Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis.

Mediation analysis aims to assess if, and how, a certain exposure influences an outcome of interest through intermediate variables. This problem has recently gained a surge of attention due to the tremendous need for such analyses in scientific fields. Testing for the mediation effect (ME) is greatly challenged by the fact that the underlying null hypothesis (i.e. the absence of MEs) is composite. Most existing mediation tests are overly conservative and thus underpowered. To overcome this significant methodological hurdle, we develop an adaptive bootstrap testing framework that can accommodate different types of composite null hypotheses in the mediation pathway analysis. Applied to the product of coefficients test and the joint significance test, our adaptive testing procedures provide type I error control under the composite null, resulting in much improved statistical power compared to existing tests. Both theoretical properties and numerical examples of the proposed methodology are discussed.

Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies.

Data sharing barriers present paramount challenges arising from multicenter clinical studies where multiple data sources are stored and managed in a distributed fashion at different local study sites. Merging such data sources into a common data storage for a centralized statistical analysis requires a data use agreement, which is often time-consuming. Data merging may become more burdensome when propensity score modeling is involved in the analysis because combining many confounding variables, and systematic incorporation of this additional modeling in a meta-analysis has not been thoroughly investigated in the literature. Motivated from a multicenter clinical trial of basal insulin treatment for reducing the risk of post-transplantation diabetes mellitus, we propose a new inference framework that avoids the merging of subject-level raw data from multiple sites at a centralized facility but needs only the sharing of summary statistics. Unlike the architecture of federated learning, the proposed collaborative inference does not need a center site to combine local results and thus enjoys maximal protection of data privacy and minimal sensitivity to unbalanced data distributions across data sources. We show theoretically and numerically that the new distributed inference approach has little loss of statistical power compared to the centralized method that requires merging the entire data. We present large-sample properties and algorithms for the proposed method. We illustrate its performance by simulation experiments and the motivating example on the differential average treatment effect of basal insulin to lower risk of diabetes among kidney-transplant patients compared to the standard-of-care.

A Randomized Controlled Trial to Improve Unmet Social Needs and Clinical Outcomes Among Adults with Diabetes.

BACKGROUND: Adults with type 1 or type 2 diabetes often face financial challenges and other unmet social needs to effective diabetes self-management. OBJECTIVE: Whether a digital intervention focused on addressing socioeconomic determinants of health improves diabetes clinical outcomes more than usual care. DESIGN: Randomized trial from 2019 to 2023. PARTICIPANTS: A total of 600 adults with diabetes, HbA1c ≥ 7.5%, and self-reported unmet social needs or financial burden from a health system and randomized to the intervention or standard care. INTERVENTION: CareAvenue is an automated, e-health intervention with eight videos that address unmet social needs contributing to poor outcomes. MEASURES: Primary outcome was HbA1c, measured at baseline, and 6 and 12 months after randomization. Secondary outcomes included systolic blood pressure and reported met social needs, cost-related non-adherence (CRN), and financial burden. We examined main effects and variation in effects across predefined subgroups. RESULTS: Seventy-eight percent of CareAvenue participants completed one or more modules of the website. At 12-month follow-up, there were no significant differences in HbA1c changes between CareAvenue and control group (p = 0.24). There were also no significant between-group differences in systolic blood pressure (p = 0.29), met social needs (p = 0.25), CRN (p = 0.18), and perceived financial burden (p = 0.31). In subgroup analyses, participants with household incomes 100-400% FPL (1.93 (SE = 0.76), p < 0.01), 201-400% FPL (1.30 (SE = 0.62), p < 0.04), and > 400% FPL (1.27 (SE = 0.64), p < 0.05) had significantly less A1c decreases compared to the control group. CONCLUSIONS: On average, CareAvenue participants did not achieve better A1c lowering, met needs, CRN, or perceived financial burden compared to control participants. CareAvenue participants with higher incomes achieved significantly less A1c reductions than control. Further research is needed on social needs interventions that consider tailored approaches to population subgroups. CLINICAL TRIALS REGISTRY: ClinicalTrials.gov ID NCT03950973, May 2019.

