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BackgroundAntipsychotic-induced metabolic adverse effects are risk factors for cardiometabolic comorbidities. Whether dose lowering could mitigate such effects remains unclear. The present study aims to investigate the associations between clozapine doses and modifications of weight, blood pressure, blood glucose, and lipid levels.Study DesignLinear mixed-effects models of weight changes over 1 year and of variations of other metabolic parameters over 4 months were applied to a prospective cohort of 115 patients. Age- and sex-stratified analyses of weight changes were also performed.Study ResultsEach 100 mg dose increment of clozapine was associated on average with a +0.48% weight increase (P = .004) over 1 year of treatment. Weight increase was greater for treatment duration ≤3 vs >3 months (+0.84% and +0.47% per month, respectively, P < .001), with a significant association with the dose for durations >3 months (+0.54%, P = .004) and a trend for durations ≤3 months (+0.33%, P = .075). Dose increments of 100 mg were also associated with weight increases of +0.71% among adults (P = .001), +1.91% among the elderly (P < .001) and +1.32% among men (P < .001) with no associations among women (P = .62). Among young adults, weight change was positively associated with doses ≤300 mg/day (+2.19% per 100 mg, P = .001), whereas no association was found with doses >300 mg/day (P = .60). No significant effect of clozapine dose on other metabolic parameters was found.ConclusionsThis study reports a modest effect of clozapine dose increases on weight gain over 1 year with differences among age categories and sexes and no dose effect on other metabolic parameters over 4 months.

Patients suffering from mental disorders are at high risk of developing cardiovascular diseases, leading to a reduction in life expectancy. Genetic variants can display greater influence on cardiometabolic features in psychiatric cohorts compared to the general population. The difference is possibly due to an intricate interaction between the mental disorder or the medications used to treat it and metabolic regulations. Previous genome wide association studies (GWAS) on antipsychotic-induced weight gain included a low number of participants and/or were restricted to patients taking one specific antipsychotic. We conducted a GWAS of the evolution of body mass index (BMI) during early (i.e., ≤ 6) months of treatment with psychotropic medications inducing metabolic disturbances (i.e., antipsychotics, mood stabilizers and some antidepressants) in 1135 patients from the PsyMetab cohort. Six highly correlated BMI phenotypes (i.e., BMI change and BMI slope after distinct durations of psychotropic treatment) were considered in the analyses. Our results showed that four novel loci were associated with altered BMI upon treatment at genome-wide significance (p −8): rs7736552 (near MAN2A1), rs11074029 (in SLCO3A1), rs117496040 (near DEFB1) and rs7647863 (in IQSEC1). Associations between the four loci and alternative BMI-change phenotypes showed consistent effects. Replication analyses in 1622 UK Biobank participants under psychotropic treatment showed a consistent association between rs7736552 and BMI slope (p = 0.017). These findings provide new insights into metabolic side effects induced by psychotropic drugs and underline the need for future studies to replicate these associations in larger cohorts.

Given the unpredictable rapid onset and ubiquitous consequences of weight gain induced by antipsychotics (AP), there is a pressing need to get insights into the underlying processes at the brain system level that will allow stratification of “at risk” patients. The pathophysiological hypothesis at hand is focused on brain networks governing impulsivity that are modulated by neuro-inflammatory processes. To this aim, we investigated brain anatomy and functional connectivity in patients with early psychosis (median age: 23 years, IQR = 21–27) using anthropometric data and magnetic resonance imaging acquired one month to one year after initiation of AP medication. Our analyses included 19 patients with high and rapid weight gain (i.e., ≥ 5% from baseline weight after one month) and 23 patients with low weight gain (i.e., P = 0.03). Here, the observed brain differences comprised nodes of impulsivity networks - reduced frontal lobe grey matter volume (Pcorrected=0.007) and higher striatal volume (Pcorrected=0.048) paralleled by disruption of fronto-striatal functional connectivity (R=-0.32; P = 0.04). Weight gain was associated with the inflammatory biomarker plasminogen activator inhibitor-1 (β = 4.9, P = 0.002). There was no significant association between increased BMI or weight gain and brain anatomy characteristics in both cohorts of young and middle-aged healthy individuals. Our findings support the notion of weight gain in treated psychotic patients associated with poor impulse control, impulsivity-related brain networks and chronic inflammation.

Background Several psychotropic drugs can induce weight gain and metabolic alterations. The authors compared metabolic evolutions of patients switching versus continuing psychotropic treatments with different risk profiles. Methods Patients either switched from a high- to a medium- (N = 36) or low-risk drug (N = 27), from a medium- to a low-risk drug (N = 71), or to a same-risk drug (N = 61). Controls were kept using either a high- (N = 35), medium- (N = 155), or low-risk drug (N = 47). The evolution over 2 years of weight and metabolic parameters was analyzed using linear mixed-effect models, also examining the influence of polygenic risk scores for body mass index (BMI) or BMI and psychiatric disorders. Study Results High-, medium-, or low-risk controls gained on average 1.32%, 0.42%, and 0.36% more weight per month than patients switching from or within these risk categories (P Conclusion Psychotropic switches to a lower- or same-risk drug can attenuate weight gain, with only switching high to low resulting in weight loss.

