Abstract Background Accurate identification of Parkinson’s disease (PD) in large electronic health record (EHR) population-based databases is challenging due to diagnostic heterogeneity in routine care, with a substantial proportion of individuals diagnosed with PD had not been diagnosed by a specialist. Our aim was to develop and validate a simplified rule-based medication algorithm to identify PD in a nationwide healthcare registry and apply it to estimate long-term incidence, prevalence, and pre-diagnostic diagnoses. Methods Using Clalit Health Services EHR data covering over five million individuals (2005–2025), we constructed a medication-based algorithm incorporating predefined inclusion and exclusion criteria and two levels of diagnostic certainty (probable/possible PD). Validation was performed against two independent specialist-confirmed PD cohorts and FDOPA PET/CT and a non-PD neurological cohort. Incidence rates per 100,000 were calculated annually with 95% confidence intervals (CIs) assuming a Poisson distribution. Age-adjusted incidence rates were computed using the WHO standard population. motor and non-motor diagnoses preceding PD were examined up to 18 years before the index date using matched controls. Results The algorithm identified 34,368 PD patients (56.5% male; mean age at index 75.2 ± 10.5 years). Sensitivity was 94.8% (95% CI 90.4–97.2) in the FDOPA PET/CT cohort, 94.8% (95% CI 92.1–96.6) in the private clinic cohort, and 94.7% (95% CI 90.9–96.9) in the movement disorder clinic cohort. Specificity was 85.2% (95% CI 77.8–90.6). Incidence increased markedly with age but declined significantly over time (overall annual percent change [APC] - 4.47%, 95% CI -4.90 – -4.03). Age-adjusted incidence rates (≥20 years) declined 2.4-fold between 2005 and 2024 (55 [95% CI 53–58] to 23 [95% CI 21–24] per 100,000). Overall prevalence declined modestly (APC -0.78%, 95% CI -0.84 – -0.72), with increases in younger age groups and declines in older groups. Constipation, depression, and tremor diagnoses were more frequent years before diagnosis, whereas smoking-related codes were less frequent among future PD patients. Conclusions This validated medication-based algorithm provides a reproducible framework for PD identification in large registries. Applied over two decades in a nationwide cohort, it demonstrated high diagnostic performance and revealed age-dependent declines in PD incidence alongside heterogeneous prevalence trends.

The Parkinson's Progression Markers Initiative (PPMI) has remained at the vanguard of Parkinson's disease (PD) research through evolving recruitment strategies that mirror advances in PD biology. PPMI was initially focused on untreated, early-stage PD. Over time, it has expanded to include genetic cohorts, prodromal participants identified via olfactory testing and risk factor questionnaires targeting people with rapid eye movement (REM) sleep behavior disorder (RBD). Most recently, PPMI has focused on the biology underlying PD and sought to recruit individuals defined by neuronal alpha-synuclein disease (NSD) biomarkers. This evolution from clinical to biological enrollment demonstrates the feasibility of biomarker-driven cohort design and establishes a scalable model for early detection and prevention research in neurodegenerative disease. ANN NEUROL 2026.

Alanine aminotransferase (ALT), aspartate aminotransferase (AST), and the neutrophil-to-lymphocyte ratio (NLR) are routinely measured; lower ALT and higher NLR have been reported in Parkinson's disease (PD), but longitudinal evidence remains limited. In this prospective cohort study, we analyzed longitudinal data from the BEAT-PD cohort, comparing 175 individuals with PD (63 idiopathic, 51 LRRK2 -PD, 61 GBA1 -PD) to 345 non-manifesting individuals (74 LRRK2 , 94 GBA1 , 177 healthy controls). ALT was consistently lower in PD and declined longitudinally, while increasing slightly in controls over a median 4-year follow-up. Lower ALT was associated with longer disease duration independent of dopaminergic medication dosage, suggesting the decline may reflect disease biology rather than a pharmacological effect. AST and NLR differences were less consistent. Analyte levels did not differ significantly across genetic PD subgroups. These findings support ALT as a candidate biomarker of PD risk and progression, though effect sizes were small and not currently clinically actionable.