Iron Deficiency and Vitamin D Deficiency Are Associated with Sleep in Females of Reproductive Age: An Analysis of NHANES 2005-2018 Data.

BACKGROUND: Iron and vitamin D deficiencies have been implicated in sleep disturbance. Although females are more susceptible to these deficiencies and frequently report sleep-related issues, few studies have examined these associations in females. OBJECTIVE: This study investigates the association of iron and vitamin D deficiencies on sleep in a nationally representative sample of females of reproductive age. METHODS: We used 2 samples of 20-49-y-old non-pregnant females from National Health and Nutrition Examination Survey (NHANES) 2005-2008 (N = 2497) and NHANES 2005-2010 and 2015-2018 (N = 6731) to examine the associations of iron deficiency (ID), iron deficiency anemia (IDA), vitamin D deficiency (VDD), vitamin D inadequacy (VDI), and the joint association of both deficiencies with sleep duration, latency, and quality. Sleep outcomes were measured using a self-reported questionnaire. We used the body iron model based on serum ferritin and serum soluble transferrin receptor to identify ID, along with hemoglobin to identify IDA cases. In addition, 25-hydroxyvitamin D levels were used to determine VDD and VDI cases. Logistic regression was used to evaluate these associations, adjusting for potential confounders. In addition, we assessed the multiplicative and additive interactions of both deficiencies. RESULTS: ID and IDA were associated with poor sleep quality, with 1.42 [95% confidence interval (CI): 1.02, 2.00)] and 2.08 (95% CI: 1.29, 3.38) higher odds, respectively, whereas VDD and VDI were significantly associated with short sleep duration, with 1.26 (95% CI: 1.02, 1.54) and 1.22 (95% CI: 1.04, 1.44) higher odds, respectively. Subjects with both nutritional deficiencies had significantly higher odds of poorer sleep quality compared with subjects with neither condition. For sleep quality, a significant multiplicative interaction was observed between ID and VDD (P value = 0.0005). No associations were observed between study exposures and sleep latency. CONCLUSIONS: Among females of reproductive age, iron and vitamin D deficiencies are associated with sleep health outcomes. The potential synergistic effect of both deficiencies warrants further assessment.

Supervised Learning of Physical Activity Features From Functional Accelerometer Data.

OBJECTIVE: We propose a new health informatics framework to analyze physical activity (PA) from accelerometer devices. Accelerometry data enables scientists to extract personal digital features useful for precision health decision making. Existing methods in accelerometry data analysis typically begin with discretizing summary counts by certain fixed cutoffs into activity categories. One well-known limitation is that the chosen cutoffs are often validated under restricted settings, and cannot be generalizable across populations, devices, or studies. METHODS: We develop a data-driven approach to overcome this bottleneck in PA data analysis, in which we holistically summarize a subject's activity profile using Occupation-Time curves (OTCs), which describe the percentage of time spent at or above a continuum of activity count levels. We develop multi-step adaptive learning algorithms to perform supervised learning via a scalar-on-function model that involves OTC as the functional predictor of interest as well as other scalar covariates. Our learning analytic first incorporates a hybrid approach of fused lasso for clustering and Hidden Markov Model for changepoint detection, then executes refinement procedures to determine activity windows of interest. RESULTS: We evaluate and illustrate the performance of the proposed learning analytic through simulation experiments and real-world data analyses to assess the influence of PA on biological aging. Our findings indicate a different directional relationship between biological age and PA depending on the specific outcome of interest. SIGNIFICANCE: Our bioinformatics methodology involves the biomedical outcome of interest to detect different critical points, and is thus adaptive to the specific data, study population, and health outcome under investigation.

Real-Time Regression Analysis of Streaming Clustered Data With Possible Abnormal Data Batches.

