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Phenotypic Differences among Recently Diagnosed Drug-Naïve Patients with Parkinson’s Disease with or without SNCA Polymorphisms
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20 July 2023
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20 July 2023
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Abstract
Some studies show that patients with mutations in the SNCA gene, which codifies for the alpha-synuclein protein, show a particular phenotype. The effects of SNCA Single Nucleotide Polymorphism (SNPs) in recently diagnosed, drug-naïve patients with PD have been less explored. Therefore, we set out to explore the differences in the clinical characteristics of recently diagnosed drug-naïve sporadic PD patients with or without SNCA rs3910105 or rs356181 SNPs. Patients with a clinical diagnosis of PD in the Parkinson’s Progression Markers Initiative (PPMI) database entered the study. We excluded those with missing data, dementia, psychiatric conditions, a diagnosis change over the first five years from the initial PD diagnosis, or with a familial history of PD. Subjects were evaluated with the MDS-Unified PD Rating Scale (MDS-UPDRS), DAT imaging, the Geriatric Depression Scale (GDS), the State-Trait Anxiety Inventory (STAI), the Montreal Cognitive Assessment (MoCA), the SCOPA-AUT for autonomic function, the Epworth Sleepiness Scale (ESS), the RBD Questionnaire, and the University of Pennsylvania Smell Identification Test (UPSIT). We included 308 PD patients fulfilling all inclusion and exclusion criteria. A logistic regression analysis and Machine-Learning models did not disclose any difference between patients either with or without the SNCA rs3910105 SNP or with or without the SNCA rs3910105 polymorphism. Our results suggest that the SNCA polymorphisms rs3910105 and rs356181 have no impact on the phenotype of idiopathic, sporadic, recently diagnosed, drug naïve PD patients.
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Medicine and Pharmacology - Neuroscience and Neurology1. Introduction
Parkinson’s disease (PD) is the second most frequent neurodegenerative disorder worldwide [1]. Patients are affected by motor and non-motor symptoms, the latter being the most disturbing [2]. The major motor symptoms of the disease are bradykinesia, resting tremor, rigidity, and postural abnormalities [3]. Non-motor symptoms include mood disorders, troubled sleep, dysautonomia, cognitive disfunction and pain, among others [3]. Pathogenically, PD likely encompasses many genetic–molecular entities, resulting in lesions in different structures within the central or peripheral nervous system. The deposition of alpha-synuclein in the cellular soma, leading to the formation of Lewy bodies, appears to be one of the main events leading to neurodegeneration and, eventually, dementia [2]. Other factors contributing to the neurodegenerative process include mitochondrial dysfunction, synaptic alterations, the disruption of calcium homeostasis, and neuroinflammation [2].
PD can be sporadic or, when autosomal mutations are present, familial [4, 5]. However, recent evidence indicates that genetic mutations also contribute to sporadic PD in non-negligible ways [6]. A recent meta-analysis of genome-wide association studies (GWAS) included the analysis of 7.8M single nucleotide polymorphisms (SNPs) in 37.7K cases, 18.6K UK Biobank proxy-cases (having a first-degree relative with PD), and 1.4M controls [6]. The authors could identify 90 variants that explained 16–36% of the heritable risk of PD depending on prevalence.
Interestingly, patients with mutations in the SNCA gene, which codifies for the alpha-synuclein protein, show a particular phenotype. For example, in a study involving 230 PD patients, the SNCA (Synuclein alpha gene) rs356182 GG genotype was associated with a more tremor-predominant phenotype and predicted a slower rate of motor progression [7]. The SNCA p.Ala53Thr variant among monogenic forms of PD manifests with a rapidly evolving rate of PD progression and early emergence of levodopa complications, while the p.His50Gln variant often manifests as a tremor-dominant subtype with cognitive impairment [8]. Patients with this variant are also frequently affected by a high rate of psychotic symptoms and depression, early onset of cognitive decline, and autonomic dysfunction [8]. REM (rapid eye movement) sleep behavior disorder can also be found more frequently among patients with the SNCA-A53T_rs104893877 variant [9]. The effects of SNCA SNPs in recently diagnosed, drug-naïve patients with PD have been less explored. Therefore, we set out to explore the differences in the clinical characteristics of recently diagnosed drug-naïve sporadic PD patients with or without SNCA rs3910105 or rs356181 SNPs.
