Journal of Parkinson’s Disease

BackgroundMinor hallucinations (MH) affect 30-60% of patients with Parkinsons disease (PD), and are considered precursors to structured visual hallucinations and cognitive decline. While the link between structured visual hallucinations and dementia is well established, the neuropsychological correlates of MH in PD remain unclear; most studies finding no significant cognitive differences between patients with MH and those without any hallucinations. ObjectivesPresence hallucinations (PH) being among the most prevalent MH in PD, we used a robotic procedure delivering somatomotor conflicts inducing PH experimentally to investigate whether sensitivity to such robot-induced PH aids in detecting cognitive differences between patients with MH and without hallucinations. Methods31 PD patients with MH (PD-MH) and 37 without hallucinations (PD-nH) underwent neuropsychological assessment and the robotic procedure inducing PH. The sensitivity to report robot-induced PH was analyzed in relation to cognitive performance in neuropsychological tests. ResultsPD-MH patients reported more robot-induced PH than PD-nH patients, supporting previous findings. While both groups showed comparable performance in neuropsychological testing, we found a significant association between increased sensitivity to the PH-induction and poorer performance in frontal subcortical functions (executive functions), in PD-MH patients, but not in PD-nH patients. ConclusionsThese findings demonstrate that sensitivity to robot-induced PH reveals a previously undetected link between MH and frontal subcortical cognitive deficits in PD, pointing to shared underlying mechanisms between executive dysfunction and somatomotor processes involved in MH. This approach offers a novel and clinically valuable means of identifying early cognitive vulnerability that assessments relying only on standard testing may overlook. Plain language summaryO_ST_ABSInduced minor hallucinations are linked to executive function alteration in Parkinsons diseaseC_ST_ABSThis study investigated two groups of Parkinsons disease (PD) patients: one group with minor hallucinations (MH) and another without any hallucinations. Using cognitive tests and a robotic task designed to temporarily induce a presence hallucination (hallucination of someone being there when no one is actually present), the study examined the relationship between cognitive impairment and sensitivity to robot-induced presence hallucinations (riPH). While both groups performed similarly on traditional cognitive tests, patients with MH were more sensitive to the riPH procedure. This increased riPH sensitivity was linked to difficulties with cognitive functions, especially executive functions, which are generally supported by the brains frontal and deeper regions (frontal subcortical network). These results from both tests (riPH, neuropsychology) suggest that elevated sensitivity to riPH is associated with mild signs of cognitive decline in PD patients that traditional tests might not detect. The use of the robotic induction of hallucinations as a tool for assessing cognitive deficits could offer a more sensitive method for identifying cognitive issues earlier in PD, potentially enabling earlier interventions.

BackgroundImpulse control disorders (ICD) in Parkinsons disease (PD) patients mainly occur as adverse effects of dopamine replacement therapy. Despite several known risk factors associated with ICD development, this cannot yet be accurately predicted at PD diagnosis. ObjectivesWe aimed to investigate the predictability of incident ICD by baseline measures of demographic, clinical, dopamine transporter single photon emission computed tomography (DAT-SPECT), and genetic variables. MethodsWe used demographic and clinical data of medication-free PD patients from two longitudinal datasets; Parkinsons Progression Markers Initiative (PPMI) (n=311) and Amsterdam UMC (n=72). We extracted radiomic and latent features from DAT-SPECT. We used single nucleotic polymorphisms (SNPs) from PPMIs NeuroX and Exome sequencing data. Four machine learning classifiers were trained on combinations of the input feature sets, to predict incident ICD at any follow-up assessment. Classification performance was measured with 10x5-fold cross-validation. ResultsICD prevalence at any follow-up was 0.32. The highest performance in predicting incident ICD (AUC=0.66) was achieved by the models trained on clinical features only. Anxiety severity and age of PD onset were identified as the most important features. Performance did not improve with adding features from DAT-SPECT or SNPs. We observed significantly higher performance (AUC=0.74) when classifying patients who developed ICD within four years from diagnosis compared with those tested negative for seven or more years. ConclusionsPrediction accuracy for later ICD development, at the time of PD diagnosis, is limited; however, it increases for shorter time-to-event predictions. Neither DAT-SPECT nor genetic data improve the predictability obtained using demographic and clinical variables alone.