This paper develops an incremental learning algorithm based on quadratic inference function (QIF) to analyze streaming datasets with correlated outcomes such as longitudinal data and clustered data. We propose a renewable QIF (RenewQIF) method within a paradigm of renewable estimation and incremental inference, in which parameter estimates are recursively renewed with current data and summary statistics of historical data, but with no use of any historical subject-level raw data. We compare our renewable estimation method with both offline QIF and offline generalized estimating equations (GEE) approach that process the entire cumulative subject-level data all together, and show theoretically and numerically that our renewable procedure enjoys statistical and computational efficiency. We also propose an approach to diagnose the homogeneity assumption of regression coefficients via a sequential goodness-of-fit test as a screening procedure on occurrences of abnormal data batches. We implement the proposed methodology by expanding existing Spark's Lambda architecture for the operation of statistical inference and data quality diagnosis. We illustrate the proposed methodology by extensive simulation studies and an analysis of streaming car crash datasets from the National Automotive Sampling System-Crashworthiness Data System (NASS CDS). The supplementary material is available online.

Fathers Matter: Black Fathers' Relationships with their Partners during Pregnancy and Postpartum.

PURPOSE: To describe the characteristics of participants in the Fathers Matter study for a better understanding of fathers of the baby who engage in pregnancy research involving primarily Black couples and their relationships with their partners. STUDY DESIGN AND METHODS: The Fathers Matter Study uses a prospective design, identifying father-mother dyads during pregnancy and following them until birth as part of the Biosocial Impacts on Black Births Study. Participants completed prenatal and postpartum questionnaires. RESULTS: Our analyses are based on 111 fathers. Nearly all ( n = 101, 91.1%) of fathers identified as Black and 51.4% ( n = 57) had a high school diploma, graduate equivalency diploma, or higher. About half ( n = 57, 51.4%) reported annual incomes of $10,000 or less. Most reported that relationships with the mother were very close both before ( n = 100, 89.9%) and during ( n = 85, 76.6%) pregnancy. However, substantial variability was found in relationship satisfaction, involvement in the pregnancy, financial support provided, and scales of conflict and support. CLINICAL IMPLICATIONS: We found homogeneity in sociodemographic and basic relationship measures. Complex measures of the father-mother relationships demonstrated considerable variability. Data from fathers may identify their contributions to successful birth outcomes. Understanding relationships between fathers and mothers could identify risk or protective characteristics to be addressed at the family or community levels.

Cross-sectional associations between phthalates, phenols, and parabens with metabolic syndrome risk during early-to-mid adolescence among a cohort of Mexican youth.

BACKGROUND: Epidemiological studies on children and adults have linked toxicants from plastics and personal care products to metabolic disruption. Yet, the impact of endocrine-disrupting chemicals (EDCs) on adolescent metabolic syndrome (MetS) risk during early and mid-adolescence is unclear. METHODS: To examine the links between exposure to EDCs and MetS risk and its components, cross-sectional data from 344 Mexican youth in early-to-mid adolescence (10-17 years) were analyzed. Urinary biomarker concentrations of phthalates, phenol, and paraben analytes were measured from a single spot urine sample collected in 2015; study personnel obtained anthropometric and metabolic measures. We examined associations between summary phthalates and metabolites, phenol, and paraben analytes with MetS risk z-scores using linear regression, adjusted for specific gravity, sex, age, pubertal status, smoking, alcohol intake, physical activity level, and screen time. As a secondary aim, mediation analysis was conducted to evaluate the role of hormones in the association between summary phthalates with lipids and MetS risk z-scores. RESULTS: The mean (SD) age was 13.2 (1.9) years, and 50.9% were female. Sex-stratified analyses revealed associations between summary phthalates and lipids ratio z-scores, including Σ DEHP [ß = 0.21 (95% CI: 0.04, 0.37; p < 0.01)], phthalates from plastic sources (Σ Plastic) [ß = 0.22 (95% CI: 0.05, 0.39; p < 0.01)], anti-androgenic phthalates (Σ AA) [ß = 0.22 (95% CI: 0.05, 0.39; p < 0.01)], and individual phthalate metabolites (MEHHP, MEOHP, and MECPP) among males. Among females, BPA [ß = 0.24 (95% CI: 0.03, 0.44; p < 0.05)] was positively associated with lipids ratio z-score and one phenol (2,5 DCP) [ß = 0.09 (95% CI: 0.01, 0.18); p < 0.05)] was associated with increased waist circumference z-score. Results showed no evidence of mediation by hormone concentrations in the association between summary phthalates with lipids ratio or MetS risk z-scores. CONCLUSION: Higher EDC exposure was positively associated with serum lipids during adolescence, particularly among males.