2. Materials and Methods
2.1. Participants
The Parkinson’s Progression Markers Initiative (PPMI) is an ongoing multicenter observational study focused on identifying disease biomarkers in PD patients attending clinical centers all over the world [10]. Identifying markers of disease progression through this initiative serves to accelerate therapeutic trials aimed at reducing PD disabilities. The review board of each clinical center participating in the initiative approves the study protocol and all participating patients are required to sign a written informed consent form. Information is de-identified and shared with all investigators. We extracted information only from each participant’s baseline visit.
For our study, we selected patients with a clinical diagnosis of idiopathic PD based on the UKPDBBS or the MDS criteria within two years before the date of inclusion. Patients with missing data, demented patients, those being treated with any antiparkinsonian drug, those whose PD diagnosis was changed during the first five years after the initial diagnosis, those with psychiatric conditions, or those with a familial history of PD were excluded.
2.2. Patient evaluation
Subjects were evaluated with the MDS-Unified PD Rating Scale (MDS-UPDRS) and additional clinical tests for cognition, depression, anxiety, autonomic function, sleep, and olfaction [10]. All patients underwent DAT imaging. Depression was assessed using the Geriatric Depression Scale (GDS), and anxiety with the State-Trait Anxiety Inventory (STAI). Cognitive testing included the Montreal Cognitive Assessment (MoCA). Autonomic function was examined using the SCOPA-AUT. Diurnal somnolence and REM sleep behavior disorder (RDB) were assessed using the Epworth Sleepiness Scale (ESS) and RBD Questionnaire, respectively. Finally, hyposmia/anosmia was evaluated using the University of Pennsylvania Smell Identification Test (UPSIT).
2.3. Genomic data processing
As part of the screening/baseline visit, whole-genome sequencing was performed on whole blood-extracted DNA samples using a Macrogen Inc. sequencer [10]. One microgram of each DNA sample was fragmented using the Covaris System and prepared following the Illumina TruSeq DNA Sample preparation guide to obtain a final library of 300-400 bp average insert size. Libraries were multiplexed and sequenced on the Illumina HiSeq X platform. Paired-end read sequences were aligned to the GRCh37-hs37d5 genome using the Burrows-Wheeler aligner-maximal exact matches algorithm (BWA-MEM v0.7.13). The Bamsormadup2 tool (v2.0.87) was used to filter duplicates and sort aligned bam files. After filtering duplicated read sequences, the reads were realigned and recalibrated using the GATK pipeline (v3.5). A haplotype caller in the GATK pipeline was used to call variants, including single nucleotide variants (SNVs) and small indels, and to generate genome VCFs. Using the hg38 aligned cohort VCF files from the whole-genome sequencing data, genotype information was extracted with BCF tools and PLINK. We focused on the SNCA rs3910105 and rs356181 SNPs.
2.4. Statistical analysis
Numerical variables were expressed as means ± standard deviation and the categorical ones in percentages. Differences between PD patients with and without SNCA polymorphisms were analyzed with Analysis of Variance (ANOVA) or the Chi-square test. P-values were corrected for multiple comparisons using the Benjamini-Hochberg method. All analyses were performed using R Statistical Software (v4.1.2; R Core Team 2021).
2.5. Machine-Learning models
We designed machine learning models to predict SNP variants and the number of alleles based on the variables evaluated in the dataset. The data was divided into training and test sets using an 80-20 split ratio. Decision trees, boosted decision trees, random forest, and Support-Vector Machine (SVM) models were trained on the training set, and a grid search was conducted to optimize the hyperparameters of each model. Each model performance was evaluated on the test set, using accuracy (the proportion of cases classified correctly), recall (the true positive rate), and the F1-Score (the harmonic mean of accuracy and recall). Analyses were carried out in Python, using the scikit-learn library and its corresponding methods.
3. Results
3.1. Characteristics of patients with or without SNCA SNPs
Three hundred and eight patients met all inclusion and exclusion criteria. Of them, 146 (47.4%) and 51 (16.5%) had 1 or 2 GG minor alleles in the rs3910105 SNP, respectively. In terms of the rs356181 SNP, 135 (43.8%) and 106 (34.4%) had one or two AG minor alleles, respectively. Three hundred and two patients (98.0%) had one or two minor alleles in one or both SNPs. One hundred and thirty-six patients (44.1%) had one or two minor alleles in both SNPs.
