Metabolomic Effects of Folic Acid Supplementation in Adults: Evidence from the FACT Trial.

BACKGROUND: Folic acid (FA) is the oxidized form of folate found in supplements and FA-fortified foods. Most FA is reduced by dihydrofolate reductase to 5-methyltetrahydrofolate (5mTHF); the latter is the form of folate naturally found in foods. Ingestion of FA increases the plasma levels of both 5mTHF and unmetabolized FA (UMFA). Limited information is available on the downstream metabolic effects of FA supplementation, including potential effects associated with UMFA. OBJECTIVE: We aimed to assess the metabolic effects of FA-supplementation, and the associations of plasma 5mTHF and UMFA with the metabolome in FA-naïve Bangladeshi adults. METHODS: Sixty participants were selected from the Folic Acid and Creatine Trial; half received 800 µg FA/day for 12 weeks and half placebo. Plasma metabolome profiles were measured by high-resolution mass spectrometry, including 170 identified metabolites and 26,541 metabolic features. Penalized regression methods were used to assess the associations of targeted metabolites with FA-supplementation, plasma 5mTHF, and plasma UMFA. Pathway analyses were conducted using Mummichog. RESULTS: In penalized models of identified metabolites, FA-supplementation was associated with higher choline. Changes in 5mTHF concentrations were positively associated with metabolites involved in amino acid metabolism (5-hydroxyindoleacetic acid, acetylmethionine, creatinine, guanidinoacetate, hydroxyproline/n-acetylalanine) and 2 fatty acids (docosahexaenoic acid and linoleic acid). Changes in 5mTHF concentrations were negatively associated with acetylglutamate, acetyllysine, carnitine, propionyl carnitine, cinnamic acid, homogentisate, arachidonic acid, and nicotine. UMFA concentrations were associated with lower levels of arachidonic acid. Together, metabolites selected across all models were related to lipids, aromatic amino acid metabolism, and the urea cycle. Analyses of nontargeted metabolic features identified additional pathways associated with FA supplementation. CONCLUSION: In addition to the recapitulation of several expected metabolic changes associated with 5mTHF, we observed additional metabolites/pathways associated with FA-supplementation and UMFA. Further studies are needed to confirm these associations and assess their potential implications for human health. TRIAL REGISTRATION NUMBER: This trial was registered at https://clinicaltrials.gov as NCT01050556.

Exploring Relevant Time Windows in the Association Between PM2.5 Exposure and Amyotrophic Lateral Sclerosis: A Case-Control Study in Denmark.

Studies suggest a link between particulate matter less than or equal to 2.5 µm in diameter (PM2.5) and amyotrophic lateral sclerosis (ALS), but to our knowledge critical exposure windows have not been examined. We performed a case-control study in the Danish population spanning the years 1989-2013. Cases were selected from the Danish National Patient Registry based on International Classification of Diseases codes. Five controls were randomly selected from the Danish Civil Registry and matched to a case on vital status, age, and sex. PM2.5 concentration at residential addresses was assigned using monthly predictions from a dispersion model. We used conditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for confounding. We evaluated exposure to averaged PM2.5 concentrations 12-24 months, 2-6 years, and 2-11 years pre-ALS diagnosis; annual lagged exposures up to 11 years prediagnosis; and cumulative associations for exposure in lags 1-5 years and 1-10 years prediagnosis, allowing for varying association estimates by year. We identified 3,983 cases and 19,915 controls. Cumulative exposure to PM2.5 in the period 2-6 years prediagnosis was associated with ALS (OR = 1.06, 95% CI: 0.99, 1.13). Exposures in the second, third, and fourth years prediagnosis were individually associated with higher odds of ALS (e.g., for lag 1, OR = 1.04, 95% CI: 1.00, 1.08). Exposure to PM2.5 within 6 years before diagnosis may represent a critical exposure window for ALS.

Unintended impacts of the Open Streets program on noise complaints in New York City.