Longitudinal self-learning of individualized treatment rules in a nutrient supplementation trial with missing data.

Longitudinal outcomes are prevalent in clinical studies, where the presence of missing data may make the statistical learning of individualized treatment rules (ITRs) a much more challenging task. We analyzed a longitudinal calcium supplementation trial in the ELEMENT Project and established a novel ITR to reduce the risk of adverse outcomes of lead exposure on child growth and development. Lead exposure, particularly in the form of in utero exposure, can seriously impair children's health, especially their cognitive and neurobehavioral development, which necessitates clinical interventions such as calcium supplementation intake during pregnancy. Using the longitudinal outcomes from a randomized clinical trial of calcium supplementation, we developed a new ITR for daily calcium intake during pregnancy to mitigate persistent lead exposure in children at age 3 years. To overcome the technical challenges posed by missing data, we illustrate a new learning approach, termed longitudinal self-learning (LS-learning), that utilizes longitudinal measurements of child's blood lead concentration in the derivation of ITR. Our LS-learning method relies on a temporally weighted self-learning paradigm to synergize serially correlated training data sources. The resulting ITR is the first of this kind in precision nutrition that will contribute to the reduction of expected blood lead concentration in children aged 0-3 years should this ITR be implemented to the entire study population of pregnant women.

Associations between exposure to phthalates, phenols, and parabens with objective and subjective measures of sleep health among Mexican women in midlife: a cross-sectional and retrospective analysis.

Endocrine-disrupting chemicals (EDCs) may impact sleep during the menopausal transition by altering sex hormones. However, these studies are scarce among Latin American women. This investigation utilized cross-sectional and retrospective data from midlife women enrolled in the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) study to examine associations between exposure to EDCs (phthalates, phenols, and parabens) and sleep health measures. For cross-sectional analyses, single spot urine samples were collected between 2017-2019 from a pilot sample of women (N = 91) of midlife age to estimate the urinary concentration of individual phthalates, phenols, and parabens and to calculate the summary concentration of phthalate mixtures. Seven-day nightly sleep duration, midpoint, and fragmentation were obtained from wrist-actigraphy devices and estimated from the actigraphy data using a pruned dynamic programming algorithm. Self-reported poor sleep quality was assessed by one item from the Pittsburgh Sleep Quality Index (PSQI). We examined associations between urinary summary phthalate mixtures, phthalate metabolites, phenol, and paraben analytes with each sleep measure using linear or logistic (to compute odds of poor sleep quality only) regression models adjusted for specific gravity, age, and socioeconomic status. We ran similar regression models for retrospective analyses (N = 74), except that urine exposure biomarker data were collected in 2008 when women were 24-50 years old. At the 2017-2019 midlife visit, 38% reported poor sleep quality. Cross-sectionally, EDCs were associated with longer sleep duration, earlier sleep timing, and more fragmented sleep. For example, every 1-unit IQR increase in the phenol triclosan was associated with a 26.3 min per night (95% CI: 10.5, 42.2; P < 0.05) longer sleep duration and marginally associated with 0.2 decimal hours (95% CI: -0.4, 0.0; P < 0.10) earlier sleep midpoint; while every 1-unit IQR increase in the phthalate metabolite MEHP was associated with 1.1% higher sleep fragmentation (95% CI: 0.1, 2.1; P < 0.05). Retrospective study results generally mirrored cross-sectional results such that EDCs were linked to longer sleep duration, earlier sleep timing, and more fragmented sleep. EDCs were not significantly associated with odds of self-reported poor sleep quality. Results from cross-sectional and retrospective analyses revealed that higher exposure to EDCs was predictive of longer sleep duration, earlier sleep timing, and more fragmented sleep among midlife women.

