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Shared decision making (SDM) is a process whereby decisions are made together by patients and/or families and clinicians. Nevertheless, few patients are aware of its proven benefits. This study investigated the feasibility, acceptability and impact of an intervention to raise public awareness of SDM in public libraries. A 1.5 hour interactive workshop to be presented in public libraries was co-designed with Quebec City public library network officials, a science communication specialist and physicians. A clinical topic of maximum reach was chosen: antibiotic overuse in treatment of acute respiratory tract infections. The workshop content was designed and a format, whereby a physician presents the information and the science communication specialist invites questions and participation, was devised. The event was advertised to the general public. An evaluation form was used to collect data on participants' sociodemographics, feasibility and acceptability components and assess a potential impact of the intervention. Facilitators held a post-workshop focus group to qualitatively assess feasibility, acceptability and impact. All 10 planned workshops were held. Out of 106 eligible public participants, 89 were included in the analysis. Most participants were women (77.6%), retired (46.1%) and over 45 (59.5%). Over 90% of participants considered the workshop content to be relevant, accessible, and clear. They reported substantial average knowledge gain about antibiotics (2.4, 95% Confidence Interval (CI): 2.0-2.8; P < .001) and about SDM (4.0, 95% CI: 3.4-4.5; P < .001). Self-reported knowledge gain about SDM was significantly higher than about antibiotics (4.0 versus 2.4; P < .001). Knowledge gain did not vary by sociodemographic characteristics. The focus group confirmed feasibility and suggested improvements. A public library intervention is feasible and effective way to increase public awareness of SDM and could be a new approach to implementing SDM by preparing potential patients to ask for it in the consulting room.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care. Drawing on 51,269 participants across 9 independent, geographically diverse datasets, an AI model identifies the etiologies contributing to dementia in individuals, harnessing a broad array of data, including demographics, medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging.

To determine the test performance of cutaneous phosphorylated alpha-synuclein (P-SYN) in dementia with Lewy bodies (DLB), individuals with reduced Montreal Cognitive Assessment (MoCA) and healthy controls. This is the first subgroup analysis of the Synuclein-One study, a prospective, blinded study evaluating P-SYN detection from skin biopsies in 218 subjects with a referral diagnosis of control (N = 151) and DLB (N = 67). All subjects completed detailed examinations, questionnaires, and had skin biopsies for detection of P-SYN. DLB patients were included if meeting the 4th DLB consensus probable criteria. Control subjects, aged 40-99, had no history, examination findings, or symptoms suggestive of a synucleinopathy or neurodegenerative disease. An expert review panel, blinded to pathological data, determined the final diagnosis. Controls with reduced MoCA (MoCA < 26, N = 26) at screening were analyzed separately. After expert panel review, only 50/67 patients met consensus criteria for DLB, 26/151 controls had a reduced MoCA, and 120/151 controls had a normal MoCA. The proportions of subjects with cutaneous P-SYN detected by skin biopsy were 96.0% (48 of 50) of the DLB group, 31% (8 of 26) of the controls with reduced MoCA, and 3.3% (4 of 120) of the controls with normal MoCA. In this prospective, blinded, cross-sectional study, a high proportion of subjects meeting clinical consensus criteria for DLB had P-SYN detected in skin biopsies. Almost 1/3 of subjects with reduced MoCA testing also had P-SYN detected. These results support a role for skin biopsy detection of P-SYN in patients with DLB. NCT04700722.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer’s disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis. Here the authors present a deep learning framework for dementia diagnosis, which can identify persons with normal cognition, mild cognitive impairment, Alzheimer’s disease, and dementia due to other etiologies.

Background: Research using reconstructed exposure histories has suggested an association between heavy metal exposures, including lead, and Parkinson's disease (PD), but the only study that used bone lead, a biomarker of cumulative lead exposure, found a nonsignificant increase in risk of PD with increasing bone lead. Objectives: We sought to assess the association between bone lead and PD. Methods: Bone lead concentrations were measured using ¹⁰⁹Cd excited K-shell X-ray fluorescence from 330 PD patients (216 men, 114 women) and 308 controls (172 men, 136 women) recruited from four clinics for movement disorders and general-community cohorts. Adjusted odds ratios (ORs) for PD were calculated using logistic regression. Results: The average age of cases and controls at bone lead measurement was 67 (SD = 10) and 69 (SD = 9) years of age, respectively. In primary analyses of cases and controls recruited from the same groups, compared with the lowest quartile of tibia lead, the OR for PD in the highest quartile was 3.21 [95% confidence interval (CI), 1.17-8.83]. Results were similar but slightly weaker in analyses restricted to cases and controls recruited from the movement disorders clinics only (fourth-quartile OR = 2.57; 95% CI, 1.11-5.93) or when we included controls recruited from sites that did not also contribute cases (fourth-quartile OR = 1.91; 95% CI, 1.01-3.60). We found no association with patella bone lead. Conclusions: These findings, using an objective biological marker of cumulative lead exposure among typical PD patients seen in our movement disorders clinics, strengthen the evidence that cumulative exposure to lead increases the risk of PD.

