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Biomedicines
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Challenges in the Diagnosis and Treatment of Parkinson’s Disease
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Challenges in the Diagnosis and Treatment of Parkinson’s Disease

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Neurobiology and Clinical Neuroscience".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4538

Special Issue Editors

Dr. Iro Boura

E-Mail Website
Guest Editor
1. School of Medicine, University of Crete, Heraklion, Greece
2. Neurology Department, University General Hospital of Heraklion, Crete, Greece
3. Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
Interests: Parkinson’s disease; movement disorders; neurogenetics
Prof. Dr. Cleanthe Spanaki

E-Mail Website
Guest Editor
1. School of Medicine, University of Crete, Heraklion, Greece
2. Neurology Department, University General Hospital of Heraklion, Crete, Greece
Interests: Parkinson’s disease; movement disorders; neurogenetics

Special Issue Information

Dear Colleagues,

Parkinson’s disease (PD) is a highly heterogeneous disorder, presenting significant challenges in diagnosis and treatment. While our understanding of the clinical and pathophysiological aspects of PD has deepened, many elements of the disease remain elusive, especially in the early stages when symptoms often overlap with other neurodegenerative conditions. This diagnostic uncertainty frequently leads to delayed treatment, complicating disease management and reducing the potential for optimal patient outcomes. Recent clinical research has focused on refining diagnostic criteria for both PD and prodromal PD. Identifying early clinical, genetic, imaging, laboratory, and digital biomarkers indicative of onset and progression may enhance diagnostic accuracy.

Therapeutically, there is growing interest in strategies that target not only symptom management but also the underlying disease mechanisms, with molecularly informed treatments offering new avenues for intervention. Neuroprotective approaches, including non-pharmaceutical interventions such as exercise, are particularly promising. Equally important are treatment biomarkers that can detect responses to therapy. PD is thought to encompass distinct subtypes, differentiated clinically by motor or non-motor symptom profiles and their pattern of progression. These endophenotypes may be associated with specific genetic substrates or molecular pathways, suggesting underlying differences in disease mechanisms, which could drive personalized treatment approaches. Despite these advancements, significant gaps remain in translating these findings into routine clinical practice. Additionally, although various interventional therapies are available for advanced-stage PD, their management continues to pose challenges for both patients and clinicians.

This Special Issue of Biomedicines highlights clinical research addressing the challenges in diagnosing and treating PD while integrating molecular perspectives where relevant. Key areas of interest include the detection of PD subtypes and the identification of diagnostic, progression, and treatment biomarkers, along with their incorporation into clinical workflows. We also focus on the development of early-stage intervention strategies and diagnostic tools, as well as treatment approaches for both early and advanced-stage PD, including the management of drug-induced complications and updates on novel targeted treatments or interventional therapies.

Our goal is to inspire further research and clinical innovation, ultimately enhancing diagnostic precision, improving treatment outcomes, and supporting the development of personalized approaches to patient care.

Dr. Iro Boura
Prof. Dr. Cleanthe Spanaki
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Parkinson’s disease
  • diagnosis
  • treatment
  • pathophysiology
  • biomarkers
  • prodromal
  • advanced therapies
  • subtypes
  • imaging
  • genetic

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Published Papers (4 papers)

