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.

[Antipsychotic prescribing in adults : what to watch out for ?] / Prescription d'antipsychotiques chez les adultes : à quoi faut-il être attentif ?

Antipsychotics are known to produce frequent and/or potentially serious adverse effects, including neurological, cardiovascular, metabolic and endocrine effects. The side-effects of antipsychotics vary according to their affinity for different central and peripheral receptors, and individual vulnerabilities. Some of these side-effects are dose-dependent, while others are little or not ; thus, management strategies need to be adapted. Good management of adverse events is important to encourage patients' medication adherence and to reduce the cardiovascular morbidity and mortality of side effects. Good collaboration between psychiatrists and general practitioners or specialists is essential.

Les antipsychotiques sont connus pour engendrer des effets indésirables fréquents et/ou potentiellement graves, notamment neurologiques, cardiovasculaires, métaboliques et endocriniens. Les effets secondaires des antipsychotiques varient selon leur profil d'affinités pour les différents récepteurs cérébraux et périphériques et selon les vulnérabilités individuelles. Certains d'entre eux sont dose-dépendants, d'autres peu ou pas ; les stratégies de prise en charge sont donc à adapter. Une bonne gestion des effets indésirables est importante pour favoriser l'adhésion médicamenteuse des patients et atténuer leur impact en termes de morbimortalité. Une bonne collaboration entre médecins psychiatres et généralistes ou spécialistes est nécessaire.

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.

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.

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.

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.

Body Composition Assessment by Dual-Energy X-Ray Absorptiometry: A Useful Tool for the Diagnosis of Lipedema.

INTRODUCTION: Lipedema is a poorly known condition. Diagnosis is based almost exclusively on clinical criteria, which may be subjective and not always reliable. This study aimed to investigate regional body composition (BC) by dual-energy X-ray absorptiometry (DXA) in patients with lipedema and healthy controls and to determine cut-off values of fat mass (FM) indices to provide an additional tool for the diagnosis and staging of this condition. METHODS: This study is a single-center case-control study performed at Lausanne University Hospital, Switzerland. Women with clinically diagnosed lipedema underwent regional BC assessment by DXA. The control group without clinical lipedema was matched for age and body mass index (BMI) at a ratio of 1:2 and underwent similar examination. Regional FM (legs, arms, legs and arms, trunk, android and gynoid FM) was measured in (kg) and divided by FM index (FMI) (kg/m2) and total FM (kg). The trunk/legs and android/gynoid ratios were calculated. For all indices of FM distribution showing a significant difference between cases and controls, we defined the receiver operating characteristic (ROC) curves, calculating the area under the curve (AUC), sensitivity, specificity, and Youden's index. Types and stages of lipedema were compared in terms of FM indices. Correlation analyses between all FM distribution indices and lipedema stages were performed. RESULTS: We included 222 women (74 with lipedema and 148 controls). Overall, the mean age was 41 years (standard deviation [SD] 11), and mean BMI was 30.9 kg/m2 (SD 7.6). A statistically significant difference was observed for all DXA-derived indices of FM distribution between groups, except for arm FM indices. The ROC curve analysis of leg FM/total FM, as a potential indicator of lipedema, resulted in an AUC of 0.90 (95% confidence interval 0.86-0.94). According to Youden's index, optimal cut-off value identifying lipedema was 0.384. Sensitivity and specificity were 0.95 and 0.73, respectively. We found no significant differences between lipedema types and stages in terms of FM indices, nor significant correlations between the latter and lipedema stages. DISCUSSION/CONCLUSION: BC assessment by DXA, and particularly calculation of the leg FM/total FM index, is a simple tool that may help clinicians rule out lipedema in doubtful cases.

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.

Valproate is associated with early decrease of high-density lipoprotein cholesterol levels in the psychiatric population.

Few studies have evaluated the influence of valproate on the deterioration of the lipid profile in psychiatric patients. This observational study aimed to compare the evolution of metabolic parameters in a sample of adult patients starting valproate (n = 39) with a control group (n = 39) of patients starting aripiprazole, a drug associated with a low risk of metabolic deterioration. Data were obtained from a prospective study including psychiatric patients with metabolic parameters monitored during the first year of treatment. During the first month of treatment with valproate (median: 31 days [IQR: 25-36]), mean body mass index increased significantly (from 24.8 kg/m2 at baseline to 25.2 kg/m2 after one month; P = .03) and mean HDL-C levels decreased significantly (from 1.39 mmol/L to 1.27 mmol/L; P = .02). In comparison, these metabolic variables remained stable during the first month of treatment with aripiprazole. The proportion of patients with early (ie during the first month of treatment) HDL-C decrease of ≥ 5% was significantly higher under valproate (54%) than aripiprazole (15%) treatment (P < .001). These findings remind the importance of a prospective metabolic monitoring in patients who initiate valproate treatment. Further research should be conducted on larger samples and should focus on finding effective interventions to prevent such metabolic adverse effects.

