# Biomarker signatures as predictors of future impulsivity in schizophrenia: a multi-center study

## TL;DR

This study shows that routine biomarkers can predict future impulsivity in schizophrenia patients, with machine learning models performing well and sex-specific patterns emerging.

## Contribution

The study introduces a machine learning model using routine biomarkers to predict impulsivity in schizophrenia patients, highlighting sex-specific biomarker patterns.

## Key findings

- CatBoost model achieved an AUROC of 0.749 in cross-validation and 0.719 in external testing for predicting impulsivity.
- Combining biomarkers with clinical data improved prediction accuracy, increasing AUROC from 0.652 to 0.749 in cross-validation.
- Sex-specific patterns were observed, with uric acid showing a modified relationship with impulsivity in exploratory analysis.

## Abstract

While clinical scales for impulsivity assessment in psychiatric settings are widely used, evidence linking laboratory biomarkers to impulsivity remains limited. This study evaluated the prognostic value of routinely collected biomarkers for future impulsivity risk and developed a machine learning–based prediction model.

We analyzed data from 1,496 first-admission schizophrenia (SCZ) patients across four specialized psychiatric hospitals (2016–2023). A total of 99 features, including 91 routinely tested biomarker measurements, four treatment-related indicators, and four demographic or psychometric variables, were evaluated. Impulsivity was assessed using the Impulsive Behavior Risk Assessment Scale within one week of admission. Five machine learning models were trained with 10-fold cross-validation (n=993) and externally validated in an independent cohort (n=503). Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and biomarker importance was evaluated using SHapley Additive exPlanations (SHAP).

Of 1,496 SCZ patients, 882 (59.0%) exhibited high impulsivity. CatBoost outperformed other models, achieving an AUROC of 0.749 in cross-validation and 0.719 in external testing. SHAP values identified key biomarkers, revealing heterogeneous response patterns for uric acid (UA), globulin (GLO), apolipoprotein E (APOE), and others. Combining biomarkers with clinical data improved prediction, increasing AUROC from 0.652 to 0.749 in cross-validation and from 0.655 to 0.721 in external testing. Subgroup analyses revealed sex-specific patterns, with exploratory analysis suggesting sex-modified relationships between UA and impulsivity.

These findings highlight the utility of routine biomarkers for early identification of high-risk individuals with SCZ and suggest the importance of incorporating sex-specific factors in predictive modeling.