Dopaminergic pathway is the most disrupted pathway in the pathogenesis of Parkinson's disease. Several studies reported associations of dopaminergic genes with the occurrence of adverse events of dopaminergic treatment. However, none of these studies adopted a pathway based approach. The aim of this study was to comprehensively evaluate the influence of selected single nucleotide polymorphisms of key dopaminergic pathway genes on the occurrence of motor and non-motor adverse events of dopaminergic treatment in Parkinson's disease. In total, 231 Parkinson's disease patients were enrolled. Demographic and clinical data were collected. Genotyping was performed for 16 single nucleotide polymorphisms from key dopaminergic pathway genes. Logistic and Cox regression analyses were used for evaluation. Results were adjusted for significant clinical data. We observed that carriers of at least one COMT rs165815 C allele had lower odds for developing visual hallucinations (OR = 0.34; 95% CI = 0.16–0.72; p = 0.004), while carriers of at least one DRD3 rs6280 C allele and CC homozygotes had higher odds for this adverse event (OR = 1.88; 95% CI = 1.00–3.54; p = 0.049 and OR = 3.31; 95% CI = 1.37–8.03; p = 0.008, respectively). Carriers of at least one DDC rs921451 C allele and CT heterozygotes had higher odds for orthostatic hypotension (OR = 1.86; 95% CI = 1.07–3.23; p = 0.028 and OR = 2.30; 95% CI = 1.26–4.20; p = 0.007, respectively). Heterozygotes for DDC rs3837091 and SLC22A1 rs628031 AA carriers also had higher odds for orthostatic hypotension (OR = 1.94; 95% CI = 1.07–3.51; p = 0.028 and OR = 2.57; 95% CI = 1.11–5.95; p = 0.028, respectively). Carriers of the SLC22A1 rs628031 AA genotype had higher odds for peripheral edema and impulse control disorders (OR = 4.00; 95% CI = 1.62–9.88; p = 0.003 and OR = 3.16; 95% CI = 1.03–9.72; p = 0.045, respectively). Finally, heterozygotes for SLC22A1 rs628031 and carriers of at least one SLC22A1 rs628031 A allele had lower odds for dyskinesia (OR = 0.48; 95% CI = 0.24–0.98, p = 0.043 and OR = 0.48; 95% CI = 0.25–0.92; p = 0.027, respectively). Gene-gene interactions, more specifically DDC-COMT, SLC18A2-SV2C, and SLC18A2-SLC6A3, also significantly influenced the occurrence of some adverse events. Additionally, haplotypes of COMT and SLC6A3 were associated with the occurrence of visual hallucinations (AT vs. GC: OR = 0.34; 95% CI = 0.16–0.72; p = 0.005) and orthostatic hypotension (ATG vs. ACG: OR = 2.48; 95% CI: 1.01–6.07; p = 0.047), respectively. Pathway based approach allowed us to identify new potential candidates for predictive biomarkers of adverse events of dopaminergic treatment in Parkinson's disease, which could contribute to treatment personalization.

Background Inflammation and oxidative stress are recognized as important contributors to Parkinson’s disease pathogenesis. As such, genetic variability in these pathways could have a role in susceptibility for the disease as well as in the treatment outcome. Dopaminergic treatment is effective in management of motor symptoms, but poses a risk for motor and non-motor adverse events. Our aim was to evaluate the impact of selected single-nucleotide polymorphisms in genes involved in inflammation and oxidative stress on Parkinson’s disease susceptibility and the occurrence of adverse events of dopaminergic treatment. Methods In total, 224 patients were enrolled, and their demographic and clinical data on the disease course were collected. Furthermore, a control group of 146 healthy Slovenian blood donors were included for Parkinson’s disease’ risk evaluation. Peripheral blood was obtained for DNA isolation. Genotyping was performed for NLRP3 rs35829419, CARD8 rs2043211, IL1β rs16944, IL1β rs1143623, IL6 rs1800795, CAT rs1001179, CAT rs10836235, SOD2 rs4880, NOS1 rs2293054, NOS1 rs2682826, TNF-α rs1800629, and GPX1 rs1050450. Logistic regression was used for analysis of possible associations. Results We observed a nominally significant association of the IL1β rs1143623 C allele with the risk for Parkinson’s disease ( OR = 0.59; 95%CI = 0.38–0.92, p = 0.021). CAT rs1001179 A allele was significantly associated with peripheral edema (OR = 0.32; 95%CI = 0.15–0.68; p = 0.003). Other associations observed were only nominally significant after adjustments: NOS1 rs2682826 A allele and excessive daytime sleepiness and sleep attacks (OR = 1.75; 95%CI = 1.00–3.06, p = 0.048), SOD2 rs4880 T allele and nausea/vomiting (OR = 0.49, 95%CI = 0.25–0.94; p = 0.031), IL1β rs1143623 C allele and orthostatic hypotension (OR = 0.57, 95%CI = 0.32–1.00, p = 0.050), and NOS1 rs2682826 A allele and impulse control disorders (OR = 2.59; 95%CI = 1.09–6.19; p = 0.032). We did not find any associations between selected polymorphisms and motor adverse events. Conclusions Apart from some nominally significant associations, one significant association between CAT genetic variability and peripheral edema was observed as well. Therefore, the results of our stud...

