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Differences in the expression of non-motor symptoms (NMS) by Parkinson’s disease (PD) patients may have important implications for their management and prognosis. Gender is a basic epidemiological variable that could influence such expression. The present study evaluated the prevalence and severity of NMS by gender in an international sample of 951 PD patients, 62.63% males, using the non-motor symptoms scale (NMSS). Assessments for motor impairment and complications, global severity, and health state were also applied. All disease stages were included. No significant gender differences were found for demographic and clinical characteristics. For the entire sample, the most prevalent symptoms were Nocturia (64.88%) and Fatigue (62.78%) and the most prevalent affected domains were Sleep/Fatigue (84.02%) and Miscellaneous (82.44%). Fatigue, feelings of nervousness, feelings of sadness, constipation, restless legs, and pain were more common and severe in women. On the contrary, daytime sleepiness, dribbling saliva, interest in sex, and problems having sex were more prevalent and severe in men. Regarding the NMSS domains, Mood/Apathy and Miscellaneous problems (pain, loss of taste or smell, weight change, and excessive sweating) were predominantly affected in women and Sexual dysfunction in men. No other significant differences by gender were observed. To conclude, in this study significant differences between men and women in prevalence and severity of fatigue, mood, sexual and digestive problems, pain, restless legs, and daytime sleepiness were found. Gender-related patterns of NMS involvement may be relevant for clinical trials in PD.

Non-motor symptoms are present in Parkinson's disease (PD) and a key determinant of quality of life. The Non-motor Symptoms Scale (NMSS) is a validated scale that allows quantifying frequency and severity (burden) of NMS. We report a proposal for using NMSS scores to determine levels of NMS burden (NMSB) and to complete PD patient classification. This was an observational, cross-sectional international study of 935 consecutive patients. Using a distribution of NMSS scores by quartiles, a classification based on levels from 0 (no NMSB at all) to 4 (very severe NMSB) was obtained and its relation with Hoehn and Yahr (HY) staging, motor and health-related quality of life scales was analyzed. Concordance between NMSB levels and grouping based on clinician's global impression of severity, using categorical regression, was determined. Disability and HRQoL predictors were identified by multiple regression models. The distribution of motor and QoL scales scores by HY and NMSB levels was significantly discriminative. The difference in the classification of cases for both methods, HY and NMSB, was significant (gamma = 0.45; ASE = 0.032). Concordance between NMSB and global severity-based levels from categorical regression was 91.8%, (kappaw = 0.97). NMS score was predictor of disability and QoL. Current clinical practice does not address a need for inclusion of non-motor scores in routine assessment of PD in spite of the overwhelming influence of NMS on disability and quality of life. Our data overcome the problems of \"pure motor assessment\" and we propose a combined approach with addition of NMSB levels to standard motor assessments.

BackgroundCognitive deficits and reduced dopamine transporter (DAT) binding ratio have been reported in Parkinson’s disease psychosis (PDP). However, it remains unclear whether DAT striatal binding ratio (SBR) may contribute to worsening cognitive performance in PDP.ObjectivesWe examined this using data from the Parkinson’s Progression Markers Initiative.MethodsWe analysed data from 392 PD patients, from baseline to year 4 follow-up, and classified patients into PD with psychosis (PDP) and without psychosis (PDnP). DAT SBR was available from 123I-FP-CIT-SPECT [(123) I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl) nortropane single photon emission computed tomography] imaging. We examined all cognitive measures assessed at each time point; sociodemographic characteristics, neuropsychiatric and PD-specific symptoms were entered as covariates of interest.FindingsPDP patients had lower DAT SBR compared with PDnP patients (b=−0.092, p=0.035) over all time points, which remained significant after controlling for age, sex and ethnicity. PDP patients also reported worse trajectory of task performance on the Montreal Cognitive Assessment (MoCA) (b=−0.238, p=0.001) and symbol digit modality (b=−0.534, p=0.016) compared with PDnP patients. Declining performance in symbol digit modality (Group×Time×DAT SBR interaction, b=0.683, p=0.028) but not MoCA was differentially associated with the decline in DAT SBR over time. MoCA scores declined more in PDP compared with PDnP patients over all timepoints (Group×Time interaction, b=−0.284, p=0.016).ConclusionsDecline in striatal presynaptic dopamine function may specifically underlie longitudinal decline in performance in the symbol digit modality task that engages processing speed, associative learning and working memory in PD psychosis. Whether striatal presynaptic dopamine changes explain accelerated longitudinal decline in other cognitive domains in people with PDP remains to be tested.

In people with Parkinson's disease, neuropsychiatric signs and symptoms are common throughout the disease course. These symptoms can be disabling and as clinically relevant as motor symptoms, and their presentation can be similar to, or distinct from, their counterparts in the general population. Correlates and risk factors for developing neuropsychiatric signs and symptoms include demographic, clinical, and psychosocial characteristics. The underlying neurobiology of these presentations is complex and not well understood, with the strongest evidence for neuropathological changes associated with Parkinson's disease, mechanisms linked to dopaminergic therapy, and effects not specific to Parkinson's disease. Assessment instruments and formal diagnostic criteria exist, but there is little routine screening of these signs and symptoms in clinical practice. Mounting evidence supports a range of pharmacological and non-pharmacological interventions, but relatively few efficacious treatment options exist. Optimising the management of neuropsychiatric presentations in people with Parkinson's disease will require additional research, raised awareness, specialised training, and development of innovative models of care.

