Atypical Antipsychotics for Irritability in Pediatric Autism: A Systematic Review and Network Meta-Analysis.

OBJECTIVE: Irritability is common in pediatric autism spectrum disorder (ASD) patients. This can have major implications in child development, receptivity to behavioral therapy, as well as child and caregiver well-being. A systematic review and network meta-analysis were conducted to assess the efficacy and safety of atypical antipsychotics in treating irritability in these patients. METHODS: Studies were identified from Medline, Embase, and PsycINFO from inception to March 2018. The clinical trials database was reviewed. Studies were included if they were a double-blind, randomized controlled trial utilizing the Aberrant Behavior Checklist Irritability (ABC-I) to measure the efficacy of atypical antipsychotic monotherapy. Data extraction was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-analyses for network meta-analysis guidelines. The main outcome was the reduction in irritability score using the ABC-I subscale from baseline. RESULTS: Eight trials comparing four interventions-risperidone, aripiprazole, lurasidone, and placebo in 878 patients, were included. Both risperidone and aripiprazole had significantly reduced ABC-I scores than placebo. Estimates of mean differences (95% credible intervals) were risperidone, -6.89 (-11.14, -2.54); aripiprazole, -6.62 (-10.88, -2.22); and lurasidone, -1.61 (-9.50, 6.23). Both risperidone and aripiprazole had similar safety. There were only eight studies included in the analysis, however, sample sizes were not small. Variance in reporting of adverse effects limited the quality of safety analysis. CONCLUSION: Risperidone and aripiprazole were the two best drugs, with comparable efficacy and safety in pediatric ASD patients. These two medications could be beneficial in improving irritability in these patients.

Statistical models and computational algorithms for discovering relationships in microbiome data.

Microbiomes, populations of microscopic organisms, have been found to be related to human health and it is expected further investigations will lead to novel perspectives of disease. The data used to analyze microbiomes is one of the newest types (the result of high-throughput technology) and the means to analyze these data is still rapidly evolving. One of the distributions that have been introduced into the microbiome literature, the Dirichlet-Multinomial, has received considerable attention. We extend this distribution's use uncover compositional relationships between organisms at a taxonomic level. We apply our new method in two real microbiome data sets: one from human nasal passages and another from human stool samples.

Harmonization of Food-Frequency Questionnaires and Dietary Pattern Analysis in 4 Ethnically Diverse Birth Cohorts.

BACKGROUND: Canada is an ethnically diverse nation, which introduces challenges for health care providers tasked with providing evidence-based dietary advice. OBJECTIVES: We aimed to harmonize food-frequency questionnaires (FFQs) across 4 birth cohorts of ethnically diverse pregnant women to derive robust dietary patterns to investigate maternal and newborn outcomes. METHODS: The NutriGen Alliance comprises 4 prospective birth cohorts and includes 4880 Canadian mother-infant pairs of predominantly white European [CHILD (Canadian Healthy Infant Longitudinal Development) and FAMILY (Family Atherosclerosis Monitoring In earLY life)], South Asian [START (SouTh Asian birth cohoRT)-Canada], or Aboriginal [ABC (Aboriginal Birth Cohort)] origins. CHILD used a multiethnic FFQ based on a previously validated instrument designed by the Fred Hutchinson Cancer Research Center, whereas FAMILY, START, and ABC used questionnaires specifically designed for use in white European, South Asian, and Aboriginal people, respectively. The serving sizes and consumption frequencies of individual food items within the 4 FFQs were harmonized and aggregated into 36 common food groups. Principal components analysis was used to identify dietary patterns that were internally validated against self-reported vegetarian status and externally validated against a modified Alternative Healthy Eating Index (mAHEI). RESULTS: Three maternal dietary patterns were identified-"plant-based," "Western," and "health-conscious"-which collectively explained 29% of the total variability in eating habits observed in the NutriGen Alliance. These patterns were strongly associated with self-reported vegetarian status (OR: 3.85; 95% CI: 3.47, 4.29; r2 = 0.30, P < 0.001; for a plant-based diet), and average adherence to the plant-based diet was higher in participants in the fourth quartile of the mAHEI than in the first quartile (mean difference: 46.1%; r2 = 0.81, P < 0.001). CONCLUSION: Dietary data collected by using FFQs from ethnically diverse pregnant women can be harmonized to identify common dietary patterns to investigate associations between maternal dietary intake and health outcomes.