Metabolomic alteration induced by psychotropic drugs: Short-term metabolite profile as a predictor of weight gain evolution.

Psychotropic drugs can induce strong metabolic adverse effects, potentially increasing morbidity and/or mortality of patients. Metabolomic profiling, by studying the levels of numerous metabolic intermediates and products in the blood, allows a more detailed examination of metabolism dysfunctions. We aimed to identify blood metabolomic markers associated with weight gain in psychiatric patients. Sixty-two patients starting a treatment known to induce weight gain were recruited. Two hundred and six selected metabolites implicated in various pathways were analyzed in plasma, at baseline and after 1 month of treatment. Additionally, 15 metabolites of the kynurenine pathway were quantified. This latter analysis was repeated in a confirmatory cohort of 24 patients. Among the 206 metabolites, a plasma metabolomic fingerprint after 1 month of treatment embedded 19 compounds from different chemical classes (amino acids, acylcarnitines, carboxylic acids, catecholamines, nucleosides, pyridine, and tetrapyrrole) potentially involved in metabolic disruption and inflammation processes. The predictive potential of such early metabolite changes on 3 months of weight evolution was then explored using a linear mixed-effects model. Of these 19 metabolites, short-term modifications of kynurenine, hexanoylcarnitine, and biliverdin, as well as kynurenine/tryptophan ratio at 1 month, were associated with 3 months weight evolution. Alterations of the kynurenine pathway were confirmed by quantification, in both exploratory and confirmatory cohorts. Our metabolomic study suggests a specific metabolic dysregulation after 1 month of treatment with psychotropic drugs known to induce weight gain. The identified metabolomic signature could contribute in the future to the prediction of weight gain in patients treated with psychotropic drugs.

Socio-economic position as a moderator of cardiometabolic outcomes in patients receiving psychotropic treatment associated with weight gain: results from a prospective 12-month inception cohort study and a large population-based cohort.

Weight gain and metabolic complications are major adverse effects of many psychotropic drugs. We aimed to understand how socio-economic status (SES), defined as the Swiss socio-economic position (SSEP), is associated with cardiometabolic parameters after initiation of psychotropic medications known to induce weight gain. Cardiometabolic parameters were collected in two Swiss cohorts following the prescription of psychotropic medications. The SSEP integrated neighborhood-based income, education, occupation, and housing condition. The results were then validated in an independent replication sample (UKBiobank), using educational attainment (EA) as a proxy for SES. Adult patients with a low SSEP had a higher risk of developing metabolic syndrome over one year versus patients with a high SSEP (Hazard ratio (95% CI) = 3.1 (1.5-6.5), n = 366). During the first 6 months of follow-up, a significant negative association between SSEP and body mass index (BMI), weight change, and waist circumference change was observed (25 ≤ age < 65, n = 526), which was particularly important in adults receiving medications with the highest risk of weight gain, with a BMI difference of 0.86 kg/m2 between patients with low versus high SSEP (95% CI: 0.03-1.70, n = 99). Eventually, a causal effect of EA on BMI was revealed using Mendelian randomization in the UKBiobank, which was notably strong in high-risk medication users (beta: -0.47 SD EA per 1 SD BMI; 95% CI: -0.46 to -0.27, n = 11,314). An additional aspect of personalized medicine was highlighted, suggesting the patients' SES represents a significant risk factor. Particular attention should be paid to patients with low SES when initiating high cardiometabolic risk psychotropic medications.

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 (ß = 0.1; 0.11; 0.09; and 0.1, pcorrected = 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 (ß = 0.13, pcorrected = 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.

Evaluation of Cardiometabolic Risk in a Large Psychiatric Cohort and Comparison With a Population-Based Sample in Switzerland.