BACKGROUND: During the COVID-19 pandemic, several cities allocated more public spaces for physical activity and recreation instead of road transport through Open Streets. This policy locally reduces traffic and provides experimental testbeds for healthier cities. However, it may also generate unintended impacts. For instance, Open Streets may impact the levels of exposure to environmental noise but there are no studies assessing these unintended impacts. OBJECTIVES: Using noise complaints from New York City (NYC) as a proxy of annoyance caused by environmental noise, we estimated associations at the census tract level between same-day proportion of Open Streets in a census tract and noise complaints in NYC. METHODS: Using data from summer 2019 (pre-implementation) and summer 2021 (post-implementation), we fit regressions to estimate the association between census tract-level proportion of Open Streets and daily noise complaints, with random effects to account for within-tract correlation and natural splines to allow non-linearity in the estimated association. We accounted for temporal trends and other potential confounders, such as population density and poverty rate. RESULTS: In adjusted analyses, daily street/sidewalk noise complaints were nonlinearly associated with an increasing proportion of Open Streets. Specifically, compared to the mean proportion of Open Streets in a census tract (0.11%), 5% of Open Streets had a 1.09 (95% CI: 0.98, 1.20) and 10% had a 1.21 (95% CI: 1.04, 1.42) times higher rate of street/sidewalk noise complaints. Our results were robust to the choice of data source for identifying Open Streets. CONCLUSION: Our findings suggest that Open Streets in NYC may be linked to an increase in street/sidewalk noise complaints. These results highlight the necessity to reinforce urban policies with a careful analysis for potential unintended impacts to optimize and maximize the benefits of these policies.

Fine particulate matter composition in American Indian vs. Non-American Indian communities.

BACKGROUND: Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US. METHODS: We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time. RESULTS: Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties. CONCLUSIONS: This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands.

Long-term Traffic-related Air Pollutant Exposure and Amyotrophic Lateral Sclerosis Diagnosis in Denmark: A Bayesian Hierarchical Analysis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Limited evidence suggests ALS diagnosis may be associated with air pollution exposure and specifically traffic-related pollutants. METHODS: In this population-based case-control study, we used 3,937 ALS cases from the Danish National Patient Register diagnosed during 1989-2013 and matched on age, sex, year of birth, and vital status to 19,333 population-based controls free of ALS at index date. We used validated predictions of elemental carbon (EC), nitrogen oxides (NO x ), carbon monoxide (CO), and fine particles (PM 2.5 ) to assign 1-, 5-, and 10-year average exposures pre-ALS diagnosis at study participants' present and historical residential addresses. We used an adjusted Bayesian hierarchical conditional logistic model to estimate individual pollutant associations and joint and average associations for traffic-related pollutants (EC, NO x , CO). RESULTS: For a standard deviation (SD) increase in 5-year average concentrations, EC (SD = 0.42 µg/m 3 ) had a high probability of individual association with increased odds of ALS (11.5%; 95% credible interval [CrI] = -1.0%, 25.6%; 96.3% posterior probability of positive association), with negative associations for NO x (SD = 20 µg/m 3 ) (-4.6%; 95% CrI = 18.1%, 8.9%; 27.8% posterior probability of positive association), CO (SD = 106 µg/m 3 ) (-3.2%; 95% CrI = 14.4%, 10.0%; 26.7% posterior probability of positive association), and a null association for nonelemental carbon fine particles (non-EC PM 2.5 ) (SD = 2.37 µg/m 3 ) (0.7%; 95% CrI = 9.2%, 12.4%). We found no association between ALS and joint or average traffic pollution concentrations. CONCLUSIONS: This study found high probability of a positive association between ALS diagnosis and EC concentration. Further work is needed to understand the role of traffic-related air pollution in ALS pathogenesis.

Air Pollution in American Indian Versus Non-American Indian Communities, 2000-2018.

Objectives. To compare fine particulate matter (PM2.5) concentrations in American Indian (AI)-populated with those in non-AI-populated counties over time (2000-2018) in the contiguous United States. Methods. We used a multicriteria approach to classify counties as AI- or non--AI-populated. We ran linear mixed effects models to estimate the difference in countywide annual PM2.5 concentrations from well-validated prediction models and monitoring sites (modeled and measured PM2.5, respectively) in AI- versus non-AI-populated counties. Results. On average, adjusted modeled PM2.5 concentrations in AI-populated counties were 0.38 micrograms per cubic meter (95% confidence interval [CI] = 0.23, 0.54) lower than in non-AI-populated counties. However, this difference was not constant over time: in 2000, modeled concentrations in AI-populated counties were 1.46 micrograms per cubic meter (95% CI = 1.25, 1.68) lower, and by 2018, they were 0.66 micrograms per cubic meter (95% CI = 0.45, 0.87) higher. Over the study period, adjusted modeled PM2.5 mean concentrations decreased by 2.13 micrograms per cubic meter in AI-populated counties versus 4.26 micrograms per cubic meter in non-AI-populated counties. Results were similar for measured PM2.5. Conclusions. This study highlights disparities in PM2.5 trends between AI- and non-AI-populated counties over time, underscoring the need to strengthen air pollution regulations and prevention implementation in tribal territories and areas where AI populations live. (Am J Public Health. 2022;112(4): 615-623. https://doi.org/10.2105/AJPH.2021.306650).

