Addiction最新文献

Background and aims: Previous studies on response inhibition deficits in smoking have often been conducted in small, young, age-homogeneous samples, without controlling for covariates or testing moderating effects. The primary research question compared response inhibition between a large, age-diverse smoking sample and non-smoking controls, and examined whether deficits were exacerbated toward smoking-related stimuli. By accounting for key covariates and moderators, this study aimed to extend understanding of individual differences in response inhibition deficits in smoking.

Design and setting: Cross-sectional study conducted at a university laboratory in Munich, Germany.

Participants: The large (n = 122, 57% female), age-diverse (M_age = 41.4, range: 21-70 years) smoking group comprised individuals with moderate to severe tobacco dependence participating in a smoking reduction intervention study. Controls comprised n = 69 healthy individuals with no smoking history.

Measurements: Primary outcomes were commission error (CE) rates and mean reaction times in Go trials (Go-RT) in general and smoking-specific Go/No-Go tasks (GNGTs). Covariates included age, sex and IQ. Smoking-related variables were cigarettes per day (CPD), tobacco dependence severity and craving.

Findings: General GNGT: The smoking group exhibited significantly higher CE rates (P-value < 0.001, medium effect, BF₁₀ = 9.06) than the control group. Higher craving was associated with faster Go-RTs (β = -1.487, P-value = 0.041). Smoking-specific GNGT: CE rates were significantly higher in the smoking group only when controlling for covariates (β = 1.272, P-value = 0.040). Higher craving was associated with higher CE rates during smoking-related trials (β = 0.108, P-value = 0.010). The smoking group showed significantly faster Go-RTs in response to smoking-related compared with neutral stimuli, relative to the control group (β = -3.326, P-value = 0.027). Preliminary evidence indicated that greater deficits were associated with higher scores in smoking-related variables, but only in older individuals.

Conclusions: Individuals who smoke appear to exhibit response inhibition deficits, although these are not uniform and seem to be exacerbated during higher reported craving or in response to smoking-related stimuli. Age may moderate the relationship between deficits and smoking-related variables.

Background and aims: Substance use disorders (SUD) lead to a high burden of disease, yet treatment options are limited. Cannabidiol (CBD) is being investigated as a potential therapeutic target due to its pharmacological properties and mode of action in the endocannabinoid system. Recent systematic reviews (SR) on CBD and SUDs have shown inconsistent results. The objective of this umbrella review was to determine whether CBD alone or in combination with Δ-9-tetrahydrocannabinol (THC) is effective for managing and treating SUDs.

Methods: Following a registered protocol, we searched PubMed, Web of Science and Epistemonikos databases for SRs, with or without a meta-analysis, of randomized controlled trials focusing on interventions dispensing CBD, alone or in combination with THC, to treat SUDs, published from 1 January 2000 to 15 October 2024. Screening, data extraction and quality assessment with the AMSTAR 2 tool were performed by two researchers in parallel and duplicated.

Results: 22 SRs were included, 5 of which performed a meta-analysis. We found mixed evidence regarding the efficacy of CBD to manage and treat SUDs. Findings were interpreted in light of the quality of the SRs. Nabiximols, which contains CBD and THC, demonstrated positive effects on cannabis withdrawal and craving symptoms. Evidence supporting the efficacy of CBD is limited and inconclusive for abstinence, reduction or cessation of use of cannabis, tobacco, alcohol, opiates and other psychoactive substances.

Conclusion: Cannabidiol (CBD) monotherapy does not appear to be efficacious for treatment of substance use disorders. CBD primarily exhibits effects on cannabis withdrawal and craving when combined with Δ-9-tetrahydrocannabinol (THC). Existing data on the efficacy of CBD alone with regard to other outcomes related to substance use disorders are limited.

Background and aims: Long-term treatment of opioid dependence with the opioid agonist therapy (OAT) methadone can lead to significant weight gain. This study investigated whether OAT patients with a body mass index (BMI) deemed overweight (≥25) are at increased risk of mortality.

Methods: A retrospective case-control study was performed with anonymised data collected from living and deceased persons registered as receiving OAT from Derbyshire or Teesside drug and alcohol services in the United Kingdom. Height, weight, age, gender, OAT type and dose, smoking status and postcode of usual address were collected. Cause of death was collected from deceased individuals. Adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) were calculated using regression models.

