Journal of Addictive Diseases最新文献

Background: Cannabis use disorder (CUD) commonly co-occurs with depression, post-traumatic stress disorder (PTSD), anxiety, and attention-deficit/hyperactivity disorder (ADHD), resulting in poorer outcomes and underscoring the need for tailored interventions. Contingency management (CM) is one of the most effective behavioral treatments for substance use disorders, and emerging applications of artificial intelligence (AI) may enhance CM by predicting relapse risk and personalizing incentives. This review evaluates evidence on integrated interventions for CUD with co-occurring disorders and the developing role of AI-enhanced CM.

Methods: A systematic search of PubMed, PsycINFO, Embase, and Web of Science (through October 2025) identified clinical studies and systematic reviews on tailored interventions for CUD with depression, PTSD, anxiety, or ADHD, as well as research on AI-driven CM. Data were extracted on study design, interventions, and outcomes.

Results: Thirty-eight studies met inclusion criteria. Integrated cognitive-behavioral therapies improved psychiatric symptoms and reduced cannabis use, particularly in depression and PTSD. Pharmacotherapies showed inconsistent benefits, while ADHD-focused behavioral and stimulant-based approaches demonstrated promising reductions in cannabis use. AI applications such as machine-learning prediction of relapse using smartphone/sensor data, remote CM delivery, and reinforcement-learning-based incentive optimization-improved attendance, abstinence verification, and reward efficiency.

Conclusion: Integrated psychosocial approaches and AI-augmented CM offer complementary pathways for improving outcomes in individuals with CUD and co-occurring disorders. Combining personalized psychotherapy with adaptive, technology-assisted reinforcement may strengthen treatment efficacy.

Background: Alcohol withdrawal delirium (AWD) is the most severe and potentially fatal manifestation of alcohol withdrawal syndrome (AWS). Early identification of patients at risk is critical for preventing medical complications and optimizing withdrawal management. Although the Clinical Institute Withdrawal Assessment for Alcohol Scale, Revised (CIWA-Ar), is widely used to assess AWS severity, its predictive value for AWD remains unclear, particularly in Asian inpatient settings.

Objective: To examine the incidence of AWS and AWD among patients with alcohol use disorder (AUD) in a Japanese rehabilitation center and to identify admission factors associated with AWD onset.

Methods: Among 295 consecutively admitted patients, 273 were analyzed; 22 were excluded for ongoing AWD at admission. AWS was evaluated using CIWA-Ar throughout hospitalization. Associations between admission variables and AWD onset were analyzed using logistic regression and receiver operating characteristic (ROC) analysis.

Results: Of 273 patients, 152 (55.7%) exhibited AWS and 24 (8.8%) developed AWD during hospitalization. Stepwise logistic regression identified only the CIWA-Ar score at admission as a significant predictor (odds ratio = 1.50; 95% confidence interval, 1.28-1.74; p < 0.001). ROC analysis yielded an area under the curve (AUC) of 0.906 with a CIWA-Ar cutoff of 4 (sensitivity 91.7%, specificity 84.3%).

Conclusions: The admission CIWA-Ar score is a strong and practical predictor of AWD in AUD inpatients. Even mild withdrawal symptoms may indicate early autonomic hyperactivity and high AWD risk, underscoring the need for vigilant monitoring and timely intervention.

Background: Substance use disorder (SUD) is associated with systemic inflammation and cognitive vulnerability. Peripheral biomarkers may help characterize biological risk across substances and comorbidities. Turkey has experienced a recent rise in methamphetamine and pregabalin misuse, underscoring the need for biomarker-based clinical profiling.

Methods: A total of 321 inpatients were evaluated in a single inpatient addiction treatment unit in Turkey (2019-2025). Exclusion criteria included severe somatic disease, preexisting psychiatric diagnoses, and incomplete records. Admission data included demographics, primary substance, DSM-5 comorbidities, biomarkers (C-reactive protein [CRP], neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio, Systemic Immune-Inflammation Index, Hemoglobin × Albumin/Leukocyte × Platelet Index, Inflammatory Burden Index [IBI]). Analyses used appropriate parametric/non-parametric tests, analysis of covariance, and logistic regression adjusted for age, sex, and comorbidities.

Results: IBI and CRP differed significantly across substance groups, with higher inflammatory burden among methamphetamine, alcohol, and MDMA users. After multiple-comparison correction, IBI remained statistically significant, whereas CRP required cautious interpretation. Longer duration of use correlated with higher inflammatory burden. Depressive (14.6%) and anxiety disorders (16.2%) were the most common comorbidities. Bipolar disorder, although infrequent, clustered with higher inflammatory markers. Gender patterns reflected international trends, with greater alcohol/cannabis use in male and earlier, faster dependence progression in female. Multivariate models showed that methamphetamine and alcohol independently predicted elevated IBI after adjustment.