Prenatal exposure to polybrominated diphenyl ethers and BMI Z-scores from 5 to 14 years.

BACKGROUND: Polybrominated diphenyl ethers (PBDEs) are flame-retardant compounds widely used in household products until phase out in 2004. PBDEs are endocrine disruptors and are suggested to influence signaling related to weight control. Prenatal exposures to PBDEs may alter childhood adiposity, yet few studies have examined these associations in human populations. METHODS: Data were collected from a birth cohort of Dominican and African American mother-child pairs from New York City recruited from 1998 to 2006. PBDE congeners BDE-47, - 99, - 100, and - 153 were measured in cord plasma (ng/µL) and dichotomized into low (< 80th percentile) and high (>80th percentile) exposure categories. Height and weight were collected at ages 5, 7, 9, 11, and an ancillary visit from 8 to 14 years (n = 289). Mixed-effects models with random intercepts for participant were used to assess associations between concentrations of individual PBDE congeners or the PBDE sum and child BMI z-scores (BMIz). To assess associations between PBDEs and the change in BMIz over time, models including interactions between PBDE categories and child age and (child age)2 were fit. Quantile g-computation was used to investigate associations between BMIz and the total PBDE mixture. Models were adjusted for baseline maternal covariates: ethnicity, age, education, parity, partnership status, and receipt of public assistance, and child covariates: child sex and cord cholesterol and triglycerides. RESULTS: The prevalence of children with obesity at age 5 was 24.2% and increased to 30% at age 11. Neither cord levels of individual PBDEs nor the total PBDE mixture were associated with overall BMIz in childhood. The changes in BMIz across childhood were not different between children with low or high PBDEs. Results were similar when adjusting for postnatal PBDE exposures. CONCLUSIONS: Prenatal PBDE exposures were not associated with child growth trajectories in a cohort of Dominican and African American children.

Impact of Intraoperative Data on Risk Prediction for Mortality After Intra-Abdominal Surgery.

BACKGROUND: Risk prediction models for postoperative mortality after intra-abdominal surgery have typically been developed using preoperative variables. It is unclear if intraoperative data add significant value to these risk prediction models. METHODS: With IRB approval, an institutional retrospective cohort of intra-abdominal surgery patients in the 2005 to 2015 American College of Surgeons National Surgical Quality Improvement Program was identified. Intraoperative data were obtained from the electronic health record. The primary outcome was 30-day mortality. We evaluated the performance of machine learning algorithms to predict 30-day mortality using: 1) baseline variables and 2) baseline + intraoperative variables. Algorithms evaluated were: 1) logistic regression with elastic net selection, 2) random forest (RF), 3) gradient boosting machine (GBM), 4) support vector machine (SVM), and 5) convolutional neural networks (CNNs). Model performance was evaluated using the area under the receiver operator characteristic curve (AUROC). The sample was randomly divided into a training/testing split with 80%/20% probabilities. Repeated 10-fold cross-validation identified the optimal model hyperparameters in the training dataset for each model, which were then applied to the entire training dataset to train the model. Trained models were applied to the test cohort to evaluate model performance. Statistical significance was evaluated using P < .05. RESULTS: The training and testing cohorts contained 4322 and 1079 patients, respectively, with 62 (1.4%) and 15 (1.4%) experiencing 30-day mortality, respectively. When using only baseline variables to predict mortality, all algorithms except SVM (area under the receiver operator characteristic curve [AUROC], 0.83 [95% confidence interval {CI}, 0.69-0.97]) had AUROC >0.9: GBM (AUROC, 0.96 [0.94-1.0]), RF (AUROC, 0.96 [0.92-1.0]), CNN (AUROC, 0.96 [0.92-0.99]), and logistic regression (AUROC, 0.95 [0.91-0.99]). AUROC significantly increased with intraoperative variables with CNN (AUROC, 0.97 [0.96-0.99]; P = .047 versus baseline), but there was no improvement with GBM (AUROC, 0.97 [0.95-0.99]; P = .3 versus baseline), RF (AUROC, 0.96 [0.93-1.0]; P = .5 versus baseline), and logistic regression (AUROC, 0.94 [0.90-0.99]; P = .6 versus baseline). CONCLUSIONS: Postoperative mortality is predicted with excellent discrimination in intra-abdominal surgery patients using only preoperative variables in various machine learning algorithms. The addition of intraoperative data to preoperative data also resulted in models with excellent discrimination, but model performance did not improve.

