Parkinsonism & related disorders最新文献

Introduction: Recent studies have demonstrated an association between hypoglycemic medications and neuroprotective action in neurodegenerative diseases, such as Parkinson's disease (PD). Therefore, in this meta-analysis, our objective was to evaluate the efficacy of these medications, compared to placebo, as disease-modifying therapy in patients with PD.

Methods: We systematically searched PubMed, Embase, and Cochrane for studies comparing the use of hypoglycemic drugs and placebo in patients with PD. Statistical analyses were performed using R Studio 4.3.2. Mean difference (MD) with 95 % confidence intervals (CI) were pooled across trials. Outcomes of interest were change in Movement Disorders Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) parts I, II, III, IV, and Parkinson's Disease Questionnaire 39 (PDQ-39).

Results: This meta-analysis included six randomized controlled trials (RCT) reporting data on 787 patients. Among them, 480 (61 %) received hypoglycemic drugs. Follow-up ranged from 36 to 61 weeks. At the end of follow-up, improvement in MDS-UPDRS part III score during OFF state occurred when subjects received any hypoglycemic agents at their lowest dose (MD -1.36; 95 % IC -2.78 to -0.47; I2 = 38 %), as well as highest doses (MD -1.58; 95 % IC -3.07 to -0.09; I2 = 50 %). Changes in MDS-UPDRS part III score in patients examined in the ON state who received any dose of any hypoglycemic agents (MD -3.32; 95 % IC -5.28 to -1.36; I2 = 0 %) were significant. There was no significant difference between groups MDS-UPDRS parts I, II, IV, and PDQ-39.

Conclusion: In patients with PD, the use of hypoglycemic agents showed efficacy on symptomatic PD treatment with an improvement in MDS-UPDRS part III.

Introduction: The first-line treatment for Parkinson's disease (PD) involves dopamine-replacement therapies; however, significant variability exists in patient responses. Pharmacogenomics has been explored as a potential approach to understanding and predicting treatment outcomes. This review aims to evaluate the current state of knowledge regarding the role of pharmacogenomics in PD, focusing on identifying challenges and proposing future directions.

Methods: We conducted a systematic review following PRISMA 2020 guidelines. The PubMed database was searched for original, English-language studies using the R package 'RISmed.' Data were extracted and analyzed based on sample size, population origin, evaluated genes and polymorphisms, outcomes, and methodological approaches.

Results: Out of 183 identified articles, 76 met the inclusion criteria. The COMT-rs4680 polymorphism was the most frequently studied, and levodopa-related motor complications were the most commonly assessed outcomes. All but two studies employed a candidate gene approach. In 75 % of the studies, the sample size was fewer than 225 individuals. There was a notable underrepresentation of Latino participants, with a lack of studies from Latin American countries other than Brazil. None of the studies produced consistent results across investigations.

Conclusions: The variability in patient responses to PD treatments suggests a genetic predisposition. While current research has enhanced our understanding of PD medication metabolism, it has not yet fully elucidated the complex genetic interactions involved in PD pharmacogenomics. Novel approaches, larger and more genetically diverse cohorts, and improved data collection are essential for advancing pharmacogenomics in PD clinical practice.

Parkinson's disease (PD) is a complex neurodegenerative disorder with significant heterogeneity in disease presentation and progression. Subtype identification remains a top priority in the field of PD clinical research. Several PD subtypes have been identified. Hypothesis-driven subtypes refer to pre-defined subtypes based on specific criteria. Under hypothesis-driven subtypes, motor subtypes are the most common empirical subtype in both research and clinical settings. The concept of the non-motor symptoms (NMS) subtypes is relatively new and less well studied. Mild cognitive impairment (MCI) is one of the more prevalent NMS subtypes of PD. Data-driven subtyping is a hypothesis-free approach, that defines disease phenotypes by comprehensively evaluating multidimensional data. In this review, we summarize the main features for the different PD subtypes: from hypothesis-driven subtypes to data-driven subtypes. NMS and data-driven subtypes are still not yet well understood particularly with regard to biomarker and progression characterization. Future PD subtyping based on specific biological makers will enable us to better reflect the underlying pathophysiological underpinnings and enhance our search for specific therapeutic targets. The goal is to develop a simple algorithm to subtype PD patients at an early stage of PD that will enable good prognostication of their disease course, targeted therapies to be delivered, and proactive prevention of complications. Understanding PD subtypes and heterogeneity will also guide future clinical trial design and aid clinicians to better manage PD patients that will enable targeted disease surveillance and personalized treatment. The graphical abstract can be seen below.