Associations of Valproate Doses With Weight Gain in Adult Psychiatric Patients: A 1-Year Prospective Cohort Study

Objective: The aim of this study was to evaluate valproate dose association with weight change, blood glucose, lipid levels, and blood pressure in a psychiatric population.

Methods: Data from 215 patients taking valproate for up to 1 year were collected from 2 longitudinal studies that monitored metabolic variables between 2007 and 2022. Linear mixed-effect models and logistic regressions were used to analyze the associations between valproate doses and metabolic outcomes.

Results: An increase in valproate dose of 500 mg was associated with a weight change of +0.52% per month over a year (P < .001). The association between valproate dose and weight change was evident both before and after 3 months of treatment. Weight increase was greater for treatment durations of < 3 months compared to ≥ 3 months (+0.56%, P < .001 and +0.12%, P = .02 per month, respectively). Using piecewise regression, a significant association between dose and weight gain was observed in patients receiving doses equal to or above the median dose (1,300 mg/d), with a +0.50% increase in weight for each dose increment of 500 mg (P = .004). Among men, each 500 mg dose increment was associated with weight increases of +0.59% per month (P = .004), whereas a trend was observed for women (+0.40%, P = .09). No associations were found between valproate doses and blood glucose, lipid levels, or blood pressure over a 6-month treatment period.

Conclusions: This study provides evidence that valproate dose, mainly for doses at or above 1,300 mg/d, is associated with weight gain in psychiatric patients, suggesting that the lowest effective doses should be prescribed to minimize weight gain.

Associations between antipsychotics-induced weight gain and brain networks of impulsivity

Given the unpredictable rapid onset and ubiquitous consequences of weight gain induced by antipsychotics, 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., <5% from baseline weight after one month). We replicated our analyses in young (26 years, IQR = 22-33, N = 102) and middle-aged (56 years, IQR = 51-62, N = 875) healthy individuals from the general population. In early psychosis patients, higher weight gain was associated with poor impulse control score (β = 1.35; 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.

DNA methylation may partly explain psychotropic drug-induced metabolic side effects: results from a prospective 1-month observational study

BACKGROUND

Metabolic side effects of psychotropic medications are a major drawback to patients' successful treatment. Using an epigenome-wide approach, we aimed to investigate DNA methylation changes occurring secondary to psychotropic treatment and evaluate associations between 1-month metabolic changes and both baseline and 1-month changes in DNA methylation levels. Seventy-nine patients starting a weight gain inducing psychotropic treatment were selected from the PsyMetab study cohort. Epigenome-wide DNA methylation was measured at baseline and after 1 month of treatment, using the Illumina Methylation EPIC BeadChip.

RESULTS

A global methylation increase was noted after the first month of treatment, which was more pronounced (p < 2.2 × 10-16) in patients whose weight remained stable (< 2.5% weight increase). Epigenome-wide significant methylation changes (p < 9 × 10-8) were observed at 52 loci in the whole cohort. When restricting the analysis to patients who underwent important early weight gain (≥ 5% weight increase), one locus (cg12209987) showed a significant increase in methylation levels (p = 3.8 × 10-8), which was also associated with increased weight gain in the whole cohort (p = 0.004). Epigenome-wide association analyses failed to identify a significant link between metabolic changes and methylation data. Nevertheless, among the strongest associations, a potential causal effect of the baseline methylation level of cg11622362 on glycemia was revealed by a two-sample Mendelian randomization analysis (n = 3841 for instrument-exposure association; n = 314,916 for instrument-outcome association).

CONCLUSION

These findings provide new insights into the mechanisms of psychotropic drug-induced weight gain, revealing important epigenetic alterations upon treatment, some of which may play a mediatory role.

Metabolic disturbances are risk factors for readmission to psychiatric hospitals in non-smokers but not in smokers: results from a Swiss psychiatric cohort and in first-episode psychosis patients

Background

Psychiatric patients are at high risk of readmission, and a high body mass index has previously been shown as a risk factor. We sought to replicate this finding and 1) to prospectively assess the association of metabolic syndrome and its five components with readmission in psychiatric hospitals and 2) to identify other clinical and sociodemographic predictors of readmission.

Methods

Between 2007 and 2019, data on 16727 admissions of 7786 adult and elderly patients admitted to the Department of Psychiatry of the Lausanne University Hospital, were collected. Metabolic syndrome was defined according to the International Diabetes Federation definition. Cox frailty models were used to investigate the associations between readmission and metabolic disturbances.

