Category: Parkinson's Disease: Non-Motor Symptoms
Objective: To examine the relationship between fatigue, clinical demographics and NMS in PD patients to inform targeted treatment approaches.
Background: Non-motor symptoms (NMS) in Parkinson’s disease (PD) have garnered increasing attention due to their substantial impact on patients’ quality of life.Fatigue, affecting over 50% of individuals with PD1–5,is often underappreciated in clinical trials and underrecognized in practice.
Method: Data from the Parkinson’s Progression Markers Initiative (PPMI) database were used in this study. We quantified fatigue as a binary variable based on the Unified Parkinson’s Disease Rating Scale (UPDRS) part I.The analysis incorporated a range of independent variables: REM Sleep Behavior Disorder Questionnaire (RBDSC), Epworth Sleepiness Scale (ESS), Body Mass Index (BMI), Geriatric Depression Scale – Short Form (GDS), State-Trait Anxiety Inventory (STAI), Scales for Outcomes in PD-Autonomic Dysfunction (SCOPA-AUT), sex, age at visit, months from symptom onset, and the UPDRS-I question on Apathy.Logistic regression assessed the impact of these variables on fatigue probability.Subsequent point-biserial correlation measured the strength of association with the influential regression variables.
Results: Logistic regression analysis on a dataset encompassing 4742 visits from 1055 PD patients revealed a significant association between fatigue and multiple factors. Elevated risk of fatigue were linked to higher scores on the RBDSC (OR: 1.09,95% CI:1.06-1.11), GDS (OR:1.07,95% CI: 1.03-1.11), STAI (OR:1.01,95% CI: 1.00-1.02), SCOPA-AUT (OR:1.05, 95% CI:1.03-1.06), and ESS (OR:1.06,95%CI:1.04-1.08).Conversely, absence of apathy significantly decreased the risk of fatigue (OR: 0.35, 95% CI:0.29-0.41). The model’s area under the receiver operator curve for predicting fatigue had an accuracy of 0.76, indicating reasonable discriminatory power.The other variables showed no significant associations. Point-biserial correlation analysis revealed statistically significant yet weak to moderate positive associations between fatigue and key variables: SCOPA (r = 0.277, p<0.001), GDS (r=0.279,p<0.001), STAI (r=0.279, p<0.001), RBDSC (r=0.224,p<0.001), and ESS (r=0.226,p<0.001)
Conclusion: These findings underscore the multifactorial and multidimensional relationships between nonmotor aspects of PD that contribute to fatigue.Perhaps these components can be leveraged in future interventional studies to treat PD fatigue.
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2. Friedman, J. H. et al. Fatigue in Parkinson’s disease: A review. Movement Disorders vol. 22 297–308 Preprint at https://doi.org/10.1002/mds.21240 (2007).
3. Kluger, B. M. et al. Parkinson’s disease-related fatigue: A case definition and recommendations for clinical research. Mov. Disord. 31, 625–631 (2016).
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5. Siciliano, M. et al. Fatigue in Parkinson’s disease: A systematic review and meta-analysis. Movement Disorders vol. 33 1712–1723 Preprint at https://doi.org/10.1002/mds.27461 (2018).
To cite this abstract in AMA style:
F. Sarmento, G. Lamp, V. Srikar Lavu, A. S. Madamangalam, J. K. Wong. Decoding Fatigue in Parkinson’s Disease: Exploring Multifactorial Associations [abstract]. Mov Disord. 2024; 39 (suppl 1). https://www.mdsabstracts.org/abstract/decoding-fatigue-in-parkinsons-disease-exploring-multifactorial-associations/. Accessed October 15, 2024.« Back to 2024 International Congress
MDS Abstracts - https://www.mdsabstracts.org/abstract/decoding-fatigue-in-parkinsons-disease-exploring-multifactorial-associations/