The proposed Neuronal α-Synuclein Disease Integrated Staging System (NSD-ISS) was recently applied to early Parkinson's disease (PD) cohorts. We applied this research framework to the BioFIND study cohort, which includes more moderately advanced PD participants with clinically established PD. Disease durations within each ISS stage were highly variable. Cognitive and non-motor anchors had little weight in determining staging. The analysis highlights strengths and limitations of NSD-ISS to guide further refinement of an integrated staging system.

Background Parkinson disease (PD) is a debilitating and chronic neurodegenerative disease resulting in ambulation difficulties. Natural walking activity often declines early in disease progression despite the relative stability of motor impairments. In this study, we propose a paradigm shift with a “connected behavioral approach” that targets real-world walking using cognitive-behavioral training and mobile health (mHealth) technology. Methods/design The Walking and mHealth to Increase Participation in Parkinson Disease (WHIP-PD) study is a twelve-month, dual site, two-arm, randomized controlled trial recruiting 148 participants with early to mid-stage PD. Participants will be randomly assigned to connected behavioral or active control conditions. Both conditions will include a customized program of goal-oriented walking, walking-enhancing strengthening exercises, and eight in-person visits with a physical therapist. Participants in the connected behavioral condition also will (1) receive cognitive-behavioral training to promote self-efficacy for routine walking behavior and (2) use a mHealth software application to manage their program and communicate remotely with their physical therapist. Active control participants will receive no cognitive-behavioral training and manage their program on paper. Evaluations will occur at baseline, three-, six-, and twelve-months and include walking assessments, self-efficacy questionnaires, and seven days of activity monitoring. Primary outcomes will include the change between baseline and twelve months in overall amount of walking activity (mean number of steps per day) and amount of moderate intensity walking activity (mean number of minutes per day in which > 100 steps were accumulated). Secondary outcomes will include change in walking capacity as measured by the six-minute walk test and ten-meter walk test. We also will examine if self-efficacy mediates change in amount of walking activity and if change in amount of walking activity mediates change in walking capacity. Discussion We expect this study to show the connected behavioral approach will be more effective than the active control condition in increasing the amount and intensity of real-world walking activity and improving walking capacity. Determining effective physical activity interventions for persons with PD is important for preserving mobility and essential for maintaining quality of life. Clinical trials registration NCT03517371, May 7, 2018. Trial registration ClinicalTrials.gov: NCT03517371 . Date of registration: May 7, 2018. Protocol version: Original.

In the outpatient setting, it can be difficult to effectively manage the complex medical and rehabilitation needs of people with Parkinson disease (PD). A multidisciplinary approach in the inpatient rehabilitation environment may be a viable alternative. The purposes of this study were: (1) to investigate the effectiveness of an inpatient rehabilitation program for people with a primary diagnosis of PD, (2) to determine whether gains made were clinically meaningful, and (3) to identify predictors of rehabilitation outcome. Sixty-eight subjects with a diagnosis of PD were admitted to an inpatient rehabilitation hospital with a multidisciplinary movement disorders program. Subjects participated in a rehabilitation program consisting of a combination of physical therapy, occupational therapy, and speech therapy for a total of 3 hours per day, 5 to 7 days per week, in addition to pharmacological adjustments based on data collected daily. A pretest-posttest design was implemented. The differences between admission and discharge scores on the Functional Independence Measure (FIM) (total, motor, and cognitive scores), Timed \"Up & Go\" Test, 2-Minute Walk Test, and Finger Tapping Test were analyzed. An analysis of data obtained for the 68 subjects admitted with a diagnosis of PD revealed significant improvements across all outcome measures from admission to discharge. Subjects with PD whose medications were not adjusted during their admission (rehabilitation only) (n=10) showed significant improvements in FIM total, motor, and cognitive scores. Improvements exceeded the minimal clinically important difference in 71% of the subjects. Prior level of function at admission accounted for 20% of the variance in the FIM total change score. The results suggest that subjects with a diagnosis of PD as a primary condition benefited from an inpatient rehabilitation program designed to improve functional status.

