Parkinson's disease updates: Addressing the pathophysiology, risk factors, genetics, diagnosis, along with the medical and surgical treatment

A crazy trio in Parkinson's disease: metabolism alteration, α-synuclein aggregation, and oxidative stress

Parkinson's disease (PD) is an aging-associated neurodegenerative disorder, characterized by the progressive loss of dopaminergic neurons in the pars compacta of the substantia nigra and the presence of Lewy bodies containing α-synuclein within these neurons. Oligomeric α-synuclein exerts neurotoxic effects through mitochondrial dysfunction, glial cell inflammatory response, lysosomal dysfunction and so on. α-synuclein aggregation, often accompanied by oxidative stress, is generally considered to be a key factor in PD pathology. At present, emerging evidences suggest that metabolism alteration is closely associated with α-synuclein aggregation and PD progression, and improvement of key molecules in metabolism might be potentially beneficial in PD treatment. In this review, we highlight the tripartite relationship among metabolic changes, α-synuclein aggregation, and oxidative stress in PD, and offer updated insights into the treatments of PD, aiming to deepen our understanding of PD pathogenesis and explore new therapeutic strategies for the disease.

Nicotinic Acetylcholine Receptors in Glial Cells as Molecular Target for Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by resting tremor, bradykinesia, rigidity, and postural instability that also includes non-motor symptoms such as mood dysregulation. Dopamine (DA) is the primary neurotransmitter involved in this disease, but cholinergic imbalance has also been implicated. Current intervention in PD is focused on replenishing central DA, which provides remarkable temporary symptomatic relief but does not address neuronal loss and the progression of the disease. It has been well established that neuronal nicotinic cholinergic receptors (nAChRs) can regulate DA release and that nicotine itself may have neuroprotective effects. Recent studies identified nAChRs in nonneuronal cell types, including glial cells, where they may regulate inflammatory responses. Given the crucial role of neuroinflammation in dopaminergic degeneration and the involvement of microglia and astrocytes in this response, glial nAChRs may provide a novel therapeutic target in the prevention and/or treatment of PD. In this review, following a brief discussion of PD, we focus on the role of glial cells and, specifically, their nAChRs in PD pathology and/or treatment.

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson's Disease: A Study on Speaker Diarization and Classification Techniques

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King's College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life.

Animal Approaches to Studying Risk Factors for Parkinson's Disease: A Narrative Review

Despite recent efforts to search for biomarkers for the pre-symptomatic diagnosis of Parkinson's disease (PD), the presence of risk factors, prodromal signs, and family history still support the classification of individuals at risk for this disease. Human epidemiological studies are useful in this search but fail to provide causality. The study of well-known risk factors for PD in animal models can help elucidate mechanisms related to the disease's etiology and contribute to future prevention or treatment approaches. This narrative review aims to discuss animal studies that investigated four of the main risk factors and/or prodromal signs related to PD: advanced age, male sex, sleep alterations, and depression. Different databases were used to search the studies, which were included based on their relevance to the topic. Although still in a reduced number, such studies are of great relevance in the search for evidence that leads to a possible early diagnosis and improvements in methods of prevention and treatment.

Differential expression of PSMC4, SKP1, and HSPA8 in Parkinson's disease: insights from a Mexican mestizo population

Parkinson's disease (PD) is a complex neurodegenerative condition characterized by alpha-synuclein aggregation and dysfunctional protein degradation pathways. This study investigates the differential gene expression of pivotal components (UBE2K, PSMC4, SKP1, and HSPA8) within these pathways in a Mexican-Mestizo PD population compared to healthy controls. We enrolled 87 PD patients and 87 controls, assessing their gene expression levels via RT-qPCR. Our results reveal a significant downregulation of PSMC4, SKP1, and HSPA8 in the PD group (p = 0.033, p = 0.003, and p = 0.002, respectively). Logistic regression analyses establish a strong association between PD and reduced expression of PSMC4, SKP1, and HSPA8 (OR = 0.640, 95% CI = 0.415-0.987; OR = 0.000, 95% CI = 0.000-0.075; OR = 0.550, 95% CI = 0.368-0.823, respectively). Conversely, UBE2K exhibited no significant association or expression difference between the groups. Furthermore, we develop a gene expression model based on HSPA8, PSMC4, and SKP1, demonstrating robust discrimination between healthy controls and PD patients. Notably, the model's diagnostic efficacy is particularly pronounced in early-stage PD. In conclusion, our study provides compelling evidence linking decreased gene expression of PSMC4, SKP1, and HSPA8 to PD in the Mexican-Mestizo population. Additionally, our gene expression model exhibits promise as a diagnostic tool, particularly for early-stage PD diagnosis.