Results: Data were collected for 1574 living and 233 deceased individuals. Mean BMI of all individuals (25.75 ± 6.56) was above the threshold considered a healthy weight, and was higher in deceased (26.63 ± 7.87) than living individuals (25.61 ± 6.34; P < 0.05). A BMI of 30 corresponded to an average 7.7% increased risk of mortality, increasing to 37.2% for those with a BMI of 35 and 107.3% for those with a BMI of 40. Risk of death also increased by an average of 43.6% for those deemed underweight (BMI 15). People who were older (mean aOR = 1.019; 95% CI = 1.002-1.036), smoked (mean aOR = 2.917; 95% CI = 1.726-4.934) and lived in more deprived areas (mean aOR = 0.891; 95% CI = 0.831-0.956) were more likely to have died, as were those prescribed methadone (vs buprenorphine) (mean aOR = 1.916; 95% CI = 1.138-3.227). There was no significant effect of gender (mean aOR = 0.844; 95% CI = 0.612-1.162) or methadone dose (aOR = 0.995; 95% CI = 0.988-1.001) on incidence of death. Acute drug toxicity was the predominant underlying cause of death for healthy and overweight people (46% of cases in both groups), with cancer (21% of cases) and infection (18% of cases) greater contributors to cause of death in underweight people.

Conclusions: Opioid agonist therapy (OAT) patients with a body mass index outside of the 'healthy' range appear to have an increased risk of death compared with OAT patients within the 'healthy' range.

Background and aims: There is growing evidence that tobacco smoking causes depression, but it is unclear which constituents of tobacco smoke (e.g. nicotine, carbon monoxide) may be responsible. We used Mendelian randomisation (MR) to measure the independent effect of nicotine on depression, by adjusting the effect of circulating nicotine exposure [via nicotine metabolite ratio (NMR)] for the overall effect of smoking heaviness [via cigarettes per day (CPD)] to account for the non-nicotine constituents of tobacco smoke.

Design: Univariable MR and multivariable MR (MVMR) were used to measure the total and independent effects of genetic liability to NMR and CPD on major depressive disorder (MDD). Our primary method was inverse variance weighted (IVW) regression, with other methods as sensitivity analyses.

Setting and participants: For the exposures, we used genome-wide association study (GWAS) summary statistics among European ancestry individuals for CPD (n = 143 210) and NMR (n = 5185). For the outcome, a GWAS of MDD stratified by smoking status was conducted using individual-level data from UK Biobank (n = 35 871-194 881).

Measurements: Genetic variants associated with NMR (n = 6) and CPD (n = 53).

Findings: Univariable MR-IVW indicated a causal effect of CPD on MDD [odds ratio (OR) = 1.13, 95% confidence interval (CI) = 1.04-1.23, P = 0.003] but no clear evidence for an effect of NMR on MDD (OR = 0.98, 95% CI = 0.97-1.00, P = 0.134). MVMR indicated a causal effect of CPD on MDD when accounting for NMR (IVW: OR = 1.19, 95% CI = 1.03-1.37, P = 0.017; Egger: OR = 1.13, 95% CI = 0.89-1.43, P = 0.300) and weak evidence of a small effect of NMR on MDD when accounting for CPD (IVW: OR = 0.98, 95% CI = 0.96-1.00, P = 0.057; Egger: OR = 0.98, 95% CI = 0.96-1.00, P = 0.038).

Conclusions: The role of nicotine exposure in risk of depression cannot be entirely dismissed. However, the causal effect of tobacco smoking increasing depression risk appears to be largely independent of circulating nicotine exposure, which implies the role of alternative causal pathways.

Background and aims: Substance-induced psychoses (SIP) have the potential to relapse and convert into schizophrenia-spectrum disorders. However, risk factors associated with SIP relapse remain unknown. The aim of this study was to measure the incidence and risk of SIP relapse and associating risk factors.

Design, setting and participants: Population based register study that identified people with first-time SIP between 2006 and 2016 (n = 7320) from Swedish nation-wide registers to examine incidence of relapse and associated risk factors during 2-year follow-up. Participants were censored to death, emigration and the diagnosis of other psychotic illness. Risk factors associated with relapse were studied using multivariable Cox models.

Measurements: SIP was measured via related diagnostic codes (ICD-10: F1x.5) collected from the National Patient Register (NPR). SIP relapse was measured as hospitalization due to SIP during 2-year follow-up also collected from the NPR. Potential risk factors included demographic characteristics, psychiatric comorbidities, sickness absence and disability pension collected from nationwide registers.

Findings: Of the study population (n = 7320), 20.0% (n = 1463) had a SIP relapse during the follow-up (median time 126 days, interquartile range 56-321) and 83.3% had the same type of SIP as their first SIP. Relapse was most common for those whose first SIP was induced by cannabis (25.7%), followed by multi-substance use (23.8%) and (meth)amphetamine (19.7%). Factors associated with SIP relapse were previous substance use disorder [hazard ratio (HR) = 1.37, 95% confidence interval (CI) = 1.20-1.56], younger age (16-29 years, HR = 1.29, 95% CI = 1.05-1.58, versus 50-65), being born abroad (HR = 1.23, 95% CI = 1.07-1.41), attention deficit hyperactivity disorder (HR = 1.21, 95% CI = 1.05-1.39), having had 1-90 days sick leave during the previous year (HR = 1.19, 95% CI = 1.01-1.44), and cannabis- (HR = 2.42, 95% CI = 1.98-2.96), (meth)amphetamine- (HR = 1.49, 95% CI = 1.23-1.81) or multi-substance- (HR = 1.81, 95% CI = 1.52-2.15) induced psychosis compared with alcohol-induced psychosis.