3.2. Machine-Learning models
All machine learning failed to converge in a model with sufficient accuracy, recall, and F1-sscore (Table 3).
4. Discussion
PD patients with autosomal mutations in the SNCA and those with sporadic PD bearing SNCA polymorphisms differ phenotypically from non-carrier PD patients. Indeed, in a study involving 230 PD patients, the SNCA rs356182 GG genotype was associated with a more tremor-predominant phenotype and predicted a slower rate of motor progression [7]. Regarding monogenic forms of PD, the SNCA p.Ala53Thr variant manifests with a rapidly evolving rate of PD and early emergence of levodopa complications, while the p.His50Gln variant often manifests as a tremor-dominant subtype, with cognitive impairment [8]. These latter patients are also frequently affected by a high rate of psychotic symptoms and depression, early onset of cognitive decline, and autonomic dysfunction [8]. REM sleep behavior disorder is also found more frequently among patients with the SNCA-A53T_rs104893877 variant [9]. However, a recent meta-analysis showed considerable variability of design among studies aimed at establishing the phenotypic consequences of SNCA mutations, and a general absence of any significant association [11]. Similarly, no major differences were found between idiopathic, sporadic, recently diagnosed, drug naïve PD patients with or without SNCA rs3910105 or rs356181 SNPs.
Our results are in agreement with those of Szwedo and colleagues, who analyzed the impact of SNCA polymorphisms on the clinical presentation of 433 newly diagnosed PD patients [9]. The effects of five SNCA polymorphisms (rs2870004, rs356182, rs5019538, rs356219, and rs763443) were analyzed by these authors. While the rs356219 was associated with faster cognitive decline, no significant associations were found between each of the five SNCA SNPs and the development of motor or functional impairment. Considered together, these results suggest that common SNCA SNPs do not contribute to motor impairment.
Based on our findings, two hypotheses can be entertained. Either SNCA polymor-phisms do not significantly affect a patient’s phenotype — supported by the results of the meta-analysis showing no overall associations between the polymorphisms and pheno-types[11] — or the effects of SNCA polymorphism are possibly stronger during later stages of the disease. The first scenario would imply that the association between SNCA polymorphisms and phenotypes is the result of biased studies. Alpha-synuclein deposition may result from previous pathological events like altered calcium homeostasis, synaptic changes, mitochondrial dysfunction, neuroinflammation, apoptosis, and failure in protein degradation [2]. Then, alterations in alpha-synuclein structure may gain importance in later disease stages, whereas factors affecting any of these mechanisms may be more important early in the disease, which would be in agreement with our second hypothesis.
5. Conclusions
We found that SNCA polymorphisms rs3910105 and rs356181 had no impact on the phenotype of idiopathic, sporadic, recently diagnosed, drug naïve PD patients. These findings agree with those of previous studies showing no major effects of other SNCA polymorphisms on the characteristic of early PD patients.
6. Future directions
Further studies are required to determine differences in the effects of SNCA SNPs between early and late stages of PD disease. Monitoring recently diagnosed patients would allow us to compare the characteristics of patients with or without polymorphisms at different stages of the disease, avoiding a major source of bias. The constraint of this and similar studies in terms of sample size could be surmounted by combining the results from different cohorts using meta-analytic techniques.
Author Contributions
Conceptualization, G.C. and S.P.LL; methodology, A.E., G.C., S.B., H.B.; statistical analysis, G.C., S.P.LL; writing, review and editing, A.E., M.O-L, G.C., S.B., H.B., S.P.LL., F.J.B.; supervision, S.P.LL. and F.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
We used a public database that was fully de-identified. Therefore, this approval was not necessary.
Informed Consent Statement
Not required for this study.
Data Availability Statement
Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org.
Acknowledgments
PPMI — a public-private partnership — is funded by the Michael J. Fox Foundation for Parkinson’s Research funding partners 4D Pharma, Abbvie, Acurex Therapeutics, Allergan, Amathus Therapeutics, ASAP, Avid Radiopharmaceuticals, Bial Biotech, Biogen, BioLegend, Bristol-Myers Squibb, Calico, Celgene, Dacapo Brain Science, Denali, The Edmond J. Safra Foundation, GE Healthcare, Genentech, GlaxoSmithKline, Golub Capital, Handl Therapeutics, Insitro, Janssen Neuroscience, Lilly, Lundbeck, Merck, Meso Scale Discovery, Neurocrine Biosciences, Pfizer, Piramal, Prevail, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, Verily, and Voyager Therapeutics. The authors did not receive any other funding for this study.
