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Substance use and psychiatric illness, particularly psychotic disorders, contribute to violence in emergency healthcare settings. However, there is limited research regarding the relationship between specific substances, psychotic symptoms and violent behaviour in such settings. We investigated the interaction between recent cannabinoid and stimulant use, and acute psychotic symptoms, in relation to violent behaviour in a British emergency healthcare setting.
We used electronic medical records from detentions of 1089 individuals under Section 136 of the UK Mental Health Act (1983 amended 2007), an emergency police power used to detain people for 24–36 h for psychiatric assessment. The relationship between recent cannabinoids and/or stimulant use, psychotic symptoms, and violent behaviour, was estimated using logistic regression.
There was evidence of recent alcohol or drug use in 64.5% of detentions. Violent incidents occurred in 12.6% of detentions. Psychotic symptoms increased the odds of violence by 4.0 [95% confidence intervals (CI) 2.2–7.4; p < 0.0001]. Cannabinoid use combined with psychotic symptoms increased the odds of violence further [odds ratios (OR) 7.1, 95% CI 3.7–13.6; p < 0.0001]. Recent use of cannabinoids with stimulants but without psychotic symptoms was also associated with increased odds of violence (OR 3.3, 95% CI 1.4–7.9; p < 0.0001).
In the emergency setting, patients who have recently used cannabinoids and exhibit psychotic symptoms are at higher risk of violent behaviour. Those who have used both stimulants and cannabinoids without psychotic symptoms may also be at increased risk. De-escalation protocols in emergency healthcare settings should account explicitly for substance use.
The genetic component of Cannabis Use Disorder may overlap with influences acting more generally on early stages of cannabis use. This paper aims to determine the extent to which genetic influences on the development of cannabis abuse/dependence are correlated with those acting on the opportunity to use cannabis and frequency of use.
A cross-sectional study of 3303 Australian twins, measuring age of onset of cannabis use opportunity, lifetime frequency of cannabis use, and lifetime DSM-IV cannabis abuse/dependence. A trivariate Cholesky decomposition estimated additive genetic (A), shared environment (C) and unique environment (E) contributions to the opportunity to use cannabis, the frequency of cannabis use, cannabis abuse/dependence, and the extent of overlap between genetic and environmental factors associated with each phenotype.
Variance components estimates were A = 0.64 [95% confidence interval (CI) 0.58–0.70] and E = 0.36 (95% CI 0.29–0.42) for age of opportunity to use cannabis, A = 0.74 (95% CI 0.66–0.80) and E = 0.26 (95% CI 0.20–0.34) for cannabis use frequency, and A = 0.78 (95% CI 0.65–0.88) and E = 0.22 (95% CI 0.12–0.35) for cannabis abuse/dependence. Opportunity shares 45% of genetic influences with the frequency of use, and only 17% of additive genetic influences are unique to abuse/dependence from those acting on opportunity and frequency.
There are significant genetic contributions to lifetime cannabis abuse/dependence, but a large proportion of this overlaps with influences acting on opportunity and frequency of use. Individuals without drug use opportunity are uninformative, and studies of drug use disorders must incorporate individual exposure to accurately identify aetiology.
Genes involved in pathways regulating body weight may operate differently in men and women. To determine whether sex-limited genes influence the obesity-related phenotype body mass index (BMI), we have conducted a general non- scalar sex-limited genome-wide linkage scan using variance components analysis in Mx (Neale, 2002). BMI measurements and genotypic data were available for 2053 Australian female and male adult twins and their siblings from 933 families. Clinical measures of BMI were available for 64.4% of these individuals, while only self-reported measures were available for the remaining participants. The mean age of participants was 39.0 years of age (SD 12.1 years). The use of a sex-limited linkage model identified areas on the genome where quantitative trait loci (QTL) effects differ between the sexes, particularly on chromosome 8 and 20, providing us with evidence that some of the genes responsible for BMI may have different effects in men and women. Our highest linkage peak was observed at 12q24 (–log10p = 3.02), which was near the recommended threshold for suggestive linkage (–log10p = 3.13). Previous studies have found evidence for a quantitative trait locus on 12q24 affecting BMI in a wide range of populations, and candidate genes for non- insulin-dependent diabetes mellitus, a consequence of obesity, have also been mapped to this region. We also identified many peaks near a –log10p of 2 (threshold for replicating an existing finding) in many areas across the genome that are within regions previously identified by other studies, as well as in locations that harbor genes known to influence weight regulation.
