In Parkinson’s disease (PD), alpha-synuclein (a-syn) can be detected in biological fluids including saliva. Although previous studies found reduced a-syn total (a-syntotal) concentration in saliva of PD patients, no studies have previously examined salivary a-syn oligomers (a-synolig) concentrations or assessed the correlation between salivary a-syntotal, a-synolig and clinical features in a large cohort of PD patients. Is well known that a-synolig exerts a crucial neurotoxic effect in PD. We collected salivary samples from 60 PD patients and 40 age- and sex-comparable healthy subjects. PD was diagnosed according to the United Kingdom Brain Bank Criteria. Samples of saliva were analyzed by specific anti-a-syn and anti-oligomeric a-syn ELISA kits. A complete clinical evaluation of each patient was performed using MDS-Unified Parkinson's Disease Rating Scale, Beck Depression Inventory, Montreal Cognitive Assessment and Frontal Assessment Battery. Salivary a-syntotal was lower, whereas a-synolig was higher in PD patients than healthy subjects. The a-synolig/a-syntotal ratio was also higher in patients than in healthy subjects. Salivary a-syntotal concentration negatively correlated with that of a-synolig and correlated with several patients’ clinical features. In PD, decreased salivary concentration of a-syntotal may reflect the reduction of a-syn monomers (a-synmon), as well as the formation of insoluble intracellular inclusions and soluble oligomers. The combined detection of a-syntotal and a-synolig in the saliva might help the early diagnosis of PD.

HighlightsThis systematic review focuses on structural and functional neuroimaging findings in PD patients with FOG.The existing neuroimaging literature may explain several mechanisms underpinning FOG in PD.FOG in PD reflect structural or functional damage in brain regions responsible for human locomotion.

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Freezing of gait (FOG) is a disabling disorder that often affects Parkinson's disease (PD) patients in advanced stages of the disease. To study structural gray matter (GM) and white matter (WM) changes in PD patients with and without FOG, twenty-one PD patients with FOG (PD-FOG), 16 PD patients without FOG (PD-nFOG) and 19 healthy subjects (HS) underwent a standardized MRI protocol. For the gray matter evaluation, cortical volume (CV), cortical thickness (CTh), and surface area (SA) were analyzed using the FreeSurfer pipeline. For the white matter evaluation, DTI images were analyzed using tracts constrained by underlying anatomy (TRACULA) toolbox in FreeSurfer. PD-FOG patients exhibited lower CTh than HS in the mesial surface of both cerebral hemispheres, including the superior frontal gyrus, paracentral lobule, posterior cingulate cortex, precuneus and pericalcarine cortex, and in the right dorsolateral prefrontal cortex. Moreover, significant WM changes were observed in PD-FOG patients in comparison with HS in the superior longitudinal fasciculus, uncinate fasciculus, cingulum cingulate gyrus and inferior longitudinal fasciculus (prevalently in the right hemisphere) and in the frontal radiations of the corpus callosum. DTI abnormalities in specific WM bundles correlated significantly with cognitive measures. The damage of multiple cortical areas involved in high-level gait control together with WM disruption between motor, cognitive and limbic structures may represent the anatomical correlate of FOG.

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Objective:To perform a simultaneous evaluation of potential risk/protective factors of Parkinson’s disease (PD) in order to identify independent risk/protective factors, assess interaction among factors and determine whether identified risk factors predict etiological subtypes of PD.Methods:We designed a large case-control study assessing 31 protective/risk factors of PD, including environmental and lifestyle factors, comorbidities, and drugs. The study enrolled 694 PD patients and 640 healthy controls from six neurological centers. Data were analyzed by logistic regression models, additive interaction models, and cluster analysis.Results:The simultaneous assessment of 31 putative risk/protective factors of PD showed that only coffee consumption (odds ratio [OR]: 0.6; 95% confidence interval [CI]: 0.4-0.9), smoking (OR: 0.7; 95% CI: 0.6-0.9), physical activity (OR: 0.8; 95% CI: 0.7-0.9), family history of PD (OR: 3.2; 95% CI: 2.2- 4.8), dyspepsia (OR: 1.8; 95% CI:1.3-2.4), exposure to pesticides (OR: 2.3; 95% CI:1.3- 4.2), oils (OR: 5.6; 95% CI: 2.3-13.7), metals (OR: 2.8; 95% CI: 1.5-5.4), and general anesthesia (OR: 6.1; 95% CI: 2.9-12.7) were independently associated with PD. There was no evidence of interaction among risk/protective factors, but cluster analysis identified four subtypes with different risk factor profiles. In Group 1, all patients had a family history of PD, while dyspepsia or exposure to toxic agents were present in 30% of patients. In Group 2 and 3, a family history of PD was lacking, while exposure to toxic agents (Group 2) and dyspepsia (Group 3) played major roles. Group 4 consisted of patients with no risk factors.Conclusions:This study demonstrated that nine factors independently modify PD risk by coexisting in the same patient rather than interacting with others. Our study suggests the need for future preventive strategies aimed at reducing the coexistence of different risk factors within the same subject.

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.