Séverine Crettol

Dyslipidemia affects patients with severe mental illness to varying degrees, partly due to medication side effects and genetic variations that modify drug-induced lipid changes. To identify these genetics contributors, we conducted a genome-wide association study (GWAS) on 829 patients of the PsyMetab cohort whose total-, LDL-, HDL- cholesterol and triglyceride levels were recorded pre- and up to one-year post psychotropic treatment. Seven different loci were associated at genome wide significance level ( P < 5 × 10 ⁻⁸ ) with treatment-induced lipids changes. Three genetic variants were associated with total cholesterol change following aripiprazole, quetiapine, and combination of quetiapine, olanzapine, clozapine treatment (rs17313064 G > A, rs12543987 G > A, rs58331124 G > A, respectively). Two markers were associated with HDL-cholesterol change following lithium and mirtazapine treatment (rs12097296 C > T, rs10933971 C > T, respectively). One variant was associated with triglycerides change following risperidone treatment (rs10260518 G > T) and finally one genetic variation was associated with triglycerides change considering any drug treatment (rs56286230 T > G). To compensate for the lack of classical replication, sensitivity analyses were conducted by assessing the robustness of effects using different post-treatment interval lengths. Notably rs10933971 effect on HLD-cholesterol was consistently found across different patients and time-frames. These findings point to genes previously associated with total lipids level in the general population and highlight novel drug-specific associations which provide promising insights toward personalized psychotropic therapy.

Several psychotropic drugs can induce important weight gain. The present study aims to elucidate the metabolomic fingerprint of this adverse effect. From the Psymetab cohort, 151 follow-ups from 135 patients starting a weight-inducing psychotropic drug were selected. Untargeted metabolomics analyses were applied to plasma samples collected at treatment baseline and at another time point during the follow-up. Dimension reduction and feature selection approaches such as Minimum-Redundancy/Maximum-Relevance coupled with a bootstrap of 5000 Least Absolute Shrinkage and Selection Operator were applied to select metabolic features associated with weight change per month of treatment. Our selection algorithm identified 56 metabolic features. Out of the 56, 10 were identified, of which 6 were confirmed by standard references. Consistent with previous studies, increases in the intensity changes of glycerophospholipids such as lysophosphatidylcholine (18:2/0:0), and tryptophan metabolites such as acetylkynurenine and 5-hydroxy-L-tryptophan were positively associated with weight change. Moreover, increases in the intensity changes of the amino acids citrulline and N-Acetyl-L-aspartic, and of the xanthine 1-methyluric acid and of the pyrimidine nucleoside uridine were positively associated with weight changes, whereas increases in the intensity changes of the amino acid glutamine and of the steroidal glycoside glycochenodeoxycholic acid 3-glucuronide were negatively associated with the outcome. The present study highlighted novel potential biomarkers for weight change under psychotropic treatment, confirming an alteration in the pathways of glycerophospholipids and tryptophan metabolites. These results emphasise the potential use of metabolomics for clarifying metabolic changes and for defining biomarkers with a prospective use in clinical practice.

Caffeine metabolic ratios (CMR) following monitored caffeine intake are the gold standard to probe cytochrome P450 (CYP) 1A2 activity, which metabolizes antipsychotics like clozapine and olanzapine. Given caffeine’s ubiquity, we tested whether random CMR from dietary caffeine were associated with (1) clinical, genetic, and epigenetic factors linked to CYP1A2 activity; (2) plasma concentrations of clozapine and olanzapine; and (3) psychotropic treatment response. First, we analyzed two population-based studies (CoLaus|PsyCoLaus, N = 4898; SKIPOGH, N = 2054) to investigate random CMR associations with clinical, genome-wide, and epigenome-wide factors associated with CYP1A2 activity. Second, in psychiatric cohorts, we tested CMR associations with dose-normalized plasma concentrations (C/D) of clozapine (N = 164) and olanzapine (N = 222) and with psychotropic treatment response, including hospital admission risk (N = 1019) and prolonged stays (N = 1349). CMR were positively associated with age, CYP1A2 inducers including smoking, and negatively with female sex. CMR were negatively associated with clozapine C/D, explaining up to 14.9% of the variance; over six-fold the variance explained by genetic factors. A one-unit increase in CMR was associated with a 26% increased likelihood of hospital admission (p = 0.002) and reduced short-stay chance by 11% (p < 10 ⁻³ ). Random CMR provides a useful method to probe CYP1A2 activity, contributing, alongside other variables, to personalizing clozapine doses and identifying psychiatric patients at risk of hospital admission and lengthy stays. Incorporating routine measurement of random CMR before introduction of clozapine could be considered to allow early assessment of CYP1A2 activity, a key determinant of personalized clozapine dose titration.