Results

A total of 2697 (35%) patients were readmitted to our psychiatric hospital. Novel risk factors for readmission in non-smokers were identified, including being overweight (HR=1.26; 95%CI=[1.05; 1.51]) or obese (HR=1.33; 95%CI=[1.08; 1.62]), displaying hypertriglyceridemia (HR=1.21; 95%CI=[1.04; 1.40]) and metabolic syndrome (HR=1.26; 95%CI=[1.02; 1.55]). Central obesity and hyperglycemia increased the risk of readmission when considering the Health of the Nation Outcome Scales variable. In first-episode psychosis patients, obesity (HR=2.23; 95%CI=[1.14; 4.30]) and high-density lipoprotein hypocholesterolemia (HR=1.90; 95%CI=[1.14; 3.20]) doubled the risk of readmission.

Conclusion

The observed interaction between smoking and metabolic variables are compatible with a ceiling effect; metabolic variables increase the risk of readmission in non-smokers but not in smokers who are already at higher risk. Future studies should determine whether better metabolic monitoring and treatment can reduce readmission risk.

Identification of four novel loci associated with psychotropic drug-induced weight gain in a Swiss psychiatric longitudinal study: A GWAS analysis

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 < 5 × 10-8): 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.

Is Clozapine-induced Weight Gain Dose-dependent? Results From a Prospective Cohort Study

BACKGROUND

Antipsychotic-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 DESIGN

Linear 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 RESULTS

Each 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.

CONCLUSIONS

This 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.

Evolutions of Metabolic Parameters Following Switches of Psychotropic Drugs: A Longitudinal Cohort Study

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 < .001, P < .001, and P = .003, respectively). High-to-high or high-to-medium switches resulted in a greater weight increase than switching to lower-risk categories (+0.77% and + 0.39% respectively, P < .001). No difference was found between switching medium-to-medium and medium-to-low (P ≈ 1). Switching high-to-low resulted in 10% weight loss after 2 years, with the greatest loss occurring the first 6 months after the switch. Compared with high-risk controls, lower total cholesterol (-0.27 mmol/l, P = .043) in the high-to-low group, and lower glucose (-0.44 mmol/l, P = .032) and systolic blood pressure (-5.50 mmHg, P = .034) in the low-to-low group were found. Polygenic scores were not associated with weight changes in controls or after switching.

CONCLUSION

Psychotropic switches to a lower- or same-risk drug can attenuate weight gain, with only switching high to low resulting in weight loss.

The psychosis metabolic risk calculator (PsyMetRiC) for young people with psychosis: International external validation and site-specific recalibration in two independent European samples

Background

Cardiometabolic dysfunction is common in young people with psychosis. Recently, the Psychosis Metabolic Risk Calculator (PsyMetRiC) was developed and externally validated in the UK, predicting up-to six-year risk of metabolic syndrome (MetS) from routinely collected data. The full-model includes age, sex, ethnicity, body-mass index, smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein, and triglyceride concentrations; the partial-model excludes biochemical predictors.

Methods

To move toward a future internationally-useful tool, we externally validated PsyMetRiC in two independent European samples. We used data from the PsyMetab (Lausanne, Switzerland) and PAFIP (Cantabria, Spain) cohorts, including participants aged 16-35y without MetS at baseline who had 1-6y follow-up. Predictive performance was assessed primarily via discrimination (C-statistic), calibration (calibration plots), and decision curve analysis. Site-specific recalibration was considered.

Findings

We included 1024 participants (PsyMetab n=558, male=62%, outcome prevalence=19%, mean follow-up=2.48y; PAFIP n=466, male=65%, outcome prevalence=14%, mean follow-up=2.59y). Discrimination was better in the full- compared with partial-model (PsyMetab=full-model C=0.73, 95% C.I., 0.68-0.79, partial-model C=0.68, 95% C.I., 0.62-0.74; PAFIP=full-model C=0.72, 95% C.I., 0.66-0.78; partial-model C=0.66, 95% C.I., 0.60-0.71). As expected, calibration plots revealed varying degrees of miscalibration, which recovered following site-specific recalibration. PsyMetRiC showed net benefit in both new cohorts, more so after recalibration.