Urinary phthalates, phenols, and parabens in relation to sleep health markers among a cohort of Mexican adolescents.

INTRODUCTION: 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. MATERIAL AND METHODS: In 2015, spot urine samples were analyzed for exposure biomarkers of 14 phthalate metabolites, seven 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. RESULTS: 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. CONCLUSIONS: 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.

Sleep Classification With Artificial Synthetic Imaging Data Using Convolutional Neural Networks.

OBJECTIVE: We propose a new analytic framework, "Artificial Synthetic Imaging Data (ASID) Workflow," for sleep classification from a wearable device comprising: 1) the creation of ASID from data collected by a non-invasive wearable device that permits real-time multi-modal physiological monitoring on heart rate (HR), 3-axis accelerometer, electrodermal activity, and skin temperature, denoted as "Temporal E4 Data" (TED) and 2) the use of an image classification supervised learning algorithm, convolutional neural network (CNN), to classify periods of sleep. METHODS: We investigate ASID Workflow under 6 settings (3 data resolutions × 2 HR scenarios). Competing machine/deep learning classification algorithms, including logistic regression, support vector machine, random forest, k-nearest neighbors, and Long Short-Term Memory, are applied to TED as comparisons, termed "Competing Workflow." RESULTS: The ASID Workflow achieves excellent performance with mean weighted accuracy across settings of 94.7%, and is superior to the Competing Workflow with high and low resolution data regardless of the inclusion of HR modality. This superiority is maximized for low resolution data without HR. Additionally, CNN has a relatively low subject-wise test computational cost compared with competing algorithms. CONCLUSION: We demonstrate the utility of creating ASID from multi-modal physiological data and applying a preexisting image classification algorithm to achieve better classification accuracy. We shed light on the influence of data resolution and HR modality on the Workflow's performance. SIGNIFICANCE: Applying CNN to ASID allows us to capture both temporal and spatial dependency among physiological variables and modalities by using 2D images' topological structure that competing algorithms fail to utilize.

Trimester two gestational exposure to bisphenol A and adherence to mediterranean diet are associated with adolescent offspring oxidative stress and metabolic syndrome risk in a sex-specific manner.

Background: Exposure to prenatal bisphenol A (BPA) and Mediterranean Diet Score (MDS) has been linked to metabolic risk in child offspring. It remains unclear if independent and interactive effects persist in adolescence. Methods: We examined prenatal BPA and MDS on adolescent offspring metabolic syndrome risk score (MRS) and 8-isoprostane (8-iso), a biomarker of oxidative stress. Data from maternal-adolescent dyads from a Mexico City cohort were utilized, including trimester-specific prenatal BPA from spot urine and MDS from food frequency questionnaires. Offspring socio-demographic data and biomarkers to estimate MRS and 8-iso were obtained during peri-adolescence. Results: Adjusted linear regression models examined associations between trimester-specific BPA, MDS, and BPA*MDS on outcomes. Sex-stratified analyses revealed a significant association between MDS with increased 8-iso (ß = 0.064, p < 0.05), and a marginal association between trimester two BPA with increased 8-iso (ß = 0.237), while MDS modified the marginal association between BPA and 8-iso in females (ß = 0.046). A negative, marginal association was observed between trimester two BPA and MRS (ß = - 0.728), while BPA * MDS was marginally, positively associated with MRS (ß = 0.152) in males. Conclusions: Study findings indicate that trimester two prenatal BPA and maternal adherence to a Mediterranean diet may have sexually dimorphic effects on adolescent offspring oxidative stress and metabolic syndrome risk.

Measurement and Validation of the Comprehensive Score for Financial Toxicity (COST) in a Population With Diabetes.

