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.

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.