Interpretation

The study provides evidence of PsyMetRiC's generalizability in Western Europe, although further local and international validation studies are required. In future, PsyMetRiC could help clinicians internationally to identify young people with psychosis who are at higher cardiometabolic risk, so interventions can be directed effectively to reduce long-term morbidity and mortality.

Funding

NIHR Cambridge Biomedical Research Centre (BRC-1215-20014); The Wellcome Trust (201486/Z/16/Z); Swiss National Research Foundation (320030-120686, 324730- 144064, and 320030-173211); The Carlos III Health Institute (CM20/00015, FIS00/3095, PI020499, PI050427, and PI060507); IDIVAL (INT/A21/10 and INT/A20/04); The Andalusian Regional Government (A1-0055-2020 and A1-0005-2021); SENY Fundacion Research (2005-0308007); Fundacion Marques de Valdecilla (A/02/07, API07/011); Ministry of Economy and Competitiveness and the European Fund for Regional Development (SAF2016-76046-R and SAF2013-46292-R).For the Spanish and French translation of the abstract see Supplementary Materials section.

CYP2C19 expression modulates affective functioning and hippocampal subiculum volume-a large single-center community-dwelling cohort study

Given controversial findings of reduced depressive symptom severity and increased hippocampus volume in CYP2C19 poor metabolizers, we sought to provide empirical evidence from a large-scale single-center longitudinal cohort in the community-dwelling adult population-Colaus|PsyCoLaus in Lausanne, Switzerland (n = 4152). We looked for CYP2C19 genotype-related behavioral and brain anatomy patterns using a comprehensive set of psychometry, water diffusion- and relaxometry-based magnetic resonance imaging (MRI) data (BrainLaus, n = 1187). Our statistical models tested for differential associations between poor metabolizer and other metabolizer status with imaging-derived indices of brain volume and tissue properties that explain individuals' current and lifetime mood characteristics. The observed association between CYP2C19 genotype and lifetime affective status showing higher functioning scores in poor metabolizers, was mainly driven by female participants (ß = 3.9, p = 0.010). There was no difference in total hippocampus volume between poor metabolizer and other metabolizer, though there was higher subiculum volume in the right hippocampus of poor metabolizers (ß = 0.03, p_FDRcorrected = 0.036). Our study supports the notion of association between mood phenotype and CYP2C19 genotype, however, finds no evidence for concomitant hippocampus volume differences, with the exception of the right subiculum.

An International External Validation and Revision of the PsyMetRiC Cardiometabolic Risk Prediction Algorithm for Young People with Psychosis

Introduction

The comorbidity between cardiometabolic and psychotic disorders develops early. This is a crucial window of opportunity to reduce excess morbidity and mortality. Recently, a cardiometabolic risk prediction algorithm for young people with psychosis, the psychosis metabolic risk calculator (PsyMetRiC) was developed and externally validated in the UK. However, its international transportability is unknown.

Objectives

We performed the first international validation study of PsyMetRiC in Lausanne, Switzerland, and examined whether additional variables (clinical and/or genetic) may improve the predictive performance of the algorithm

Methods

We included people aged 16-35y with psychosis from the PsyMetab cohort, who did not have MetS at baseline, and who had 1-6y follow-up data. The PsyMetRiC partial (age, sex, ethnicity, body mass index, smoking status, and prescription of a metabolically-active antipsychotic) and full (also including high-density lipoprotein and triglycerides) algorithms were applied. Predictive performance was assessed using measures of discrimination (C-statistic) and calibration (calibration plots). Recalibration steps included refitting the intercept and/or slope if necessary. Additional variables (e.g. speed of weight gain, polygenic risk scores) were added to the model and predictive performance was reassessed.

Results

We included 545 participants. The discrimination performance of both PsyMetRiC algorithms was good (C>0.75), and calibration plots showed good agreement between observed and predicted risk. Additional analyses to be conducted.

Conclusions

PsyMetRiC is likely to be generalizable for use in Switzerland, suggesting that PsyMetRiC may also be suitable for use in other European populations. While additional international validations are required, these findings are an encouraging step toward an international cardiometabolic risk prediction algorithm for young people with psychosis.

Disclosure

No significant relationships.

Metabolic disturbances are associated with psychiatric readmission: results from a Swiss psychiatric cohort

Introduction

High BMI has been associated with psychiatric rehospitalisation.

