⚡ Quick Summary
This study explored the potential of sleep biomarkers in accurately profiling various neurodegenerative disorders (NDD), demonstrating a remarkable agreement of over 75% with clinical diagnoses. The findings suggest that sleep biomarker-based profiling could pave the way for early detection and intervention in neurodegenerative diseases. 💤
🔍 Key Details
- 📊 Dataset: 381 recordings from patients with clinically diagnosed NDDs and a control group.
- 🧩 Disorders studied: Alzheimer’s disease dementia (AD), Lewy body dementia (LBD), isolated REM sleep behavior disorder (iRBD), Parkinson’s disease (PD), and mild cognitive impairment (MCI).
- ⚙️ Technology: Four-class machine-learning algorithm trained on age and nine sleep biomarkers.
- 🏆 Performance: Agreement with clinical diagnoses exceeded 75% for AD, LBD, and control groups.
🔑 Key Takeaways
- 🧠 Sleep biomarkers show promise in profiling neurodegenerative disorders.
- 💡 Machine learning was effectively utilized to analyze sleep data.
- 📈 High agreement (over 75%) with clinical diagnoses for AD, LBD, and control groups.
- 🔄 Test-retest consistency was observed in 88% of LBD and 86% of AD participants.
- 🌟 Potential for early detection and intervention in neurodegenerative diseases.
- 📊 Profiles for iRBD, PD, and MCI reflected the heterogeneity of disease severities.
- 🔍 Further research is needed for prospective validation in larger cohorts.
📚 Background
Neurodegenerative disorders pose significant challenges in healthcare, often leading to debilitating symptoms and a decline in quality of life. Early detection is crucial for effective intervention and management. Recent studies have highlighted the bi-directional relationship between sleep and neurodegeneration, suggesting that sleep disturbances could serve as valuable biomarkers for identifying individuals at risk.
🗒️ Study
The study aimed to assess the ability of sleep biomarkers to accurately characterize specific neurodegenerative disorders. Researchers trained a machine-learning algorithm using data from patients diagnosed with various NDDs, including AD, LBD, and iRBD, alongside a control group. The algorithm was validated with a larger dataset, encompassing a diverse range of neurodegenerative conditions.
📈 Results
The results indicated that the machine-learning model achieved a high level of agreement with clinical diagnoses, exceeding 75% for AD, LBD, and control groups. Test-retest consistency was also impressive, with 88% of LBD and 86% of AD participants showing similar profiles upon reassessment. Notably, the study highlighted the variability in profiles for iRBD, PD, and MCI, reflecting the complexity of these disorders.
🌍 Impact and Implications
The findings from this study could significantly impact the field of neurodegenerative disease research and management. By leveraging sleep biomarkers for profiling, healthcare professionals may be able to identify individuals at risk earlier, allowing for timely interventions and potentially slowing disease progression. This innovative approach could lead to improved patient outcomes and a better understanding of the relationship between sleep and neurodegeneration.
🔮 Conclusion
This study underscores the potential of sleep biomarkers in the early detection and profiling of neurodegenerative disorders. The promising results call for further research and validation in larger cohorts to fully realize the benefits of this approach. As we continue to explore the intersection of sleep and neurodegeneration, we may uncover new pathways for intervention and treatment. 🌙
💬 Your comments
What are your thoughts on the use of sleep biomarkers for neurodegenerative disorder profiling? We would love to hear your insights! 💬 Join the conversation in the comments below or connect with us on social media:
Concordance and test-retest consistency of sleep biomarker-based neurodegenerative disorder profiling.
Abstract
Biomarkers that aid in early detection of neurodegeneration are needed to enable early symptomatic treatment and enable identification of people who may benefit from neuroprotective interventions. Increasing evidence suggests that sleep biomarkers may be useful, given the bi-directional relationship between sleep and neurodegeneration and the prominence of sleep disturbances and altered sleep architectural characteristics in several neurodegenerative disorders. This study aimed to demonstrate that sleep can accurately characterize specific neurodegenerative disorders (NDD). A four-class machine-learning algorithm was trained using age and nine sleep biomarkers from patients with clinically-diagnosed manifest and prodromal NDDs, including Alzheimer’s disease dementia (AD = 27), Lewy body dementia (LBD = 18), and isolated REM sleep behavior disorder (iRBD = 15), as well as a control group (CG = 58). The algorithm was validated in a total of 381 recordings, which included the training data set plus an additional AD = 10, iRBD = 18, Parkinson disease without dementia (PD = 29), mild cognitive impairment (MCI = 78) and CG = 128. Test-retest consistency was then assessed in LBD = 10, AD = 9, and CG = 46. The agreement between the NDD profiles and their respective clinical diagnoses exceeded 75% for the AD, LBD, and CG, and improved when NDD participants classified Likely Normal with NDD indications consistent with their clinical diagnosis were considered. Profiles for iRBD, PD and MCI participants were consistent with the heterogeneity of disease severities, with the majority of overt disagreements explained by normal sleep characterization in 27% of iRBD, 21% of PD, and 26% of MCI participants. For test-retest assignments, the same or similar NDD profiles were obtained for 88% of LBD, 86% in AD, and 98% of CG participants. The potential utility for NDD subtyping based on sleep biomarkers demonstrates promise and requires further prospective development and validation in larger NDD cohorts.
Author: [‘Levendowski DJ’, ‘Tsuang D’, ‘Chahine LM’, ‘Walsh CM’, ‘Berka C’, ‘Lee-Iannotti JK’, ‘Salat D’, ‘Fischer C’, ‘Mazeika G’, ‘Boeve BF’, ‘Strambi LF’, ‘Lewis SJG’, ‘Neylan TC’, ‘Louis EKS’]
Journal: Sci Rep
Citation: Levendowski DJ, et al. Concordance and test-retest consistency of sleep biomarker-based neurodegenerative disorder profiling. Concordance and test-retest consistency of sleep biomarker-based neurodegenerative disorder profiling. 2024; 14:31234. doi: 10.1038/s41598-024-82528-y