Conclusions: In Sweden, 20% of people with substance-induced psychosis between 2006 and 2016 had a relapse within 2 years follow-up. Cannabis-induced psychosis had the shortest time lapse between episodes. Risk factors for relapse included attention deficit hyperactivity disorder, substance-use disorder, younger age, previous sickness absence and being born outside Sweden.

Background & aim: Globally, more than 100 000 people die annually from opioid overdose. Although strongly implicated in heroin overdose deaths, acute opioid-induced respiratory depression is poorly understood, and few laboratory studies have been completed in human subjects. It is an area of undone science. Using a human laboratory overdose model, our research question was: what is the strength of the association between increasing dose of diamorphine and degree of respiratory depression in people prescribed injectable diamorphine for heroin use disorder?

Design: Single-blind, Phase IV, non-randomised, dose-escalation clinical trial.

Setting: King's Clinical Research Facility, London, UK.

Participants: Four participants prescribed injectable diamorphine as treatment for heroin use disorder [all male, median (range) age 63 (59-72)].

Interventions: The following dosing schedule was implemented (as a % of participant's usual prescribed diamorphine dose): visit 1-100%; visit 2-110%; visit 3-120%; visit 4-100%. Usual dose: 97.5 mg (30 mg-200 mg).

Measurements: Physiological measures included: pulse oximetry (SpO₂%), end-tidal CO₂ (ETCO₂%), transcutaneous CO₂, respiratory rate and parasternal electromyography to measure neural respiratory drive index (NRDI). Recordings were made continuously from 3 mins pre-dose to 60 mins post-dose.

Findings: Respiratory measures from baseline to post-dose across all dose sessions had ranges of: 89.7%-99.5% SpO₂%; 4.8%-7.7% ETCO₂%; 5.2-13.4 breaths/minute respiratory rate; 51.2 min^-1-165.9 min^-1 NRDI across all participants. All diamorphine doses caused some reduction in respiratory function. There was no clear difference between diamorphine dose and the degree of respiratory depression, based on descriptive analyses.

Conclusions: A dose-escalation clinical trial of people prescribed injectable diamorphine for heroin addiction found that the degree of respiratory depression caused by diamorphine does not appear to be dose dependent; however, the changes seen at diamorphine doses to which participants were accustomed suggest that participants had only partial tolerance to the respiratory depressant effect of diamorphine.

Background and aim: Alcohol consumption in China poses significant public health challenges. Between 2005 and 2010, alcohol production and by implication, consumption, surged, leading to increased alcohol-related harms due to weak regulatory frameworks. This study examined recent patterns of alcohol consumption in China using public production data as a proxy.

Method: Statistical analysis was conducted using data from the National Bureau of Statistics of China on the production of beer, wine, and liquor, from the years 2009 to 2023.

Results: The production of liquor and wine in China increased from 7.1 and 0.96 million tons in 2009 to 13.6 and 1.14 million tons in 2016, respectively. Inferred alcohol per capita consumption (APC) rose from 4.38 L in 2009 to 6.45 L in 2016, representing a 47% increase. However, from 2016 onwards, alcohol production declined to 4.5 and 0.14 million tons for liquor and wine in 2023, respectively. Consequently, inferred APC decreased from 6.45 L to 2.85 L, an overall reduction of 55.8%.

Conclusions: Alcohol production and inferred consumption in China appear to have substantially declined since 2016. Potential factors contributing to this decline include stricter government policies, stricter market regulation and tax enforcement, public health campaigns, and demographic and cultural shifts. However, many global measures such as higher taxes, price controls, advertising restrictions, and a legal drinking age, have not been (adequately) implemented in China.

Background and aims: Comparing regulatory models for nicotine vaping products in different countries can provide insights into the most effective regulatory approach but can be confounded by cross-national differences. This study compared two neighbouring countries with very different vaping regulatory models but with similar tobacco control policies and population demographics. Australia has a highly restrictive, prescription-only vaping policy, while New Zealand adopted a regulated consumer model.

Methods: This study compared trends in daily smoking and vaping among adults and youth from 2016 to 2023 in Australia (adults ≥14 years and ≥15 years; youth 12-17 years) and New Zealand (adults ≥15 years, youth 14-15 years), using published statistics from large nationally representative surveys.