Addressing patient's unmet social needs: disparities in access to social services in the United States from 1990 to 2014, a national times series study.

BACKGROUND: To address patient's unmet social needs and improve health outcomes, health systems have developed programs to refer patients in need to social service agencies. However, the capacity to respond to patient referrals varies tremendously across communities. This study assesses the emergence of disparities in spatial access to social services from 1990 to 2014. METHODS: Social service providers in the lower 48 continental U.S. states were identified annually from 1990 to 2014 from the National Establishment Times Series (NETS) database. The addresses of providers were linked in each year to 2010 US Census tract geometries. Time series analyses of annual counts of services per Km2 were conducted using Generalized Estimating Equations with tracts stratified into tertiles of 1990 population density, quartiles of 1990 poverty rate and quartiles of 1990 to 2010 change in median household income. RESULTS: Throughout the period, social service agencies/Km2 increased across tracts. For high population density tracts, in the top quartile of 1990 poverty rate, compared to tracts that experienced the steepest declines in median household income from 1990 to 2010, tracts that experienced the largest increases in income had more services (+ 1.53/Km2, 95% CI 1.23, 1.83) in 1990 and also experienced the steepest increases in services from 1990 to 2010: a 0.09 services/Km2/year greater increase (95% CI 0.07, 0.11). Similar results were observed for high poverty tracts in the middle third of population density, but not in tracts in the lowest third of population density, where there were very few providers. CONCLUSION: From 1990 to 2014 a spatial mismatch emerged between the availability of social services and the expected need for social services as the population characteristics of neighborhoods changed. High poverty tracts that experienced further economic decline from 1990 to 2010, began the period with the lowest access to services and experienced the smallest increases in access to services. Access was highest and grew the fastest in high poverty tracts that experienced the largest increases in median household income. We theorize that agglomeration benefits and the marketization of welfare may explain the emergence of this spatial mismatch.

Registration of 24-hour accelerometric rest-activity profiles and its application to human chronotypes.

By collecting data continuously over 24 hours, accelerometers and other wearable devices can provide novel insights into circadian rhythms and their relationship to human health. Existing approaches for analyzing diurnal patterns using these data, including the cosinor model and functional principal components analysis, have revealed and quantified population-level diurnal patterns, but considerable subject-level variability remained uncaptured in features such as wake/sleep times and activity intensity. This remaining informative variability could provide a better understanding of chronotypes, or behavioral manifestations of one's underlying 24-hour rhythm. Curve registration, or alignment, is a technique in functional data analysis that separates "vertical" variability in activity intensity from "horizontal" variability in time-dependent markers like wake and sleep times; this data-driven approach is well-suited to studying chronotypes using accelerometer data. We develop a parametric registration framework for 24-hour accelerometric rest-activity profiles represented as dichotomized into epoch-level states of activity or rest. Specifically, we estimate subject-specific piecewise linear time-warping functions parametrized with a small set of parameters. We apply this method to data from the Baltimore Longitudinal Study of Aging and illustrate how estimated parameters give a more flexible quantification of chronotypes compared to traditional approaches.

Objectively Measured Physical Activity and Sedentary Time Among Adults With and Without Stroke: A National Cohort Study.