Functionality is considered the third health indicator, complementing the traditional mortality and morbidity metrics. However, functionality is rarely assessed systematically in Parkinsons disease (PD) clinical practice and research, where symptom-based scales predominate. Identifying declines across various dimensions of functionality in PD is essential for patient education, disease management, and guiding new intervention strategies. The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a comprehensive assessment tool developed by the World Health Organization (WHO) based on the International Classification of Functioning, Disability and Health (ICF) to standardize the measurement of functionality and disability impacts across diverse health conditions and cultural contexts. This study aimed to characterize functionality across PD severity stages using the WHODAS 2.0, independent of age, sex, socioeconomic status (SEC), and education. A total of 352 patients were divided into four clinically severity PD stage groups according to the Hoehn & Yahr (H&Y) scale. The age, sex, SEC and education levels were controlled to guarantee paired groups. All participants were evaluated remotely using the Telephone - Montreal Cognitive Assessment (T-MoCA), Beck Depression Inventory (BDI), Movement Disorder Society - Unified Parkinsons Disease Rating Scale, Part I (MDS-UPDRS I) and Part II (MDS-UPDRS II) and WHODAS 2.0. The most affected functionality dimensions were Mobility, Activities of Daily Life related to the Household, and Participation. The nonparametric Kruskal-Wallis test revealed a significant effect of the group for all functionality dimensions. Notably, Mobility, Activities of Daily Life related to the Household, and Self-Care showed a gradual decline starting from stage 1 of H&Y. In contrast, Cognition, Getting Along and Participation exhibited progressive impairment only from stage 2 of H&Y. This study is the first to describe functionality in PD using WHODAS 2.0 across severity stages controlling for key demographic factors. Findings highlight that WHODAS captures functional limitations not identified by symptom-focused scales such as MDS-UPDRS. Incorporating WHODAS into routine assessment may improve patient-centered care by informing interventions targeting functional limitations from early disease stages. HighlightsWHODAS 2.0 captures functionality domains not assessed by MDS-UPDRS. Functional decline in mobility, self-care, and household tasks starts at early PD stages. Participation, cognition, and interpersonal relationships decline from stage 2 onwards. Functionality decline is independent of age, sex, education, and socioeconomic status. WHODAS provides a biopsychosocial assessment essential for person-centered PD care.

BackgroundThe causes of neuropsychiatric symptoms in Parkinsons disease (PD) remain ill-defined. Disruptions in dopamine-dependent reward learning, a consequence of midbrain dopamine loss, potentially represent a mechanism that could underlie the neuropsychiatric symptoms of apathy, depression, and impulsivity, all of which have been proposed to reflect aberrant goal-directed behaviour. However no large-scale investigation that jointly considers these symptoms in PD has ever been undertaken. We aimed to determine if reward learning is associated with apathy, depression and impulsivity symptom in two samples of PD patients. MethodsTwo samples of PD patients (nsample1=81, nsample2=90), tested in their medicated state, completed two widely used reward learning tasks (probabilistic stimulus selection task, probabilistic reward task) from which we derived five summary measures of performance. Apathy, depression, and impulsivity were evaluated using validated self-report questionnaires. ResultsOverall, there was poor consistency in the relationship between reward learning and neuropsychiatric symptom severity. Greater depressive symptom severity was associated with slower reward learning performance in sample 1, but with both slower and faster reward learning performance in sample 2. Greater impulsivity symptom severity was associated with slower reward learning performance in sample 1 but not in sample 2. There were no associations between apathy and reward learning. ConclusionsWe found inconsistent relationships between symptoms of apathy, depression, and impulsivity and reward learning performance across two samples of PD patients. While this doesnt rule out the possibility that reward learning impairments contribute to these symptoms, it suggests any effect is likely to be small and overshadowed by other non-measured factors.

IntroductionMotor complications are major determinants of disability in Parkinsons disease (PD), yet clinician-rated motor complication severity does not fully explain variability in health-related quality of life (HRQoL). Research questionTo examine the contribution of illness perceptions and cognitive-behavioural responses to HRQoL alongside motor complication severity in people with PD. MethodsThis multi-centre, cross-sectional study recruited 58 people with idiopathic PD (median age 68 years; 55.2% male; 48.3% from minoritised ethnic backgrounds; Hoehn & Yahr stage 2-3). All underwent assessment of motor complications (Movement Disorder Society-Unified Parkinsons Disease Rating Scale; MDS-UPDRS Part IV) and HRQoL (Parkinsons Disease Questionnaire-39 Summary Index; PDQ-39 SI). Illness perceptions were measured with Illness Perception Questionnaire-Revised (IPQ-R) Part 2, and cognitive-behavioural responses with Cognitive and Behavioural Responses Questionnaire (CBRQ). Regression models were adjusted for age, sex, disease duration, motor severity (MDS-UPDRS Part III), levodopa equivalent daily dose (LEDD), anxiety, depression, and cognitive function. A subset (n=47) completed 7-day Parkinsons KinetiGraph monitoring. ResultsDemographic and clinical covariates explained 77.3% of variance in HRQoL (R{superscript 2}=0.773). Adding motor complication severity explained a significant additional 3.7% ({Delta}R{superscript 2}=0.037, P=0.004). Subsequent inclusion of illness consequences (IPQ-R) and catastrophising (CBRQ) explained a further 4.1% ({Delta}R{superscript 2}=0.041, P=0.004), yielding a final adjusted R{superscript 2} of 0.815. In the fully adjusted model, catastrophising (B=0.797, P=0.027) and perceived consequences (B=0.767, P=0.013) remained independently associated with HRQoL. ConclusionHRQoL in PD appears to depend not only on motor complication severity, but also on patients interpretations and responses. Clinicians should assess both to guide holistic care and support adaptive coping.