Parkinson’s disease (PD) is the second commonest neurodegenerative disorder in the world with a rising prevalence. The pathophysiology is multifactorial but aggregation of misfolded α-synuclein is considered to be a key underpinning mechanism. Amyloid-β (Aβ) and tau deposition are also comorbid associations and especially Aβ deposition is associated with cognitive decline in PD. Some existing evidence suggests that low cerebrospinal fluid (CSF) Aβ42 is predictive of future cognitive impairment in PD. Recent studies also show that CSF Aβ is associated with the postural instability and gait difficulties (PIGD) or the newly proposed cholinergic subtype of PD, a possible risk factor for cognitive decline in PD. The glial-lymphatic system, responsible for convective solute clearance driven by active fluid transport through aquaporin-4 water channels, may be implicated in brain amyloid deposition. A better understanding of the role of this system and more specifically the role of Aβ in PD symptomatology, could introduce new treatment and repurposing drug-based strategies. For instance, apomorphine infusion has been shown to promote the degradation of Aβ in rodent models. This is further supported in a post-mortem study in PD patients although clinical implications are unclear. In this review, we address the clinical implication of cerebral Aβ deposition in PD and elaborate on its metabolism, its role in cognition and motor function/gait, and finally assess the potential effect of apomorphine on Aβ deposition in PD.

Early-morning off periods, causing early-morning akinesia, can lead to significant motor and nonmotor morbidity in levodopa-treated fluctuating Parkinson’s disease (PD) cases. Despite validated bedside scales in clinical practice, such early-morning off periods may remain undetected unless specific wearable technologies, such as the Parkinson’s KinetiGraph™ (PKG) watch, are used. We report five PD cases for whom the PKG detected early-morning off periods that were initially clinically undetected and as such, untreated. These five cases serve as exemplars of this clinical gap in care. Post-PKG assessment, clinicians were alerted and targeted therapies helped abolish the early-morning off periods.

Background Identifying predictors of incident cognitive impairment (CI), one of the most problematic long‐term outcomes, in Parkinson's disease (PD) is highly relevant for personalized medicine and prognostic counseling. The Nonmotor Symptoms Scale (NMSS) provides a global clinical assessment of a range of NMS, reflecting NMS burden (NMSB), and thus may assist in the identification of an “at‐risk” CI group based on overall NMSB cutoff scores. Methods To investigate whether specific patterns of PD NMS profiles predict incident CI, we performed a retrospective longitudinal study on a convenience sample of 541 nondemented PD patients taking part in the Nonmotor Longitudinal International Study (NILS) cohort, with Mini‐Mental State Examination (MMSE), NMSS, and Scales for Outcomes in PD Motor Scale (SCOPA Motor) scores at baseline and last follow‐up (mean 3.2 years) being available. Results PD patients with incident CI (i.e., MMSE score ≤ 25) at last follow‐up (n = 107) had severe overall NMSB level, significantly worse NMSS hallucinations/perceptual problems and higher NMSS attention/memory scores at baseline. Patients with CI also were older and with more advanced disease, but with no differences in disease duration, dopamine replacement therapy, sex, and comorbid depression, anxiety, and sleep disorders. Conclusions Our findings suggest that a comprehensive baseline measure of NMS and in particular hallucinations and perceptual problems assessed with a validated single instrument can be used to predict incident CI in PD. This approach provides a simple, holistic strategy to predict future CI in this population. A comprehensive baseline measure of NMS and in particular hallucinations and perceptual problems assessed with a validated single instrument can be used to predict incident cognitive impairment in PD. This approach provides a simple, holistic strategy to predict future cognitive impairment in this population.

Pain is a commonly occurring non-motor symptom of Parkinson’s disease (PD). Treatment of pain in PD remains less than optimal and a better understanding of the underlying mechanisms would facilitate discovery of improved analgesics. Animal models of PD have already proven helpful for furthering the understanding and treatment of motor symptoms of PD, but could these models offer insight into pain in PD? This review addresses the current position regarding pain in preclinical models of PD, covering the face and predictive validity of existing models and their use so far in advancing understanding of the mechanisms contributing to pain in PD. While pain itself is not usually measured in animals, nociception in the form of thermal, mechanical or chemical nociceptive thresholds offers a useful readout, given reduced nociceptive thresholds are commonly seen in PD patients. Animal models of PD including the reserpine-treated rat and neurodegenerative models such as the MPTP-treated mouse and 6-hydroxydopamine (6-OHDA)-treated rat each exhibit reduced nociceptive thresholds, supporting face validity of these models. Furthermore, some interventions known clinically to relieve pain in PD, such as dopaminergic therapies and deep brain stimulation of the subthalamic nucleus, restore nociceptive thresholds in one or more models, supporting their predictive validity. Mechanistic insight gained already includes involvement of central and spinal dopamine and opioid systems. Moving forward, these preclinical models should advance understanding of the cellular and molecular mechanisms underlying pain in PD and provide test beds for examining the efficacy of novel analgesics to better treat this debilitating non-motor symptom.

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson’s and Alzheimer’s disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as “bio-psycho-social” conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.