Background: Atypical antipsychotics can induce metabolic side effects, but whether they are dose-dependent remains unclear. Objective: To assess the effect of risperidone and/or paliperidone dosing on weight gain and blood lipids, glucose, and blood pressure alterations. Methods: Data for 438 patients taking risperidone and/or its metabolite (paliperidone) for up to 1 year were obtained between 2007 and 2018 from a longitudinal study monitoring metabolic parameters. Results: For each milligram increase in dose, we observed a weight increase of 0.16% at 1 month of treatment (P = .002) and increases of 0.29%, 0.21%, and 0.25% at 3, 6, and 12 months of treatment, respectively (P < .001 for each). Moreover, dose increases of 1 mg raised the risk of a ≥ 5% weight gain after 1 month (OR = 1.18; P = .012), a strong predictor of important weight gain in the long term. When we split the cohort into age categories, the dose had an effect on weight change after 3 months of treatment (up to 1.63%, P = .008) among adolescents (age ≤ 17 years), at 3 (0.13%, P = .013) and 12 (0.13%, P = .036) months among adults (age > 17 and < 65 years), and at each timepoint (up to 1.58%, P < .001) among older patients (age ≥ 65 years). In the whole cohort, for each additional milligram we observed a 0.05 mmol/L increase in total cholesterol (P = .018) and a 0.04 mmol/L increase in LDL cholesterol (P = .011) after 1 year. Conclusions: Although of small amplitude, these results show an effect of daily risperidone dose on weight gain and blood cholesterol levels. Particular attention should be given to the decision of increasing the drug dose, and minimum effective dosages should be preferred.

European archives of psychiatry and clinical neuroscience (2022). doi:10.1007/s00406-022-01542-1, Published by Steinkopff, Darmstadt

Introduction The atypical antipsychotic quetiapine is known to induce weight gain and other metabolic complications. The underlying mechanisms are multifactorial and poorly understood with almost no information on the effect of dosage. Concerns were thus raised with the rise in low-dose quetiapine off-label prescription (i. e., Methods In this study, we evaluated the influence of quetiapine dose for 474 patients included in PsyMetab and PsyClin studies on weight and metabolic parameter evolution. Weight, blood pressure, lipid, and glucose profiles were evaluated during a follow-up period of 3 months after treatment initiation. Results Significant dose-dependent metabolic alterations were observed. The daily dose was found to influence weight gain and increase the risk of undergoing clinically relevant weight gain (≥7% from baseline). It was also associated with a change in plasma levels of cholesterol (total cholesterol, LDL cholesterol, and HDL cholesterol) as well as with increased odds of developing hypertriglyceridemia, as well as total and LDL hypercholesterolemia. No impact of a dose increase on blood pressure and plasma glucose level was observed. Discussion The dose-dependent effect highlighted for weight gain and lipid alterations emphasizes the importance of prescribing the minimal effective dose. However, as the effect size of a dose increase on metabolic worsening is low, the potential harm of low-dose quetiapine should not be dismissed. Prescriptions must be carefully evaluated and regularly questioned in light of side effect onset.

Objective: We first sought to examine the relationship between plasma levels of methylxanthines (caffeine and its metabolites) and sleep disorders, and secondarily between polygenic risk scores (PRS) of caffeine consumption or sleep duration with methylxanthine plasma levels and/or sleep disorders in a psychiatric cohort. Methods: Plasma levels of methylxanthines were quantified by ultra-high performance liquid chromatography/tandem mass spectrometry. In inpatients, sleep disorder diagnosis was defined using ICD-10 "F51.0," sedative drug intake before bedtime, or hospital discharge letters, while a subgroup of sedative drugs was used for outpatients. The PRS of coffee consumption and sleep duration were constructed using publicly available GWAS results from the UKBiobank. Results: 1,747 observations (1,060 patients) were included (50.3% of observations with sleep disorders). Multivariate analyses adjusted for age, sex, body mass index, setting of care and psychiatric diagnoses showed that patients in the highest decile of plasma levels of methylxanthines had more than double the risk for sleep disorders compared to the lowest decile (OR = 2.13, p = 0.004). PRS of caffeine consumption was associated with plasma levels of caffeine, paraxanthine, theophylline and with their sum (β = 0.1; 0.11; 0.09; and 0.1, p corrected = 0.01; 0.02; 0.02; and 0.01, respectively) but not with sleep disorders. A trend was found between the PRS of sleep duration and paraxanthine levels (β = 0.13, p corrected = 0.09). Discussion: Very high caffeine consumption is associated with sleep disorders in psychiatric in- and outpatients. Future prospective studies should aim to determine the benefit of reducing caffeine consumption in high caffeine-consuming patients suffering from sleep disorders.