Posttraumatic stress disorder (PTSD) has been associated with advanced epigenetic age cross-sectionally, but the association between these variables over time is unclear. This study conducted meta-analyses to test whether new-onset PTSD diagnosis and changes in PTSD symptom severity over time were associated with changes in two metrics of epigenetic aging over two time points.

We conducted meta-analyses of the association between change in PTSD diagnosis and symptom severity and change in epigenetic age acceleration/deceleration (age-adjusted DNA methylation age residuals as per the Horvath and GrimAge metrics) using data from 7 military and civilian cohorts participating in the Psychiatric Genomics Consortium PTSD Epigenetics Workgroup (total N = 1,367).

Meta-analysis revealed that the interaction between Time 1 (T1) Horvath age residuals and new-onset PTSD over time was significantly associated with Horvath age residuals at T2 (meta β = 0.16, meta p = 0.02, p-adj = 0.03). The interaction between T1 Horvath age residuals and changes in PTSD symptom severity over time was significantly related to Horvath age residuals at T2 (meta β = 0.24, meta p = 0.05). No associations were observed for GrimAge residuals.

Results indicated that individuals who developed new-onset PTSD or showed increased PTSD symptom severity over time evidenced greater epigenetic age acceleration at follow-up than would be expected based on baseline age acceleration. This suggests that PTSD may accelerate biological aging over time and highlights the need for intervention studies to determine if PTSD treatment has a beneficial effect on the aging methylome.

First branchial arch abnormalities are rare. Surgical excision is the mainstay of treatment and described in the literature. Excision can be associated with significant complications. We describe factors influencing operative and non-operative management of patients.

Case review was conducted between January 2012 and September 2022 of patients with first branchial arch abnormalities at Alder Hey Children’s Hospital, UK. Analysis of electronic patient records, operation notes and extraction of clinical outcomes were obtained.

Four patients were identified with an average age of 2 years and 4 months. Three were female. Three underwent operative intervention, one of which had a recurrence post-operatively and was manged conservatively. The non-operatively managed patient remains conservatively managed with no complications.

First branchial arch abnormalities can be managed operatively and non-operatively, depending on patient factors. Steps to improve surgical planning such as constructive interference in steady state magnetic resonance imaging may help decision making and risk stratification of operative management.

3q29 deletion syndrome (3q29del) is a rare (~1:30 000) genomic disorder associated with a wide array of neurodevelopmental and psychiatric phenotypes. Prior work by our team identified clinically significant executive function (EF) deficits in 47% of individuals with 3q29del; however, the nuances of EF in this population have not been described.

We used the Behavior Rating Inventory of Executive Function (BRIEF) to perform the first in-depth assessment of real-world EF in a cohort of 32 individuals with 3q29del (62.5% male, mean age = 14.5 ± 8.3 years). All participants were also evaluated with gold-standard neuropsychiatric and cognitive assessments. High-resolution structural magnetic resonance imaging was performed on a subset of participants (n = 24).

We found global deficits in EF; individuals with 3q29del scored higher than the population mean on the BRIEF global executive composite (GEC) and all subscales. In total, 81.3% of study subjects (n = 26) scored in the clinical range on at least one BRIEF subscale. BRIEF GEC T scores were higher among 3q29del participants with a diagnosis of attention deficit/hyperactivity disorder (ADHD), and BRIEF GEC T scores were associated with schizophrenia spectrum symptoms as measured by the Structured Interview for Psychosis-Risk Syndromes. BRIEF GEC T scores were not associated with cognitive ability. The BRIEF-2 ADHD form accurately (sensitivity = 86.7%) classified individuals with 3q29del based on ADHD diagnosis status. BRIEF GEC T scores were correlated with cerebellar white matter and subregional cerebellar cortex volumes.

Together, these data expand our understanding of the phenotypic spectrum of 3q29del and identify EF as a core feature linked to both psychiatric and neuroanatomical features of the syndrome.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

The ability to remotely monitor cognitive skills is increasing with the ubiquity of smartphones. The Mobile Toolbox (MTB) is a new measurement system that includes measures assessing Executive Functioning (EF) and Processing Speed (PS): Arrow Matching, Shape-Color Sorting, and Number-Symbol Match. The purpose of this study was to assess their psychometric properties.