Patients with Parkinson's disease chronically treated with levodopa commonly have delayed or unpredictable onset of its benefits after oral intake. In this study, we assessed the safety and efficacy of CVT-301, a self-administered levodopa oral inhalation powder, for the treatment of patients with Parkinson's disease during off periods. In this randomised, double-blind, placebo-controlled, phase 3 trial, patients were recruited at 65 sites in Canada, Poland, Spain, and the USA. Eligible participants were patients with Parkinson's disease aged 30–85 years, who had daily off periods of 2 h or longer and showed an improvement of 25% or greater in the Unified Parkinson's Disease Rating Scale (UPDRS) motor score from off to on state after use of an oral levodopa plus a dopa-decarboxylase inhibitor combination. Patients were assigned (1:1:1) with a computer-generated randomisation code, in fixed blocks of six, to either CVT-301 60 mg, CVT-301 84 mg, or placebo. Spirometry results and modified Hoehn and Yahr disease stage at screening were used for stratification of treatment groups. Patients, the sponsor, and site personnel were masked to treatment assignment. Each study dose consisted of two capsules administered with an inhaler. Patients were instructed to use the study drug as needed for off periods, and could self-administer up to five doses per day. The primary endpoint was the change in UPDRS motor score from predose to 30 min postdose, assessed at week 12 during an in-clinic off period, in the CVT-301 84 mg group compared with the placebo group. Analysis was by intention to treat. Safety was assessed in all patients who received at least one dose of experimental treatment. This trial is registered with ClinicalTrials.gov, number NCT02240030. Between Dec 4, 2014, and Aug 26, 2016, 351 patients were enrolled and randomly assigned to receive CVT-301 60 mg (115 patients), CVT-301 84 mg (120 patients), or placebo (116 patients). Of these, 339 received the assigned study treatment (CVT-301 60 mg, n=113; CVT-301 84 mg, n=114; placebo, n=112) and 290 completed the study (CVT-301 60 mg, n=96; CVT-301 84 mg, n=97; placebo, n=97). The least-squares mean difference in UPDRS motor score change from predose to 30 min postdose was −5·91 (SE 1·50, 95% CI −8·86 to −2·96) for the placebo group and −9·83 (1·51; −12·79 to −6·87) for the CVT-301 84 mg group (between-group difference −3·92 [–6·84 to −1·00]; p=0·0088). Treatments were safe and well tolerated. Severe adverse events were reported by 2 (2%) of 112 patients in the placebo group, 7 (6%) of 113 in the CVT-301 60 mg group, and 5 (4%) of 114 in the CVT-301 84 mg group, with no severe adverse event occurring in more than one patient in any treatment group. 11 (3%) of 339 patients had 19 serious adverse events (three [3%] of 112 patients in placebo, six [5%] of 113 in CVT-301 60 mg, and two [2%] of 114 in CVT-301 84 mg). Of these, hypotension and atrial fibrillation were assessed by investigators to be possibly related to the study drug. CVT-301 can improve UPDRS motor scores of patients with Parkinson's disease during in-clinic off periods, with few severe or serious adverse events. The long-term safety and efficacy of CVT-301 need to be investigated in future studies. Acorda Therapeutics.

Huntington’s disease (HD) is a neurodegenerative disorder characterized by motor, cognitive, and behavioral disturbances. It is caused by the expansion of the HTT CAG repeat, which is the major determinant of age at onset (AO) of motor symptoms. Aberrant function of N -methyl- D -aspartate receptors and/or overexposure to dopamine has been suggested to cause significant neurotoxicity, contributing to HD pathogenesis. We used genetic association analysis in 1,628 HD patients to evaluate candidate polymorphisms in N -methyl- D -aspartate receptor subtype genes ( GRIN2A rs4998386 and rs2650427, and GRIN2B rs1806201) and functional polymorphisms in genes in the dopamine pathway ( DAT1 3′ UTR 40-bp variable number tandem repeat (VNTR), DRD4 exon 3 48-bp VNTR, DRD2 rs1800497, and COMT rs4608) as potential modifiers of the disease process. None of the seven polymorphisms tested was found to be associated with significant modification of motor AO, either in a dominant or additive model, after adjusting for ancestry. The results of this candidate-genetic study therefore do not provide strong evidence to support a modulatory role for these variations within glutamatergic and dopaminergic genes in the AO of HD motor manifestations.