Early Predictors of Parkinson's Disease


  • Kaja Ceglar University of Ljubljana


Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, bradykinesia, and postural instability. Detecting PD in its early stages is crucial for implementing timely interventions to alleviate symptoms, slow disease progression, and improve patients' quality of life. This talk will include reviews of current literature on early predictors of PD, aiming to explain why, when, and how we can predict the onset of this devitalizing condition, and its relevance to cognitive science.

The significance of early detection lies in the potential to intervene before irreversible damage occurs in the dopaminergic system, which underlies the motor symptoms of PD. Detecting PD before the onset of motor symptoms presents a window of opportunity for implementing neuroprotective strategies and personalized treatments tailored to individual patients. Moreover, early diagnosis facilitates enrollment in clinical trials aimed at disease-modifying therapies, fostering advancements in PD research and treatment.

Several studies have investigated potential biomarkers and risk factors for predicting PD onset. Parashos et al. proposed a comprehensive framework for measuring disease progression in early PD, emphasizing the importance of identifying subtle motor and non-motor symptoms indicative of prodromal PD [1]. Postuma and Montplaisir highlighted the multifactorial nature of PD prediction, advocating for a combination of clinical, genetic, and imaging markers to enhance predictive accuracy [2]. Stern and Siderowf introduced the concept of "Parkinson's at-risk syndrome," suggesting that certain individuals exhibit prodromal features preceding the clinical diagnosis of PD, including olfactory dysfunction, rapid eye movement sleep behavior disorder, and subtle motor abnormalities [3].

Emerging research has explored innovative approaches to early PD prediction, such as machine learning algorithms analyzing multimodal data from wearable sensors, neuroimaging techniques detecting alterations in brain structure and function, and genetic profiling identifying susceptibility genes associated with PD risk. Integrating these diverse modalities may yield more robust predictive models capable of stratifying individuals based on their likelihood of developing PD.

Methodologically, this talk will compare different approaches to early PD detection, including clinical assessments, imaging studies, and computational models, to identify the most promising strategies. The main contribution of this research is to synthesize these interdisciplinary insights to refine predictive algorithms and suggest targeted interventions for delaying or preventing PD onset.

In conclusion, early prediction of PD holds promise for improving patient outcomes and advancing our understanding of disease pathogenesis. By leveraging insights from multidisciplinary research and harnessing technological advancements, we can refine predictive algorithms and implement targeted interventions to delay or prevent the onset of PD. Collaborative efforts between clinicians, researchers, and industry stakeholders are essential for translating scientific findings into clinical practice and ultimately mitigating the burden of PD on individuals and society.


[1] S. A. Parashos et al., “Measuring disease progression in early parkinson disease,” JAMA Neurology, vol. 71, no. 6, p. 710, Jun. 2014. doi:10.1001/jamaneurol.2014.391

[2] R. B. Postuma and J. Montplaisir, “Predicting parkinson’s disease – why, when, and how?,” Parkinsonism & Related Disorders, vol. 15, Dec. 2009. doi:10.1016/s1353-8020(09)70793-x

[3] M. B. Stern and A. Siderowf, “Parkinson’s at risk syndrome: Can parkinson’s disease be predicted?,” Movement Disorders, vol. 25, no. S1, Jan. 2010. doi:10.1002/mds.22719