MTB measures were developed for smartphone administration based on constructs measured in the NIH Toolbox® (NIHTB). Psychometric properties of the resulting measures were evaluated in three studies with participants ages 18 to 90. In Study 1 (N = 92), participants completed MTB measures in the lab and were administered both equivalent NIH TB measures and other external measures of similar cognitive constructs. In Study 2 (N = 1,021), participants completed the equivalent NIHTB measures in the lab and then took the MTB measures on their own, remotely. In Study 3 (N = 168), participants completed MTB measures twice remotely, two weeks apart.

All three measures exhibited very high internal consistency and strong test-retest reliability, as well as moderately high correlations with comparable NIHTB tests and moderate correlations with external measures of similar constructs. Phone operating system (iOS vs. Android) had a significant impact on performance for Arrow Matching and Shape-Color Sorting, but no impact on either validity or reliability.

Results support the reliability and convergent validity of MTB EF and PS measures for use across the adult lifespan in remote, self-administered designs.

There is an urgent need to address pervasive inequities in health and healthcare in the USA. Many areas of health inequity are well known, but there remain important unexplored areas, and for many populations in the USA, accessing data to visualize and monitor health equity is difficult.

We describe the development and evaluation of an open-source, R-Shiny application, the “Health Equity Explorer (H2E),” designed to enable users to explore health equity data in a way that can be easily shared within and across common data models (CDMs).

We have developed a novel, scalable informatics tool to explore a wide variety of drivers of health, including patient-reported Social Determinants of Health (SDoH), using data in an OMOP CDM research data repository in a way that can be easily shared. We describe our development process, data schema, potential use cases, and pilot data for 705,686 people who attended our health system at least once since 2016. For this group, 996,382 unique observations for questions related to food and housing security were available for 324,630 patients (at least one answer for all 46% of patients) with 65,152 (20.1% of patients with at least one visit and answer) reporting food or housing insecurity at least once.

H2E can be used to support dynamic and interactive explorations that include rich social and environmental data. The tool can support multiple CDMs and has the potential to support distributed health equity research and intervention on a national scale.

To compare the agreement and cost of two recall methods for estimating children’s minimum dietary diversity (MDD).

We assessed child’s dietary intake on two consecutive days: an observation on day one, followed by two recall methods (list-based recall and multiple-pass recall) administered in random order by different enumerators at two different times on day two. We compared the estimated MDD prevalence using survey-weighted linear probability models following a two one-sided test equivalence testing approach. We also estimated the cost-effectiveness of the two methods.

Cambodia (Kampong Thom, Siem Reap, Battambang, and Pursat provinces) and Zambia (Chipata, Katete, Lundazi, Nyimba, and Petauke districts).

Children aged 6–23 months: 636 in Cambodia and 608 in Zambia.

MDD estimations from both recall methods were equivalent to the observation in Cambodia but not in Zambia. Both methods were equivalent to the observation in capturing most food groups. Both methods were highly sensitive although the multiple-pass method accurately classified a higher proportion of children meeting MDD than the list-based method in both countries. Both methods were highly specific in Cambodia but moderately so in Zambia. Cost-effectiveness was better for the list-based recall method in both countries.

The two recall methods estimated MDD and most other infant and young child feeding indicators equivalently in Cambodia but not in Zambia, compared to the observation. The list-based method produced slightly more accurate estimates of MDD at the population level, took less time to administer and was less costly to implement.

To present validation evidence for the first eight cognitive measures available through Mobile Toolbox (MTB). These measures use a remote self-administered platform to assess language, working memory, episodic memory, executive function, and processing speed.

We used two separate samples, recruited as part of a larger study, to validate MTB measures. Sample I, comprised of 92 English-speaking adults ages 18-85, was used to assess internal consistency and construct validity. Participants were first administered “gold standard” cognitive measures (Wechsler Memory Scale-IV Verbal Paired Associates I and II; Wechsler Adult Intelligence Scale-IV Symbol Search, Digit Span, Coding, and Letter-Number Sequencing; Delis-Kaplan Executive Function System Color-Word Interference Test, Peabody Picture Vocabulary Test, Wechsler Individual Achievement Test-4 Spelling, and the Wisconsin Card Sorting Test), after which they completed MTB (pre-loaded on a study-provided smartphone) on their own. Internal consistency was evaluated using measure-appropriate indices (split-half reliability, Cronbach’s alpha or IRT-based indices). Pearson correlation coefficients between MTB tests and measures of similar constructs were used to evaluate concurrent validity. For two tests with timing-dependent scores, Arrow Matching and Shape-Color Sorting, separate analyses were performed for iOS and Android devices. Sample II, with 1,120 English-speaking participants ages 18-90, was used to evaluate age-related change. Participants completed MTB measures remotely on their own smartphones, in a preset order, within a 14-day period. Spearman correlation coefficients, corrected for education, were calculated to evaluate relationships between age and test scores.