Objectives

We aimed to replicate this finding in a large Swiss psychiatric cohort and to examine whether other metabolic disturbances are independently associated with psychiatric readmission.

Methods

Data on 16’727 hospitalizations of 7’786 patients admitted between January 1st, 2007 and December 31st, 2019 at the Department of Psychiatry of the Lausanne University Hospital, were collected. Metabolic syndrome was defined according to International Diabetes Federation definition. Generalized Linear Mixed Models were used to investigate the associations between psychiatric readmission and metabolic syndrome and/or its five components.

Results

The readmitted population (N=2’935; 37.7% patients) had higher BMI, and were more likely to have central obesity, hypertriglyceridemia, and hypertension. Multivariate analyses confirmed that having a BMI ≥ 25 kg.m-2 was associated with psychiatric readmission (25 kg.m-2≤ BMI< 30 kg.m-2: OR = 1.88; 95%CI [1.55-2.29]; BMI≥30 kg.m-2: OR = 3.5; 95%CI [2.85-4.30]) when compared to patients with 18.5≤BMI<25 kg.m-2. Interestingly, novel factors associated with readmission were identified including metabolic syndrome (OR = 1.57, 95%CI [1.05-2.33]), central obesity (OR = 1.81, 95%CI [1.33-2.46]), hypertriglyceridemia (OR = 1.59; 95%CI [1.38-1.83]), HDL hypocholesterolemia (OR = 1.22; 95%CI [1.06-1.40]) and hyperglycemia (OR = 1.58; 95%CI [1.35-1.85]).

Conclusions

Metabolic syndrome, central obesity, hypertriglyceridemia, HDL hypocholesterolemia, hyperglycemia and obesity were associated with psychiatric readmission. Possible causes will be presented and discussed (e.g. reduced adherence to treatment in patients with metabolic disorders, multiple psychotropic treatments in non-responders increasing the risk of metabolic worsening).

Disclosure

No significant relationships.

DNA methylation may mediate psychotropic drug-induced metabolic side effects: results from a 1-month observational study

Introduction

Metabolic side effects of psychotropic medications are a major drawback to patients’ effective treatment. Among the mechanisms underlying their development, DNA methylation may be involved.

Objectives

The aim of this study was to estimate DNA methylation changes occurring secondary to psychotropic treatment and evaluate associations between 1-month metabolic changes and baseline DNA methylation or 1-month DNA methylation changes, using an epigenome-wide approach.

Methods

Seventy-nine psychiatric patients recruited as part of PsyMetab study, who started a treatment with either an antipsychotic, a mood stabilizer or mirtazapine were selected. Epigenome-wide DNA methylation was measured using the Illumina Methylation EPIC BeadChip at baseline and after one month of treatment.

Results

A global methylation increase was observed after 1 month of treatment, which was more pronounced in patients whose weight remained stable (i.e., <2.5% weight increase). Epigenome-wide significant methylation changes were observed at 52 loci in the whole cohort and at one site, namely cg12209987, located in an intergenic region within an enhancer, specifically in patients who underwent important early weight gain (i.e., ≥5% weight increase) during the same period of treatment (p<5*10^-8). Multivariable analysis confirmed an association between an increase in methylation at this locus and weight gain in the whole cohort (p=0.004). Epigenome-wide association analyses failed to identify any significant link between other metabolic changes (e.g. glucose or lipid levels) and methylation data.

Conclusions

These findings give new insight into the mechanisms of psychotropic drug-induced weight gain. With improved understanding of the metabolic side effects, the use of precision medicine with epigenetics may become possible

Disclosure

No significant relationships.

Effects of psychotropic switches on weight change: a prospective cohort study

Introduction

Many psychotropic drugs can induce weight gain with differences in their metabolic risk profiles (i.e. high, medium or low-risk).

Objectives

To compare the weight evolution of patients switching versus patients keeping their psychotropic drugs with different risk-profiles.

Methods

Data for patients switching or keeping the same drug were obtained from the Psyclin (from 2007 to 2015) and Psymetab (2007- 2019) cohort studies, conducted at the Lausanne University Hospital, Switzerland. Patients either switched from a high to a low-risk, a high to a medium-risk, a medium to a low-risk drug, or for a drug with the same risk category. Patients not switching either kept a high, medium or low-risk drug. The evolution of weight is currently being analyzed using a linear mixed-effect model.

