Diagnostic discrimination of social network indicators in alcohol use disorder: Initial examination using high-resolution and brief assessments.

OBJECTIVE: Social network analysis (SNA) characterizes the structure and composition of a person's social relationships. Network features have been associated with alcohol consumption in observational studies, primarily of university undergraduates. No studies have investigated whether indicators from a person's social network can accurately identify the presence of alcohol use disorder (AUD), offering an indirect strategy for identifying AUD. METHOD: Two cross-sectional case-control designs examined the clinical utility of social network indicators for identifying individuals with AUD (cases) versus demographically matched drinkers without AUD (controls). Study 1 (N = 174) used high-resolution egocentric SNA assessment, whereas Study 2 (N = 189) used a brief assessment. RESULTS: In Study 1, significant differences between AUD+ participants and controls were present for network alcohol severity (i.e., heavy drinking days; d = 1.23) and frequency (d = 0.35), but not network structural features. Network alcohol severity exhibited very good classification of AUD+ individuals versus controls (area under the curve [AUC] = 0.80), whereas network frequency did not (AUC = 0.61). In Study 2, significant differences were present for network alcohol severity (d = 1.02), quantity (d = 0.74), and frequency (d = 0.43), and severity exhibited good differentiation (AUC = 0.76). CONCLUSIONS: Social network indicators of alcohol involvement robustly differentiated AUD+ individuals from matched controls, and the brief assessment performed almost as well as the high-resolution assessment. These findings provide proof-of-concept for severity-related SNA indicators as promising novel clinical assessments for AUD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Leveraging related health phenotypes for polygenic prediction of impulsive choice, impulsive action, and impulsive personality traits in 1534 European ancestry community adults.

Impulsivity refers to a number of conceptually related phenotypes reflecting self-regulatory capacity that are considered promising endophenotypes for mental and physical health. Measures of impulsivity can be broadly grouped into three domains, namely, impulsive choice, impulsive action, and impulsive personality traits. In a community-based sample of ancestral Europeans (n = 1534), we conducted genome-wide association studies (GWASs) of impulsive choice (delay discounting), impulsive action (behavioral inhibition), and impulsive personality traits (UPPS-P), and evaluated 11 polygenic risk scores (PRSs) of phenotypes previously linked to self-regulation. Although there were no individual genome-wide significant hits, the neuroticism PRS was positively associated with negative urgency (adjusted R2 = 1.61%, p = 3.6 × 10-7 ) and the educational attainment PRS was inversely associated with delay discounting (adjusted R2 = 1.68%, p = 2.2 × 10-7 ). There was also evidence implicating PRSs of attention-deficit/hyperactivity disorder, externalizing, risk-taking, smoking cessation, smoking initiation, and body mass index with one or more impulsivity phenotypes (adjusted R2 s: 0.35%-1.07%; FDR adjusted ps = 0.05-0.0006). These significant associations between PRSs and impulsivity phenotypes are consistent with established genetic correlations. The combined PRS explained 0.91%-2.46% of the phenotypic variance for individual impulsivity measures, corresponding to 8.7%-32.5% of their reported single-nucleotide polymorphism (SNP)-based heritability, suggesting a non-negligible portion of the SNP-based heritability can be recovered by PRSs. These results support the predictive validity and utility of PRSs, even derived from related phenotypes, to inform the genetics of impulsivity phenotypes.

Corrigendum: A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain.

[This corrects the article DOI: 10.3389/fnins.2021.668293.].

Is E-cigarette Use Associated With Persistence or Discontinuation of Combustible Cigarettes? A 24-Month Longitudinal Investigation in Young Adult Binge Drinkers.

INTRODUCTION: It remains unclear whether electronic cigarette (e-cigarette) use promotes persistent combustible tobacco use or smoking discontinuation over time. Alcohol use is associated with a greater risk of adverse health effects of tobacco, and higher likelihood of e-cigarette use, making drinkers a high-priority subpopulation. AIMS AND METHODS: This study examined longitudinal patterns of combustible tobacco and e-cigarette use over 24 months in young adult binge drinkers. A pooled dataset of 1002 (58.5% female; M age = 22.14) binge drinkers from the United States (60%) and Canada (40%) was used. The primary outcomes were past month combustible tobacco and e-cigarette use. Nicotine dependence was measured using the Fagerström Test of Cigarette Dependence. Alcohol severity was measured using the Young Adult Alcohol Consequences Questionnaire. Latent transition analysis (LTA) was used to identify patterns of cigarette smoking and e-cigarette use over 24 months. RESULTS: The LTA yielded a four-class solution: (1) e-cigarettes-only users (prevalence over time: 7.75%-10.10%), (2) dual-product users (2.61%-9.89%), (3) combustible-only smokers (8.12%-20.70%), and (4) nonusers (61.66%-80.06%). Dual-product users predominantly transitioned to complete abstinence or exclusively e-cigarette use. In combustible-only smokers, the most common transition was to abstinence, followed by persistence of combustible-only status. At 24 months, 63% of e-cigarettes-only users transitioned to abstinence, with 37% continuing e-cigarettes-only use and 0% transitioning to dual or combustible cigarette use. CONCLUSIONS: Dual-product use in young adult binge drinkers was associated with discontinuation of combustible tobacco over time, and e-cigarette-only use was not associated with subsequent combustible tobacco use. IMPLICATIONS: These findings suggest that concurrent or exclusive e-cigarette use is not a risk factor for the persistence or development of combustible tobacco use in this subpopulation, with dual-product use reflecting a transitional pattern away from combustible use, toward discontinuation.

A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain.

Studying the molecular development of the human brain presents unique challenges for selecting a data analysis approach. The rare and valuable nature of human postmortem brain tissue, especially for developmental studies, means the sample sizes are small (n), but the use of high throughput genomic and proteomic methods measure the expression levels for hundreds or thousands of variables [e.g., genes or proteins (p)] for each sample. This leads to a data structure that is high dimensional (p ≫ n) and introduces the curse of dimensionality, which poses a challenge for traditional statistical approaches. In contrast, high dimensional analyses, especially cluster analyses developed for sparse data, have worked well for analyzing genomic datasets where p ≫ n. Here we explore applying a lasso-based clustering method developed for high dimensional genomic data with small sample sizes. Using protein and gene data from the developing human visual cortex, we compared clustering methods. We identified an application of sparse k-means clustering [robust sparse k-means clustering (RSKC)] that partitioned samples into age-related clusters that reflect lifespan stages from birth to aging. RSKC adaptively selects a subset of the genes or proteins contributing to partitioning samples into age-related clusters that progress across the lifespan. This approach addresses a problem in current studies that could not identify multiple postnatal clusters. Moreover, clusters encompassed a range of ages like a series of overlapping waves illustrating that chronological- and brain-age have a complex relationship. In addition, a recently developed workflow to create plasticity phenotypes (Balsor et al., 2020) was applied to the clusters and revealed neurobiologically relevant features that identified how the human visual cortex changes across the lifespan. These methods can help address the growing demand for multimodal integration, from molecular machinery to brain imaging signals, to understand the human brain's development.