Characteristics of Patients Served by a Statewide Child Psychiatry Access Program.

OBJECTIVE: Maryland's Behavioral Health Integration in Pediatric Primary Care (BHIPP) is a child psychiatry access program offering child-adolescent psychiatry consultation, resource and referral networking, and direct-to-patient mental health intervention. This study investigated characteristics of patients for whom primary care providers sought BHIPP services. METHODS: Data from 6,939 unique patient contacts between October 2012 and March 2020 were collected on service type, demographic characteristics, presenting concerns, clinical severity, clinicians' diagnostic impressions, current treatments, and BHIPP recommendations. Descriptive statistics and latent class analysis were used. RESULTS: Of the 6,939 patient contacts, 38.6% were for direct-to-patient mental health intervention, 27.3% for child-adolescent psychiatry consultation, and 34.2% for resource and referral networking. In total, 50.3% of patients were female, 58.7% were White, and 32.7% were already receiving mental health services. Latent class analysis identified four classes of presenting concerns: anxiety only (44.2%); behavior problems only (30.7%); mood and anxiety (17.1%); and attention, behavior, and learning problems (8.0%). Compared with patients in the anxiety-only class, those in the attention, behavior, and learning problems class were more likely to receive direct-to-patient mental health intervention (OR=3.59), and BHIPP clinicians were more likely to recommend in-office behavioral interventions for those in the mood and anxiety class (OR=1.62) and behavior problems-only class (OR=1.55). CONCLUSIONS: Patients supported through BHIPP varied in presenting concerns, condition severity and complexity, current receipt of services, and BHIPP utilization. Latent class analysis yielded more clinically useful information about the nature and complexity of patients' concerns than did consideration of individual presenting concerns.

A function-based approach to model the measurement error in wearable devices.

Physical activity (PA) is an important risk factor for many health outcomes. Wearable-devices such as accelerometers are increasingly used in biomedical studies to understand the associations between PA and health outcomes. Statistical analyses involving accelerometer data are challenging due to the following three characteristics: (i) high-dimensionality, (ii) temporal dependence, and (iii) measurement error. To address these challenges we treat accelerometer-based measures of PA as a single function-valued covariate prone to measurement error. Specifically, in order to determine the relationship between PA and a health outcome of interest, we propose a regression model with a functional covariate that accounts for measurement error. Using regression calibration, we develop a two-step estimation method for the model parameters and establish their consistency. A test is also proposed to test the significance of the estimated model parameters. Simulation studies are conducted to compare the proposed methods with existing alternative approaches under varying scenarios. Finally, the developed methods are used to assess the relationship between PA intensity and BMI obtained from the National Health and Nutrition Examination Survey data.

Rhythmic Fluctuations in Levels of Liver Enzymes During Menstrual Cycles of Healthy Women and Effects of Body Weight.

BACKGROUND & AIMS: Female sex hormones affect several non-reproductive organs, but little is known about their effects on the liver during a normal menstrual cycle. We aimed to investigate the association between sex hormones and liver enzymes in healthy menstruating women. METHODS: We performed a post-hoc analysis of data from the BioCycle study, a longitudinal cohort study designed to determine the association of sex hormones with markers of oxidative stress during the menstrual cycle. We analyzed data collected from 259 menstruating women, over 1-2 menstrual cycles, who had as many as 16 separate office visits, timed by fertility monitors. Levels of liver enzymes, including alanine aminotransferase (ALT), aspartate aminotransferase, and alkaline phosphatase (ALKP), bilirubin, and lipids were measured by laboratory assays. RESULTS: We found a natural cyclic pattern for liver enzymes, with transaminases and ALKP peaking in the mid-follicular phase and reaching a trough in the late luteal phase; the peak to trough differences were 4.0 ± 4.9 U/L for ALT and 8.8 ± 4.0 U/L for ALKP. Levels of ALT were significantly and negatively associated with levels of progesterone on the preceding visit (P = 5x10-4), whereas level of ALKP was negatively associated with level of estrogen (P = .007) and progesterone (P = 1x10-11). Food and alcohol intake did not modify the association. The amplitude of ALT fluctuation was greater in African Americans and decreased with age. Fluctuations in levels of ALT were smaller in women with body mass indices >30 kg/m2 (P = .03). During menstrual fluctuation, 49% of participants had ALT values both above and below the normal cut-off value (19 U/L). CONCLUSIONS: Levels of liver enzymes fluctuate during the normal menstrual cycle, possibly mediated by progesterone, and the fluctuation varies with age and body mass index. These findings indicate the importance of accounting for phase of menstrual cycle when interpreting liver enzyme measurements in menstruating women.

A functional U-statistic method for association analysis of sequencing data.

Although sequencing studies hold great promise for uncovering novel variants predisposing to human diseases, the high dimensionality of the sequencing data brings tremendous challenges to data analysis. Moreover, for many complex diseases (e.g., psychiatric disorders) multiple related phenotypes are collected. These phenotypes can be different measurements of an underlying disease, or measurements characterizing multiple related diseases for studying common genetic mechanism. Although jointly analyzing these phenotypes could potentially increase the power of identifying disease-associated genes, the different types of phenotypes pose challenges for association analysis. To address these challenges, we propose a nonparametric method, functional U-statistic method (FU), for multivariate analysis of sequencing data. It first constructs smooth functions from individuals' sequencing data, and then tests the association of these functions with multiple phenotypes by using a U-statistic. The method provides a general framework for analyzing various types of phenotypes (e.g., binary and continuous phenotypes) with unknown distributions. Fitting the genetic variants within a gene using a smoothing function also allows us to capture complexities of gene structure (e.g., linkage disequilibrium, LD), which could potentially increase the power of association analysis. Through simulations, we compared our method to the multivariate outcome score test (MOST), and found that our test attained better performance than MOST. In a real data application, we apply our method to the sequencing data from Minnesota Twin Study (MTS) and found potential associations of several nicotine receptor subunit (CHRN) genes, including CHRNB3, associated with nicotine dependence and/or alcohol dependence.