Background: Due to limited inpatient care resources and high healthcare expenditures, understanding factors that affect lengths of stay (LOS) is highly relevant. We aimed to investigate associations between metabolic disturbances and LOS in a psychiatric hospital and to identify other clinical and sociodemographic LOS predictors. Methods: Patients admitted to one of the units of the general psychiatric or psychogeriatric departments between January 1, 2007 and December 31, 2020, were included. Metabolic disturbances (i.e., the metabolic syndrome and its five components) were defined using the International Diabetes Federation definition. Cox frailty regression models with time-varying coefficients were used to investigate the association between metabolic disturbances and LOS. Hazard ratios (HR) >1 and HR < 1 indicated the relative likelihood of shorter and extended LOS, respectively. Results: A total of 7,771 patients for 16,959 hospital stays throughout 14 years of follow-up were included. Central obesity (HR = 0.82; 95% confidence interval [CI] = [0.76–0.89]), hyperglycemia (HR = 0.83; 95% CI = [0.78–0.89]), hypertriglyceridemia (HR = 0.87; 95% CI = [0.80–0.92]), and the metabolic syndrome (HR = 0.76; 95% CI = [0.70–0.82]) were associated with an increased risk of extended LOS in the psychiatric hospital, while underweight (HR = 1.30, 95% CI = [1.09–1.56]) and HDL hypocholesterolemia (HR = 1.10, 95% CI = [1.03–1.18]) were associated with a higher likelihood of shorter LOS. In first-episode psychosis patients, hypertriglyceridemia (HR = 0.82; 95% CI = [0.67–0.99]) and hypertension (HR = 0.76, 95% CI = [0.58–0.99]) were associated with extended LOS when considering all stays, while no association was found when considering the first stay per patient. Conclusion: Future studies should determine whether better metabolic monitoring and treatment of metabolic disturbances can contribute to reducing LOS.

The cytochrome P450 2D6 (CYP2D6) metabolizes around 20% of currently prescribed medications, including the antipsychotic risperidone. Previous studies reported greater odds of switching antipsychotic medication from risperidone among CYP2D6 poor metabolizers (PM) without considering treatment duration up to the switch. Risperidone treatment failure, defined as risperidone treatment duration up to switching medication, was analyzed among 515 patients of the PsyMetab cohort using Kaplan–Meier estimates with log‐rank tests and Cox multivariate regression. Risperidone‐to‐paliperidone ratios were higher among CYP2D6 PMs (median: 2.78) vs. the other phenotypes (median: 0.14, P < 0.001). After 1 year of treatment, the proportion of patients who switched from risperidone was 44%. This proportion was increased to 70% among PMs, vs. 42% among the other CYP2D6 phenotypes (P = 0.026). PMs' risk of switching increased over time (interaction PM*treatment duration: 1.01; P = 0.011), becoming statistically significant after 3 months of treatment, with 1.79 (P = 0.028), 3.7 times (P < 0.001) and 16.3 times higher (P = 0.001) risk of switch at 3, 6, and 12 months, respectively (95% confidence intervals: 1.07–3.01, 1.91–7.17, and 3.13–85.37, respectively). Considering a pharmacogenetic‐guided treatment, the number of patients needed to genotype to find one PM and lower the switching proportion from 70% to 42% would be 65. In conclusion, CYP2D6 PM status presented an increased risk of switching from risperidone over 1 year of treatment, the risk increasing over time and becoming statistically significant after the first 3 months of treatment.