OBJECTIVE: The Comprehensive Score for Financial Toxicity-Functional Assessment of Chronic Illness Therapy (COST-FACIT) is a validated instrument measuring financial distress among people with cancer. The reliability and construct validity of the 11-item COST-FACIT were examined in adults with diabetes and high A1C. RESEARCH DESIGN AND METHODS: We examined the factor structure (exploratory factor analysis), internal consistency reliability (Cronbach α), floor/ceiling effects, known-groups validity, and predictive validity among a sample of 600 adults with diabetes and high A1C. RESULTS: COST-FACIT demonstrated a two-factor structure with high internal consistency: general financial situation (7-items, α = 0.86) and impact of illness on financial situation (4-items, α = 0.73). The measure demonstrated a ceiling effect for 2% of participants and floor effects for 7%. Worse financial toxicity scores were observed among adults who were women, were below the poverty line, had government-sponsored health insurance, were middle-aged, were not in the workforce, and had less educational attainment (P < 0.01). Worse financial toxicity was observed for those engaging in cost coping behaviors, such as taking less or skipping medicines, delaying care, borrowing money, "maxing out" the limit on credit cards, and not paying bills (P < 0.01). In regression models for the full measure and its two factors, worse financial toxicity was correlated with higher A1C (P < 0.01), higher levels of diabetes distress (P < 0.01), more chronic conditions (P < 0.01), and more depressive symptoms (P < 0.01). CONCLUSIONS: Findings support both the reliability and validity of the COST-FACIT tool among adults with diabetes and high A1C levels. More research is needed to support the use of the COST-FACIT tool as a clinically relevant patient-centered instrument for diabetes care.

A Pattern Dictionary Method for Anomaly Detection.

In this paper, we propose a compression-based anomaly detection method for time series and sequence data using a pattern dictionary. The proposed method is capable of learning complex patterns in a training data sequence, using these learned patterns to detect potentially anomalous patterns in a test data sequence. The proposed pattern dictionary method uses a measure of complexity of the test sequence as an anomaly score that can be used to perform stand-alone anomaly detection. We also show that when combined with a universal source coder, the proposed pattern dictionary yields a powerful atypicality detector that is equally applicable to anomaly detection. The pattern dictionary-based atypicality detector uses an anomaly score defined as the difference between the complexity of the test sequence data encoded by the trained pattern dictionary (typical) encoder and the universal (atypical) encoder, respectively. We consider two complexity measures: the number of parsed phrases in the sequence, and the length of the encoded sequence (codelength). Specializing to a particular type of universal encoder, the Tree-Structured Lempel-Ziv (LZ78), we obtain a novel non-asymptotic upper bound, in terms of the Lambert W function, on the number of distinct phrases resulting from the LZ78 parser. This non-asymptotic bound determines the range of anomaly score. As a concrete application, we illustrate the pattern dictionary framework for constructing a baseline of health against which anomalous deviations can be detected.

Trimester-specific phthalate exposures in pregnancy are associated with circulating metabolites in children.

BACKGROUND: Prenatal phthalates exposures have been related to adiposity in peripuberty in a sex-specific fashion. Untargeted metabolomics analysis to assess circulating metabolites offers the potential to characterize biochemical pathways by which early life exposures influence the development of cardiometabolic risk during childhood and adolescence, prior to becoming evident in clinical markers. METHODS: Among mother-child dyads from the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) birth cohort, we measured 9 phthalate metabolites and bisphenol A in maternal spot urine samples obtained during each trimester of pregnancy, corrected for urinary specific gravity and natural log-transformed. In 110 boys and 124 girls aged 8-14 years, we used a mass-spectrometry based untargeted metabolomics platform to measure fasting serum metabolites, yielding 572 annotated metabolites. We estimated the associations between trimester-specific urinary toxicants and each serum metabolite, among all children or stratified by sex and adjusting for child age, BMI z-score, and pubertal onset. We accounted for multiple comparisons using a 10% false discovery rate (q<0.1). RESULTS: Associations between exposures and metabolites were observed among all children and in sex-stratified analyses (q<0.1). First trimester MEP, MiBP, and MCPP were associated with decreased 2-deoxy-D-glucose among all children. Among girls, third trimester concentrations of MECPP, MEHHP, MEHP, and MCPP were associated with 15, 13, 1, and 10 metabolites, respectively, including decreased choline and increased acylcarnitines and saturated FAs (FA). Among boys, third trimester MIBP was positively associated with 9 features including long chain saturated FAs, and second trimester MBzP was inversely associated with thyroxine. CONCLUSIONS: Metabolomics biomarkers may reflect sex- and exposure timing-specific responses to prenatal phthalate exposures manifesting in childhood that may not be detected using standard clinical markers of cardiometabolic risk.