Parkinson's disease (PD) is a sporadic progressive neurodegenerative brain disorder with a relatively strong genetic background. We have reviewed the current literature about the genetic factors that could be indicative of pathophysiological pathways of PD and their applications in everyday clinical practice. Information on novel risk genes is coming from several genome-wide association studies (GWASs) and their meta-analyses. GWASs that have been performed so far enabled the identification of 24 loci as PD risk factors. These loci take part in numerous cellular processes that may contribute to PD pathology: protein aggregation, protein, and membrane trafficking, lysosomal autophagy, immune response, synaptic function, endocytosis, inflammation, and metabolic pathways are among the most important ones. The identified single nucleotide polymorphisms are usually located in the non-coding regions and their functionality remains to be determined, although they presumably influence gene expression. It is important to be aware of a very low contribution of a single genetic risk factor to PD development; therefore, novel prognostic indices need to account for the cumulative nature of genetic risk factors. A better understanding of PD pathophysiology and its genetic background will help to elucidate the underlying pathological processes. Such knowledge may help physicians to recognize subjects with the highest risk for the development of PD, and provide an opportunity for the identification of novel potential targets for neuroprotective treatment. Moreover, it may enable stratification of the PD patients according to their genetic fingerprint to properly personalize their treatment as well as supportive measures.

Molecular mechanisms of Parkinson's disease (PD) have already been investigated in various different omics landscapes. We reviewed the literature about different omics approaches between November 2005 and November 2017 to depict the main pathological pathways for PD development. In total, 107 articles exploring different layers of omics data associated with PD were retrieved. The studies were grouped into 13 omics layers: genomics–DNA level, transcriptomics, epigenomics, proteomics, ncRNomics, interactomics, metabolomics, glycomics, lipidomics, phenomics, environmental omics, pharmacogenomics, and integromics. We discussed characteristics of studies from different landscapes, such as main findings, number of participants, sample type, methodology, and outcome. We also performed curation and preliminary synthesis of multiple omics data, and identified overlapping results, which could lead toward selection of biomarkers for further validation of PD risk loci. Biomarkers could support the development of targeted prognostic/diagnostic panels as a tool for early diagnosis and prediction of progression rate and prognosis. This review presents an example of a comprehensive approach to revealing the underlying processes and risk factors of a complex disease. It urges scientists to structure the already known data and integrate it into a meaningful context.

The response to dopaminergic treatment in Parkinson’s disease depends on many clinical and genetic factors. The very common motor fluctuations (MF) and dyskinesia affect approximately half of patients after 5 years of treatment with levodopa. We did an evaluation of a combined effect of 16 clinical parameters and 34 single nucleotide polymorphisms to build clinical and clinical-pharmacogenetic models for prediction of time to occurrence of motor complications and to compare their predictive abilities. In total, 220 Parkinson’s disease patients were included in the analysis. Their demographic, clinical, and genotype data were obtained. The combined effect of clinical and genetic factors was assessed using The Least Absolute Shrinkage and Selection Operator penalized regression in the Cox proportional hazards model. Clinical and clinical-pharmacogenetic models were constructed. The predictive capacity of the models was evaluated with the cross-validated area under time-dependent receiver operating characteristic curve. Clinical-pharmacogenetic model included age at diagnosis (HR = 0.99), time from diagnosis to initiation of levodopa treatment (HR = 1.24), COMT rs165815 (HR = 0.90), DRD3 rs6280 (HR = 1.03), and BIRC5 rs9904341 (HR = 0.95) as predictive factors for time to occurrence of MF. Furthermore, clinical-pharmacogenetic model for prediction of time to occurrence of dyskinesia included female sex (HR = 1.07), age at diagnosis (HR = 0.97), tremor-predominant Parkinson’s disease (HR = 0.88), beta-blockers (HR = 0.95), alcohol consumption (HR = 0.99), time from diagnosis to initiation of levodopa treatment (HR = 1.15), CAT rs1001179 (HR = 1.27), SOD2 rs4880 (HR = 0.95), NOS1 rs2293054 (HR = 0.99), COMT rs165815 (HR = 0.92), and SLC22A1 rs628031 (HR = 0.80). Areas under the curves for clinical and clinical-pharmacogenetic models for MF after 5 years of levodopa treatment were 0.68 and 0.70, respectively. Areas under the curves for clinical and clinical-pharmacogenetic models for dyskinesia after 5 years of levodopa treatment were 0.71 and 0.68, respectively. These results show that clinical-pharmacogenetic models do not have better ability to predict time to occurrence of motor complications in comparison to the clinical ones despite the significance of several polymorphisms. Models could be improved by a larger sample size and by additional polymorphisms, epigenetic predictors or serum biomarkers.

