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.

Therapeutic Drug Monitoring of Olanzapine: Effects of Clinical Factors on Plasma Concentrations in Psychiatric Patients.

BACKGROUND: Therapeutic drug monitoring (TDM) is strongly recommended for olanzapine due to its high pharmacokinetic variability. This study aimed to investigate the impact of various clinical factors on olanzapine plasma concentrations in patients with psychiatric disorders. METHODS: The study used TDM data from the PsyMetab cohort, including 547 daily dose-normalized, steady-state, olanzapine plasma concentrations (C:D ratios) from 248 patients. Both intrinsic factors (eg, sex, age, body weight) and extrinsic factors (eg, smoking status, comedications, hospitalization) were examined. Univariate and multivariable, linear, mixed-effects models were employed, with a stepwise selection procedure based on Akaike information criterion to identify the relevant covariates. RESULTS: In the multivariable model (based on 440 observations with a complete data set), several significant findings emerged. Olanzapine C:D ratios were significantly lower in smokers (ß = -0.65, P < 0.001), valproate users (ß = -0.53, P = 0.002), and inpatients (ß = -0.20, P = 0.025). Furthermore, the C:D ratios decreased significantly as the time since the last dose increased (ß = -0.040, P < 0.001). The male sex had a significant main effect on olanzapine C:D ratios (ß = -2.80, P < 0.001), with significant interactions with age (ß = 0.025, P < 0.001) and body weight (ß = 0.017, P = 0.011). The selected covariates explained 30.3% of the variation in C:D ratios, with smoking status accounting for 7.7% and sex contributing 6.9%. The overall variation explained by both the fixed and random parts of the model was 67.4%. The model facilitated the prediction of olanzapine C:D ratios based on sex, age, and body weight. CONCLUSIONS: The clinical factors examined in this study, including sex, age, body weight, smoking status, and valproate comedication, remarkably influence olanzapine C:D ratios. Considering these factors, in addition to TDM and the clinical situation, could be important for dose adjustment.

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.

AI algorithm combined with RNA editing-based blood biomarkers to discriminate bipolar from major depressive disorders in an external validation multicentric cohort.

Bipolar disorder (BD) is a leading cause of disability worldwide, as it can lead to cognitive and functional impairment and premature mortality. The first episode of BD is usually a depressive episode and is often misdiagnosed as major depressive disorder (MDD). Growing evidence indicates that peripheral immune activation and inflammation are involved in the pathophysiology of BD and MDD. Recently, by developing a panel of RNA editing-based blood biomarkers able to discriminate MDD from depressive BD, we have provided clinicians a new tool to reduce the misdiagnosis delay observed in patients suffering from BD. The present study aimed at validating the diagnostic value of this panel in an external independent multicentric Switzerland-based cohort of 143 patients suffering from moderate to major depression. The RNA-editing based blood biomarker (BMK) algorithm developped allowed to accurately discriminate MDD from depressive BD in an external cohort, with high accuracy, sensitivity and specificity values (82.5 %, 86.4 % and 80.8 %, respectively). These findings further confirm the important role of RNA editing in the physiopathology of mental disorders and emphasize the possible clinical usefulness of the biomarker panel for optimization treatment delay in patients suffering from BD.

Prediction of antipsychotics efficacy based on a polygenic risk score: a real-world cohort study.