The impact of genetic structure on sequencing analysis.

BACKGROUND: Genome-wide association studies have made substantial progress in identifying common variants associated with human diseases. Despite such success, a large portion of heritability remains unexplained. Evolutionary theory and empirical studies suggest that rare mutations could play an important role in human diseases, which motivates comprehensive investigation of rare variants in sequencing studies. To explore the association of rare variants with human diseases, many statistical approaches have been developed with different ways of modeling genetic structure (ie, linkage disequilibrium). Nevertheless, the appropriate strategy to model genetic structure of sequencing data and its effect on association analysis have not been well studied. METHODS: We investigate 3 statistical approaches that use 3 different strategies to model the genetic structure of sequencing data. We proceed by comparing a burden test that assumes independence among sequencing variants, a burden test that considers pairwise linkage disequilibrium (LD), and a functional analysis of variance (FANOVA) test that models genetic data through fitting continuous curves on individuals' genotypes. RESULTS: Through simulations, we find that FANOVA attains better or comparable performance to the 2 burden tests. Overall, the burden test that considers pairwise LD has comparable performance to the burden test that assumes independence between sequencing variants. However, for 1 gene, where the disease-associated variant is located in an LD block, we find that considering pairwise LD could improve the test's performance. CONCLUSIONS: The structure of sequencing variants is complex in nature and its patterns vary across the whole genome. In certain cases (eg, a disease-susceptibility variant is in an LD block), ignoring the genetic structure in the association analysis could result in suboptimal performance. Through this study, we show that a functional-based method is promising for modeling the underlying genetic structure of sequencing data, which could lead to better performance.

Development and Validation of Stability-Indicating Method for Estimation of Chlorthalidone in Bulk and Tablets with the Use of Experimental Design in Forced Degradation Experiments.

Chlorthalidone was subjected to various forced degradation conditions. Substantial degradation of chlorthalidone was obtained in acid, alkali, and oxidative conditions. Further full factorial experimental design was applied for acid and alkali forced degradation conditions, in which strength of acid/alkali, temperature, and time of heating were considered as independent variables (factors) and % degradation was considered as dependent variable (response). Factors responsible for acid and alkali degradation were statistically evaluated using Yates analysis and Pareto chart. Furthermore, using surface response curve, optimized 10% degradation was obtained. All chromatographic separation was carried out on Phenomenex HyperClone C 18 column (250 × 4.6 mm, 5 µ), using mobile phase comprising methanol : acetonitrile : phosphate buffer (20 mM) (pH 3.0 adjusted with o-phosphoric acid): 30 : 10 : 60% v/v. The flow rate was kept constant at 1 mL/min and eluent was detected at 241 nm. In calibration curve experiments, linearity was found to be in the range of 2-12 µg/mL. Validation experiments proved good accuracy and precision of the method. Also there was no interference of excipients and degradation products at the retention time of chlorthalidone, indicating specificity of the method.

Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models.

Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study.

Low Dose Loxapine: Neuromotor Side Effects and Tolerability in Autism Spectrum Disorders.

OBJECTIVE: New and repurposed drugs are urgently needed to treat individuals with autism spectrum disorders (ASD). Loxapine (LOX) in low doses of 5-15 mg/day resembles an atypical antipsychotic (Stahl 2002 ). Our recent open pilot study of LOX found significant behavioral improvements and overall weight neutrality in 16 adolescents and adults with ASD. The present study examined an outpatient sample for LOX neuromotor tolerability. METHODS: Consecutive outpatients with Diagnostic and Statistical Manual of Mental Disorders, 4th ed, Text Revision (DSM-IV-TR) ASD diagnoses receiving LOX were examined for tardive dyskinesia (TD) and extrapyramidal side effects (EPS) using the Dyskinesia Identification System: Condensed User Scale (DISCUS), and for akathisia using the Barnes Akathisia Rating Scale. Data were also then retrospectively extracted from clinic charts regarding age, gender, diagnoses, LOX doses, treatment duration, concomitant medications, and LOX dosage reductions. RESULTS: Thirty-four subjects (25 male, 9 female) participated. Mean age was 23.4 years at LOX initiation (range 8-52). Thirteen subjects (38.2%) received loxapine for ≥5 years. Mean LOX dose was 8.9 mg/day (range 5-30 mg) and mean duration was 4.2 years (range 0.8-13). Fourteen subjects (41.2%) received concomitant atypical antipsychotics. Benztropine was prescribed in 5 of 34 subjects (14.7%). Three subjects manifested tics at baseline, but lower final DISCUS scores. Subject 26, with Prader-Willi syndrome, manifested TD. Apart from LOX 5 mg daily he received paroxetine 40 mg daily, which reduces LOX metabolism significantly. Akathisia objective scores were positive in 6 subjects (17.6%): Subject 2 scored 3 (pacing was present also at baseline); subjects 6, 7, and 11 each scored 1; and subjects 18 and 23 each scored 2. Six of 9 subjects (66.7%) with expressive language were positive for subjective akathisia. CONCLUSIONS: Low dose LOX was well tolerated, with lower than expected TD rates. This confirms clinical resemblance to an atypical antipsychotic. Individuals with neuromuscular problems including Prader-Willi Syndrome receiving LOX require close monitoring. Further study of LOX in ASD is warranted.