Isolated REM Sleep Behavior Disorder (iRBD) is a strong predictor of neurodegenerative diseases, particularly synucleinopathies. Current diagnosis requires overnight video-polysomnography (vPSG) in sleep laboratories. Limited access to vPSG and differences in sleep habits result in diagnostic challenges. Here we aimed to evaluate the feasibility of identifying iRBD from a lumbar-mounted wearable sensor in the home setting and explored night-to-night variability. Seventy-three participants (15 iRBD, 58 controls) underwent vPSG, followed by six nights of wearing a lower-back inertial measurement unit at home. iRBD participants showed distinct mobility patterns compared to controls. Machine learning models were trained on mobility features and classified iRBD with high sensitivity and moderate specificity. Performance improved with increased nights, plateauing at five nights recorded at home. Principal component analysis identified substantial differences between lab and home data. Our findings suggest that lumbar-mounted wearables can support sensitive, multi-night home-based detection of nocturnal motor patterns associated with iRBD, with potential utility as part of a staged screening approach and for enriching cohorts for further evaluation.

BACKGROUND:Parkinson's disease (PD) is increasingly recognized as a neurodegenerative disorder with a broad clinical spectrum and diverse biomarkers enabling early detection. α-synuclein seed amplification assays (SAA) and genetic testing now allow identification of PD pathology in asymptomatic individuals. As preventive strategies remain unavailable, it is important to understand at-risk individuals' attitudes toward disclosure of genetic and biomarker information. OBJECTIVES:To examine awareness, emotional responses, and behavioral intentions among first-degree relatives of PD patients who underwent genetic testing and counseling. METHODS:A cross-sectional questionnaire was administered to first-degree relatives tested and counseled at Tel Aviv Sourasky Medical Center. The survey assessed recall of testing and results, adequacy of information, emotional reactions, willingness to undergo biological testing (blood vs. lumbar puncture), and readiness to modify lifestyle following positive biomarker results. RESULTS:Among 240 non-manifesting relatives (NMNC = 145; LRRK2 = 40; GBA1 = 49; dual = 6; 72% response), the mean interval from testing to survey was 5.3 ± 2.4 years. Despite prior counseling, only 70.3% recalled being tested and 66.5% remembered results. About half felt adequately informed about PD (50.9%) and their genetic status (53.7%). Willingness for blood testing was high (86.6%) but lower for lumbar puncture (41.5%). Mutation carriers reported greater distress, while 87.6% indicated readiness to adopt lifestyle changes if results were positive. CONCLUSIONS:First-degree relatives favored minimally invasive testing and showed strong motivation for lifestyle adaptation. Limited recall of results highlights the need for improved communication and ongoing counseling in preventive neurology.

Research on Parkinson's disease (PD) has documented significant deficits in verb production, with more robust results in single word retrieval tasks than in connected speech, yet the underlying causes of these deficits are disputable, especially concerning connected speech production. We analyzed picture descriptions provided by 48 individuals with PD and 48 age-matched healthy controls, and examined the percent of nouns and verbs of all words, the number of described events, verbs denoting activity, verbs in active morpho-syntactic patterns, and transitive verbs. Individuals with PD produced a lower percent of verbs than did control participants, but the groups differed in no other variable. Scores on a cognitive screening task associated with the percent of verbs and the number of events. We suggest that verb retrieval in connected speech in PD reflects no specific difficulty with action semantics, but rather the spread of PD pathology into more diffuse verb-specific neural networks.

Early diagnosis of Parkinson’s disease (PD) can assist in designing efficient treatments. Reduced facial expressions are considered a hallmark of PD, making advanced artificial intelligence (AI) image processing a potential non-invasive clinical decision support tool for PD detection. This study aims to determine the sensitivity of image-to-text AI, which matches facial frames recorded in home settings with descriptions of PD facial expressions, in identifying patients with PD. Facial image of 67 PD patients and 52 healthy-controls (HCs) were collected via standard video recording. Using clinical knowledge, we compiled descriptive sentences detailing facial characteristics associated with PD. The facial images were analyzed with OpenAI’s CLIP model to generate probability scores, indicating the likelihood of each image matching the PD-related descriptions. These scores were used in an XGBoost model to identify PD patients based on the total, motor, and facial-expression item of the MDS-UPDRS, a common scale for assessing disease severity. The image-to-text AI technology showed the best results in identifying PD patients based on the facial expression item (AUC = 0.78 ± 0.05), especially for those with ‘mild’ facial symptoms (AUC = 0.87 ± 0.04). The motor MDS-UPDRS score followed (AUC = 0.69 ± 0.05), while the total MDS-UPDRS score showed the lowest performance in identifying PD patients (AUC = 0.59 ± 0.05). PD matching probabilities between facial images and sentences revealed significant correlations across all MDS-UPDRS components (r > 0.23, p < 0.0001). Our results demonstrate the feasibility of using advanced AI in a clinical decision support tool for PD diagnosis, suggesting a novel approach for home-based screening to identify PD patients. This method represents a significant innovation, transforming clinical knowledge into practical algorithms that can serve as effective screening tools. MOH_2023-04-16_012535