Background: Response to antipsychotics is subject to a wide interindividual variability, due to genetic and non-genetic factors. Several single nucleotide polymorphisms (SNPs) have been associated with response to antipsychotics in genome-wide association studies (GWAS). Polygenic risk scores (PRS) are a powerful tool to aggregate into a single measure the small effects of multiple risk alleles. Materials and methods: We studied the association between a PRS composed of SNPs associated with response to antipsychotics in GWAS studies (PRSresponse) in a real-world sample of patients (N = 460) with different diagnoses (schizophrenia spectrum, bipolar, depressive, neurocognitive, substance use disorders and miscellaneous). Two other PRSs composed of SNPs previously associated with risk of schizophrenia (PRSschizophrenia1 and PRSschizophrenia2) were also tested for their association with response to treatment. Results: PRSresponse was significantly associated with response to antipsychotics considering the whole cohort (OR = 1.14, CI = 1.03-1.26, p = 0.010), the subgroup of patients with schizophrenia, schizoaffective disorder or bipolar disorder (OR = 1.18, CI = 1.02-1.37, p = 0.022, N = 235), with schizophrenia or schizoaffective disorder (OR = 1.24, CI = 1.04-1.47, p = 0.01, N = 176) and with schizophrenia (OR = 1.27, CI = 1.04-1.55, p = 0.01, N = 149). Sensitivity and specificity were sub-optimal (schizophrenia 62%, 61%; schizophrenia spectrum 56%, 55%; schizophrenia spectrum plus bipolar disorder 60%, 56%; all patients 63%, 58%, respectively). PRSschizophrenia1 and PRSschizophrenia2 were not significantly associated with response to treatment. Conclusion: PRSresponse defined from GWAS studies is significantly associated with response to antipsychotics in a real-world cohort; however, the results of the sensitivity-specificity analysis preclude its use as a predictive tool in clinical practice.

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.

Aripiprazole dose associations with metabolic adverse effect: Results from a longitudinal study.

OBJECTIVE: Weight gain, blood lipids and/or glucose dysregulation can follow aripiprazole treatment onset. Whether aripiprazole dosage is associated with an increase in these metabolic parameters remains uncertain. The present study investigates aripiprazole dose associations with weight change, blood glucose, lipids, and blood pressure. METHODS: 422 patients taking aripiprazole for a minimum of three weeks to one year were selected from PsyMetab and PsyClin cohorts. Associations between aripiprazole dose and metabolic outcomes were examined using linear mixed-effect models. RESULTS: Aripiprazole dose was associated with weight change when considering its interaction with treatment duration (interaction term: -0.10, p < 0.001). This interaction resulted in greater weight gain for high versus low doses at the beginning of the treatment, this result being overturned at approximately five months, with greater weight increase for low versus high doses thereafter. LDL and HDL cholesterol levels were associated with aripiprazole dose over five months independently of treatment duration, with an average of 0.06 and 0.02 mmol/l increase for each 5 mg increment, respectively (p = 0.033 and p = 0.016, respectively). Furthermore, mean dose increases were associated with greater odds (+30 % per 5 mg increase) of clinically relevant weight gain (i.e., ≥7 %) over one year (p = 0.025). CONCLUSION: Aripiprazole dose was associated with one-year weight changes when considering its interaction with treatment duration. Increasing its dose could lead to metabolic worsening over the first five months of treatment, during which minimum effective doses should be particularly preferred.

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.

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.

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.

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.

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.

Parent-child agreement and prevalence estimates of diagnoses in childhood: direct interview versus family history method.

Diagnostic information on children is typically elicited from both children and their parents. The aims of the present paper were to: (1) compare prevalence estimates according to maternal reports, paternal reports and direct interviews of children [major depressive disorder (MDD), anxiety and attention-deficit and disruptive behavioural disorders]; (2) assess mother-child, father-child and inter-parental agreement for these disorders; (3) determine the association between several child, parent and familial characteristics and the degree of diagnostic agreement or the likelihood of parental reporting; (4) determine the predictive validity of diagnostic information provided by parents and children. Analyses were based on 235 mother-offspring, 189 father-offspring and 128 mother-father pairs. Diagnostic assessment included the Kiddie-schedule for Affective Disorders and Schizophrenia (K-SADS) (offspring) and the Diagnostic Interview for Genetic Studies (DIGS) (parents and offspring at follow-up) interviews. Parental reports were collected using the Family History - Research Diagnostic Criteria (FH-RDC). Analyses revealed: (1) prevalence estimates for internalizing disorders were generally lower according to parental information than according to the K-SADS; (2) mother-child and father-child agreement was poor and within similar ranges; (3) parents with a history of MDD or attention deficit hyperactivity disorder (ADHD) reported these disorders in their children more frequently; (4) in a sub-sample followed-up into adulthood, diagnoses of MDD, separation anxiety and conduct disorder at baseline concurred with the corresponding lifetime diagnosis at age 19 according to the child rather than according to the parents. In conclusion, our findings support large discrepancies of diagnostic information provided by parents and children with generally lower reporting of internalizing disorders by parents, and differential reporting of depression and ADHD by parental disease status. Follow-up data also supports the validity of information provided by adolescent offspring.