Conflicts of Interest
The authors declare no conflict of interest.
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Table 1.
Characteristics of PD patients with the SNCA rs3910105 SNP.
| Genotype | AA | GA | GG | P-value |
| (N=111) | (N=146) | (N=51) | ||
| Sex | ||||
| Female | 32 (28.8%) | 55 (37.7%) | 23 (45.1%) | 0.213 |
| Male | 79 (71.2%) | 91 (62.3%) | 28 (54.9%) | |
| Years of education | 15.7 (2.98) | 15.5 (3.03) | 15.2 (2.98) | 0.784 |
| Age | 62.3 (9.48) | 61.4 (9.53) | 63.1 (9.27) | 0.719 |
| Age at PD onset | 61.8 (9.40) | 60.9 (9.48) | 62.7 (9.13) | 0.664 |
| Results of the DAT scan | ||||
| Caudate contralateral | 1.84 (0.552) | 1.78 (0.606) | 1.86 (0.503) | 0.821 |
| Caudate ipsilateral | 2.16 (0.533) | 2.09 (0.659) | 2.17 (0.566) | 0.772 |
| Putamen contralateral | 0.691 (0.275) | 0.661 (0.224) | 0.715 (0.356) | 0.637 |
| Putamen ipsilateral | 0.967 (0.380) | 0.922 (0.399) | 0.949 (0.370) | 0.837 |
| Elixhauser comorbidity score | 0.901 (2.48) | 1.40 (3.08) | 0.902 (2.39) | 0.471 |
| Non-motor symptoms | ||||
| ESS score | 5.40 (3.14) | 5.68 (3.32) | 6.80 (3.81) | 0.104 |
| Diurnal somnolence | 12 (10.8%) | 21 (14.4%) | 13 (25.5%) | |
| GDS score | 2.03 (2.32) | 2.36 (2.53) | 3.00 (2.73) | 0.151 |
| Depression | 14 (12.6%) | 19 (13.0%) | 13 (25.5%) | |
| RBD Questionnaire score | 3.98 (2.48) | 4.15 (2.70) | 4.62 (3.11) | 0.584 |
| Probable RBD | 39 (35.1%) | 55 (37.7%) | 21 (41.2%) | |
| SCOPA gastrointestinal score | 2.17 (2.10) | 2.06 (1.97) | 2.25 (1.96) | 0.941 |
| SCOPA urinary score | 4.50 (3.24) | 4.06 (2.85) | 3.88 (2.45) | 0.544 |
| SCOPA cardiovascular score | 0.432 (0.921) | 0.418 (0.651) | 0.608 (0.827) | 0.505 |
| SCOPA thermoregulation score | 1.13 (1.32) | 1.14 (1.30) | 1.41 (1.83) | 0.641 |
| SCOPA visual score | 0.378 (0.557) | 0.418 (0.722) | 0.392 (0.532) | 0.969 |
| SCOPA sexual dysfunction score | 1.30 (1.63) | 1.05 (1.39) | 1.33 (1.85) | 0.563 |
| STAIT anxiety score | 22.0 (8.82) | 23.3 (9.54) | 24.9 (9.95) | 0.301 |
| Anxiety | 4 (3.6%) | 16 (9.6%) | 3. (5.9%) | 0.303 |
| UPSIT | 23.2 (7.78) | 22.6 (8.78) | 21.8 (7.64) | 0.788 |
| MoCA score | 27.2 (2.47) | 27.3 (2.31) | 27.3 (2.20) | 0.995 |
| Cognitive deterioration | 32 (28.8%) | 50 (34.2%) | 16 (31.4%) | 0.790 |
| MDS-UPDRS I | 1.21 (1.85) | 1.09 (1.36) | 1.55 (1.46) | 0.358 |
| Motor assessment | ||||
| Hoehn & Yahr | 1.55 (0.500) | 1.58 (0.496) | 1.67 (0.476) | 0.572 |
| MDS-UPDRS II+III | 26.1 (10.8) | 26.8 (10.6) | 30.2 (13.2) | 0.190 |
| Oro-buccal score | 2.65 (2.44) | 2.56 (2.44) | 3.10 (2.57) | 0.608 |
| Eating score | 1.45 (0.842) | 1.29 (0.807) | 1.61 (1.06) | 0.139 |
| Mobility score | 1.74 (1.76) | 1.88 (1.94) | 2.69 (2.44) | 0.521 |