Results

Preliminary results showed that switching from a high to low-risk molecule had the strongest impact on weight changes. The analysis being ongoing, the quantitative results will be presented at the congress.

Conclusions

Switching from a high-risk to a low-risk molecule is likely to have the strongest impact on weight changes.

Disclosure

No significant relationships.

CYP2C19 expression modulates affective functioning and brain anatomy – a large single-center community-dwelling cohort study

Introduction

The association between CYP2C19 poor metabolizer status, depressive symptom severity and hippocampal volume in humans is controversial. Progress in understanding not only the pathophysiology of depression but also potential protective mechanisms is important both for daily clinical practice and for the development of new antidepressant therapies.

Objectives

To test and validate previous findings regarding the impact of CYP2C19 status on depressive symptoms and to examine whether it could influence hippocampus subregions and brain tissue microstructure.

Methods

A total of 4152 individuals from the Longitudinal cohort in the community-dwelling adult population - Colaus|PsyCoLaus in Lausanne, Switzerland were included. They have participated in at least one psychiatric evaluation. Brain anatomy patterns using a comprehensive set of psychometry, water diffusion- and relaxometry-based magnetic resonance imaging data were analysed in a subset of the cohort (BrainLaus, n=1187).

Results

In this population-based cohort study, better lifetime global assessment of functioning scores were observed in poor metabolizers when compared to other metabolizers, this result was mainly driven by female participants (ß=3.9, P=0.01). Examination of brain imaging data revealed that higher right hippocampal subiculum volume was related to poor metabolizer status (ß=0.03, P=0.006). In addition, associations were observed between metabolizer status and white matter microstructure in the left uncinate fasciculus (ß=-0.01, P=0.01) and the left cingulum bundle (ß=-0.01, P=0.01).

Conclusions

CYP2C19 status is associated with modifications in lifetime global functioning, and brain anatomy. Such differences in brain structures can contribute to explain the protective effect of CYP2C19 poor metabolizer status.

Disclosure

No significant relationships.

Insomnia disorders are associated with increased cardiometabolic disturbances and death risks from cardiovascular diseases in psychiatric patients treated with weight-gain-inducing psychotropic drugs: results from a Swiss cohort

STUDY OBJECTIVES

Insomnia disorders as well as cardiometabolic disorders are highly prevalent in the psychiatric population compared to the general population. We aimed to investigate their association and evolution over time in a Swiss psychiatric cohort.

METHODS

Data for 2861 patients (8954 observations) were obtained from two prospective cohorts (PsyMetab and PsyClin) with metabolic parameters monitored routinely during psychotropic treatment. Insomnia disorders were based on the presence of ICD-10 "F51.0" diagnosis (non-organic insomnia), the prescription of sedatives before bedtime or the discharge letter. Metabolic syndrome was defined using the International Diabetes Federation definition, while the 10-year risk of cardiovascular event or death was assessed using the Framingham Risk Score and the Systematic Coronary Risk Estimation, respectively.

RESULTS

Insomnia disorders were observed in 30% of the cohort, who were older, predominantly female, used more psychotropic drugs carrying risk of high weight gain (olanzapine, clozapine, valproate) and were more prone to suffer from schizoaffective or bipolar disorders. Multivariate analyses showed that patients with high body mass index (OR = 2.02, 95%CI [1.51-2.72] for each ten-kg/m² increase), central obesity (OR = 2.20, [1.63-2.96]), hypertension (OR = 1.86, [1.23-2.81]), hyperglycemia (OR = 3.70, [2.16-6.33]), high density lipoprotein hypocholesterolemia in women (OR = 1.51, [1.17-1.95]), metabolic syndrome (OR = 1.84, [1.16-2.92]) and higher 10-year risk of death from cardiovascular diseases (OR = 1.34, [1.17-1.53]) were more likely to have insomnia disorders. Time and insomnia disorders were associated with a deterioration of cardiometabolic parameters.

CONCLUSIONS

Insomnia disorders are significantly associated with metabolic worsening and risk of death from cardiovascular diseases in psychiatric patients.

Daily Dose Effects of Risperidone on Weight and Other Metabolic Parameters: A Prospective Cohort Study

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.