BACKGROUND: Psychiatric patients are known to be at high risk of developing cardiovascular diseases (CVDs), leading to an increased mortality rate. OBJECTIVE: To assess the CVD risk (presence of metabolic syndrome [MetS] and calculated 10-year CVD risk) in a Swiss psychiatric cohort taking weight gain-inducing psychotropic drugs, compare the findings to a Swiss population-based cohort, and evaluate the prevalence of participants treated for metabolic disruptions in both cohorts. METHODS: Data for 1,216 psychiatric patients (of whom 634 were aged 35-75 years) were obtained between 2007 and 2017 from a study with metabolic parameters monitored during psychotropic treatment and between 2003 and 2006 for 6,733 participants from the population-based CoLaus|PsyCoLaus study. RESULTS: MetS as defined by the International Diabetes Federation (IDF) was identified in 33% of the psychiatric participants and 24.7% of the population-based subjects. Specifically, prevalence per the IDF definition was more than 3 times higher in the psychiatric cohort among women aged 35 to 49 years (25.6% vs 8.0%; P < 10-4). The psychiatric and population-based cohorts, respectively, had comparable predicted CVD risk (10-year risk of CVD event > 20%: 0% vs 0.1% in women and 0.3% vs 1.8% [P = .01] in men; 10-year risk of CVD death > 5%: 8.5% vs 8.4% [P = .58] in women and 13.4% vs 16.6% [P = .42] in men). No difference was observed among the proportion of participants with MetS treated for metabolic disturbances in the two cohorts, with the exception of women aged 35-49 years, for whom those in the psychiatric cohort were half as likely to receive treatment compared to participants in CoLaus|PsyCoLaus (17.8% vs 38.8% per the IDF definition; P = .0004). CONCLUSIONS: These findings emphasize the concern that psychiatric patients present an altered metabolic profile and that they do not receive adequate treatment for metabolic disruptions. Presence of metabolic disturbances should be routinely assessed, and adequate follow-up is needed to intervene early after illness onset.

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 (Cmin,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 Cmin,ss under several dosage regimens, and was combined with a direct Emax model to describe the prolactin data. The effect of model-based average amisulpride concentrations over 24 h (Cav) 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). Cmin,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 Emax parameter was 53% lower in males compared with females. Model-predicted prolactin levels were above the normal values for Cmin,ss within the reference range. Weight gain did not depend on Cav. 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.

Psychological trauma occurring during adolescence is associated with an increased risk of greater waist circumference in Early Psychosis patients treated with psychotropic medication.

BACKGROUND: It has been suggested that exposure to Childhood Trauma [CT] may play a role in the risk of obesity in Early Psychosis [EP] patients; however, whether this is independently of age at exposure to CT and the medication profile has yet to be investigated. METHODS: 113 EP-patients aged 18-35 were recruited from the Treatment and Early Intervention in Psychosis Program [TIPP-Lausanne]. Body Mass Index [BMI], Weight Gain [WG] and Waist Circumference [WC] were measured prospectively at baseline and after 1, 2, 3, 6 and 12 months of weight gain inducing psychotropic treatment. Patients were classified as Early-Trauma and Late-Trauma if the exposure had occurred before age 12 or between ages 12 and 16 respectively. Generalized Linear Mixed-Models were adjusted for age, sex, socioeconomic status, baseline BMI, medication and for diagnosis of depression. RESULTS: Late-Trauma patients, when compared to Non-Trauma patients showed greater WCs during the follow-up [p = 0.013]. No differences were found in any of the other follow-up measures. CONCLUSIONS: Exposition to CT during adolescence in EP-patients treated with psychotropic medication is associated with greater WC during the early phase of the disease. Further investigation exploring mechanisms underlying the interactions between peripubertal stress, corticoids responsiveness and a subsequent increase of abdominal adiposity is warranted.

Psychotropic drug-induced genetic-epigenetic modulation of CRTC1 gene is associated with early weight gain in a prospective study of psychiatric patients.