Background and Purpose: We examined differences in the volume and pattern of physical activity (PA) and sedentary behavior between adults with and without stroke. Methods: We studied cohort members with an adjudicated or self-reported stroke (n=401) and age-, sex-, race-, region of residence-, and body mass index-matched participants without a history of stroke (n=1203) from the REGARDS study (Reasons for Geographic and Racial Differences in Stroke). Sedentary behavior (total volume and bouts), light-intensity PA, and moderate-to-vigorous-intensity PA were objectively measured for 7 days via hip-worn accelerometer. Results: Sedentary time (790.5±80.4 versus 752.4±81.9 min/d) and mean sedentary bout duration (15.7±12.6 versus 11.9±8.1 min/d) were higher and PA (light-intensity PA: 160.5±74.6 versus 192.9±73.5 min/d and moderate-to-vigorous-intensity PA: 9.0±11.9 versus 14.7±17.0 min/d) lower for stroke survivors compared with controls (P<0.001). Stroke survivors also accrued fewer activity breaks (65.5±21.9 versus 73.31±18.9 breaks/d) that were shorter (2.4±0.7 versus 2.7±0.8 minutes) and lower in intensity (188.4±60.8 versus 217.9±72.2 counts per minute) than controls (P<0.001). Conclusions: Stroke survivors accrued a lower volume of PA, higher volume of sedentary time, and exhibited accrual patterns of more prolonged sedentary bouts and shorter, lower intensity activity breaks compared with persons without stroke.

The association between ambient temperature variability and myocardial infarction in a New York-State-based case-crossover study: An examination of different variability metrics.

BACKGROUND: Short-term temperature variability has been consistently associated with mortality, with limited evidence for cardiovascular outcomes. Previous studies have used multiple metrics to measure temperature variability; however, those metrics do not capture hour-to-hour changes in temperature. OBJECTIVES: We assessed the correlation between sub-daily temperature-change-over-time metrics and previously-used metrics, and estimated associations with myocardial infarction (MI) hospitalizations. METHODS: Hour-to-hour change-over-time was measured via three metrics: 24-hr mean absolute hourly first difference, 24-hr maximum absolute hourly first difference, and 24-hr mean hourly first difference. We first assessed the Spearman correlations between these metrics and four previously-used metrics (24-hr standard deviation of hourly temperature, 24-hr diurnal temperature range, 48-hr standard deviation of daily minimal and maximal temperatures, and 48-hr difference of daily mean temperature), using hourly data from the North America Land Data Assimilation System-2 Model. Subsequently, we estimated the association between these metrics and primary MI hospitalization in adult residents of New York State for 2000-2015 using a time-stratified case-crossover design. RESULTS: The hour-to-hour change-over-time metrics were correlated, but not synonymous, with previously-used metrics. We observed 809,259 MI, 45% of which were among females and the mean (standard deviation) age was 70 (15). An increase from mean to 90th percentile in mean absolute first difference of temperature was associated with a 2.04% (95% Confidence Interval [CI]: 1.30-2.78%) increase in MI rate. An increase from mean to 90th percentile in mean first difference also yielded a positive association (1.86%; 95%CI: 1.09-2.64%). We observed smaller- or similar-in-magnitude positive associations for previously-used metrics. DISCUSSION: First, short-term hour-to-hour temperature change was positively associated with MI risk. Second, all other variability metrics yielded positive associations with MI, with varying magnitude. In future research on temperature variability, researchers should define their research question, including which aspects of variability they intend to measure, and apply the appropriate metric. ALTERNATIVE: All metrics of temperature variability, including short-term hour-to-hour temperature changes, were positively associated with MI risk, though the magnitude of effect estimates varied by metric.

Parkinson's disease aggravation in association with fine particle components in New York State.