Alcohol dependence symptoms and consumption measures were examined for stability and heritability. Data were collected from 12,045 individuals (5376 twin pairs, 1293 single twins) aged 19 to 90 years in telephone interviews conducted in three collection phases. Phases 1 and 2 were independent samples, but Phase 3 targeted families of smokers and drinkers from the Phase 1 and 2 samples. The stability of dependence symptoms and consumption was examined for 1158 individuals interviewed in both Phases 1 and 3 (mean interval = 11.0 years). For 1818 individuals interviewed in Phases 2 and 3 (mean interval = 5.5 years) the stability of consumption was examined. Heritability was examined for each collection phase and retest samples from the selected Phase 3 collection. The measures examined were a dependence score, based on DSM-IIIR and DSM-IV criteria for substance dependence, and a quantity × frequency measure. Measures were moderately stable, with test–retest correlations ranging from .58 to .61 for dependence and from .55 to .64 for consumption. However, the pattern of changes over time for dependence suggested that the measure may more strongly reflect recent than lifetime experience. Similar to previous findings, heritabilities ranged from .42 to .51 for dependence and from .31 to .51 for consumption. Consumption was significantly less heritable in the younger Phase 2 cohort (23–39 years) compared to the older Phase 1 cohort (28–90 years).
The aim of this study is to characterize the relationship between major depression and the metabolic syndrome in a large community based sample of Australian men and women aged 26–90 years. A lifetime history of major depression was assessed by telephone interview following the DSM–III-R. A current history of metabolic syndrome was assessed following the United States National Cholesterol Education Program Adult Treatment Panel III (NCEP AP-III) guidelines 1 to 3 years later. Logistic regression was used to estimate the association between depression and the metabolic syndrome, and its component criteria, controlling for age, sex and alcohol dependence. There was no association between a lifetime history of major depression and the presence of the metabolic syndrome. There was a weak association between depression and low high-density lipoprotein cholesterol but not with other component criteria of the metabolic syndrome. Despite calls for interventions directed at depression to reduce the onset of the metabolic syndrome there are important failures to replicate in large samples such as this, no consensus regarding the threshold at which depression may pose a significant risk even allowing for heterogeneity across populations, and no consensus regarding confounders that may explain inter-study differences. The absence of any dosage effect of depression on the associated risk for the metabolic syndrome in other unselected samples does not support a direct causal relationship. The call for intervention studies on the basis of the currently published evidence base is unwarranted.
We investigated the genetic and environmental contributions to covariation between smoking age-at-onset, cigarette consumption and smoking persistence.
Multivariate biometrical modelling methods were applied to questionnaire data from Australian twins and their siblings (14 472 individuals from 6247 families). The contributions of genetic and environmental factors to covariation between the three traits were estimated, allowing for sex differences in both trait prevalence and the magnitude of genetic and environmental effects.
All traits were moderately heritable in males and females (estimates between 0·40 and 0·62), but there were sex differences in the extent to which additive genetic influences were shared across traits. Twin-specific environmental factors accounted for a substantial proportion of the variance in smoking age-at-onset in females (0·19) and males (0·12), but had little influence (<0·08) on other traits. Unique environmental factors were estimated to have a moderate influence on smoking age-at-onset (0·17 for females, 0·19 for males), but a stronger influence on other traits (between 0·39 and 0·49).
These results provide some insight into observed sex differences in smoking behaviour, and suggest that searching for pleiotropic genes may prove fruitful. However, further work on phenotypic definitions of smoking behaviour, particularly persistence, is warranted.
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