Reserve is a physiological capacity used under demanding situations. The concept was developed to account for the discrepancy between pathology and clinical manifestation. In neuroscience, motor, brain and cognitive reserves are abstract measures, conceptually defined yet elusive to quantify. Reserve is indirectly assessed using proxies such as years of education and brain volume, limiting its utility. Moreover, the dichotomy in definitions of cognitive and motor reserves is artificial, as daily function requires an intricate network of connections between these domains. Here, we assessed the validity of a newly developed graded motor cognitive 'stress test' to quantify the combined motor and cognitive reserve (MCR). The study included 144 participants (ages between 18 and 85, 50% women) with a range of reserve capacities (i.e. healthy young and older adults and individuals with Parkinson's disease, Alzheimer's disease, dementia with Lewy bodies and mild cognitive impairment). The assessment included walking on a treadmill while negotiating motor and cognitive challenges delivered using virtual reality. To establish an MCR index score, we used a semi-supervised machine learning algorithm. The model includes performance measures from completing the stress test and measures obtained from wearable sensors used during the test. Validation of the proposed MCR index was examined through: (i) model face validity-reflecting decline of performance as challenge increased; (ii) known-groups validity-classification of scores according to neurological status; (iii) construct validity (convergent)-association with common MCRs proxies as well as MRI-derived regional brain volumes. The model's face validity revealed decreased performance with increased motor and cognitive challenges (both domains P < 0.001). The index accurately discriminated between healthy controls and those diagnosed with neurological conditions with an area under the curve of 0.89 [95% CI: 0.79-0.99] which was significantly higher than all other commonly used proxies. Statistically significant Spearman's ρ correlations were observed with all commonly used motor and cognitive proxies (0.56 ≤ r ≤ 0.79, after multiplicity correction all P < 0.05), reflecting construct validity. In addition, statistically significant correlations were observed between the MCR index and whole-brain grey matter and white matter volumes (r = 0.63 and 0.55), as well as the pre-defined left and right caudate nucleus (r = 0.56 and 0.68) and inferior-frontal gyrus (r = 0.47 and 0.58). This proof-of-concept study shows that the novel MCR index is valid, with high sensitivity to neurological deficits and is able to quantify reserve on an individual level. This new innovative tool can assist in screening for motor cognitive deficits and potentially, for predicting motor and cognitive decline associated with neurodegenerative disease.

Alpha-synuclein (αS) aggregation is a widely regarded hallmark of Parkinson’s disease (PD) and can be detected through synuclein amplification assays (SAA). This study investigated the association between cerebrospinal fluid (CSF) radiological measures in 41 PD patients (14 iPD, 14 GBA1-PD, 13 LRRK2-PD) and 14 age-and-sex-matched healthy controls. Quantitative measures including striatal binding ratios (SBR), whole-brain and deep gray matter volumes, neuromelanin-MRI (NM-MRI), functional connectivity (FC), and white matter (WM) diffusion-tensor imaging (DTI) were calculated. Nine LRRK2-PD patients were SAA-negative (PD-SAA−). PD-SAA+ patients showed lower whole-brain gray matter, putamenal, brainstem, and substantia nigra volumes, reduced FC in the left caudate, and lower fractional anisotropy in the left fronto-occipital fasciculus compared to PD-SAA−. Taken together, αS aggregation was observed in iPD, GBA1-PD, and 38% of LRRK2-PD patients, and this was associated with reduced regional brain volumes, altered caudal FC, and SBRs. These changes were less pronounced in PD-SAA−, possibly suggesting a milder neurodegenerative process.