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Research

20 pages, 10972 KiB  
Article
Renalase Overexpression-Mediated Excessive Metabolism of Peripheral Dopamine, DOPAL Accumulation, and α-Synuclein Aggregation in Baroreflex Afferents Contribute to Neuronal Degeneration and Autonomic Dysfunction
by Xue Xiong, Yin-Zhi Xu, Yan Zhang, Hong-Fei Zhang, Tian-Min Dou, Xing-Yu Li, Zhao-Yuan Xu, Chang-Peng Cui, Xue-Lian Li and Bai-Yan Li
Biomedicines 2025, 13(5), 1243; https://doi.org/10.3390/biomedicines13051243 - 20 May 2025
Abstract
Background/Objectives: Increasing evidence reveals the likely peripheral etiology of Parkinson’s disease; however, the mechanistic insight into α-Synuclein aggregation in the periphery remains unclear. This study aimed to explore the effect of abnormal expression of renalase on dopamine metabolism, toxic DOPAL generation, and [...] Read more.
Background/Objectives: Increasing evidence reveals the likely peripheral etiology of Parkinson’s disease; however, the mechanistic insight into α-Synuclein aggregation in the periphery remains unclear. This study aimed to explore the effect of abnormal expression of renalase on dopamine metabolism, toxic DOPAL generation, and subsequently, α-Synuclein aggregation. Methods: Blood pressure (BP) was monitored while changing the body position of rats; the serum level of renalase was detected by ELISA; the mRNA/protein of renalase and α-Synuclein were determined by qRT-PCR/Western blot; DOPAL was measured using HPLC; renalase distribution was explored by immunostaining; cell viability and ultrastructure were examined by TUNEL and electron microscopy, respectively. Results: The results showed that, in PD model rats, the serum level of renalase was increased time-dependently with up-regulated renalase gene/protein expression in the nodose ganglia, nucleus tractus solitarius, and heart; a reduced dopamine content was also detected by the renalase overexpression in PC12 cells. Strikingly, up-regulated renalase and orthostatic BP changes were observed before the behavior changes in the model rats. Meanwhile, the levels of DOPAL and α-Synuclein were increased time-dependently. Intriguingly, the low molecular weight of α-Synuclein declined coordinately with the increase in the higher molecular weight of α-Synuclein. Clear ultrastructure damages at the cellular level supported the notion of molecular findings. Notably, the α-Synuclein aggregation-induced impairment of the axonal transport function predates neuronal degeneration mediated by renalase overexpression. Conclusions: Our results demonstrate that abnormal peripheral dopamine metabolism mediated by overexpressed renalase promotes the DOPAL-induced α-Synuclein and leads to baroreflex afferent neuronal degeneration and early autonomic failure. Full article
(This article belongs to the Special Issue Challenges in the Diagnosis and Treatment of Parkinson’s Disease)
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15 pages, 2532 KiB  
Article
The Utilization and Impact of Dopamine Transporter Imaging in Diagnosing Movement Disorders at a Tertiary Care Hospital in Greece
by Georgia Xiromerisiou, Iro Boura, Eleni Barmpounaki, Panagiotis Georgoulias, Efthimios Dardiotis, Cleanthe Spanaki and Varvara Valotassiou
Biomedicines 2025, 13(4), 970; https://doi.org/10.3390/biomedicines13040970 - 16 Apr 2025
Viewed by 310
Abstract
Background/Objectives: The introduction of dopamine transporter scan (DaTscan) in clinical diagnostics has revolutionized the way clinicians approach movement disorders, offering valuable insights into presynaptic striatal dopaminergic deficits and revealing subjacent neurodegeneration. The aim of our study was to evaluate the impact of [...] Read more.
Background/Objectives: The introduction of dopamine transporter scan (DaTscan) in clinical diagnostics has revolutionized the way clinicians approach movement disorders, offering valuable insights into presynaptic striatal dopaminergic deficits and revealing subjacent neurodegeneration. The aim of our study was to evaluate the impact of DaTscan on diagnostic decisions regarding movement disorders, particularly Parkinson’s disease (PD) and atypical parkinsonian syndromes, under real-world circumstances in Greece. Methods: We retrospectively analyzed data from 360 patients who underwent a DaTscan examination between 2018 and 2023 at a tertiary hospital in Greece, including referrals from both movement disorder specialists and general neurologists, either hospital-based or in private practice. Demographics, primary referral symptoms, and both pre-scan and post-scan diagnoses were collected and analyzed. Results: The mean age in our cohort was 60 ± 13.5 years, and tremor was the leading referral symptom (40.8%). The initial diagnosis changed in nearly half of the cases (48.3%) following DaTscan. Significant shifts included transitions from an “Unclear” or “Dystonia” diagnosis to “Parkinson’s disease” in 78.1% and 72.7% of patients, respectively. However, the particularly high concordance rates between pre-scan and post-scan diagnosis for “Vascular parkinsonism” (100%), “Parkinson’s disease” (89.3%), and “Essential/Dystonic Tremor” (86%) suggest that the test may have been over-utilized or ordered beyond its intended indications. Conclusions: DaTscan markedly enhances diagnostic accuracy for movement disorders, particularly for general neurologists, addressing the complexities of overlapping clinical presentations. Continuous medical training is essential to ensure the cost-effective utilization of DaTscan in routine clinical practice; ongoing technological advancements will further refine and expand their applications, benefiting both patients and the broader medical community. Full article
(This article belongs to the Special Issue Challenges in the Diagnosis and Treatment of Parkinson’s Disease)
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20 pages, 512 KiB  