BACKGROUND: Long-term exposure to fine particulate matter (PM2.5) has been associated with neurodegenerative diseases, including disease aggravation in Parkinson's disease (PD), but associations with specific PM2.5 components have not been evaluated. OBJECTIVE: To characterize the association between specific PM2.5 components and PD first hospitalization, a surrogate for disease aggravation. METHODS: We obtained data on hospitalizations from the New York Department of Health Statewide Planning and Research Cooperative System (2000-2014) to calculate annual first PD hospitalization counts in New York State per county. We used well-validated prediction models at 1 km2 resolution to estimate county level population-weighted annual black carbon (BC), organic matter (OM), nitrate, sulfate, sea salt (SS), and soil particle concentrations. We then used a multi-pollutant mixed quasi-Poisson model with county-specific random intercepts to estimate rate ratios (RR) of one-year exposure to each PM2.5 component and PD disease aggravation. We evaluated potential nonlinear exposure-outcome relationships using penalized splines and accounted for potential confounders. RESULTS: We observed a total of 197,545 PD first hospitalizations in NYS from 2000 to 2014. The annual average count per county was 212 first hospitalizations. The RR (95% confidence interval) for PD aggravation was 1.06 (1.03, 1.10) per one standard deviation (SD) increase in nitrate concentrations and 1.06 (1.04, 1.09) for the corresponding increase in OM concentrations. We also found a nonlinear inverse association between PD aggravation and BC at concentrations above the 96th percentile. We found a marginal association with SS and no association with sulfate or soil exposure. CONCLUSION: In this study, we detected associations between the PM2.5 components OM and nitrate with PD disease aggravation. Our findings support that PM2.5 adverse effects on PD may vary by particle composition.

Prenatal exposure to air pollution is associated with childhood inhibitory control and adolescent academic achievement.

BACKGROUND: Prenatal air pollution exposure is associated with reductions in self-regulation and academic achievement. Self-regulation has been separately linked with academic achievement. Understudied, however, are the contributions of pollution exposure to inhibitory control, a facet of self-regulation, and whether pollution-related inhibitory control deficits are associated with impairment in academic achievement. METHODS: Participants were recruited from a prospective birth cohort. Measures of prenatal airborne polycyclic aromatic hydrocarbons (PAH) during the third trimester of pregnancy, inhibitory control (NEPSY Inhibition) at mean age = 10.4 years, and Woodcock-Johnson Tests of Achievement-III at mean age = 13.7 were available for N = 200 participants. Multiple linear regression examined sex-dependent and sex independent associations among prenatal PAH, childhood inhibitory control, and academic achievement during adolescence, and whether childhood inhibitory control mediated associations between prenatal PAH and academic achievement during adolescence, controlling for ethnicity, maternal country of birth, language of prenatal interview, maternal marital status, maternal years of education, material hardship, quality of home caregiving environment, and early life stress. RESULTS: Across all participants, higher prenatal PAH was significantly associated with worse spelling skills (WJ-III Spelling, ß = -0.16, 95%Confidence Interval [CI]: 0.30, -0.02, p = .02). Trend level associations between higher prenatal PAH and worse reading comprehension (WJ-III Passage Comprehension, ß = -0.13, 95%CI: 0.28, 0.01, p = .07) and math skills (WJ-III Broad Math, ß = -0.11, 95%CI: 0.25, 0.03, p = .11) were detected. Across all participants, higher PAH was significantly associated with worse inhibitory control (ß = -0.15, 95%CI: 0.29,-0.01 p = .03). Better inhibitory control was significantly associated with better reading comprehension (WJ-III Passage Comprehension, ß = 0.22, 95%CI: 0.09, 0.36, p < .002) and math skills (WJ-III Broad Math Index, ß = 0.32, 95%CI: 0.19, 0.45, p < .001), and trend level associations with better spelling skills (WJ-III Spelling, ß = 0.12, 95%CI: 0.02, 0.26, p = .10). Inhibitory control significantly mediated PAH-related achievement effects for Passage Comprehension (ß = -0.61, 95%CI: 1.49, -0.01) and Broad Math Index (ß = -1.09, 95%CI: 2.36, -0.03). CONCLUSIONS: Higher prenatal PAH exposure and lower childhood inhibitory control were associated with worse spelling, passage comprehension, and math in adolescence. Notably, childhood inhibitory control mediated PAH exposure-related effects on achievement in adolescents. Identifying these potential exposure-related phenotypes of learning problems may promote interventions that target inhibitory control deficits rather than content specific deficits.

Rethinking interhemispheric imbalance as a target for stroke neurorehabilitation.

