Jojanneke Bruins

The effects of lifestyle interventions on (long-term) weight management, cardiometabolic risk and depressive symptoms in people with psychotic disorders: a meta-analysis.

Aims The aim of this study was to estimate the effects of lifestyle interventions on bodyweight and other cardiometabolic risk factors in people with psychotic disorders. Additionally, the long-term effects on body weight and the effects on depressive symptoms were examined. Material and Methods We searched four databases for randomized controlled trials (RCTs) that compared lifestyle interventions to control conditions in patients with psychotic disorders. Lifestyle interventions were aimed at weight loss or weight gain prevention, and the study outcomes included bodyweight or metabolic parameters. Results The search resulted in 25 RCTs -only 4 were considered high quality- showing an overall effect of lifestyle interventions on bodyweight (effect size (ES) = −0.63, p<0.0001). Lifestyle interventions were effective in both weight loss (ES = −0.52, p<0.0001) and weight-gain-prevention (ES = −0.84, p = 0.0002). There were significant long-term effects, two to six months post-intervention, for both weight-gain-prevention interventions (ES = −0.85, p = 0.0002) and weight loss studies (ES = −0.46, p = 0.02). Up to ten studies reported on cardiometabolic risk factors and showed that lifestyle interventions led to significant improvements in waist circumference, triglycerides, fasting glucose and insulin. No significant effects were found for blood pressure and cholesterol levels. Four studies reported on depressive symptoms and showed a significant effect (ES = −0.95, p = 0.05). Conclusion Lifestyle interventions are effective in treating and preventing obesity, and in reducing cardiometabolic risk factors. However, the quality of the studies leaves much to be desired.

Cannabis use in people with severe mental illness: The association with physical and mental health – a cohort study. A Pharmacotherapy Monitoring and Outcome Survey study

Objective: In the general population cannabis use is associated with better cardiometabolic outcomes. Patients with severe mental illness frequently use cannabis, but also present increased cardiometabolic risk factors. We explore the association between cannabis use and cardiometabolic risk factors in patients with severe mental illness. Method: A total of 3169 patients with severe mental illness from a Dutch cohort were included in the study. The association of cannabis use with body mass index, waist circumference, blood pressure, cholesterol, triglycerides, glucose, glycated hemoglobin and Positive and Negative Syndrome Scale was examined with separate univariate AN(C)OVA. Changes in metabolic risk factors and Positive and Negative Syndrome Scale were examined after a follow-up interval of 9–24 months, for patients who continued, discontinued, started or were never using cannabis between the two assessments. Results: Cannabis users at baseline had lower body mass index, smaller waist circumference, lower diastolic blood pressure, and more severe psychotic symptoms than non-users. Patients who discontinued their cannabis use after the first assessment had a greater increase in body mass index, waist circumference, diastolic blood pressure and triglyceride concentrations than other patients, and the severity of their psychotic symptoms had decreased more compared to continued users and non-users. Conclusion: Extra attention should be paid to the monitoring and treatment of metabolic parameters in patients who discontinue their cannabis use.

Cannabis and a lower BMI in psychosis: What is the role of AKT1?

Cannabis use has been associated with favorable outcomes on metabolic risk factors. The cause of this relation is still unknown. In this study we investigated whether this effect is mediated by the AKT1 gene, as activation of the related enzyme by cannabis may cause metabolic changes. Six Single Nucleotide Polymorphisms (SNPs) of the AKT1 gene (rs1130214, rs1130233, rs2494732, rs2498784, rs3730358, and rs3803300) of patients with psychotic disorders (n=623) were related to Body Mass Index (BMI), levels of glycosylated hemoglobin (HBA1c) and total metabolic risk. Next, mediation analysis was performed with BMI as outcome, cannabis as predictor, and AKT1 as mediator. Cannabis use was inversely related to BMI but not with levels of HBA1c and total metabolic risk. Moreover, out of 6 AKT1 SNPs, rs2494732 was associated with cannabis use, but AKT1 did not mediate the effect of cannabis on BMI. In conclusion, cannabis use is likely to be associated with a lower BMI in patients with a psychotic disorder. Moreover, AKT1 risk alleles may increase the incidence of cannabis use in patients with a psychotic disorder, but AKT1 does not appear to mediate the effect of cannabis on BMI.

The relation between AKT1, cannabis use and metabolic risk factors in psychosis

Background: Cardiovascular and metabolic problems combined with a bad lifestyle are a major cause of a shortened life expectancy in chronic psychotic disorders. While the incidence of cannabis use is twice as high in psychosis compared to the general population, use of cannabis has been associated with better outcomes on cardiometabolic risk factors. This study investigates whether this effect is mediated by the AKT1 gene, as activation of the related enzyme by cannabis may cause changes in metabolism. Methods: Patients with a recent onset psychosis (n=623) were included from a cohort study (GROUP). Cannabis use, based on self-report and urine screening, was related to Body Mass index (BMI). Next the mediating effects of six AKT1 polymorphisms (rs1130214, rs1130233, rs2494732, rs2498784, rs3730358 and rs3803300) were investigated. Results: There was a strong, negative association between BMI and cannabis use. Moreover, two SNPs (rs1130233 and rs2494732) showed an association with cannabis use, but did not mediate the effect of cannabis on BMI. Conclusion: In conclusion, cannabis use results in a lower body weight in patients with a psychosis. While AKT1 is related to cannabis use, it does not affect body mass via AKT1. Instead, AKT1 risk alleles may increase the incidence of cannabis use. Future studies may investigate whether other genes mediate the effect between cannabis and metabolic risk factors.

Cannabis Use in Psychosis: the Effects On Metabolic Health

Introduction Studies in the general population show cannabis use has a beneficial effect on metabolic disorders. Given the increased cardiometabolic risk in patients with psychotic disorders, as well as their prevalent use of cannabis, we aim to investigate whether such effects are also evident in these patients. Method 3176 patients with chronic psychotic disorders from mental health institutions in the Netherlands were included in the study. With multivariate regression analyses we examined the effects of cannabis use on metabolic risk factors; BMI, waist circumference, blood pressure (BP), cholesterol, HDL-C, LDL-C, triglycerides, glucose and HbA 1c . Age, sex, smoking, alcohol use and antipsychotic drugs were included as confounders. Next, we examined change in metabolic risk factors after one-year follow up for cannabis users, non-users, discontinuers and starters. Results We found a significant negative association between cannabis use and BMI (p=0.003), waist circumference (p>0.001), diastolic BP (p=0.015) and HbA 1c (0.004). One year later, patients who had discontinued their cannabis use had a greater increase of BMI (p=0.002) and waist circumference (p=0.011) than other patients. They also had a greater increase of diastolic BP than non-users (p=0.036) or starters (p=0.004). Conclusion Discontinuation of cannabis use increased metabolic risk. To stop cannabis use is often an important treatment goal, because it reduces psychotic symptoms. However, physicians should be aware of the increased metabolic risk in patients who discontinue the use of cannabis. Extra attention should be paid to monitoring and treatment of metabolic parameters in these patients to prevent cardiovascular diseases and premature cardiovascular mortality.