Conclusions: IBI and CRP appear to be the most sensitive indicators of systemic inflammation in SUD, particularly in stimulant and alcohol users. Elevation of IBI supports its potential role as a cognition-related biomarker. Findings highlight biological heterogeneity across substances, genders, and comorbidities, and emphasize the need for future prospective studies with neurocognitive testing and structured tobacco documentation.

Background: Marijuana usage is on the rise. It is becoming more dangerous because of the significantly greater amounts of tetrahydrocannabinol (THC) and more accessible because of legalization. This paper aims to examine the usage of cannabis in adolescents aged 12-17 and determine the multifaceted effects and dangers that come with the usage.

Methods: This study queried data from the National Survey on Drug Use and Health. The database was queried and tested until significant numbers were found. Age was added as a control variable to ensure the data was of just adolescents aged 12-17 and not a wide array of ages. We compared adolescents who use marijuana to adolescents who did not use marijuana in four topics: gender, race, number of school days missed,and grades.

Results: The overall pooled prevalence of cannabis use in adolescents ages 12-17 is 11.4% (95% CI: 10.70-12.20%). Cannabis use was slightly more prominent among females than males and in white adolescents, Hispanics, and blacks. The higher the marijuana use, the more likely adolescents are to skip class and the less likely they are to perform well grade-wise.

Conclusion: Approximately 1 in 10 individuals who try marijuana get develop a use disorder.When narrowing the pool down to just teenagers, that number increases to one in six adolescentswho try marijuana being with marijuana addiction.Therefore, there is a need to educate teenagers and their families about the effects and consequences of marijuana use. These consequences are greater and more severe than most think.

Background: Individuals with tobacco use disorder (TUD) may be particularly vulnerable to the challenges following long COVID.

Objective: This study assessed whether individuals with TUD and no prior neuropsychiatric conditions developed new symptoms following long COVID-19 infection.

Methods: A cohort of 104 adults with TUD completed psychological and biological assessments before the COVID-19 pandemic and were reevaluated four months post-long COVID diagnosis. Evaluations covered mood symptoms, sleep, perceived stress, quality of life, and serum cortisol.

Results: The participants exhibited marked increases in depressive symptoms, anxiety, insomnia, and perceived stress, accompanied by significant declines in sleep quality and quality of life (all p-values < 0.001). Serum cortisol levels decreased significantly, indicating altered HPA axis activity.

Conclusion: This study suggests that long COVID may disproportionately influence addictive disorders, not only by exacerbating existing vulnerabilities, but potentially contributing to the onset of new mental health challenges.

Background: Machine learning (ML) is increasingly explored for opioid overdose and opioid use disorder (OUD) detection and prevention. Regional burden is uneven: the United States currently has among the highest rates of drug-overdose deaths worldwide, underscoring the urgent need for operationalized ML tools in US clinical and public-health settings. While many models exist, few have been tested in live settings. Understanding how deployed systems perform and are governed is essential for safe, equitable use in addiction medicine.

Methods: We systematically searched PubMed, Embase, IEEE Xplore, and Scopus from January 2019 to August 2025 for studies describing real-world ML deployments for overdose or OUD. Eligible studies required live integration into clinical, public health, or consumer workflows with prospective or concurrent evaluation. Risk of bias and operational robustness were assessed using the PROBAST-R framework (a PROBAST extension for deployed ML; 'risk of bias' here denotes systematic error in reported performance due to study design, data, or reporting).

Results: Fifteen studies were included, spanning health systems, public health surveillance, emergency services, and wearable detection. Most achieved good discrimination (AUC > 0.80) or high precision, though tradeoffs between sensitivity and specificity were common. Governance structures were more consistently reported in large deployments, yet no study described automated monitoring, and only two examined subgroup performance. Fairness audits and human-factors evaluations were rare.

Conclusion: Deployed ML for opioid outcomes is feasible but uneven in maturity. Clinical implications: addiction medicine clinicians should (1) request subgroup performance results before adoption; (2) confirm post-deployment monitoring plans for drift and calibration; and (3) use human-in-the-loop safeguards (clinician or human verification before automated actions) to reduce harms from false positives/negatives.

