to proceed. A KPD program can be viewed as a directed graph with NDDs and pairs as vertices and potential transplants as edges, where failure probabilities are associated with each vertex and edge. Transplants are carried out in the form of directed cycles among pairs and directed paths initiated by NDDs, which we refer to respectively as cycles and chains. Previous research shows that selecting disjoint subgraphs with a view to creating fallback options when failures occur generates more realized transplants than optimal selection of disjoint chains and cycles. In this paper, we define such subgraphs, which are called locally relevant (LR) subgraphs, and present an efficient algorithm to enumerate all LR subgraphs. Its computational efficiency is significantly better than the previous, more restrictive, algorithms.

The last two decades have witnessed an explosion in research focused on the development and assessment of novel biomarkers for improved prognosis of diseases. As a result, best practice standards guiding biomarker research have undergone extensive development. Currently, there is great interest in the promise of biomarkers to enhance research efforts and clinical practice in the setting of chronic kidney disease, acute kidney injury, and glomerular disease. However, some have questioned whether biomarkers currently add value to the clinical practice of nephrology. The current state of the art pertaining to statistical analyses regarding the use of such measures is critical. In December 2014, the National Institute of Diabetes and Digestive and Kidney Diseases convened a meeting, "Toward Building Better Biomarker Statistical Methodology," with the goals of summarizing the current best practice recommendations and articulating new directions for methodological research. This report summarizes its conclusions and describes areas that need attention. Suggestions are made regarding metrics that should be commonly reported. We outline the methodological issues related to traditional metrics and considerations in prognostic modeling, including discrimination and case mix, calibration, validation, and cost-benefit analysis. We highlight the approach to improved risk communication and the value of graphical displays. Finally, we address some "new frontiers" in prognostic biomarker research, including the competing risk framework, the use of longitudinal biomarkers, and analyses in distributed research networks.

Objective To assess whether adiposity measures differed according to joint categories of sleep duration and sleep variability in a sample of Mexican adolescents. Study design A sample of 528 Mexico City adolescents aged 9-17 years wore wrist actigraphs for 6-7 days. Average sleep duration was categorized as age-specific sufficient or insufficient. Sleep variability, the standard deviation of sleep duration, was split at the median into stable versus variable. Adiposity measures—body mass index (BMI)-for-age Z score (BMIz), triceps skinfolds, waist circumference, and percent body fat—were collected by trained assistants. We regressed adiposity measures on combined sleep duration and variability categories. Log binomial models were used to estimate prevalence ratios and 95% CI for obesity (>2 BMIz) by joint categories of sleep duration and variability, adjusting for sex, age, and maternal education. Results Approximately 40% of the adolescents had insufficient sleep and 13% were obese. Relative to sufficient-stable sleepers, adolescents with insufficient-stable sleep had higher adiposity across all 4 measures (eg, adjusted difference in BMIz was 0.68; 95% CI, 0.35-1.00) and higher obesity prevalence (prevalence ratio, 2.54; 95% CI, 1.36-4.75). Insufficient-variable sleepers had slightly higher BMIz than sufficient-stable sleepers (adjusted difference, 0.30; 95% CI, 0.00-0.59). Conclusions Adolescents with consistently insufficient sleep could be at greater risk for obesity. The finding that insufficient-variable sleepers had only slightly higher adiposity suggests that opportunities for “catch-up” sleep may be protective.