Kidney Paired Donation Chains Initiated by Deceased Donors.

Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.

Domain-specific effects of prenatal fluoride exposure on child IQ at 4, 5, and 6-12 years in the ELEMENT cohort.

OBJECTIVE: Prenatal exposure to fluoride has been associated with adverse neurodevelopmental outcomes. However, the neuropsychological profile of fluoride's developmental neurotoxicity at low levels and the stability of this relationship across childhood has not been characterized. We investigated the longitudinal and domain specific effect of prenatal fluoride exposure on IQ among children ages 4, 5, and 6-12 years in the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) cohort. METHODS: We measured the average of maternal urinary fluoride at each trimester of pregnancy adjusted for creatinine (MUFCRE). Children were administered the McCarthy Scales of Children's Abilities at ages 4 (N = 386) and 5 (N = 308), and the Wechsler Abbreviated Scale of Intelligence at age 6-12 (N = 278). We used generalized estimating equation (GEE) models to estimate the population averaged effect of MUFCRE concentration on longitudinal General Cognitive Index (GCI)/Full-Scale IQ (FSIQ), Verbal IQ (VIQ), and Performance IQ (PIQ) scores (N = 348). We tested for possible interactions between MUFCRE and child sex as well as for MUFCRE and time point on children's IQ. All models controlled for relevant available covariates. RESULTS: The mean/median MUFCRE concentration was 0.90/0.83 mg/L (SD = 0.39; IQR, 0.64-1.11 mg/L). A 0.5 mg/L increase in MUFCRE predicted an average 2.12-point decrease in GCI/FSIQ (95% CI: -3.49, -0.75) and 2.63-point decrease in PIQ (95% CI: -3.87, -1.40). MUFCRE was marginally associated with VIQ across time (B = -1.29, 95% CI: -2.60, 0.01). No interactions between MUFCRE and child sex or MUFCRE and time were observed. CONCLUSION: The negative association between prenatal fluoride exposure and longitudinal IQ was driven by decrements in non-verbal intelligence (i.e. PIQ), suggesting that visual-spatial and perceptual reasoning abilities may be more impacted by prenatal fluoride exposure as compared to verbal abilities.

Third-Trimester Maternal Dietary Patterns Are Associated with Sleep Health among Adolescent Offspring in a Mexico City Cohort.

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.

gom1 Mutant Mice as a Model of Otitis Media.

Otitis media (OM) disease is a common cause of hearing loss that is primarily the result of middle ear infection. At present, our understanding of the mechanisms leading to OM is limited due to the lack of animal models of OM with effusion (OME). Here, we report that the mice with genetic otitis media one (gom1) mutants are prone to OM. gom1 Mice were produced by the N-ethyl-N-nitrosourea (ENU) mutagenesis program as an animal model to study OM. These mice demonstrate many common features of OM, such as middle ear effusion and hearing impairment. We revealed that gom1 mice display various signs of middle ear and inner ear dysfunctions, including elevated thresholds of auditory-evoked brainstem response (ABR) and lack of cochlear microphonic responses. Decreased compliance in tympanometry measurements indicates tympanic membrane and ossicular chain malfunction. We confirmed through histological examinations of middle ear structures that 34/34 (100 %) of the mutant mice suffered from severe OME. While individual ears had different levels of effusion and inflammatory cells in the middle ear cavity, all had thickened middle ear mucosa and submucosa compared to control mice (B6). Moreover, the mutant mice displayed cochlear hair cell loss. These observations also suggested the craniofacial abnormalities in the gom1 mouse model. Together, these results indicate that gom1 mice could be valuable for investigating the genetic contribution to the development of middle ear disease.