BackgroundParkinsons disease (PD) is a progressive neurodegenerative disorder with complex pathophysiology. The potential of integrating biomarkers of neuronal injury, neuroinflammation, and modulators of Wnt signaling for PD diagnosis remains largely unexplored. ObjectiveThis study aimed to evaluate the diagnostic and clinical predictive value of a ten-biomarker serum panel encompassing markers of neuronal injury (NSE, UCHL1), neuroinflammation (GFAP, HMGB1), synaptic plasticity (BDNF), proteinopathy (-Synuclein, {beta}-Amyloid-42), and Wnt signaling (R-spondin-1, DKK1, Sclerostin), with a particular focus on chronic fatigue in PD. MethodsIn this case-control study, 90 PD patients and 45 healthy controls were enrolled. Serum biomarkers were quantified using ELISA. Clinical severity was assessed using the Movement Disorder Society-Unified Parkinsons Disease Rating Scale (MDS-UPDRS) and Fibro-Fatigue (FF) scales. Binary logistic regression and multiple linear regression analyses were used to evaluate the diagnostic and predictive value of biomarkers for PD diagnosis, psychiatric and motoric scores, and an FF score reflecting chronic fatigue syndrome (CFS) severity. ResultsA model incorporating NSE, DKK1, and {beta}-Amyloid-42 effectively discriminated PD patients from controls, yielding an area under the curve (AUC) of 0.932 and an overall accuracy of 83.0%. NSE and DKK1 emerged as the main predictors of overall disease severity, motor symptoms, and CFS severity. Regression analyses indicated that 41.3% of the variance in the FF score was explained by increased NSE, DKK1, {beta}-amyloid, and UCHL1, while 42.9% of the variance in psychiatric symptoms was explained by increased NSE, DKK1, and {beta}-amyloid. Increased GFAP levels were significantly associated with motor dysfunction. ConclusionThe combined presence of neuronal injury, Wnt signaling dysregulation, and amyloid pathology may represent a key pathophysiological component underlying PD, CFS-like fatigue, and psychiatric symptoms in PD. Targeting neuronal injury and Wnt signaling pathways may offer novel therapeutic strategies for managing fatigue and psychiatric manifestations in PD.

Presence hallucinations (PH), non-veridical perceptions of someone appearing around you, are frequently occurring symptoms, in Parkinsons disease (PD). They belong to the group of minor hallucinations and are predictors of the later appearance of structured visual hallucinations and PD psychosis. Despite their elevated frequency and negative clinical outcomes, differences in PH subtypes have not been examined. ObjectivesInvestigating PD patients with identified versus unidentified PH, this study determines differences in disease progression, motor and cognitive performance, and brain mechanisms. MethodsBased on detailed interviews of 77 PD patients, 27 reporting PH were grouped into those with identified PH versus unidentified PH (iPH-PD, n=12; uPH-PD, n=15). Disease progression (UPDRS, Hoehn Yahr), cognitive functions (PD-CRS), sensitivity to robot-induced PH and resting-state functional connectivity were analysed. ResultsiPH reports mostly consisted of first-degree close others (e.g., partner). iPH-PD patients had a more advanced PD stage, reported more daily-life motor impairments and more simple visual illusions than uPH-PD patients. Both groups showed similar sensitivity to robot-induced PH. Resting-state functional connectivity showed that iPH-PD patients vs. uPH-PD patients had right IFG-pMTG functional disconnection within a previously defined PH-network and the degree of this disconnection was correlated with posterior cognitive impairment. ConclusionDistinguishing iPH-PD from uPH-PD patients, we show that the former display worse daily motor functioning, more frequent visual illusions and stronger fronto-temporal disconnection, related to cognitive dysfunction. These data suggest that iPH-PD have a more advanced form of PD, possibly representing an intermediate disease stage, between uPH-PD and PD patients with structured visual hallucinations.