| Axial score | 4.43 (2.43) | 4.47 (2.23) | 5.25 (2.68) | 0.186 |
| Resting tremor | 2.76 (2.43) | 2.68 (2.36) | 2.78 (2.32) | 0.991 |
| Postural and kinetic t | 1.44 (1.55) | 1.49 (1.47) | 1.69 (1.62) | 0.812 |
| Rigidity | 3.81 (2.61) | 3.77 (2.60) | 3.86 (2.85) | 0.998 |
| Bradykinesia right | 2.85 (2.13) | 2.81 (2.49) | 2.94 (2.24) | 0.989 |
| Bradykinesia left | 2.44 (2.44) | 2.92 (2.48) | 2.88 (2.64) | 0.469 |
| Bradykinesia infra | 2.55 (2.01) | 2.94 (2.15) | 3.35 (2.56) | 0.177 |
| PD subtype | ||||
| Tremor dominant | 98 (88.3%) | 116 (79.5%) | 42 (82.4%) | 0.431 |
| PIGD | 6 (5.4%) | 17 (11.6%) | 3 (5.9%) | |
| Indeterminate | 6 (5.4%) | 13 (8.9%) | 6 (11.8%) |
ESS: Epworth Sleepiness Scale; GDS= Geriatric Depression Scale; REM= Rapid-Eye movement; STAI= State-Trait Anxiety Inventory; UPSIT= University of Pennsylvania Smell Identification Test; MoCA= Montreal Cognitive Assessment; UPDRS= Unified PD Rating Scale; PD= Parkinson’s Disease; PIGD= Postural-Instability Gait Disorder.
Table 2.
Characteristics of PD patients with the SNCA rs356181 SNP.
| Genotype | GG | AG | AA | P-value |
| (N=67) | (N=135) | (N=106) | ||
| Sex | ||||
| Female | 28 (41.8%) | 47 (34.8%) | 35 (33.0%) | 0.691 |
| Male | 39 (58.2%) | 88 (65.2%) | 71 (67.0%) | |
| Years of education | 15.3 (3.12) | 15.6 (3.15) | 15.6 (2.74) | 0.936 |
| Age | 63.3 (8.14) | 62.0 (9.52) | 61.4 (10.1) | 0.646 |
| Age at PD onset | 62.8 (8.02) | 61.4 (9.47) | 60.8 (10.1) | 0.610 |
| Results of the DAT scan | 6.48 (6.30) | 6.59 (6.51) | 6.25 (5.74) | 0.981 |
| Caudate contralateral | 1.87 (0.532) | 1.77 (0.593) | 1.84 (0.563) | 0.645 |
| Caudate ipsilateral | 2.23 (0.499) | 2.06 (0.663) | 2.15 (0.566) | 0.281 |
| Putamen contralateral | 0.723 (0.316) | 0.650 (0.232) | 0.693 (0.275) | 0.316 |
| Putamen ipsilateral | 0.962 (0.365) | 0.936 (0.399) | 0.940 (0.386) | 0.975 |
| Elixhauser comorbidity score | 0.910 (2.74) | 1.56 (3.27) | 0.755 (1.94) | 0.136 |
| Non-motor symptoms | ||||
| ESS score | 6.35 (3.48) | 5.41 (3.32) | 5.84 (3.33) | 0.321 |
| Diurnal somnolence | 12 (17.9%) | 20 (14.8%) | 14 (13.2%) | 0.851 |
| GDS score | 2.72 (2.75) | 2.16 (2.36) | 2.36 (2.52) | 0.523 |
| Depression | 11 (16.4%) | 21 (15.6%) | 14 (13.2%) | 0.939 |
| RBD Questionnaire score | 4.54 (2.95) | 3.95 (2.59) | 4.21 (2.66) | 0.542 |
| Probable RBD | 28 (41.8%) | 50 (37.0%) | 37 (34.9%) | 0.778 |
| SCOPA gastrointestinal score | 2.18 (1.84) | 2.17 (2.06) | 2.07 (2.06) | 0.980 |
| SCOPA urinary score | 3.99 (2.19) | 4.03 (3.06) | 4.52 (3.17) | 0.561 |
| SCOPA cardiovascular score | 0.493 (0.726) | 0.474 (0.818) | 0.406 (0.790) | 0.886 |
| SCOPA thermoregulation score | 1.25 (1.47) | 1.23 (1.49) | 1.07 (1.25) | 0.790 |