Background The most common psychiatric complications due to dopaminergic treatment in Parkinson’s disease are visual hallucinations and impulse control disorders. Their development depends on clinical and genetic factors. Methods We evaluated the simultaneous effect of 16 clinical and 34 genetic variables on the occurrence of visual hallucinations and impulse control disorders. Altogether, 214 Parkinson’s disease patients were enrolled. Their demographic, clinical, and genotype data were obtained. Clinical and clinical-pharmacogenetic models were built by The Least Absolute Shrinkage and Selection Operator penalized logistic regression. The predictive capacity was evaluated with the cross-validated area under the receiver operating characteristic curve (AUC). Results The clinical-pharmacogenetic index for prediction of visual hallucinations encompassed age at diagnosis (OR = 0.99), rapid eye movement (REM) sleep behavior disorder (OR = 2.27), depression (OR = 1.0002), IL6 rs1800795 (OR = 0.99), GPX1 s1050450 (OR = 1.07), COMT rs165815 (OR = 0.69), MAOB rs1799836 (OR = 0.97), DRD3 rs6280 (OR = 1.32), and BIRC5 rs8073069 (OR = 0.94). The clinical-pharmacogenetic index for prediction of impulse control disorders encompassed age at diagnosis (OR = 0.95), depression (OR = 1.75), beta-blockers (OR = 0.99), coffee consumption (OR = 0.97), NOS1 rs2682826 (OR = 1.15), SLC6A3 rs393795 (OR = 1.27), SLC22A1 rs628031 (OR = 1.19), DRD2 rs1799732 (OR = 0.88), DRD3 rs6280 (OR = 0.88), and NRG1 rs3924999 (OR = 0.96). The cross-validated AUCs of clinical and clinical-pharmacogenetic models for visual hallucinations were 0.60 and 0.59, respectively. The AUCs of clinical and clinical-pharmacogenetic models for impulse control disorders were 0.72 and 0.71, respectively. The AUCs show that the addition of selected genetic variables to the analysis does not contribute to better prediction of visual hallucinations and impulse control disorders. Conclusions Models could be improved by a larger cohort and by addition of other types of Parkinson’s disease biomarkers to the analysis.

Telomeres, which are repetitive sequences that cap the end of the chromosomes, shorten with each cell division. Besides cellular aging, there are several other factors that influence telomere length (TL), in particular, oxidative stress and inflammation, which play an important role in the pathogenesis of neurodegenerative brain diseases including Parkinson’s disease (PD). So far, the majority of studies have not demonstrated a significant difference in TL between PD patients and healthy individuals. However, studies investigating the effect of TL on the symptomatology and disease progression of PD are scarce, and thus, warranted. We analyzed TL of peripheral blood cells in a sample of 204 PD patients without concomitant autoimmune diseases and analyzed its association with several PD related phenotypes. Monochrome multiplex quantitative PCR (mmqPCR) was used to determine relative TL given as a ratio of the amount of DNA between the telomere and albumin as the housekeeping gene. We found a significant difference in the relative TL between PD patients with and without dementia, where shorter TL presented higher risk for dementia (p = 0.024). However, the correlation was not significant after adjustment for clinical factors (p = 0.509). We found no correlations between TLs and the dose of dopaminergic therapy when the analysis was adjusted for genetic variability in inflammatory or oxidative factors. In addition, TL influenced time to onset of motor complications after levodopa treatment initiation (p = 0.0134), but the association did not remain significant after adjustment for age at inclusion and disease duration (p = 0.0781). Based on the results of our study we conclude that TL contributes to certain PD-related phenotypes, although it may not have a major role in directing the course of the disease. Nevertheless, this expends currently limited knowledge regarding the association of the telomere attrition and the disease severity or motor complications in Parkinson’s disease.

Parkinson's disease (PD) is a chronic progressive neurodegenerative brain disorder presenting with motor signs and symptoms, such as akinesia, rest tremor, rigidity, and later in disease progression postural instability. However, nonmotor symptoms may harm patients' quality of life even more than the motor ones. The etiopathogenesis is not clear yet. PD may develop due to a combination of genetic and environmental factors. It is treated symptomatically with dopaminergic drugs. The gold standard of PD management is L-Dopa, however also other drugs are frequently used, such as dopamine agonists, MAOB inhibitors, COMT inhibitors, and occasionally amantadine and anticholinergic drugs. Many patients experience several adverse events of L-Dopa treatment, such as different motor complications. Furthermore, nonmotor adverse events of dopaminergic treatment may occur. The efficacy of drugs varies between patients as well. Several polymorphic genes have already been associated with treatment outcome in PD, such as metabolic enzymes, transport and receptor genes, and might serve as treatment outcome prediction factors. As gene-environment interactions were also shown to contribute to PD development, they might also be able to predict treatment response. Such genetic biomarkers could be helpful in personalized care of PD patients to prevent adverse events and inefficacy of a certain drug.