Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson's disease

Critical evaluation of the current landscape of pharmacogenomics in Parkinson's disease - What is missing? A systematic review

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.

Levodopa-induced dyskinesia in Parkinson's disease: Insights from cross-cohort prognostic analysis using machine learning

BACKGROUND

Prolonged levodopa treatment in Parkinson's disease (PD) often leads to motor complications, including levodopa-induced dyskinesia (LID). Despite continuous levodopa treatment, some patients do not develop LID symptoms, even in later stages of the disease.

OBJECTIVE

This study explores machine learning (ML) methods using baseline clinical characteristics to predict the development of LID in PD patients over four years, across multiple cohorts.

METHODS

Using interpretable ML approaches, we analyzed clinical data from three independent longitudinal PD cohorts (LuxPARK, n = 356; PPMI, n = 484; ICEBERG, n = 113) to develop cross-cohort prognostic models and identify potential predictors for the development of LID. We examined cohort-specific and shared predictive factors, assessing model performance and stability through cross-validation analyses.

RESULTS

Consistent cross-validation results for single and multiple cohort analyses highlighted the effectiveness of the ML models and identified baseline clinical characteristics with significant predictive value for the LID prognosis in PD. Predictors positively correlated with LID include axial symptoms, freezing of gait, and rigidity in the lower extremities. Conversely, the risk of developing LID was inversely associated with the occurrence of resting tremors, higher body weight, later onset of PD, and visuospatial abilities.

CONCLUSIONS

This study presents interpretable ML models for dyskinesia prognosis with significant predictive power in cross-cohort analyses. The models may pave the way for proactive interventions against dyskinesia in PD by optimizing levodopa dosing regimens and adjunct treatments with dopamine agonists or MAO-B inhibitors, and by employing non-pharmacological interventions such as dietary adjustments affecting levodopa absorption for high-risk LID patients.

Levodopa-induced dyskinesia is preceded by increased levels of anxiety and motor impairment in Parkinson's disease patients

Introduction: Dopamine replacement therapy with levodopa is the gold standard treatment of Parkinson's disease (PD); however long-term levodopa use is associated with abnormal involuntary movements known as levodopa-induced dyskinesia (LID) in most patients. LID is not preventable and represents the major limitation of PD treatment.Objective: This study was aimed to find clinical and behavioral features that could be used to identify, years in advance, PD patients that are at high risk of developing LID in the future. Method: Data from PD patients enrolled in The Parkinson's progression markers initiative (PPMI, Michael J. Fox Foundation) that developed dyskinesia during their participation in the study were compared with those who did not, and with healthy controls.Result: LID was preceded byhigher levels of trait anxiety and increased motor impairment in PD patients. Additionally, younger age at PD diagnosis, earlier need for dopaminergic therapy and higher initial levodopa dose, were associated with future development of dyskinesia.Conclusion: These findings suggest that easily detectable clinical and behavioral alterations may help to identify PD patients that are more susceptible to develop LID.

Association of Catechol-O-Methyltransferase Gene rs4680 Polymorphism and Levodopa Induced Dyskinesia in Parkinson's Disease: A Meta-Analysis and Systematic Review

INTRODUCTION

Long-term levodopa therapy for Parkinson's disease (PD) can cause levodopa induced dyskinesia (LID). Genetic predisposition has a significant role to play in inter-individual heterogeneity in the clinical manifestation of LID. Despite accumulating evidence for the role of COMT gene polymorphism (rs4680) as a genetic basis for LID, to date results have been inconsistent. Early assessment of the Catechol-O-Methyltransferase (COMT) genotype might be helpful to stratify PD patients concerning their individual risk for LID.

METHOD

In this meta-analysis, we have used 9 studies, which were selected through online databases. Statistical analysis was performed using R (v-3.6) software. 5 genetic models have been used in the present study: Allele model (A vs. G), Dominant model (AA+AG vs. GG), Homozygote model (AA vs. GG), Co-dominant/heterozygote model (AG vs. GG), and Recessive model (AA vs. AG + GG).

RESULTS

The results indicated a significant association between COMT rs4680 (Val158Met) polymorphism and LID risk. The genotype AA of COMT rs4680 is a risk factor for LID in PD patients under the recessive model (AA vs GG+AG) in the random-effect model. Analysis based on ethnicity showed that COMT rs4680 SNP allele A is a risk factor for LID development in Asian PD patients, while GG genotype is a risk factor for LID development in non-Asian PD patients using different genetic models.

CONCLUSION

The results of the present meta-analysis support that the COMT Val158Met polymorphism is a risk factor for the development of LID in PD patients having ethnic variations.

Low serum uric acid levels and levodopa-induced dyskinesia in Parkinson's disease

BACKGROUND

Levodopa is the most used and effective medication for motor symptoms of Parkinson disease (PD), its long-term use is associated with the appearance of levodopa-induced dyskinesia (LID). Uric acid (UA) is believed to play an important neuroprotective role in PD.

OBJECTIVE

To investigate if serum UA levels are related with the presence of LIDs in PD patients. Also, we investigated the associations among UA levels and clinical features of PD.