BackgroundData-driven approaches identified Mild Motor Predominant (MMP), Intermediate (IM), and Diffuse Malignant (DM) as Parkinsons Disease (PD) subtypes with different motor and non-motor impairment at diagnosis. It remains unclear whether these subtypes remain stable over time or whether they represent distinct biological substrates. The alpha-synuclein seed amplification assay in CSF (CSF-Syn-SAA) might provide further insights. Objectiveto evaluate the association between baseline CSF-Syn-SAA parameters and 10-year clinical evolution of PD subtypes. Methods323 sporadic PD patients from PPMI dataset were classified as MMP, IM, or DM at baseline and 10-year follow-up based on motor, cognitive, sleep and dysautonomia features. CSF-Syn-SAA parameters were collected at baseline using a 150-hrs protocol. CSF A{beta}1-42, tTau and pTau181, CSF and serum NfL were also considered at baseline. ResultsReaction times (T50, TTT) and area under the curve (AUC) respectively were shorter and larger in DM compared to IM/MMP. The difference in baseline amplification parameters was more evident when comparing subtypes based on 10-year clinical features (T50, 2=0.036; TTT, 2=0.031; AUC, 2=0.033; all p values < 0.05) than when comparing subtypes based on baseline clinical features (T50, 2=0.012; TTT, 2=0.012; AUC, 2=0.013; all p<0.05). Shorter T50 and TTT at baseline were associated with greater risk of DM versus MMP at 10-year follow-up (T50, OR=3.3, 95%CI: 1.3-8.1, p=0.010; TTT, OR=4.6, 95%CI: 1.8-11.6, p=0.001). A{beta}, Tau and NfL were similar between groups. ConclusionsBaseline CSF-Syn-SAA parameters predicted long-term PD progression. Faster reactions were associated with a more severe 10-year PD phenotype considering motor and non-motor features. Plain Language SummaryO_ST_ABSBackgroundC_ST_ABSThe diagnosis of Parkisons Disease is drastically changing by the development of Alpha-Synuclein Seed Amplification Assay. The assay enables, for the first time, the detection of pathological forms of alpha-synuclein in cerebrospinal fluid in living patients. Alpha-Synuclein Seed Amplification is very accurate in discerning individuals with Parkinsons Disease versus healthy subjects, but it remains unknown whether it can also inform about prognosis. ObjectiveWe assessed the ability of the assay to predict the 10-year clinical progression of Parkinsons Disease. MethodsPublic data from an international cohort were used. At time of diagnosis, we classified 323 individuals with Parkinsons Disease into three clinical subtypes: Mild Motor Predominant, Intermediate, and Diffuse Malignant. These subtypes were characterized by a progressively increasing burden of motor and non-motor symptoms. Subjects with available follow-up data were re-classified using the same subtypes after 10 years of disease. Time-dependent signal changes of Alpha-Synuclein Seed Amplification Assay in Cerebrospinal Fluid were measured at baseline and used to predict the 10-year phenotype. ResultsFirstly, we found that subtypes were not stable categories. Around a half of participants changed subtype over time, mostly shifting towards a more aggressive one. Notably, our results showed that faster reactions on Alpha-Synuclein Seed Amplification Assay at baseline were associated with a 10-year phenotype more aggressive in terms of motor symptoms, dysautonomia, sleep and cognitive impairment, i.e the Diffuse Malignant subtype. ConclusionCharacteristics of the assay underlying the final positivity or negativity outcomes performed at a milder and early stage of PD may identify a subgroup of subjects that are more likely to undergo a more rapid clinical deterioration. Further studies, however, are needed to confirm and expand this result.

Background and ObjectivesThe Montreal Cognitive Assessment (MoCA) is frequently used in cohort studies in Parkinsons disease (PD) as a simple and quick tool for assessing global cognitive abilities of patients. However, cut-off values for distinguishing between normal cognition, mild cognitive impairment (MCI), and dementia differ across the literature. We comprehensively evaluate the accuracy of the MoCA for patient stratification and whether it can improved by including additional routinely collected information. MethodsWe use longitudinal data from PD and healthy control (HC) participants of the PPMI cohort which - in addition to the MoCA - conducts detailed neuropsychological testing and records diagnoses of MCI and dementia made at the research clinics. Multilevel logistic regression was used to predict (1) impairment in detailed neuropsychological testing and (2) clinician diagnoses from the MoCA in conjunction with other routinely collected information on basic demographics or functional impairment as recorded in MDS-UPDRS 1.1. Model performance was assessed using the area under the ROC curve (AUC). Optimal cut-offs for patient stratification were derived according to Youdens J, common screening and diagnosis criteria, and an equal proportions criterion, that is, the cutoff at which the proportion of observations with the outcome equals the proportion of observations below cutoff. ResultsWe analysed data from 1,094 PD patients and of 267 HC. Education-adjusted MoCA scores predicted impairment in 2 or more domains with an AUC of 0.86 (95% CI 0.84, 0.88). Youdens J was maximized at cutoff [&le;] 24 with sensitivity 74.7 (70.5, 79.3) and specificity 83.1 (82.0, 84.2); cutoff [&le;] 21 equated proportions. The MDS-UPDRS 1.1 was a better predictor of research clinic diagnosis of PD-MCI or PDD than the MoCA. A combination of MDS-UPDRS 1.1 and education adjusted MoCA discriminated diagnosis of any impairment from no impairment with an AUC of 0.87 and dementia from no dementia with an AUC of 0.96. DiscussionOptimal MoCA cutoffs for PD-MCI or PDD depend on their purpose. For post-hoc stratification for research purposes, we recommend considering a cutoff that equates proportions. Identifying suitable cutoffs from the literature - for research or in clinic use - needs to take into account the respective PD population.

