Neurodegenerative disorders, including Alzheimer’s disease (AD), are a growing problem in aging society. The amyloid cascade hypothesis has recently been questioned, and therapies based on it have not yielded the expected results. However, the role of amyloid-β (Aβ) in AD pathogenesis cannot be rejected. It appears that some of the key players in the pathogenesis of the disease are the soluble amyloid-β oligomers. Soluble amyloid-β oligomers have neurotoxic effects by disrupting intracellular Ca²⁺ homeostasis and impairing mitochondrial function. The glymphatic system is an important pathway for the removal of soluble amyloid forms from the brain. The decline in the activity of this system is observed in aging brains, which is correlated with the occurrence of Alzheimer’s disease, primarily among the elderly population. Therefore, the question arises as to whether the glymphatic system could be another potential target for therapeutic interventions in Alzheimer’s disease. In this regard, it is imperative to pay attention to the factors that contribute to the pathogenesis of Alzheimer’s disease and also impact the glymphatic system, such as sleep, physical activity, alcohol consumption, and supplementation with polyunsaturated fatty acids. The question remains whether the glymphatic system will become the key to treating Alzheimer’s disease.

This study explores the use of machine learning models to assess the severity of Parkinson’s disease symptoms based on data from wearable and smartphone sensors. It presents models to predict the severities of individual symptoms—tremor, bradykinesia, stiffness, and dyskinesia—as well as the overall state of patients, using both clinician and patient self-assessments as labels. The dataset, although limited and imbalanced, enabled the identification of key trends. The best performance was achieved when combining data from both the MYO armband and smartphone, and when using patient self-assessments as targets. Tremor was the most predictable symptom, while others proved more challenging—especially at higher severity levels, which were poorly represented in the dataset. These results highlight the value of multimodal data and the importance of patient input in symptom monitoring. However, they also point to the need for more balanced and extensive datasets to improve prediction accuracy across all severity levels and symptoms.