OBJECTIVE: Patients with chronic stroke have been shown to have failure to release interhemispheric inhibition (IHI) from the intact to the damaged hemisphere before movement execution (premovement IHI). This inhibitory imbalance was found to correlate with poor motor performance in the chronic stage after stroke and has since become a target for therapeutic interventions. The logic of this approach, however, implies that abnormal premovement IHI is causal to poor behavioral outcome and should therefore be present early after stroke when motor impairment is at its worst. To test this idea, in a longitudinal study, we investigated interhemispheric interactions by tracking patients' premovement IHI for one year following stroke. METHODS: We assessed premovement IHI and motor behavior five times over a 1-year period after ischemic stroke in 22 patients and 11 healthy participants. RESULTS: We found that premovement IHI was normal during the acute/subacute period and only became abnormal at the chronic stage; specifically, release of IHI in movement preparation worsened as motor behavior improved. In addition, premovement IHI did not correlate with behavioral measures cross-sectionally, whereas the longitudinal emergence of abnormal premovement IHI from the acute to the chronic stage was inversely correlated with recovery of finger individuation. INTERPRETATION: These results suggest that interhemispheric imbalance is not a cause of poor motor recovery, but instead might be the consequence of underlying recovery processes. These findings call into question the rehabilitation strategy of attempting to rebalance interhemispheric interactions in order to improve motor recovery after stroke. Ann Neurol 2019;85:502-513.

Prenatal exposure to polycyclic aromatic hydrocarbons modifies the effects of early life stress on attention and Thought Problems in late childhood.

BACKGROUND: Risk for childhood psychopathology is complex and multifactorial, implicating direct and interacting effects of familial and environmental factors. The role of environmental neurotoxicants in psychiatric risk is of growing concern, including polycyclic aromatic hydrocarbons (PAH), common in air pollution. Prenatal PAH exposure is linked to adverse physical, behavioral, and cognitive outcomes as well as increasing psychiatric risk. It is unclear whether environmental exposures, like PAH, magnify the effects of exposure to early life stress (ELS), a critical risk factor for psychopathology. The current work aimed to test potential interactions between prenatal PAH exposure and psychosocial/socioeconomic stress on psychiatric symptoms in school-age children. METHODS: Data were from the Columbia Center for Children's Environmental Health Mothers and Newborns longitudinal birth cohort study. Prenatal PAH exposure was ascertained though air monitoring during pregnancy and maternal PAH-DNA adducts at delivery. Mothers reported on ELS (child age 5) and on child psychiatric symptoms across childhood (child age 5, 7, 9, and 11) using the Child Behavior Checklist (CBCL). RESULTS: Significant prenatal airborne PAH × ELS interactions (FDR-corrected) predicted CBCL Attention (ß = 0.22, t(307) = 3.47, p < .001, pfdr  = .003) and Thought Problems T-scores (ß = 0.21, t(307) = 3.29, p = .001, pfdr  = .004) at age 11 (n = 319). Relative to those with lower exposure, children with higher prenatal PAH exposure exhibited stronger positive associations between ELS and CBCL Attention and Thought Problem T-scores. This interaction was also significant examining convergent ADHD measures (Conners, DuPaul) and examining maternal PAH-DNA adducts (ß = 0.29, t(261) = 2.48, p = .01; n = 273). A three-way interaction with assessment wave indicated that the PAH × ELS interaction on Attention Problems was stronger later in development (ß = 0.03, t(1,601) = 2.19, p = .03; n = 477). CONCLUSIONS: Prenatal exposure to PAH, a common neurotoxicant in air pollution, may magnify or sustain the effects of early life psychosocial/socioeconomic stress on psychiatric outcomes later in child development. This work highlights the critical role of air pollution exposure on child mental health.

Nonnegative decomposition of functional count data.

We present a novel decomposition of nonnegative functional count data that draws on concepts from nonnegative matrix factorization. Our decomposition, which we refer to as NARFD (nonnegative and regularized function decomposition), enables the study of patterns in variation across subjects in a highly interpretable manner. Prototypic modes of variation are estimated directly on the observed scale of the data, are local, and are transparently added together to reconstruct observed functions. This contrasts with generalized functional principal component analysis, an alternative approach that estimates functional principal components on a transformed scale, produces components that typically vary across the entire functional domain, and reconstructs observations using complex patterns of cancellation and multiplication of functional principal components. NARFD is implemented using an alternating minimization algorithm, and we evaluate our approach in simulations. We apply NARFD to an accelerometer dataset comprising observations of physical activity for healthy older Americans.

