Analysis of Genetic and Non-genetic Predictors of Levodopa Induced Dyskinesia in Parkinson's Disease

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.

Dopamine Pathway and Parkinson's Risk Variants Are Associated with Levodopa-Induced Dyskinesia

BACKGROUND

Levodopa-induced dyskinesia (LID) is a common adverse effect of levodopa, one of the main therapeutics used to treat the motor symptoms of Parkinson's disease (PD). Previous evidence suggests a connection between LID and a disruption of the dopaminergic system as well as genes implicated in PD, including GBA1 and LRRK2.

OBJECTIVES

Our goal was to investigate the effects of genetic variants on risk and time to LID.

METHODS

We performed a genome-wide association study (GWAS) and analyses focused on GBA1 and LRRK2 variants. We also calculated polygenic risk scores (PRS) including risk variants for PD and variants in genes involved in the dopaminergic transmission pathway. To test the influence of genetics on LID risk we used logistic regression, and to examine its impact on time to LID we performed Cox regression including 1612 PD patients with and 3175 without LID.

RESULTS

We found that GBA1 variants were associated with LID risk (odds ratio [OR] = 1.65; 95% confidence interval [CI], 1.21-2.26; P = 0.0017) and LRRK2 variants with reduced time to LID onset (hazard ratio [HR] = 1.42; 95% CI, 1.09-1.84; P = 0.0098). The fourth quartile of the PD PRS was associated with increased LID risk (ORfourth_quartile = 1.27; 95% CI, 1.03-1.56; P = 0.0210). The third and fourth dopamine pathway PRS quartiles were associated with a reduced time to development of LID (HRthird_quartile = 1.38; 95% CI, 1.07-1.79; P = 0.0128; HRfourth_quartile = 1.38; 95% CI = 1.06-1.78; P = 0.0147).

CONCLUSIONS

This study suggests that variants implicated in PD and in the dopaminergic transmission pathway play a role in the risk/time to develop LID. Further studies will be necessary to examine how these findings can inform clinical care. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Dopamine pathway and Parkinson's risk variants are associated with levodopa-induced dyskinesia

Background

Levodopa-induced dyskinesia (LID) is a common adverse effect of levodopa, one of the main therapeutics used to treat the motor symptoms of Parkinson's disease (PD). Previous evidence suggests a connection between LID and a disruption of the dopaminergic system as well as genes implicated in PD, including GBA1 and LRRK2.

Objectives

To investigate the effects of genetic variants on risk and time to LID.

Methods

We performed a genome-wide association study (GWAS) and analyses focused on GBA1 and LRRK2 variants. We also calculated polygenic risk scores including risk variants for PD and variants in genes involved in the dopaminergic transmission pathway. To test the influence of genetics on LID risk we used logistic regression, and to examine its impact on time to LID we performed Cox regression including 1,612 PD patients with and 3,175 without LID.

Results

We found that GBA1 variants were associated with LID risk (OR=1.65, 95% CI=1.21-2.26, p=0.0017) and LRRK2 variants with reduced time to LID onset (HR=1.42, 95% CI=1.09-1.84, p=0.0098). The fourth quartile of the PD PRS was associated with increased LID risk (ORfourth_quartile=1.27, 95% CI=1.03-1.56, p=0.0210). The third and fourth dopamine pathway PRS quartiles were associated with a reduced time to development of LID (HRthird_quartile=1.38, 95% CI=1.07-1.79, p=0.0128; HRfourth_quartile=1.38, 95% CI=1.06-1.78, p=0.0147).

Conclusions

This study suggests that variants implicated in PD and in the dopaminergic transmission pathway play a role in the risk/time to develop LID. Further studies will be necessary to examine how these findings can inform clinical care.

Common and Rare Variants in TMEM175 Gene Concur to the Pathogenesis of Parkinson's Disease in Italian Patients

