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Background <p>Obstructive sleep apnea (OSA) is a common but often overlooked comorbidity in patients with Parkinson’s disease (PD), impacting both motor and non-motor symptoms and potentially affecting gait control. Early identification of PD with comorbid OSA (PD-OSA) is crucial for improving patient outcomes.</p> Methods <p>This study employed a wearable inertial sensor system to systematically collect spatiotemporal gait parameters and dynamic stability indicators during a standardized Timed Up and Go (TUG) test in 61 PD patients. Patients were categorized into PD-OSA and PD-NOSA groups based on apnea-hypopnea index (AHI). Linear mixed-effects models analyzed gait differences, and an Extreme Gradient Boosting (XGBoost) algorithm was used to develop a screening model integrating gait features and non-motor symptom scores.</p> Results <p>The PD-OSA group showed significant reductions in key gait parameters including step length, stride velocity, arm swing range, and Trunk Max Sagittal Angular Velocity compared to the PD-NOSA group. The XGBoost model demonstrated excellent performance in an independent test set with an AUC of 0.944, sensitivity of 83.3%, and specificity of 83.3%. The most predictive features aligned closely with underlying pathophysiological mechanisms.</p> Conclusion <p>A machine learning model based on wearable-derived gait parameters and clinical scores can effectively identify PD patients with comorbid OSA noninvasively at an early stage, offering potential clinical utility to enhance comprehensive management and prognosis of PD.</p>

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Utility of gait biomarkers in screening for obstructive sleep apnea among patients with Parkinson’s disease: a cross-sectional study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • Huixia Ma,
  • Hui Wang,
  • Yunqiang Zhu,
  • Liying Liang,
  • Xingyan Yang,
  • Siyu Chen,
  • Jiahao Zhao,
  • Lin Chen,
  • Wei Huang

摘要

Background

Obstructive sleep apnea (OSA) is a common but often overlooked comorbidity in patients with Parkinson’s disease (PD), impacting both motor and non-motor symptoms and potentially affecting gait control. Early identification of PD with comorbid OSA (PD-OSA) is crucial for improving patient outcomes.

Methods

This study employed a wearable inertial sensor system to systematically collect spatiotemporal gait parameters and dynamic stability indicators during a standardized Timed Up and Go (TUG) test in 61 PD patients. Patients were categorized into PD-OSA and PD-NOSA groups based on apnea-hypopnea index (AHI). Linear mixed-effects models analyzed gait differences, and an Extreme Gradient Boosting (XGBoost) algorithm was used to develop a screening model integrating gait features and non-motor symptom scores.

Results

The PD-OSA group showed significant reductions in key gait parameters including step length, stride velocity, arm swing range, and Trunk Max Sagittal Angular Velocity compared to the PD-NOSA group. The XGBoost model demonstrated excellent performance in an independent test set with an AUC of 0.944, sensitivity of 83.3%, and specificity of 83.3%. The most predictive features aligned closely with underlying pathophysiological mechanisms.

Conclusion

A machine learning model based on wearable-derived gait parameters and clinical scores can effectively identify PD patients with comorbid OSA noninvasively at an early stage, offering potential clinical utility to enhance comprehensive management and prognosis of PD.