Characterization of cardiac autonomic dysfunction in acute Schizophrenia: a cluster analysis of heart rate...

Area of Science:

• Cardiology
• Psychiatry
• Biomedical Engineering

Background:

• Schizophrenia (SCZ) is associated with significantly higher cardiac mortality, but underlying mechanisms remain unclear.
• Cardiac autonomic dysfunction (CADF) is a known physiological link between SCZ and cardiovascular disease (CVD).
• Heart Rate Variability (HRV) analysis offers a non-invasive method to assess autonomic function.

Purpose of the Study:

• To investigate the prevalence of CADF in SCZ patients using comprehensive HRV analysis.
• To identify factors associated with the severity of CADF in SCZ.
• To explore the potential of HRV-based biomarkers for CVD risk prediction in SCZ.

Main Methods:

• 119 patients with SCZ and 119 age- and sex-matched healthy controls were analyzed.
• HRV was assessed across multiple domains: time/frequency, nonlinear dynamics, complexity, symbolic dynamics, and segmented Poincaré plots.
• HRV-based clustering was employed to classify SCZ patients with or without CADF.

Main Results:

• Approximately 50% of SCZ patients demonstrated significant CADF, while the other half had normal cardiac autonomic function.
• The severity of CADF correlated positively with age, Body Mass Index (BMI), disease duration, and symptom severity.
• Significant CADF was identified as a risk factor for cardiac events in the SCZ population.

Conclusions:

• About half of SCZ patients have significant CADF, contributing to their elevated cardiac mortality risk.
• HRV analysis shows promise as a biomarker for improving CVD risk prediction and stratification in SCZ.
• Future research should integrate HRV with other biomarkers for enhanced early detection and intervention strategies for CVD in SCZ.