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 PD^1–5,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.

References: 1. Friedman, J. H. et al. Correction: Corrigendum: Fatigue in Parkinson’s disease: report from a multidisciplinary symposium. NPJ Parkinsons Dis. 3, 17001 (2017).
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).
4. Havlikova, E. et al. Impact of fatigue on quality of life in patients with Parkinson’s disease. Eur. J. Neurol. 15, 475–480 (2008).
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).

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MDS Abstracts - https://www.mdsabstracts.org/abstract/decoding-fatigue-in-parkinsons-disease-exploring-multifactorial-associations/