BackgroundNovel supportive diagnostic and prognostic biomarkers for Parkinsons disease (PD) are needed to enable its early diagnosis and inform clinical trials. Proteomic studies have identified cerebrospinal fluid (CSF) DOPA decarboxylase (DDC) as a promising biomarker candidate, but its role has not been well characterized. The aim of this study was to gain further insight into the potential of DDC as biomarker for PD. MethodsWe developed and validated a single molecule counting immunoassay for DDC quantification in CSF based on commercially available monoclonal antibodies. DDC levels were quantified in the Parkinsons Progression Markers Initiative cohort including healthy controls (n=29), dopaminergic drug-naive patients with PD (n=27) and with scans without evidence for dopaminergic deficit (SWEDD) (n=18). Their relationship with ioflupane-[123I]-single-photon emission computed tomography-based dopamine transporter (DaT-SPECT) imaging was analyzed. The prognostic potential of CSF DDC was evaluated by assessing the relationship between baseline DDC levels and yearly changes of Movement Disorder Society Unified Parkinsons Disease Rating Scale (MDS-UPDRS) scores. CSF DDC levels were also quantified three years after the diagnosis, and their relationship with the L-DOPA equivalent daily dose (LEDD) was investigated. Finally, absolute DDC levels determined by our assay were correlated with relative concentrations obtained from Olink technology. ResultsOur DDC assay detected elevated levels in CSF from dopaminergic drug-naive PD patients and discriminated them from untreated SWEDD and control with high sensitivity and specificity. There was an inverse correlation between baseline DDC levels and DaT-SPECT striatal binding ratios (SBR) from the putamen and caudate nucleus. Baseline CSF DDC levels demonstrated prognostic potential for MDS-UPDRS total change five to eight years after the diagnosis. DDC levels were further increased at the three-year follow-up visit in PD patients and were positively correlated with the LEDD. Finally, there was a strong correlation between relative CSF DDC levels determined with the Olink assay and absolute DDC levels determined with our assay. ConclusionsOur monoclonal antibody-based assay for DDC quantification provided further insight into the potential of DDC in CSF to serve as a diagnostic and prognostic biomarker for PD. The unchanged levels in SWEDD patients and the inverse correlation with DaT-SPECT SBR suggest that DDC levels in CSF are connected to dopaminergic deficit.

BackgroundThe definite diagnosis of Parkinsons disease (PD) is usually made by the detection of -synuclein aggregates in the brain post mortem with the rare exception of some genetic forms. Traces of -synuclein aggregates in extracerebral tissue biopsies may serve as an appropriate biomarker to confirm a clinical diagnosis in vivo. ObjectivesWe set out to determine whether the detection of -synuclein aggregates in submandibular gland biopsies is an effective means for diagnosing PD patients. MethodsWe examined submandibular gland biopsies from 25 patients with PD under investigation and 25 age-matched controls to detect -synuclein aggregates using real-time quaking induced conversion (RT-QuIC) as a seed amplification-assay and immunohistochemical -synuclein aggregate detection and paraffin-embedded tissue blot (PET-blot) as confirmatory methods. ResultsOur RT-QuIC assay detected -synuclein aggregates in submandibular gland biopsies with a sensitivity of 81,1%, which increased to 90% after a clinical follow-up, and a specificity of 100%. The PET-blot with mAb5C12 confirmed 50% of the RT-QuIC positives and offered a sensitivity of 45,8% (50% after clinical follow-up) and a specificity of 100%. Immunohistochemical detection using the same antibody confirmed 28% of the RT-QuIC positives, but found two of the controls to be positive and therefore provided a sensitivity of 26,1% (28,6% after clinical follow-up) and a specificity of 92%. ConclusionsThe RT-QuIC Assay demonstrated comparable sensitivity to the clinical diagnosis (when neuropathologic examination represents the gold standard) and exhibited a similar level of specificity as the PET-blot.