United network for organ sharing outcomes after heart transplantation for al compared to ATTR cardiac amyloidosis.

Light-chain (AL) cardiac amyloidosis (CA) has a worse prognosis than transthyretin (ATTR) CA. In this single-center study, we compared post-heart transplant (OHT, orthotopic heart transplantation) survival for AL and ATTR amyloidosis, hypothesizing that these differences would persist post-OHT. Thirty-nine patients with CA (AL, n = 18; ATTR, n = 21) and 1023 non-amyloidosis subjects undergoing OHT were included. Cox proportional hazards modeling was used to evaluate the impact of amyloid subtype and era (early era: from 2001 to 2007; late era: from 2008 to 2018) on survival post-OHT. Survival for non-amyloid patients was greater than ATTR (P = .034) and AL (P < .001) patients in the early era. One, 3-, and 5-year survival rates were higher for ATTR patients than AL patients in the early era (100% vs 75%, 67% vs 50%, and 67% vs 33%, respectively, for ATTR and AL patients). Survival in the non-amyloid cohort was 87% at 1 year, 81% at 3 years, and 76% at 5 years post-OHT. In the late era, AL and ATTR patients had unadjusted 1-year, 3-year, and 5-year survival rates of 100%, which was comparable to non-amyloid patients (90% vs 84% vs 81%). Overall, these findings demonstrate that in the current era, differences in post-OHT survival for AL compared to ATTR are diminishing; OHT outcomes for selected patients with CA do not differ from non-amyloidosis patients.

Joint associations of occupational standing and occupational exertion with musculoskeletal symptoms in a US national sample.

OBJECTIVE: Observational studies have linked occupational standing or walking to musculoskeletal pain. These prior studies, however, are flawed as few accounted for physical exertion; a potential confounder that accompanies many standing-based occupations. The purpose of this study was to examine the individual and joint associations of occupational standing/walking and exertion with musculoskeletal symptoms. METHODS: Data for this analysis come from the 2015 National Health Interview Survey, a US nationally representative survey. Occupational standing/walking and exertion were assessed by self-report on a 5-point Likert scale. The presence of musculoskeletal symptoms (pain, aching and stiffness) for upper extremities (neck, shoulders, elbows, wrists and fingers), lower extremities (hips, knees, ankles and toes) and lower back was also assessed. RESULTS: Occupational standing/walking was associated with a greater likelihood of upper extremity, lower extremity and lower back musculoskeletal symptoms; however, associations were attenuated and no longer significant with adjustment for exertion. When stratified by levels of occupational exertion, occupational standing/walking was associated with musculoskeletal symptoms only among the group with high exertion (eg, OR=1.69 (95% CI: 1.48 to 1.94) for lower back symptoms comparing high/high for standing or walking/exertion vs low/low). Among groups with low exertion, occupational standing/walking was not associated with musculoskeletal symptoms (eg, OR=1.00 (95% CI: 0.85 to 1.16) for lower back symptoms comparing high/low for standing or walking/exertion vs low/low). CONCLUSION: Results from this US representative survey suggest that the association between occupational standing/walking and musculoskeletal symptoms is largely driven by the co-occurrence of occupational exertion and does not provide evidence that standing or walking incurs adverse musculoskeletal symptoms.

Registration for exponential family functional data.

We introduce a novel method for separating amplitude and phase variability in exponential family functional data. Our method alternates between two steps: the first uses generalized functional principal components analysis to calculate template functions, and the second estimates smooth warping functions that map observed curves to templates. Existing approaches to registration have primarily focused on continuous functional observations, and the few approaches for discrete functional data require a pre-smoothing step; these methods are frequently computationally intensive. In contrast, we focus on the likelihood of the observed data and avoid the need for preprocessing, and we implement both steps of our algorithm in a computationally efficient way. Our motivation comes from the Baltimore Longitudinal Study on Aging, in which accelerometer data provides valuable insights into the timing of sedentary behavior. We analyze binary functional data with observations each minute over 24 hours for 592 participants, where values represent activity and inactivity. Diurnal patterns of activity are obscured due to misalignment in the original data but are clear after curves are aligned. Simulations designed to mimic the application indicate that the proposed methods outperform competing approaches in terms of estimation accuracy and computational efficiency. Code for our method and simulations is publicly available.