Abstract
Background and Hypothesis
Cardiometabolic morbidity largely explains premature mortality in people with psychotic disorders and is detectable from psychosis onset. Currently, no accurate cardiometabolic risk prediction tool exists for young people with first-episode psychosis (FEP). The Psychosis Metabolic Risk Calculator (PsyMetRiC) aims to bridge this gap, but its accuracy and potential clinical usefulness in North American populations remain unverified.
Study Design
The external validity of PsyMetRiC, developed in the United Kingdom to predict the risk of incident metabolic syndrome (MetS) up to 6 years after a FEP, was assessed using the data from the Quebec Psychosis Early Intervention Clinic. PsyMetRiC comprises 2 penalized logistic regression models: a full-model including age, sex, ethnicity, body mass index (BMI), smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein (HDL), and triglyceride concentrations; and a partial-model excluding biochemical predictors. Patients aged 16-35 years, diagnosed with FEP between 2004 and 2023 without pre-existing MetS, and with>12 months follow-up were included. Predictive performance of PsyMetRiC was assessed by discrimination (C-statistic), calibration (calibration plots), and clinical usefulness (decision curve analysis). The race and ethnicity predictor was refined to better represent the North American population.
Study Results
Among 559 included patients (mean age 24.1 years ±4.1; 22.5% female), 18.2% developed MetS during a mean follow-up of 1.7 ± 1.3 years. Compared with the UK development cohort, the Canadian sample exhibited a higher BMI, lower HDL cholesterol, lower triglycerides, lower blood glucose, and lower systolic blood pressure. Discrimination performance was acceptable (full model C = 0.74, 95% CI, 0.70-0.77; intercept = 0.225; slope = 1.278; partial model C = 0.70, 95% CI, 0.67-0.74; intercept = −0.555; slope = 0.993). After updating the model with a race and ethnicity predictor calibrated to locally representative categories, performance improved slightly (full model C = 0.74, 95% CI, 0.71-0.77; intercept = 0.000; slope = 1.001; partial model C = 0.71, 95% CI, 0.68-0.74; intercept = 0.001; slope = 1.005).
Conclusions
This study provides the first external validation of PsyMetRiC in a North American sample. Further research is essential before routine clinical implementation, but PsyMetRiC offers promise as a tool for early detection of cardiometabolic risk in early psychosis, guiding personalized treatments to diminish long-term physical health impacts.
Cardiometabolic morbidity largely explains premature mortality in people with psychotic disorders and is detectable from psychosis onset. Currently, no accurate cardiometabolic risk prediction tool exists for young people with first-episode psychosis (FEP). The Psychosis Metabolic Risk Calculator (PsyMetRiC) aims to bridge this gap, but its accuracy and potential clinical usefulness in North American populations remain unverified.
Study Design
The external validity of PsyMetRiC, developed in the United Kingdom to predict the risk of incident metabolic syndrome (MetS) up to 6 years after a FEP, was assessed using the data from the Quebec Psychosis Early Intervention Clinic. PsyMetRiC comprises 2 penalized logistic regression models: a full-model including age, sex, ethnicity, body mass index (BMI), smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein (HDL), and triglyceride concentrations; and a partial-model excluding biochemical predictors. Patients aged 16-35 years, diagnosed with FEP between 2004 and 2023 without pre-existing MetS, and with>12 months follow-up were included. Predictive performance of PsyMetRiC was assessed by discrimination (C-statistic), calibration (calibration plots), and clinical usefulness (decision curve analysis). The race and ethnicity predictor was refined to better represent the North American population.
Study Results
Among 559 included patients (mean age 24.1 years ±4.1; 22.5% female), 18.2% developed MetS during a mean follow-up of 1.7 ± 1.3 years. Compared with the UK development cohort, the Canadian sample exhibited a higher BMI, lower HDL cholesterol, lower triglycerides, lower blood glucose, and lower systolic blood pressure. Discrimination performance was acceptable (full model C = 0.74, 95% CI, 0.70-0.77; intercept = 0.225; slope = 1.278; partial model C = 0.70, 95% CI, 0.67-0.74; intercept = −0.555; slope = 0.993). After updating the model with a race and ethnicity predictor calibrated to locally representative categories, performance improved slightly (full model C = 0.74, 95% CI, 0.71-0.77; intercept = 0.000; slope = 1.001; partial model C = 0.71, 95% CI, 0.68-0.74; intercept = 0.001; slope = 1.005).
Conclusions
This study provides the first external validation of PsyMetRiC in a North American sample. Further research is essential before routine clinical implementation, but PsyMetRiC offers promise as a tool for early detection of cardiometabolic risk in early psychosis, guiding personalized treatments to diminish long-term physical health impacts.
| Original language | English |
|---|---|
| Article number | sbaf174 |
| Number of pages | 10 |
| Journal | Schizophrenia Bulletin |
| Early online date | 27 Oct 2025 |
| DOIs | |
| Publication status | E-pub ahead of print - 27 Oct 2025 |
Keywords
- Metabolic syndrome (MetS)
- North-America
- First-episode psychosis (FEP)
- external validation
- risk prediction model