IMPORTANCEWearable-based measures of walking (as proxy for physical activity) may quantify disease progression and modification thereof in early-stage Parkinsons disease (PD). OBJECTIVESEstablishing the validity of digital measures of walking and non-walking in PD. DESIGNRetrospective longitudinal analyses of data from cohorts within 3 larger studies, consisting of wearable sensor, demographic, and clinical data collected during 2017-2023, with 1-2 year follow up. SETTINGThree independent multicenter cohort studies. PARTICIPANTSPeople with PD, and age/sex matched non-PD cohort. EXPOSURESNone. MAIN OUTCOMES AND MEASURESDigital measures test-retest reliability, analyzed using intraclass correlation coefficients across consecutive monthly-aggregated data. Digital measures sensitivity: ability to detect within-participant changes, analyzed over 24 months using linear mixed-effect models, and analyzed as effect-size changes-from-baseline comparing 1- and 2-year longitudinal Cohens-d (mean and 95% CIs) vs conventional clinical endpoints. Analyses replicated in two independent PD cohorts (internal validation and external evaluation). Compared within-participant changes between PD and non-PD cohorts using linear mixed-effect model slopes. RESULTSWe analyzed 57 digital measures (51 individual, 6 composite) in a development cohort (N=171), selecting 32 (26 individual, 6 composite) for further study based on their sensitivity and test-retest reliability. During internal validation (N=101), 20 measures could detect statistically significant within-participant changes and 7 showed larger 2-year effect-size changes than conventional clinical measures; non-walking bout (NWB) duration (12.4% yearly change; 2-year Cohens-d 0.623 [95% CI: 0.461,0.811]) and 95th percentile of NWB duration (17.1% yearly change; 2-year Cohens-d, 0.623 [95% CI: 0.461,0.811]) performed best. Measures could detect significant and persisting changes from baseline at 10 months. During external evaluation (N=67), 15 measures could detect statistically significant within-participant changes and 12 showed larger 1-year effect-size changes than conventional clinical measures; 12 measures showed significantly greater change in people with PD than in matched non-PD individuals (N=171). CONCLUSION AND RELEVANCEInternal validation and external evaluation of 32 digital measures that quantified walking and non-walking behaviors in patients with early-stage PD showed that they could have greater sensitivity to detect longitudinal changes than conventional measures, and that these changes were disease-specific (e.g., separate from aging), making them candidates for disease-specific progression markers. Key PointsO_ST_ABSQuestionC_ST_ABSCan wearable sensor-based digital measures of physical activity and mobility serve as markers of disease progression in early-stage Parkinsons disease (PD)? FindingsIn two independent longitudinal cohorts of people with PD, digital measures detected statistically-significant changes in walking and non-walking behaviors after 1 and 2 years of follow-up; additionally, a comparison between people with and without PD (from a third cohort) showed that these changes were disease-specific. Compared with MDS-UPDRS-based conventional metrics, measures of non-walking behavior showed greater effect size (such as mean non-walking bout duration, with an annual increase of 12.4% and a 2-year Cohens-d of 0.623). MeaningWearable sensor-based digital measures can detect and quantify disease-specific changes in walking and non-walking behaviors over time in people with early-stage PD.

Background: Gait variability is a hallmark of Parkinson's disease (PD) and has been linked to cognitive deficits and fall risk. Rapid eye movement sleep behavior disorder (RBD) is a strong predictor of synucleinopathies, yet evidence for gait changes in RBD is inconsistent. Performing a dual task increases gait variability, an effect that can be quantified using a cost function. Objective: Determine the degree to which dual task cost differs between control, RBD, and PD participants at baseline, and between RBD converters versus non-converters at follow-up. Methods: 46 RBD, 23 control, and 14 PD participants completed standardized gait analysis at baseline. Parameters chosen for analysis included enhanced gait variability index (eGVI), functional ambulation performance (FAP), velocity, step length, cadence, base of support, and double support time. Medical records were surveilled for 3 years following participant enrollment, determining that 6 RBD participants converted to PD or dementia. Baseline gait indices and dual task costs were compared between control, RBD, and PD groups at enrollment, and between RBD stable and RBD converters at follow-up. Results: The PD group had greater eGVI, as well as greater dual task cost for FAP, cadence, width, and double support time. No differences in gait variability were identified between RBD and control groups at baseline. Compared to the stable group, RBD converters had greater dual task cost for FAP, velocity, cadence, and double support time. Conclusions: Increased gait variability during dual task may identify RBD patients at imminent risk of phenoconversion.

BackgroundFalls are a major source of morbidity in Parkinsons disease (PD), yet their evolution over time remains unclear. ObjectivesTo compare fall risk and outcomes among PD, prodromal alpha-synucleinopathy, (PAS) and healthy controls (HC); estimate fall frequency across PD progression; and characterize clinical features of PD faller subgroups. MethodsWe analyzed fall-related outcomes in the Parkinsons Progression Markers Initiative. Yearly rates of rare and frequent falls were estimated by time since diagnosis. Unique PD participants were grouped as never, rare, or frequent fallers. Clinical variables included motor, cognitive, behavioral, sleep, and autonomic measures. Outcomes included injuries and healthcare utilization. Regression models adjusted for age, sex, and disease duration, with Benjamini-Hochberg correction. ResultsAcross 6,977 visits from 3,100 participants (937 PD, 1,926 PAS, 237 HC), PD participants had higher odds of falling than PAS (OR=1.66, 95% CI [1.46-1.87]) and HC (OR=4.03, 95% CI [3.14-5.23]). PD participants were also more likely to report fall-related injuries and healthcare use than PAS (OR=1.70, 1.71) and HC (OR=3.26, 3.81). Falls occurred in 15.5% of visits at diagnosis and 69.2% after 14 years, increasing across Neuronal Synuclein Disease-Integrated Staging System (NSD-ISS) stages. Frequent fallers had longer disease duration, higher NSD-ISS, and worse clinical profiles. Women were more likely to fall than men (46.1% vs 34.9%, p=0.002) despite milder symptoms. ConclusionFalls and related morbidity increase with disease duration and NSD-ISS. Risk reflects sex and motor and non-motor factors, supporting a multifactorial model. Fall frequency may represent a practical marker of progression and guide prevention strategies in PD.