BACKGROUND: Metabolic side effects induced by psychotropic drugs represent a major health issue in psychiatry. CREB-regulated transcription coactivator 1 (CRTC1) gene plays a major role in the regulation of energy homeostasis and epigenetic mechanisms may explain its association with obesity features previously described in psychiatric patients. This prospective study included 78 patients receiving psychotropic drugs that induce metabolic disturbances, with weight and other metabolic parameters monitored regularly. Methylation levels in 76 CRTC1 probes were assessed before and after 1 month of psychotropic treatment in blood samples. RESULTS: Significant methylation changes were observed in three CRTC1 CpG sites (i.e., cg07015183, cg12034943, and cg 17006757) in patients with early and important weight gain (i.e., equal or higher than 5% after 1 month; FDR p value = 0.02). Multivariable models showed that methylation decrease in cg12034943 was more important in patients with early weight gain (≥ 5%) than in those who did not gain weight (p = 0.01). Further analyses combining genetic and methylation data showed that cg12034943 was significantly associated with early weight gain in patients carrying the G allele of rs4808844A>G (p = 0.03), a SNP associated with this methylation site (p = 0.03). CONCLUSIONS: These findings give new insights on psychotropic-induced weight gain and underline the need of future larger prospective epigenetic studies to better understand the complex pathways involved in psychotropic-induced metabolic side effects.

Lipid Disturbances in Adolescents Treated With Second-Generation Antipsychotics: Clinical Determinants of Plasma Lipid Worsening and New-Onset Hypercholesterolemia.

OBJECTIVE: Lipid disturbances following treatment with second-generation antipsychotics (SGAs) represent a major health concern. A previous study determined that early changes of plasma lipid levels ≥ 5% during the first month of treatment with SGAs predicts further lipid worsening and development of dyslipidemia. This current study aimed to determine the proportion of adolescents with early lipid changes ≥ 5% and who develop dyslipidemia during SGA treatment. METHODS: Data were obtained from a 1-year longitudinal study ongoing since 2007 including 53 adolescent psychiatric (ICD-10) patients (median age 16.5 years; interquartile range [IQR], 14.8-17.5 years) whose metabolic parameters were monitored prospectively during treatment. Plasma lipid levels (total, low-density lipoprotein, high-density lipoprotein [HDL-C], and non-high-density lipoprotein cholesterol and fasting triglycerides ) were measured at baseline and after 1, 3, and/or 12 months of SGA treatment. RESULTS: Half (n = 26; 49%) the adolescents had an early increase of total cholesterol levels by 5% or more during the first month of treatment, and one-third (n = 8/24; 33%) developed new-onset hypercholesterolemia during the first year of treatment. Hypercholesterolemia developed more frequently in female patients (P = .01) and in patients with an early increase of total cholesterol ≥ 5% (P = .02). Finally, patients whose HDL-C levels decreased by ≥ 5% during the first month of treatment had a larger HDL-C worsening after 3 months of treatment as compared with patients with early decrease of HDL-C by < 5% (P = .02). CONCLUSIONS: This study underlines the importance of prospectively monitoring metabolic parameters in adolescents after the introduction of SGAs.

Association Between Plasma Caffeine and Other Methylxanthines and Metabolic Parameters in a Psychiatric Population Treated With Psychotropic Drugs Inducing Metabolic Disturbances.

Importance: Multiple studies conducted in the general population identified an association between self-reported coffee consumption and plasma lipid levels. To date, no study assessed whether and which plasma methylxanthines (caffeine and/or its metabolites, i.e., paraxanthine, theophylline, and theobromine) are associated with plasma lipids. In psychiatric patients, an important coffee consumption is often reported and many psychotropic drugs can induce a rapid and substantial increase of plasma lipid levels. Objective: To determine whether plasma methylxanthines are associated with metabolic parameters in psychiatric patients receiving treatments known to induce metabolic disturbances. Design, Setting, and Participants: Data were obtained from a prospective study including 630 patients with metabolic parameters [i.e., body mass index (BMI), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and fasting triglycerides (TG)] monitored routinely during psychotropic treatment. Exposures: Plasma methylxanthines levels. Main Outcomes and Measures: Metabolic variables including BMI and plasma lipid levels. Results: Multivariate analyses indicated that BMI, TC, HDL-C, and non-HDL-C increased significantly with increasing total methylxanthines (p corrected ≤ 0.05). In addition, compared to patients with plasma caffeine concentration in the lowest quartile, those with caffeine concentration in the highest quartile were twice more prone to suffer from non-HDL hypercholesterolemia (p corrected = 0.05), five times more likely to suffer from hypertriglyceridemia (p corrected = 0.01) and four times more susceptible to be overweight (p corrected = 0.01). Conclusions and Relevance: This study showed that plasma caffeine and other methylxanthines are associated with worsening of metabolic parameters in patients receiving psychotropic treatments known to induce metabolic disturbances. It emphasizes that important caffeine consumption could be considered as an additional environmental risk factor for metabolic worsening in patients receiving such treatments.