Digital mobility outcomes (DMOs) have emerged as novel biomarkers offering objective, quantitative, and examiner-independent outcome measures for clinical studies. Unfortunately, research efforts on DMOs have not yet investigated the domain of clinical utility in Parkinson’s disease, i.e. providing evidence of improvements in health outcomes, diagnosis, decision-making, or prevention when compared to e.g. standard-of-care procedures. This manuscript, via a consensus building approach, aims to create a structured conceptual framework to map the knowledge generated by DMOs with clinical domains that could benefit from it. We conducted a three-round consensus-building study with 12 experts recruited from the Mobilise-D consortium’s Parkinson’s Disease Working Group. The experts designed and ranked different aspects of the conceptual framework via a 5-level Likert scale for level of agreement. Consensus for the different points evaluated was based on a double threshold: the simultaneous presence of a high level of agreement had to be accompanied by a low level of disagreement. As secondary objectives, the experts were asked to rate the practical application of DMOs by evaluating the timeline to applicability, the foreseen challenges for their implementation in clinical settings, and their main role in the decision-making process. A full consensus on the clinical utility framework was achieved after three rounds. The final framework consisted of three main categories (Disease Diagnosis, Patient Evaluation, and Treatment Evaluation) and six underlying domains (Enhancing Diagnostic Procedure, Predicting Risk, Timely Detecting Deterioration, Enhancing Clinical Judgment, Selecting Treatment, and Monitoring Treatment Response). The experts believed in the next 1–5 years DMOs will play a relevant role in clinical decision making, complementing care knowledge with useful digital biomarkers information. However, the main challenge to address is the definition of clear reference value for DMOs interpretability. This framework provides a structure for subsequent studies to build into by diversifying expert cohorts and expand our findings beyond PD. Additionally, our results support researchers planning future clinical trials where DMOs can play a valuable role for clinical decision support. Ultimately, this is the first step toward developing guidelines to assess DMOs’ clinical utility and support their integration into Real World clinical practice.

BackgroundMRI is an important tool for disease diagnosis of Creutzfeldt-Jakob disease (CJD), yet its role in identifying preclinical stages of disease remains unclear. Here, we explored subtle white matter (WM) alterations in genetic CJD (gCJD) patients and in asymptomatic E200K mutation carriers using MRI, depending on total tau protein (t-tau) levels in CSF.MethodsSix symptomatic gCJD patients and N=60 healthy relatives of gCJD patients were included. Participants underwent genetic testing for the E200K mutation, MRI scans at 3T and a lumbar puncture (LP) for t-tau. Diffusion tensor imaging (DTI) metrics were calculated along WM tracts.ResultsgCJD patients demonstrated higher mean diffusivity (MD), radial diffusivity (RD) and lower fractional anisotropy (FA) values compared with healthy relatives in several WM tracts (p<0.05). Out of the healthy relatives, 50% (N=30) were found to be carriers of the E200K mutation. T-tau levels in cerebrospinal fluid (CSF) were above the normal range (>290 pg/mL) in N=8 out of 23 carriers who underwent an LP. No significant differences in FA, MD, axial diffusivity (AD) and RD were detected between healthy mutation carriers (HMC) and healthy non-carriers within the WM tracts. Finally, significantly higher FA and lower MD, RD and AD along several WM tracts were found in HMC with elevated t-tau compared with HMC with normal t-tau (p<0.05).ConclusionsDTI abnormalities along WM tracts were found in healthy E200K mutation carriers with elevated t-tau in CSF. Longer follow-up is required to determine whether these subtle WM alterations are predictive of future conversion to symptomatic gCJD.Trial registration numberNCT05746715.

Background: Population-based research on Parkinson’s disease (PD) requires robust methods to the challenge of accurately identifying PD diagnosis and overcome diagnostic heterogeneity in routine care. Methods: We developed and validated a medication-based algorithm to define PD, using electronic health records of ~ 6 million individuals, including actual pharmacy purchase data, from Clalit Health Services, Israel’s largest healthcare provider, covering the years 2005–2025. Results: After applying exclusion criteria, the algorithm identified 34,368 patients (13,090 alive), stratified into probable and possible PD, with validation demonstrating ~95% true positive rate across diverse independent datasets. The mean age at index was 75.2 (SD 10.5) years (70.0 for those alive), with 56.5% males. Age-stratified analyses showed that incidence rates remained constant in individuals aged 20–60, but declined progressively in older groups. Over two decades, incidence decreased 2.3-fold in the [60–70) group, 2.63-fold in [70–80), 2.88-fold in [80–90), and 5.53-fold in [90–100), with consistent trends across sexes. Longitudinal analyses confirmed prodromal non-motor and motor features. Constipation was significantly more prevalent in future PD patients up to 10 years before diagnosis (12 years in males). Depressive episodes diverged 9 years prior to index, particularly after age 75. Tremor was more prevalent 16–18 years before diagnosis in both sexes. In contrast, codes reflecting tobacco use were less frequent among PD patients than controls, with differences extending 18 years prior to diagnosis, especially in males. Conclusions: This protocol provides a reproducible framework for large-scale registry studies, enabling exploration of prodromal features, evaluation of risk factors, and application of machine learning to estimate risk and identify individuals at elevated PD risk.