BackgroundLoss of smell commonly predates the diagnosis of Parkinsons disease (PD). However, smell loss has multiple causes, and the relationship between idiopathic anosmia (IA) and PD remains incompletely understood. ObjectivesTo assess the presence of prodromal features of PD in individuals with IA and to examine the relationship between anosmia duration and prodromal features. MethodsWithin the PREDICT-PD study, patients with IA investigated at specialist smell clinics were compared with healthy controls at low risk of PD (HC) and patients with PD. In-person assessments included MDS-UPDRS I-III, functional motor tasks, cognitive tasks, autonomic symptoms, orthostatic hypotension, pain, sleep and mood. We compared these features and PD risk according to the PREDICT-PD algorithm between the groups and examined the relationship between the duration of anosmia and severity of prodromal features using linear regression models. ResultsWe recruited 42 IA, 28 HC and 22 PD participants, matched for age and gender. Overall motor scores were not different between IA and HC. IA had worse cognition, sleep, pain, depressive and some symptoms of dysautonomia than HC, with trends towards worse anxiety scores and higher PD risk scores. Shorter duration of anosmia was negatively correlated with MDS-UPDRS II scores but no other prodromal feature. ConclusionsSome individuals with IA exhibit non-motor features suggestive of prodromal PD. This supports the known association of olfactory dysfunction with subsequent PD but suggests that, even after excluding secondary causes of anosmia, other conditions may underlie anosmia or smell loss may be temporally remote from PD.

BackgroundDepression in Parkinsons disease (PD) is common, disabling and responds poorly to standard antidepressant medication. Motivational symptoms of depression, such as apathy and anhedonia, are particularly prevalent in depression in PD and predict poor response to antidepressant treatment. Loss of dopaminergic innervation of the striatum is associated with emergence of motivational symptoms in PD, and mood fluctuations correlate with dopamine availability. Accordingly, optimising dopaminergic treatment for PD can improve depressive symptoms, and dopamine agonists have shown promising effects in improving apathy. However, the differential effect of antiparkinsonian medication on symptom dimensions of depression is not known. AimsWe hypothesised that there would be dissociable effects of dopaminergic medications on different depression symptom dimensions. We predicted that dopaminergic medication would specifically improve motivational symptoms, but not other symptoms, of depression. We also hypothesised that antidepressant effects of dopaminergic medications with mechanisms of action reliant on pre-synaptic dopamine neuron integrity would attenuate as pre-synaptic dopaminergic neurodegeneration progresses. MethodsWe analysed data from a longitudinal study of 412 newly diagnosed PD patients followed over five years in the Parkinsons Progression Markers Initiative cohort. Medication state for individual classes of Parkinsons medications was recorded annually. Previously validated "motivation" and "depression" dimensions were derived from the 15-item geriatric depression scale. Dopaminergic neurodegeneration was measured using repeated striatal dopamine transporter (DAT) imaging. ResultsLinear mixed-effects modelling was performed across all simultaneously acquired data points. Dopamine agonist use was associated with relatively fewer motivation symptoms as time progressed (interaction: {beta}=-0.07, 95%CI [-0.13,-0.01], p=0.015) but had no effect on the depression symptom dimension (p=0.6). In contrast, monoamine oxidase-B (MAO-B) inhibitor use was associated with relatively fewer depression symptoms across all years ({beta}=-0.41, 95%CI [-0.81,-0.01], p=0.047). No associations were observed between either depression or motivation symptoms and levodopa or amantadine use. There was a significant interaction between striatal DAT binding and MAO-B inhibitor use on motivation symptoms: MAO-B inhibitor use was associated with lower motivation symptoms in patients with higher striatal DAT binding (interaction: {beta}=-0.24, 95%CI [-0.43,-0.05], p=0.012). No other medication effects were moderated by striatal DAT binding measures. ConclusionsWe identified dissociable associations between dopaminergic medications and different dimensions of depression in PD. Dopamine agonists may be effective for treatment of motivational symptoms of depression. In contrast, MAO-B inhibitors may improve both depressive and motivation symptoms, albeit the latter effect appears to be attenuated in patients with more severe striatal dopaminergic neurodegeneration, which may be a consequence of dependence on pre-synaptic dopaminergic neuron integrity.