METHODS

We enrolled 81 PD patients (dyskinesia = 48; no dyskinesia = 33) in the present study. A blood sample was collected to evaluate serum UA levels, clinical evaluation included the following instruments: Montreal Cognitive Assessment (MoCA), Beck Depression Inventory II (BDI-II), MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Hoehn and Yahr (HY), and the sub-item 4.1 of MDS-UPDRS IV (score ≥ 1). Additional relevant clinical information was obtained by a clinical questionnaire.

RESULTS

Serum UA levels were lower in the dyskinesia group when compared with the no dyskinesia group. The same result was found in the UA levels of both men and women. The multivariate analysis showed lower uric acid levels were significantly associated with having dyskinesia (odds ratio [OR] = 0.424; 95% confidence interval [CI]: 0.221-0.746; p = 0.005). Additional analysis verified that serum UA levels are inversely correlated with depressive symptoms, disease duration, MDS-UPDRS IV and time spent with dyskinesia. A positive correlation was found with age at onset of PD symptoms.

CONCLUSIONS

The present study provides a possible role of serum UA levels in LID present in PD patients.

Metabolic Profile in Plasma AND CSF of LEVODOPA-induced Dyskinesia in Parkinson's Disease: Focus on Neuroinflammation

The existence of few biomarkers and the lack of a better understanding of the pathophysiology of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD) require new approaches, as the metabolomic analysis, for discoveries. We aimed to identify a metabolic profile associated with LID in patients with PD in an original cohort and to confirm the results in an external cohort (BioFIND). In the original cohort, plasma and CSF were collected from 20 healthy controls, 23 patients with PD without LID, and 24 patients with PD with LID. LC-MS/MS and metabolomics data analysis were used to perform untargeted metabolomics. Untargeted metabolomics data from the BioFIND cohort were analyzed. We identified a metabolic profile associated with LID in PD, composed of multiple metabolic pathways. In particular, the dysregulation of the glycosphingolipid metabolic pathway was more related to LID and was strongly associated with the severity of dyskinetic movements. Furthermore, bile acid biosynthesis metabolites simultaneously found in plasma and CSF have distinguished patients with LID from other participants. Data from the BioFIND cohort confirmed dysregulation in plasma metabolites from the bile acid biosynthesis pathway. There is a distinct metabolic profile associated with LID in PD, both in plasma and CSF, which may be associated with the dysregulation of lipid metabolism and neuroinflammation.

Current Knowledge on the Background, Pathophysiology and Treatment of Levodopa-Induced Dyskinesia-Literature Review

Levodopa remains the primary drug for controlling motor symptoms in Parkinson’s disease through the whole course, but over time, complications develop in the form of dyskinesias, which gradually become more frequent and severe. These abnormal, involuntary, hyperkinetic movements are mainly characteristic of the ON phase and are triggered by excess exogenous levodopa. They may also occur during the OFF phase, or in both phases. Over the past 10 years, the issue of levodopa-induced dyskinesia has been the subject of research into both the substrate of this pathology and potential remedial strategies. The purpose of the present study was to review the results of recent research on the background and treatment of dyskinesia. To this end, databases were reviewed using a search strategy that included both relevant keywords related to the topic and appropriate filters to limit results to English language literature published since 2010. Based on the selected papers, the current state of knowledge on the morphological, functional, genetic and clinical features of levodopa-induced dyskinesia, as well as pharmacological, genetic treatment and other therapies such as deep brain stimulation, are described.

Genetic Polymorphism of ADORA2A Is Associated With the Risk of Epilepsy and Predisposition to Neurologic Comorbidity in Chinese Southern Children

Epilepsy, a common disorder of the brain, exhibits a high morbidity rate in children. Childhood epilepsy (CE) is frequently comorbid with neurologic and developmental disorders, sharing underlying genetic factors. This study aimed to investigate the impact of ADORA2A, BDNF, and NTRK2 gene polymorphisms on the risk of childhood epilepsy and their associations with predisposition to epileptic comorbidities. A total of 444 children were enrolled in this study, and three single nucleotide polymorphisms, including ADORA2A rs2298383, BDNF rs6265, and NTRK2 rs1778929, were genotyped. The frequency distribution of genotypes was compared not only between CE patients and healthy children but also between CE patients with and without comorbidities. The results indicated that the carriers of ADORA2A rs2298383 TT genotype tended to have a lower risk of epilepsy (OR = 0.48, 95% CI = 0.30–0.76), while the CT genotype was related to a higher risk (OR = 1.56, 95% CI = 1.06–2.27). The ADORA2A rs2298383 CC genotype predisposed CE patients to comorbid neurologic disorders (OR = 2.76, 95% CI = 1.31–5.80). Genetic variations in BDNF rs6265 and NTRK2 rs1778929 had no significant association with CE and its comorbidities. Fourteen ADORA2A target genes related to epilepsy were identified by the protein–protein interaction analysis, which were mainly involved in the biological processes of “negative regulation of neuron death” and “purine nucleoside biosynthetic process” through the gene functional enrichment analysis. Our study revealed that the genetic polymorphism of ADORA2A rs2298383 was associated with CE risk and predisposition to neurologic comorbidity in children with epilepsy, and the involved mechanism might be related to the regulation of neuron death and purine nucleoside biosynthesis.