Article
Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson’s Disease from Other Forms of Parkinsonism
by Rana M. Khalil, Lisa M. Shulman, Ann L. Gruber-Baldini, Stephen G. Reich, Joseph M. Savitt, Jeffrey M. Hausdorff, Rainer von Coelln and Michael P. Cummings
Biomedicines 2025, 13(3), 572; https://doi.org/10.3390/biomedicines13030572 - 25 Feb 2025
Viewed by 845
Abstract
Background/Objectives: Parkinson’s Disease (PD) and other forms of parkinsonism share motor symptoms, including tremor, bradykinesia, and rigidity. The overlap in their clinical presentation creates a diagnostic challenge, as conventional methods rely heavily on clinical expertise, which can be subjective and inconsistent. This highlights [...] Read more.
Background/Objectives: Parkinson’s Disease (PD) and other forms of parkinsonism share motor symptoms, including tremor, bradykinesia, and rigidity. The overlap in their clinical presentation creates a diagnostic challenge, as conventional methods rely heavily on clinical expertise, which can be subjective and inconsistent. This highlights the need for objective, data-driven approaches such as machine learning (ML) in this area. However, applying ML to clinical datasets faces challenges such as imbalanced class distributions, small sample sizes for non-PD parkinsonism, and heterogeneity within the non-PD group. Methods: This study analyzed wearable sensor data from 260 PD participants and 18 individuals with etiologically diverse forms of non-PD parkinsonism, which were collected during clinical mobility tasks using a single sensor placed on the lower back. We evaluated the performance of ML models in distinguishing these two groups and identified the most informative mobility tasks for classification. Additionally, we examined the clinical characteristics of misclassified participants and presented case studies of common challenges in clinical practice, including diagnostic uncertainty at the patient’s initial visit and changes in diagnosis over time. We also suggested potential steps to address the dataset challenges which limited the models’ performance. Results: Feature importance analysis revealed the Timed Up and Go (TUG) task as the most informative for classification. When using the TUG test alone, the models’ performance exceeded that of combining all tasks, achieving a balanced accuracy of 78.2%, which is within 0.2% of the balanced diagnostic accuracy of movement disorder experts. We also identified differences in some clinical scores between the participants correctly and falsely classified by our models. Conclusions: These findings demonstrate the feasibility of using ML and wearable sensors for differentiating PD from other parkinsonian disorders, addressing key challenges in its diagnosis and streamlining diagnostic workflows. Full article
(This article belongs to the Special Issue Challenges in the Diagnosis and Treatment of Parkinson’s Disease)
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14 pages, 958 KiB  
Article
Advances in the Neuro-Rehabilitation of Parkinson’s Disease: Insights from a Personalized Multidisciplinary Innovative Pathway
by Maria Grazia Maggio, Mirjam Bonanno, Alfredo Manuli, Rosaria De Luca, Giuseppe Di Lorenzo, Angelo Quartarone and Rocco Salvatore Calabrò
Biomedicines 2024, 12(11), 2426; https://doi.org/10.3390/biomedicines12112426 - 23 Oct 2024
Cited by 3 | Viewed by 2619
Abstract
Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that requires comprehensive and personalized rehabilitation. This retrospective study focused primarily on the usability and patient acceptability of the innovative pathway. In addition, the secondary objective was to evaluate the effectiveness of a [...] Read more.
Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that requires comprehensive and personalized rehabilitation. This retrospective study focused primarily on the usability and patient acceptability of the innovative pathway. In addition, the secondary objective was to evaluate the effectiveness of a personalized and multidisciplinary rehabilitation pathway on cognitive function, especially executive functions. Methods: We conducted a retrospective study on 80 patients with PD (Hoehn and Yahr scores 1–3). Patients were divided into an experimental group (EG), which received the innovative pathway, and a control group (CG), which received traditional therapy. The rehabilitation program included three phases: initial outpatient assessment, a two-month inpatient program, and a telerehabilitation phase in a day hospital (DH) or home environment. Interventions combined traditional therapies with treatments based on robotic and virtual reality. Cognitive assessments (Mini Mental State Examination—MMSE—and frontal assessment battery—FAB), mood (Hamilton Rating Scale—Depression—HRS-D), anxiety (HRS-Anxiety—HRS-A), and goals achievement (GAS) were the primary outcome measures. Results: At baseline, there were no significant differences between the groups in terms of age, gender, education, or test scores. After rehabilitation, EG showed significant improvements in all measures (p < 0.001), particularly in cognitive tests and goal achievement. CG improved in GAS (p < 0.001) and mood (HRS-D, p = 0.0012), but less than EG. No significant changes were observed in the MMSE of CG (p = 0.23) or FAB (p = 0.003). Conclusions: This study highlights the high usability and acceptability of VR and robotics in PD rehabilitation, contributing to improved adherence and patient engagement. The experimental group showed greater cognitive benefits, particularly in executive functions. These results are in line with the existing literature on personalized technology-based rehabilitation strategies for PD. Full article
(This article belongs to the Special Issue Challenges in the Diagnosis and Treatment of Parkinson’s Disease)
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