Background. Cognitive impairment in Parkinson's disease (PD) is highly prevalent and heterogeneous. Assessing multiple cognitive domains is challenging and risks redundancy. This study evaluated whether a discriminant analysis approach could optimize the selection of specific tasks and measures for identifying attention and memory deficits in PD. Methods. Thirty PD patients and 25 cognitively unimpaired (CU) controls completed four experimental tasks: two assessing attention (flanker and spatial Stroop), one for recognition memory, one for working memory (n-back). Following group-level difference analyses, a discriminant analysis was performed to identify which tasks, and performance metrics possessed the highest sensitivity for distinguishing PD patients from CU individuals. Results. At the group level, PD patients exhibited significantly worse conflict costs in both attention tasks and lower sensitivity scores (d') in the recognition memory task compared to CU controls. The discriminant analysis revealed that time-based measures from the spatial Stroop task and the sensitivity score from the recognition memory task provided the highest discriminating power to differentiate between the two groups. Conclusion. These findings suggest that cognitive deficits in PD can be identified with high diagnostic accuracy using a targeted subset of metrics, eliminating the need for extensive and redundant neuropsychological testing batteries for attention and memory, without needing an extensive number of cognitive tasks for attention and memory.

IntroductionSerum neurofilament light chain (NfL) levels, a marker of axonal damage, are generally elevated in neurodegenerative conditions, but results in idiopathic Parkinsons Disease (iPD) have been inconsistent. The p.A53T mutation in the SNCA gene usually leads to a severe, rapidly progressing genetic form of PD. ObjectivesAssessing the possible use of serum NfL as a marker of ongoind neurodegeneration in A53T-PD. MethodsUsing a propensity score matching based technique with data from the Parkinsons Progression Markers Initiative, A53T-PD participants were matched, in a 3:1 ratio, to iPD patients and healthy controls by age and disease duration, where applicable. ResultsAfter matching, 18 A53T-PD, 54 iPD, and 54 controls were analyzed. Serum NfL was significantly higher in A53T-PD (9.82 [8.02-18.1] pg/ml) compared to HC (7.54 [5.76-10.1]) or iPD (8.59 [6.86-11.1]) [overall p=0.049]. ConclusionThe increase in serum NfL points to a more aggressive neurodegenerative process in A53T-PD compared to iPD. This finding may help design and implement Disease-modifying treatments (DMTs) in this select group of prototypical genetic PD.

BackgroundThe role of blood uric acid and more recently bilirubin as biomarkers in symptomatic motor PD has been increasingly established in the literature. ObjectiveOur present study assessed the role of serum uric acid and total bilirubin as putative biomarkers in a prodromal PD cohort followed longitudinally. MethodsLongitudinal 5-year serum uric acid and total bilirubin measurement data of 65 Prodromal PD patients (including REM Sleep Behavior disorder (RBD), N=39 and Hyposmia, N=26) with an abnormal DATSCAN imaging were downloaded from the Parkinsons Progression Markers Initiative (PPMI) database. This cohort was compared with 423 de novo sporadic PD patients and 196 healthy controls enrolled in the same study. ResultsAfter adjusting for age, sex and Body Mass Index (BMI), baseline and 5-year longitudinal serum uric acid levels were higher in the Prodromal cohort and RBD subgroup as compared to the motor PD cohort. This was also true for longitudinal measurements in the Hyposmic subgroup. In contrast, baseline and longitudinal serum total bilirubin did not differ between each prodromal group and the PD cohort. ConclusionsOur results are indicative of a role of serum uric acid (but probably not of total bilirubin) as a marker of neuroprotection, in a certain subgroup of premotor patients exhibiting exclusively non motor features (hyposmia or RBD). It is possible that an inherent antioxidant resistance of a subset of RBD or hyposmia patients with high serum uric acid level delayed or precluded the emergence of a motor PD phenotype as opposed to the PD cohort.

Background: Reliable biomarkers for Parkinson's disease (PD) pathology detection are essential for research. The alpha-synuclein (aSyn) seed amplification assay (SAA) is a validated biomarker for misfolded aSyn. Objectives: To assess the association between aSyn SAA and LRRK2-related PD (LRRK2-PD) and its link to mitochondrial genetic burden. Methods: We included N=76 LRRK2 p.Gly2019Ser variant carriers (N=22 affected, N=54 unaffected), N=714 patients with idiopathic PD (iPD), and N=411 controls from Norway. We analyzed cerebrospinal fluid (CSF)-based aSyn SAA in N=10 PD patients and N=30 unaffected LRRK2 p.Gly2019Ser carriers, alongside N=6 controls and N=56 iPD patients. A mitochondrial polygenic score (MGS) was derived from genotyping data, using PPMI as an additional cohort (iPD: N=355, LRRK2-PD: N=118). Results: Seeding was observed in 80% of patients with LRRK2-PD, and in one unaffected variant carrier (AUC=0.97, CI 0.92-1.00). In a meta-analysis across two PD cohorts, higher MGS was associated with increased aSyn seeding (pooled beta=0.38, p=0.028). Conclusions: CSF-based aSyn SAA can discriminate between LRRK2-PD and unaffected carriers. Our findings support an association with mitochondrial burden and aSyn seeding.