Parkinson's disease (PD) represents the most common neurodegenerative movement disorder. We recently identified 16 novel genes associated with PD. In this study, we focused the attention on the common and rare variants identified in the lysosomal K⁺ channel TMEM175. The study includes a detailed clinical and genetic analysis of 400 cases and 300 controls. Molecular studies were performed on patient-derived fibroblasts. The functional properties of the mutant channels were assessed by patch-clamp technique and co-immunoprecipitation. We have found that TMEM175 was highly expressed in dopaminergic neurons of the substantia nigra pars compacta and in microglia of the cerebral cortex of the human brain. Four common variants were associated with PD, including two novel variants rs2290402 (c.-10C > T) and rs80114247 (c.T1022C, p.M341T), located in the Kozak consensus sequence and TM3II domain, respectively. We also disclosed 13 novel highly penetrant detrimental mutations in the TMEM175 gene associated with PD. At least nine of these mutations (p.R35C, p. R183X, p.A270T, p.P308L, p.S348L, p. L405V, p.R414W, p.P427fs, p.R481W) may be sufficient to cause the disease, and the presence of mutations of other genes correlated with an earlier disease onset. In vitro functional analysis of the ion channel encoded by the mutated TMEM175 gene revealed a loss of the K⁺ conductance and a reduced channel affinity for Akt. Moreover, we observed an impaired autophagic/lysosomal proteolytic flux and an increase expression of unfolded protein response markers in patient-derived fibroblasts. These data suggest that mutations in TMEM175 gene may contribute to the pathophysiology of PD.

Quantitative Transcranial Sonography Evaluation of Substantia Nigra Hyperechogenicity Is Useful for Predicting Levodopa-Induced Dyskinesia in Parkinson Disease

Levodopa-induced dyskinesia (LID) is a common motor complication in Parkinson disease (PD). Abnormal substantia nigra hyperechogenicity (SN+), detected by transcranial sonography (TCS), plays an important role in the differential diagnosis of PD. The purpose of this study was to investigate the predictive performance of quantitative SN+ evaluations for LID. Five hundred sixty-two individuals were included in our analysis, and 198 individuals were followed up. These individuals were divided into two groups at baseline: the PD with LID (PD+LID) group and the PD without LID (PD-LID) group. The association between total hyperechogenic area of the SN on both sides (SN_T) and LID was analyzed by binary logistic analysis. A binary logistic regression model including SN_T was applied to establish a model for discriminating LID. At baseline, 105 (18.7%) individuals were diagnosed with LID. The PD+LID group had a longer disease duration, shorter education duration, higher levodopa equivalent doses, greater disease severity and larger SN_T. A model combining clinical features and SN_T was further established with better efficiency (area under the receiver operating characteristic curve = 0.839). One hundred ninety-eight individuals were followed up; individuals with a larger SN_T and a higher predicted probability were more likely to develop LID in our follow-up. Our study determined that quantitative TCS evaluation of SN echogenicity is useful in predicting LID in PD.

Identification of sixteen novel candidate genes for late onset Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a neurodegenerative movement disorder affecting 1-5% of the general population for which neither effective cure nor early diagnostic tools are available that could tackle the pathology in the early phase. Here we report a multi-stage procedure to identify candidate genes likely involved in the etiopathogenesis of PD.

METHODS

The study includes a discovery stage based on the analysis of whole exome data from 26 dominant late onset PD families, a validation analysis performed on 1542 independent PD patients and 706 controls from different cohorts and the assessment of polygenic variants load in the Italian cohort (394 unrelated patients and 203 controls).

RESULTS

Family-based approach identified 28 disrupting variants in 26 candidate genes for PD including PARK2, PINK1, DJ-1(PARK7), LRRK2, HTRA2, FBXO7, EIF4G1, DNAJC6, DNAJC13, SNCAIP, AIMP2, CHMP1A, GIPC1, HMOX2, HSPA8, IMMT, KIF21B, KIF24, MAN2C1, RHOT2, SLC25A39, SPTBN1, TMEM175, TOMM22, TVP23A and ZSCAN21. Sixteen of them have not been associated to PD before, were expressed in mesencephalon and were involved in pathways potentially deregulated in PD. Mutation analysis in independent cohorts disclosed a significant excess of highly deleterious variants in cases (p = 0.0001), supporting their role in PD. Moreover, we demonstrated that the co-inheritance of multiple rare variants (≥ 2) in the 26 genes may predict PD occurrence in about 20% of patients, both familial and sporadic cases, with high specificity (> 93%; p = 4.4 × 10- 5). Moreover, our data highlight the fact that the genetic landmarks of late onset PD does not systematically differ between sporadic and familial forms, especially in the case of small nuclear families and underline the importance of rare variants in the genetics of sporadic PD. Furthermore, patients carrying multiple rare variants showed higher risk of manifesting dyskinesia induced by levodopa treatment.

CONCLUSIONS

Besides confirming the extreme genetic heterogeneity of PD, these data provide novel insights into the genetic of the disease and may be relevant for its prediction, diagnosis and treatment.