Aims: Sertraline is frequently prescribed for mental health conditions in both pregnant and breastfeeding women. According to the limited available data, only small amounts of sertraline are transferred into human milk, yet with a large amount of unexplained interindividual variability. This study aimed to develop a population pharmacokinetic (popPK) model to describe the pharmacokinetics of sertraline during the perinatal period and explain interindividual variability.Methods: Pregnant women treated with sertraline were enrolled in the multicenter prospective cohort SSRI‐Breast Milk study. A popPK model for sertraline maternal plasma and breast milk concentrations was developed and allowed estimating the milk‐to‐plasma ratio (MPR). An additional fetal compartment allowed cord blood concentrations to be described. Several covariates were tested for significance. Ultimately, model‐based simulations allowed infant drug exposure through placenta and breast milk under various conditions to be predicted.Results: Thirty‐eight women treated with sertraline were included in the study and provided 89 maternal plasma, 29 cord blood and 107 breast milk samples. Sertraline clearance was reduced by 42% in CYP2C19 poor metabolizers compared to other phenotypes. Doubling milk fat content increased the MPR by 95%. Simulations suggested a median daily infant dosage of 6.9 μg kg⁻¹ after a 50 mg maternal daily dose, representing 0.95% of the weight‐adjusted maternal dose. Median cord blood concentrations could range from 3.29 to 33.23 ng mL⁻¹ after maternal daily doses between 25 and 150 mg.Conclusions: Infant exposure to sertraline, influenced by CYP2C19 phenotype and breast milk fat content, remains low, providing reassurance regarding the use of sertraline during pregnancy and breastfeeding.

Methylphenidate (MPH) is frequently employed off-label and at high uncontrolled dosages, even beyond the scope of attention deficit hyperactivity disorder (ADHD) treatment. Intoxications appear to be infrequent and are documented in only a few case reports, with monitoring of plasma concentrations not consistently conducted. In the literature, there are no case reports or studies on the tolerability of high-normal doses of MPH. Here we present a case of prolonged and excessive use of MPH without side effects before the patient was admitted to our psychiatric facility, following the consumption of illegal substances and a brief episode of acute confusion. Surprisingly, after admission, the patient's symptoms improved rapidly, before we substantially reduced the dose.

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.

Objectives: More than 40 drugs are available to treat affective disorders. Individual selection of the optimal drug and dose is required to attain the highest possible efficacy and acceptable tolerability for every patient. Methods: This review, which includes more than 500 articles selected by 30 experts, combines relevant knowledge on studies investigating the pharmacokinetics, pharmacodynamics and pharmacogenetics of 33 antidepressant drugs and of 4 drugs approved for augmentation in cases of insufficient response to antidepressant monotherapy. Such studies typically measure drug concentrations in blood (i.e. therapeutic drug monitoring) and genotype relevant genetic polymorphisms of enzymes, transporters or receptors involved in drug metabolism or mechanism of action. Imaging studies, primarily positron emission tomography that relates drug concentrations in blood and radioligand binding, are considered to quantify target structure occupancy by the antidepressant drugs in vivo. Results: Evidence is given that in vivo imaging, therapeutic drug monitoring and genotyping and/or phenotyping of drug metabolising enzymes should be an integral part in the development of any new antidepressant drug. Conclusions: To guide antidepressant drug therapy in everyday practice, there are multiple indications such as uncertain adherence, polypharmacy, nonresponse and/or adverse reactions under therapeutically recommended doses, where therapeutic drug monitoring and cytochrome P450 genotyping and/or phenotyping should be applied as valid tools of precision medicine.

La prise en charge de patients ayant développé une dépendance aux benzodiazépines et analogues est une problématique rencontrée fréquemment tant en méde...