Olanzapine-associated dose-dependent alterations for weight and metabolic parameters in a prospective cohort

Metabolic abnormalities have been associated with olanzapine treatment. We assessed if olanzapine has dose‐dependent effects on metabolic parameters with changes for weight, blood pressure, lipid and glucose profiles being modelled using linear mixed‐effects models. The risk of metabolic abnormalities including early weight gain (EWG) (≥5% during first month) was assessed using mixed‐effects logistic regression models. In 392 olanzapine‐treated patients (median age 38.0 years, interquartile range [IQR] = 26.0–53.3, median dose 10.0 mg/day, IQR = 5.0–10.0 for a median follow‐up duration of 40.0 days, IQR = 20.7–112.2), weight gain was not associated with olanzapine dose (p = 0.61) although it was larger for doses versus ≤10 mg/day (2.54 ± 5.55 vs. 1.61 ± 4.51% respectively, p = 0.01). Treatment duration and co‐prescription of >2 antipsychotics, antidepressants, benzodiazepines and/or antihypertensive agents were associated with larger weight gain (p < 0.05). Lower doses were associated with increase in total and HDL cholesterol and systolic and diastolic blood pressure (p < 0.05), whereas higher doses were associated with glucose increases (p = 0.01). Patients receiving >10 mg/day were at higher EWG risk (odds risk: 2.15, 1.57–2.97). EWG might be prominent in high‐dose olanzapine‐treated patients with treatment duration and co‐prescription of other medications being weight gain moderators. The lack of major dose‐dependent patterns for weight gain emphasizes that olanzapine‐treated patients are at weight gain risk regardless of the dose.

Effect of Quetiapine, from Low to High Dose, on Weight and Metabolic Traits: Results from a Prospective Cohort Study

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.,<150 mg/day).

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.

Associations Between High Plasma Methylxanthine Levels, Sleep Disorders and Polygenic Risk Scores of Caffeine Consumption or Sleep Duration in a Swiss Psychiatric Cohort

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.

Amisulpride: Real-World Evidence of Dose Adaptation and Effect on Prolactin Concentrations and Body Weight Gain by Pharmacokinetic/Pharmacodynamic Analyses

BACKGROUND

Amisulpride is an antipsychotic used in a wide range of doses. One of the major adverse events of amisulpride is hyperprolactinemia, and the drug might also induce body weight gain.

OBJECTIVE

The aims of this work were to characterize the pharmacokinetics of amisulpride in order to suggest optimal dosage regimens to achieve the reference range of trough concentrations at steady-state (C_min,ss) and to describe the relationship between drug pharmacokinetics and prolactin and body weight data.

METHODS

The influence of clinical and genetic characteristics on amisulpride pharmacokinetics was quantified using a population approach. The final model was used to simulate C_min,ss under several dosage regimens, and was combined with a direct E_max model to describe the prolactin data. The effect of model-based average amisulpride concentrations over 24 h (C_av) on weight was estimated using a linear model.

RESULTS

A one-compartment model with first-order absorption and elimination best fitted the 513 concentrations provided by 242 patients. Amisulpride clearance significantly decreased with age and increased with lean body weight (LBW). C_min,ss was higher than the reference range in 65% of the patients aged 60 years receiving 400 mg twice daily, and in 82% of the patients aged > 75 years with a LBW of 30 kg receiving 200 mg twice daily. The pharmacokinetic/pharmacodynamic model included 101 prolactin measurements from 68 patients. The E_max parameter was 53% lower in males compared with females. Model-predicted prolactin levels were above the normal values for C_min,ss within the reference range. Weight gain did not depend on C_av.

CONCLUSIONS

Amisulpride treatment might be optimized when considering age and body weight. Hyperprolactinemia and weight gain do not depend on amisulpride concentrations. Modification of the amisulpride dosage regimen is not appropriate to reduce prolactin concentrations and alternative treatment should be considered.

[MRI of medial-temporal atrophy as a biomarker for Alzheimer's disease]

Significant progresses have been made in the understanding of the pathological mechanisms of Alzheimer's disease (AD) and in developing tools enabling to detect its stages and its progression in vivo. At present, we know that the changes in AD pathophysiology occur many years before its clinical manifestations. Atrophy of the medial temporal lobe - containing anatomical structures essential for declarative memory, mostly impaired in AD - is one of the biomarkers detectable by magnetic resonance which can help us to predict the progression to dementia in patients with mild cognitive impairment. The atrophy assessment of the posterior cingulate cortex and the precuneus, other key hubs of the declarative memory network, can also be a useful complement.