Personality traits in children of parents with unipolar and bipolar mood disorders.

BACKGROUND: Using family study data, the following questions regarding the mechanisms of association between personality traits and mood disorders were addressed: 1) Is there an association between unipolar and bipolar mood disorders and personality traits in probands? 2) Are personality traits associated with depression in their 9 to 17 year-old children? 3) Is there an association between parental mood disorders and personality traits in offspring? 4) Are parental personality traits associated with the risk of depression in offspring? METHODS: The study included 50 probands with bipolar and 37 with unipolar mood disorder, 34 healthy controls as well as 178 of their children between 9 and 17 years. Diagnoses were made according to a best-estimate procedure based on a semi-structured interview (DIGS), medical records and family history information. Personality traits were assessed using the Eysenck Personality Questionnaire in adults and the Eysenck Personality Questionnaire Junior in offspring. RESULTS: Personality traits, and in particular Neuroticism, were found to be associated with mood disorders in currently affected as well as remitted probands and offspring. However, there was no association between mood disorders in parents and personality traits in their children, and conversely, parental personality traits were not associated with the risk of depression in offspring. LIMITATIONS: 1) Relatively small proportion of offspring who were still unaffected but likely to subsequently develop mood disorders; 2) cross-sectional design. CONCLUSIONS: The findings were best compatible with the complication or scar hypothesis, which assumes the occurrence of abnormal personality traits as a consequence of previous depressive episodes.

Use of tramadol in psychiatric care: A comprehensive review and report of two cases.

Tramadol is widely prescribed for treating acute and chronic forms of pain. It is a weak mu-receptor opioid agonist and also increases concentrations of serotonin and noradrenaline within the limbic system of the brain. The therapeutic range of tramadol is relatively wide. Compared with other opioid agonists, there is little risk for developing tolerance and for abuse. Recent models of depression emphasise the subjective experience of a depressive mood as being, in part, a psychologically painful state. It is well established that psychological stress due to social separation/loss, disruption or be-trayal of pre-existent significant interpersonal bonds is mediated by the activation of the mammalian PANIC (separation-distress) system. It is also known that this kind of stress can be soothed very effectively by very low doses of endogenous or exogenous opioid receptor agonists. These observations raise the question of whether tramadol can be an effective and safe treat-ment option for some forms of anxiety and depression in which elements of social loss or betrayal are in-volved. In support of this possibility, two clinical cases are presented, and ideas for development of new ap-proaches targeting the endogenous opioidergic system in clinical practice are discussed.

Association of variants in SH2B1 and RABEP1 with worsening of low-density lipoprotein and glucose parameters in patients treated with psychotropic drugs.

Genetic factors associated with Body Mass Index (BMI) have been widely studied over the last decade. We examined whether genetic variants previously associated with BMI in the general population are associated with cardiometabolic parameter worsening in the psychiatric population receiving psychotropic drugs, a high-risk group for metabolic disturbances. Classification And Regression Trees (CARTs) were used as a tool capable of describing hierarchical associations, to pinpoint genetic variants best predicting worsening of cardiometabolic parameters (i.e total, HDL and LDL-cholesterol, triglycerides, body mass index, waist circumference, fasting glucose, and blood pressure) following prescription of psychotropic drugs inducing weight gain in a discovery sample of 357 Caucasian patients. Significant findings were tested for replication in a second Caucasian psychiatric sample (n=140). SH2B1 rs3888190C>A was significantly associated with LDL levels in the discovery and in the replication sample, with A-allele carriers having 0.2mmol/l (p=0.005) and 0.36mmol/l (p=0.007) higher LDL levels compared to others, respectively. G-allele carriers of RABEP1 rs1000940A>G had lower fasting glucose levels compared to others in both samples (-0.16mmol/l; p<0.001 and -0.77mmol/l; p=0.03 respectively). The present study is the first to observe such associations in human subjects, which may in part be explained by a high risk towards dyslipidemia and diabetes in psychiatric patients receiving psychotropic treatments compared to population-based individuals. These results may therefore give new insight into the etiology of LDL-cholesterol and glucose regulation in psychiatric patients under psychotropic drug therapy.