Background The adaptive immune response has a role in Parkinson's disease (PD). Patients with LRRK2 or GBA1 mutations often exhibit distinct clinical characteristics. Objective To evaluate the involvement of adaptive immune response genes in three PD groups: GBA1-PD, LRRK2-PD, and non-carrier (NC)-PD. Methods Differentially expressed genes (DEGs) associated with PD were identified using four datasets. Of them, adaptive immune response genes were evaluated using whole-genome-sequencing of 201 unrelated Ashkenazi-Jewish (AJ) PD patients. Potential pathogenic variants were identified, and P2RX7 variants were assessed in 1200 AJ-PD patients. Burden analysis of rare variants (allele frequencies (AF) < 0.01) on disease risk, and association analyses of common variants (AF ≥ 0.01) with disease risk and age-at-onset (AAO) were conducted. AFs were compared to AJ-non-neuro cases reported in gnomAD. Variants associated with PD were further examined in an independent AJ cohort from AMP-PD. Results Of the four adaptive immune DEGs identified, CD8B2, P2RX7, IL27RA, and ZC3H12A, three common variants in P2RX7 were statistically significant: Tyr155His was associated with NC-PD (allelic OR = 1.15, p = 0.015) ; Arg276His was associated with LRRK2-PD (allelic OR = 2.10, p = 0.037), while Glu496Ala was associated with earlier AAO in LRRK2-PD ( p = 0.014). Burden analysis showed no significant effect on PD-risk. In the AMP-PD cohort, odds ratios of the two risk variants were similar to the primary cohort, but did not reach significance, probably due to small control sample size (n = 263). Conclusions Common variants within P2RX7 are likely associated with PD-risk and earlier AAO. These findings further suggest P2RX7's involvement in PD and its potential interplay with LRRK2.

Distinguishing Parkinson's disease (PD) subgroups may be achieved by observing network responses to external stimuli. We compared TMS-evoked potential (TEP) measures from stimulation of bilateral motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and visual cortex (V1) between 62 PD patients (age: 69.9 +/- 7.5) and 76 healthy controls (age: 69.2 +/- 4.3) using a TMS-EEG protocol. TEP measures were analyzed using two-way ANCOVA adjusted for MOCA. PD patients were divided into tremor dominant (TD), non-tremor dominant (NTD) and rapid disease progression (RDP) subgroups. PD patients showed lower wide-waveform adherence (wWFA) (p = 0.025) and interhemispheric connectivity (IHCCONN) (p < 0.001) compared to healthy controls. Lower occipital IHCCONN correlated with advanced disease stage (r = -0.37, p = 0.0039). The RDP and NTD groups showed lower wWFA in response to occipital stimulation than the TD group (p = 0.005). Occipital TEP measures identified RDP patients with 85% accuracy. These findings demonstrate occipital network involvement in early PD stages, suggesting that TEP measures offer insights into altered networks in PD subgroups.

BACKGROUND AND PURPOSE:Subtle executive dysfunction is common in people newly diagnosed with Parkinson disease (PD), even when general cognitive abilities are intact. This study examined the Short Weekly Calendar Planning Activity (WCPA-10)'s known-group construct validity, comparing persons with PD to healthy controls (HCs) and nonmanifesting carriers of LRRK2 and GBA gene mutations to HCs. Additionally, convergent and ecological validity was examined. METHODS:The study included 73 participants: 22 with idiopathic PD (iPD) who do not carry any of the founder GBA mutations or LRRK2-G2019S, 29 nonmanifesting carriers of the G2019S-LRRK2 (n = 14) and GBA (n = 15) mutations, and 22 HCs. Known-group validity was determined using the WCPA-10, convergent validity by also using the Montreal Cognitive Assessment (MoCA) and Color Trails Test (CTT), and ecological validity by using the WCPA-10, Schwab and England Activities of Daily Living Scale (SE ADL), and Physical Activity Scale for the Elderly (PASE). RESULTS:Known-group validity of the WCPA-10 was established for the iPD group only; they followed fewer rules (p = 0.020), were slower (p = 0.003) and less efficient (p = 0.001), used more strategies (p = 0.017) on the WCPA-10, and achieved significantly lower CTT scores (p < 0.001) than the HCs. The nonmanifesting carriers and HCs were similar on all cognitive tests. Convergent and ecological validity of the WCPA-10 were partially established, with few correlations between WCPA-10 outcome measures and the MoCA (r = 0.50, r = 0.41), CTT-2 (r = 0.43), SE ADL (r = 0.41), and PASE (r = 0.54, r = 0.46, r = 0.31). CONCLUSIONS:This study affirms the known-group validity for most (four) WCPA-10 scores and partially confirms its convergent and ecological validity for PD.