| SCOPA visual score | 0.388 (0.650) | 0.452 (0.688) | 0.340 (0.550) | 0.598 |
| SCOPA sexual dysfunction score | 1.33 (1.97) | 1.15 (1.48) | 1.15 (1.36) | 0.876 |
| STAIT anxiety score | 24.4 (9.34) | 23.1 (9.27) | 22.2 (9.55) | 0.524 |
| Anxiety | 4 (6.0%) | 10 (1.4%) | 7 (6.6%) | 0.984 |
| UPSIT | 22.0 (7.40) | 22.4 (8.62) | 23.5 (8.26) | 0.669 |
| MoCA score | 27.4 (2.23) | 27.4 (2.24) | 27.1 (2.56) | 0.769 |
| Cognitive deterioration | 22 (32.8%) | 43 (31.9%) | 33 (31.1%) | 0.997 |
| MDS-UPDRS I | 1.15 (1.34) | 1.22 (1.49) | 1.23 (1.81) | 0.990 |
| Motor assessment | ||||
| Hoehn & Yahr | 1.64 (0.483) | 1.56 (0.498) | 1.57 (0.498) | 0.732 |
| MDS-UPDRS II+III | 29.6 (13.0) | 26.4 (10.6) | 27.1 (11.2) | 0.250 |
| Oro-buccal score | 2.76 (2.43) | 2.60 (2.57) | 2.73 (2.34) | 0.967 |
| Eating score | 1.49 (0.975) | 1.33 (0.741) | 1.45 (0.951) | 0.564 |
| Mobility score | 2.60 (2.41) | 1.75 (1.81) | 1.83 (1.86) | 0.862 |
| Axial score | 5.10 (2.70) | 4.39 (2.33) | 4.51 (2.23) | 0.241 |
| Resting tremor | 2.87 (2.36) | 2.59 (2.24) | 2.81 (2.55) | 0.852 |
| Postural and kinetic tremor | 1.69 (1.57) | 1.44 (1.38) | 1.47 (1.66) | 0.745 |
| Rigidity | 3.79 (2.57) | 3.90 (2.82) | 3.69 (2.44) | 0.945 |
| Bradykinesia right | 3.13 (2.50) | 2.75 (2.33) | 2.78 (2.19) | 0.716 |
| Bradykinesia left | 3.00 (2.62) | 2.77 (2.44) | 2.53 (2.50) | 0.691 |
| Bradykinesia infra | 3.13 (2.40) | 2.93 (2.17) | 2.62 (2.06) | 0.479 |
| PD subtype | ||||
| Tremor dominant | 54 (80.6%) | 112 (83.0%) | 90 (84.9%) | 0.962 |
| PIGD | 6 (9.0%) | 11 (8.1%) | 9 (8.5%) | |
| Indeterminate | 7 (10.4%) | 12 (8.9%) | 6 (5.7%) |
ESS: Epworth Sleepiness Scale; GDS= Geriatric Depression Scale; REM= Rapid-Eye movement; STAI= State-Trait Anxiety Inventory; UPSIT= University of Pennsylvania Smell Identification Test; MoCA= Montreal Cognitive Assessment; UPDRS= Unified PD Rating Scale; PD= Parkinson’s Disease; PIGD= Postural-Instability Gait Disorder.
Table 3.
Performance of machine-learning models in predicting possible RBD in the “validation” subsample.
Table 3.
Performance of machine-learning models in predicting possible RBD in the “validation” subsample.
| Machine Learning model | Metric | SNCA_rs356181 | SNCA_rs3910105 |
| decision tree | Accuracy | 0.34 | 0.34 |
| Recall | 0.34 | 0.34 | |
| F1-score | 0.33 | 0.20 | |
| Boosted decision tree | Accuracy | 0.35 | 0.35 |
| Recall | 0.35 | 0.35 | |
| F1-score | 0.31 | 0.24 | |
| Random forest | Accuracy | 0.36 | 0.34 |
| Recall | 0.36 | 0.34 | |
| F1-score | 0.24 | 0.18 | |
| SVM | Accuracy | 0.38 | 0.33 |
| Recall | 0.38 | 0.33 | |
| F1-score | 0.22 | 0.16 |
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