Sample I participants were 67% female, 52% white, 99% non-Hispanic; average age=48 (SD= 17). Education was: < high school (1%); high school (55%); some college (21%); college (15%); graduate degree (8%). Internal consistency estimates ranged from 0.81 to 0.99. Pearson correlations between MTB and external measures ranged from 0.41 to 0.86 (all p < .01). Of the timed tests, only Shape-Color sorting showed significant score differences between Android and iOS devices. Sample II was 57% female, 13% Hispanic, 72% white, mean age = 45 (SD = 21). Education distribution was: < high school (2%); high school (34%); some college (34%), college (20%); graduate degree (11%). Measures of executive function (r = -0.50; r=-0.57) and processing speed (r= -0.61) showed the expected negative correlation with age (all p <0.001). Negative correlations, although weaker, were also seen on measures of working memory (r=-0.2) and episodic memory (r=-0.2, r=-0.37; p.<.001). Vocabulary performance improved with age (r=0.4; p<.001), while spelling scores remained stable (r=0.09).

Initial studies support the validity and reliability of the first eight MTB cognitive measures in two diverse samples. MTB tests showed satisfactory construct validity, as demonstrated by the associations between MTB and well-established tests. Furthermore, most MTB measures correlated with age in the expected directions. Executive function, processing speed and memory typically decrease with age and this decrease was reflected in MTB test performance. In contrast, spelling and vocabulary, typically preserved as we age, did not decrease in our sample. Our results support the use of MTB in cognitive aging research.

Prior literature has documented how motives for cannabis use predict frequency of use and cannabis use problems among adolescents. However, few studies have examined possible moderating variables that may influence the association between cannabis use motives and frequency of use. The current study examines how risky decision-making moderates this association to help better understand which individuals are at greater risk for cannabis use escalation. The current study will be the first to examine the interactive effects of motives for cannabis use (i.e., health or recreational reasons) and risky decision-making on cannabis use trajectories among a sample of adolescent cannabis users.

Data from 194 adolescent cannabis users aged 14–17 at baseline were analyzed as part of a larger longitudinal study. Participants included those who self-reported use of cannabis within six months prior to the baseline assessment. The Marijuana Reasons for Use Questionnaire (MJRUQ) was used to assess motives for cannabis use from a list of 13 items. A confirmatory factor analysis identified “health” and “recreational” factors for motives for cannabis use. Lifetime frequency of cannabis use (number of days used) was assessed through the Drug Use History Questionnaire, while risky decision-making was assessed using the Game of Dice Task. We used latent growth curve modeling and linear regression analyses to examine the interactive effects of motives for cannabis use and risky decision-making on initial levels of lifetime cannabis use at baseline, and rate of cannabis use escalation over time.

No significant interactive effects were found for health motives for cannabis use; however, we found significant main effects of health motives on initial levels of lifetime cannabis use at baseline (b = 100.82, p < .01) and rate of cannabis use escalation (b = 24.79, p < .01). Those with a greater proclivity to use cannabis for health purposes showed higher initial levels of lifetime use at baseline and steeper increases in the rate of cannabis use escalation relative to those less likely to use for health purposes. Furthermore, we found a significant interactive effect of recreational motives for use and risky decision-making on the rate of cannabis use escalation (b = -2.53, p < .01). Follow-up analyses revealed that among those less likely to use cannabis for recreational purposes, higher risky decision-making was associated with a steeper increase in the rate of cannabis use escalation relative to those who exhibited lower risky decision-making.

The current study replicated findings suggesting that cannabis use motives influence cannabis use trajectories. We found that using cannabis primarily for health reasons was associated with higher initial levels and steeper increases in use regardless of decision-making. Furthermore, we found that both motives for use and risky decision-making interacted to influence associations with cannabis use trajectories. Specifically, among individuals reporting less cannabis use for recreational reasons, those with relatively riskier decision-making showed steeper increases in the rate of cannabis use escalation. These findings inform prevention and intervention practices that focus on decision-making by tailoring approaches based on an individual’s primary motives for cannabis use.