Interplay between genetic risk and built neighborhood conditions as predictor of BMI across the transition into adulthood.

Objective: We examined BMI development across changes in the built environment during the transition from adolescence to young adulthood and explored the moderating role of genetic risk.

Methods: We used longitudinal data from individuals aged 16 to 25 years in the TRacking Adolescents' Individual Lives Survey (TRAILS) that we linked to built environment data for 2006, 2010, and 2016 from the Geoscience and Health Cohort Consortium (GECCO). We fitted a latent growth model of BMI and examined associations of changes in fast-food restaurant density and walkability with changes in BMI (n = 2735), as well as interactions of changes in fast-food restaurant density and walkability with genetic risk (n = 1676).

Maternal postpartum mental health negatively affects infants' health related quality of life.

Objectives: To assess the association of early and late postpartum maternal mental health with infants' health related quality of life (HRQoL).

Methods: The study was embedded within the POST-UP trial (n = 1843). Infants' HRQoL was assessed with the Infant and Toddler Quality of Life Questionnaire Short Form-47 at ages 1 month (1 m), and 12 m.

Like, share, and follow!: Usage of social media by pathology residency programs in the COVID-19 era.

Objectives: Social media platforms like Facebook, X (formally Twitter), and Instagram bridge pathology programs with other health professionals, prospective students, and the public, but the extent of social media usage by residency programs remains unexplored. This study investigates the current landscape of social media utilization by pathology programs.

Methods: Using the National Resident Matching Program (NRMP) Match Data from 2022, 139 anatomic and clinical pathology residency programs were analyzed and categorized into 3 prestige tiers based on Doximity ratings.

Genomic structural equation modeling of reward-related traits: exploring the genetic factor structure and its relationship with psychopathology.

Reward sensitivity has a partial genetic background, and extreme levels may increase vulnerability to psychopathology. This study explores the genetic factor structure underlying reward-related traits and examines how genetic variance links to psychopathology. We modeled GWAS data from ten reward-related traits: risk tolerance (N = 975,353), extraversion (N = 122,886), sensation seeking (N = 132,395), (lack of) premeditation (N = 132,667), (lack of) perseverance (N = 133,517), positive urgency (N = 132,132), negative urgency (N = 132,559), attentional impulsivity (N = 124,739), motor impulsivity (N = 124,104), and nonplanning impulsivity (N = 123,509) to derive their genetic factor structure.

Predicting remission following CBT for childhood anxiety disorders: a machine learning approach.

Background: The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models.

Methods: A machine learning approach was applied to predict anxiety disorder remission in a large sample of 2114 anxious youth (5-18 years).