Oxymetazoline hydrochloride 0.05% is a lipophilic sympathomimetic nasal decongestant spray available over the counter (OTC) and commonly used for allergic and chronic rhinitis. A well-known side effect of these nasal sprays is rebound congestion termed rhinitis medicamentosa (RM), but there is little literature attesting to the relationship between RM and substance use disorder. This is a case report of severe nasal spray oxymetazoline use disorder per DSM-5 criteria discovered incidentally in a 44-year-old patient receiving care at a residential addiction treatment center for long-standing polysubstance use and bipolar disorders. The patient began using oxymetazoline in 2003 for allergic rhinitis and developed rhinitis medicamentosa that progressed to an oxymetazoline use disorder. Despite medical and clinical interventions, cravings and urges prevented her from stopping the nasal spray. We discuss the pharmacological properties of oxymetazoline, the behavioral aspects of its intranasal administration, and the drug-induced rebound congestion that may contribute to its misuse. To our knowledge, this is the first reported case of oxymetazoline use disorder lasting 20 years.

Objectives: There is increasing evidence of ketamine's therapeutic potential in reducing substance use in individuals with substance use disorders. However, its effects on tobacco use disorder are unknown. We investigated the effect of a subanesthetic dose of ketamine on tobacco use.

Methods: This randomized, single-blind, placebo-controlled, pilot study administered intravenous ketamine to individuals with tobacco use disorder recruited from the local community. Participants were randomized to receive either ketamine (0.5 mg/kg) (n = 6) or saline placebo (n = 4) over 20 min. Primary outcomes included measures of drug safety and tolerability during and within an hour after the infusion. Secondary outcomes included measures of tobacco use, craving, and withdrawal before, and 24-hours after, the drug infusion study day. A follow-up visit occurred eight days after the infusion.

Results: Intravenous ketamine was well tolerated with transient side effects. No significant effects were noted on cigarette smoking, craving, or withdrawal symptoms on the post-infusion visit following overnight abstinence or on the follow-up visit (p's > 0.05).

Conclusions: Although limited by the small sample size, this pilot study extends previous research on ketamine for substance use disorders. While ketamine was well tolerated in this sample, additional research testing different ketamine doses and administration routes is necessary to determine whether ketamine has therapeutic potential for tobacco use disorder.

Background: This study investigates the effects of open and closed exercise interventions on the physical and mental health of individuals undergoing substance use disorder (SUD). We examined changes in tendency of recurrence of use, vital capacity (VC), resting heart rate (RHR), sleep quality, and choice reaction time.

Methods: Conducted over six months at the drug rehabilitation center, 95 participants were randomly assigned to closed exercise, open exercise, or control group. Outcome measures were taken at baseline, three months, and six months.

Results: Both exercise groups showed significant improvements in reduction of return-to-use risk and VC compared to baseline. Open exercise groups showed earlier significant improvements in risk of return to use at three months. No significant changes were observed in RHR. Both exercise groups showed significant improvements in sleep quality, with the open exercise group also showing significant improvements in choice reaction time. At six months, both exercise groups showed significant improvements over the control group in tendency of recurrence of use, VC, and sleep quality, with no significant differences between the exercise groups.

Conclusions: Both exercise interventions led to significant improvements in reducing the risk of return to substance use, VC, sleep quality, and choice reaction time, with the open exercise group showing the most pronounced effects in choice reaction time.

Substance use disorders (SUDs) represent a major challenge in psychiatric treatment, with significant relapse rates despite various psychotherapeutic interventions. This systematic review explores the neurobiological underpinnings of addiction and examines the efficacy of psychotherapies, such as Cognitive Behavioral Therapy (CBT), Eye Movement Desensitization and Reprocessing (EMDR), Mindfulness-Based Relapse Prevention (MBRP), and emerging therapies in treating SUDs. Additionally, the study assesses how emerging biomarkers and neuroimaging data could enhance therapeutic outcomes by guiding personalized treatments. Neurobiological markers, such as prefrontal-limbic connectivity, mesolimbic dopaminergic dysregulation, and glutamate transmission deficits, are shown to significantly influence treatment efficacy. For example, prefrontal cortex hypoactivity and amygdala hyperactivity correlate with poor impulse control and emotional regulation, making these individuals more responsive to CBT and EMDR. Similarly, dopaminergic dysfunction in the mesolimbic pathway is closely tied to reward-seeking behavior where Transcranial Magnetic Stimulation (TMS) may offer therapeutic benefits. Epigenetic modifications, primarily those affecting the glucocorticoid receptor (GR), highlight the role of stress in relapse suggesting that trauma-focused therapies can be effective for individuals with high stress vulnerability. This review finds that integrating neurobiological insights with clinically validated psychometric assessments could significantly improve treatment stratification. Future research should focus on aligning diagnostic systems, such as the DSM-5, with neurobiological markers and psychological tells to facilitate more precise and personalized interventions, potentially transforming addiction treatment outcomes.