Results: Between 2016 and 2023, the decrease in adult daily smoking in New Zealand (from 14.5% to 6.8%) was larger than in Australia (from 12.2% to 8.3%) by a factor of 0.67 [95% confidence interval (CI) = 0.43, 0.93]. However, the increase in adult vaping in New Zealand (from 0.9% to 9.7%) was larger than in Australia (0.5% to 3.5%) by a factor of 0.56 (95% CI = 0.17, 1.02). The largest smoking reductions in both countries were in young adults, who also reported the highest vaping rates. There was a more rapid decline in smoking in the lower socioeconomic groups and Indigenous people in New Zealand than in Australia. Youth smoking rates declined in both countries to very low levels. Youth vaping rates in New Zealand were higher but have started to decline since regulations were introduced.

Conclusions: If the association is causal, New Zealand's less restrictive approach to vaping (compared with Australia's more restrictive approach) may have contributed to a more rapid decline in adult smoking, and reduced social inequalities and Indigenous smoking, but at the possible expense of increased youth vaping.

Aims: Smoking rates are disproportionately high among people with mental health issues. We evaluated the effectiveness of a chat-based intervention paired with psychological support for workplace smokers with mental health symptoms.

Design and setting: A pragmatic, two-arm, randomized controlled trial in 65 companies in Hong Kong, China.

Participants: 300 employees who smoked daily (84.3% male) with stress (Perceived Stress Scale-4 ≥ 6), anxiety (General Anxiety Disorder-2 ≥ 3) or depressive symptoms (Patient Health Questionnaire-2 ≥ 3).

Interventions: Participants were randomly allocated to either the intervention group (n = 144) or the control group (n = 156). The intervention group received 20 instant messages and chat-based support delivered by trained counselors over 3 months, using Acceptance and Commitment Therapy. The control group received 6 messages on general health. Both groups received a health talk and self-help booklet at baseline, brief phone advice at follow-ups and full-course nicotine replacement therapy for continuing smokers at 6 months.

Measurements: The primary outcome was self-reported 7-day point prevalence abstinence (PPA) at 6 months after intervention initiation. Secondary outcomes included PPA at 1, 3, 9 and 12 months, changes in mental health symptoms and intervention engagement (chatting with counsellors).

Findings: By intention to treat, there was no evidence of a difference in 7-day PPA between intervention and control group at 6 months [16.0% vs. 13.5%, odds ratio (OR) = 1.22, 95% confidence interval (CI) = 0.64-2.32]. At 12 months, there was weak evidence of an intervention benefit (21.5% vs. 13.5%, 1.76, 0.96-3.24), which was statistically significant after adjusting for baseline factors (adjusted OR = 2.61, 1.22-5.58, P = 0.01). A greater reduction in anxiety symptoms over a 12-month period in the intervention group was observed (adjusted b = -0.28, 95% CI = -0.56 to -0.00, P = 0.05).

Conclusions: There appears to be weak evidence for a benefit of a personalised chat-based smoking cessation intervention plus psychological support on promoting smoking abstinence among workplace smokers with mental health symptoms.

Background and aims: Opioid agonist treatment (OAT) is established for opioid use disorder, but limited data on adverse effects exist. We aimed to review relative risks of adverse effects across opioid agonists.

Methods: Systematic review with pair-wise meta-analysis of randomized clinical trials. A search in Embase, Medline, PsycInfo, CENTRAL and the Web of Science Core Collection was performed from inception to 11 April 2024 (PROSPERO: CRD42022322722). Two reviewers independently extracted data and used the Cochrane Risk of Bias Assessment Tool. Certainty of evidence was assessed using GRADE (Grading of Recommendations Assessment, Development and Evaluation). Primary outcomes were constipation, sedation, pruritus, sweating, nausea and vomiting, headache and any non-headache pain.

Results: We identified 25 eligible trials, including 4892 participants. Reported agonists were methadone, levomethadone, methadyl acetate, buprenorphine, buprenorphine/naloxone, slow-release oral morphine (SROM), diacetylmorphine, hydromorphone and opium tincture. Buprenorphine (all formulations combined) was associated with a lower risk of sedation than methadone [risk ratio (RR) = 0.68 (95% confidence interval = 0.56-0.82)]; 1558 participants, 9 studies]. Methadone had a lower risk of sedation compared with SROM [RR = 0.63 (0.58-0.69); 356 participants, 2 studies] and a lower risk of nausea than methadyl acetate [RR = 0.56 (0.37-0.85); 913 participants, 3 studies]. There was high overall risk of bias in 92% of included trials due to limited and non-systematic outcome assessment. Certainty of evidence was low or very low for all but one comparison with moderate certainty.

Conclusions: There is currently insufficient data to determine whether the rates of adverse effects differ across opioid agonist treatments for most outcomes, with several exceptions. Moreover, the certainty of evidence is currently low or very low due to a lack of rigorous outcome assessment.