13 papers
Addiction medicine explores the complex biological and psychological roots of substance use disorders, offering hope for more effective treatments and recovery strategies. This field examines how the brain adapts to drugs, the social factors that drive dependency, and the latest clinical approaches to helping individuals regain their lives. By focusing on evidence-based solutions, researchers aim to reduce harm and improve long-term outcomes for patients worldwide.
At Gist.Science, we curate the newest preprints from medRxiv dedicated to this vital area of study. Our team processes every new submission in this category, transforming dense academic findings into accessible plain-language explanations alongside detailed technical summaries. This dual approach ensures that both the general public and specialists can stay informed about the rapidly evolving science behind addiction treatment.
Below are the latest papers in addiction medicine, freshly summarized to help you understand the cutting-edge research shaping the future of recovery.
This study reveals that food addiction symptoms are prevalent in approximately one-third of adults with alcohol use disorder and are significantly associated with higher body mass index, greater psychological distress, and increased alcohol dependence severity, regardless of treatment-seeking status.
This pre-registered study of 60 participants demonstrates that acute rTMS efficacy for tobacco use disorder is significantly predicted by higher baseline cigarette consumption, craving, and withdrawal severity, as well as distinct pre-treatment functional connectivity patterns between the insula and reward/self-referential brain regions.
This study protocol outlines a cross-sectional fNIRS experiment designed to investigate prefrontal cortical responses to gambling advertisements in individuals with varying gambling experiences, utilizing inter-subject correlation and multiband frequency analysis to identify neural markers of vulnerability and inform public health regulations.
This preliminary study of high-risk emerging adults reveals that greater cannabis use disorder severity is associated with reduced surface area and increased cortical thickness in specific orbitofrontal and medial prefrontal/anterior cingulate cortex subregions, which further correlate with depression, trauma symptoms, impulsivity, and specific cannabis use motives.
This umbrella review and meta-analysis of 11 systematic reviews involving over 3 million individuals estimates the global prevalence of problematic internet use behaviors to range from 6% for gaming to 32% for smartphone use, while highlighting substantial methodological heterogeneity and a critical need for higher-quality, geographically diverse research.
This study of European-ancestry families indicates that the intergenerational transmission of alcohol use disorder and nicotine dependence is driven primarily by direct genetic effects, with indirect genetic effects mediated by parental behavior playing only a minor role in smoking quantity.
This study demonstrates that individuals with opioid use disorder exhibit altered metabolic profiles characterized by higher adiposity and inflammation, which sequentially mediate increased risks of lipid imbalance and elevated blood glucose levels compared to matched controls.
This study introduces a two-stage machine learning-based causal framework combining graph discovery and double machine learning to analyze longitudinal ABCD Study data, successfully identifying stable, time-varying environmental and behavioral predictors of substance use initiation to inform targeted prevention strategies.
This study demonstrates that integrating multi-task learning with dynamic, longitudinal modeling significantly improves the prediction of adolescent substance use initiation compared to static baseline approaches, primarily by leveraging time-varying risk factors and shared information across different substances.
This study found that while machine learning models can detect smoking lapse risks, their modest overall performance and high inter-individual variability suggest that reducing assessment frequency or simplifying predictors does not consistently improve outcomes, indicating that such algorithms are best used in combination with rules-based approaches rather than as standalone solutions for just-in-time adaptive interventions.