Smartphone-Detected Motor Variability Correction

Parkinson's is primarily a non-familial, age-related disorder caused by α-synuclein accumulation and the progressive loss of dopamine neurons in the substantia nigra pars compacta (SNc). G protein-coupled receptor (GPCR)-cAMP signaling has been linked to a reduction in human Parkinson's incidence and α-synuclein expression. Neuronal cAMP levels are controlled by GPCRs coupled to Gs or Gi/o, which increase or decrease cAMP, respectively. Regulator of G protein signaling 6 (RGS6) powerfully inhibits Gi/o signaling. Therefore, we hypothesized that RGS6 suppresses D2 autoreceptor- Gi/o signaling in SNc dopamine neurons promoting neuronal survival and reducing α-synuclein expression. Here we provide novel evidence that RGS6 critically suppresses late-age-onset SNc dopamine neuron loss and α-synuclein accumulation. RGS6 is restrictively expressed in human SNc dopamine neurons and, despite their loss in Parkinson's, all surviving neurons express RGS6. RGS6-/- mice exhibit hyperactive D2 autor

Atremorine is a novel bioproduct obtained by nondenaturing biotechnological processes from a genetic species of Vicia faba. Atremorine is a potent dopamine (DA) enhancer with powerful effects on the neuronal dopaminergic system, acting as a neuroprotective agent in Parkinson's disease (PD). Over 97% of PD patients respond to a single dose of Atremorine (5 g, p.o.) 1 h after administration. This response is gender-, time-, dose-, and genotype-dependent, with optimal doses ranging from 5 to 20 g/day, depending upon disease severity and concomitant medication. Drug-free patients show an increase in DA levels from 12.14 ± 0.34 pg/ml to 6463.21 ± 1306.90 pg/ml; and patients chronically treated with anti-PD drugs show an increase in DA levels from 1321.53 ± 389.94 pg/ml to 16,028.54 ± 4783.98 pg/ml, indicating that Atremorine potentiates the dopaminergic effects of conventional anti-PD drugs. Atremorine also influences the levels of other neurotransmitters (adrenaline, noradrenaline) and hor

BACKGROUND: The genetic contribution to the development of levodopa-induced motor complications in Parkinson's disease (PD) remains poorly understood. OBJECTIVES: We aimed to investigate the association between selected polymorphisms of the catechol-O-methyltransferase (COMT), dopamine receptor D2 (DRD2), ankyrin repeat and kinase domain containing 1 (ANKK1) and dopamine transporter (DAT) genes and the occurrence of motor complications in the group of PD patients. METHODS: A total of 234 PD patients undergoing levodopa therapy for at least two years were genotyped for the following polymorphisms: rs4680 in COMT; rs6277, rs1076560, and rs2283265 in DRD2; rs1800497 and rs2734849 in ANKK1; and a VNTR (Variable Number of Tandem Repeats) polymorphism in the 3'-UTR (3'-untranslated region) of the DAT gene. RESULTS: Levodopa-induced dyskinesia (LID) was significantly more frequent in carriers of the AA genotype of rs4680 in COMT compared to AG and GG carriers. Motor fluctuations occurred more

Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by a progressive loss of dopamine neurons of the central nervous system. The disease determines a significant disability due to a combination of motor symptoms such as bradykinesia, rigidity and rest tremor and non-motor symptoms such as sleep disorders, hallucinations, psychosis and compulsive behaviors. The current therapies consist in combination of drugs acting to control only the symptoms of the illness by the replacement of the dopamine lost. Although patients generally receive benefits from this symptomatic pharmacological management, they also show great variability in drug response in terms of both efficacy and adverse effects. Pharmacogenetic studies highlighted that genetic factors play a relevant influence in this drug response variability. In this review, we tried to give an overview of the recent progresses in the pharmacogenetics of PD, reporting the major genetic factors identified as involve

Digital biomarker (DB) assessments provide objective measures of daily life tasks and thus hold promise to improve diagnosis and monitoring of Parkinson's disease (PD) patients especially those with advanced stages. Data from DB studies can be used in advanced analytics such as Artificial Intelligence and Machine Learning to improve monitoring, treatment and outcomes. Although early development of inertial sensors as accelerometers and gyroscopes in smartphones provided encouraging results, the use of DB remains limited due to lack of standards, harmonization and consensus for analytical as well as clinical validation. Accordingly, a number of clinical trials have been developed to evaluate the performance of DB vs traditional assessment tools with the goal of monitoring disease progression, improving quality of life and outcomes. Herein, we update current evidence on the use of DB in PD and highlight potential benefits and limitations and provide suggestions for future research study.

The past 15 years have seen the emergence of a new paradigm in medical research, namely of people living with medical conditions (whether patients, parents, or caregivers) using digital tools to conduct N-of-1 trials and scientifically grounded research on themselves, whilst using the Internet to form communities of like-minded individuals willing to self-experiment. Prominent examples can be found in amyotrophic lateral sclerosis/motor neurone disease (the 'lithium study' on PatientsLikeMe), Parkinson's disease ('digital patient' Sara Riggare), and diabetes (the 'open artificial pancreas' of the #WeAreNotWaiting movement). Through transparency, data sharing, open source code, and publication in the peer-reviewed scientific literature, such activities conform to expected scientific conventions. However, other conventions, such as ethical oversight, regulation, professionalization, and the ability to translate this new form of relatively biased data into generalizable decisions, remain

Dopamine transporter (DaT) SPECT can confirm dopaminergic deficiency in Parkinson's disease (PD) but remains costly and inaccessible. We investigated whether brief smartphone-based motor assessments could predict DaT scan results as a scalable alternative. Data from Oxford and Genoa cohorts included individuals with iRBD, PD, and controls. Machine learning models trained on smartphone-derived features classified DaT scan status and predicted striatal binding ratios, compared with MDS-UPDRS-III benchmarks. Among 100 DaT scans, the smartphone-only XGBoost model achieved AUC = 0.80, improving to 0.82 when combined with MDS-UPDRS-III (AUC's gender-corrected). A simpler logistic regression model performed better with MDS-UPDRS-III alone (AUC = 0.83) versus smartphone features, with slightly higher performance when combined (AUC = 0.85). Regression models predicted binding ratios with modest error (RMSE = 0.49, R² = 0.56). Gait, tremor, and dexterity features were most predictive. These find

Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P < 1.8 × 10-6) when compared with motor Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretati

Prevalence estimates of Parkinson's disease (PD)-the fastest-growing neurodegenerative disease-are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC

The evidence that heart rate variability (HRV) decreases during early Parkinson's disease (PD) largely depends on electrocardiogram data. In this study, we examined HRV in PD using wearable sensors and assessed various evaluation methods for detecting disease-related alterations. We evaluated 27 patients with PD and 23 disease controls. The wearable sensors POLAR V800 HR and POLAR H10 were used for the HRV measurements. The participants wore the two sensors for approximately 24 h, and long-term HRV data were acquired. We analyzed the standard deviation of normal R-R intervals (SDNN) and coefficient of variation of R-R intervals (CVRR) for every 100 consecutive beats. Focusing on the fluctuation of SDNN and CVRR, we extracted the minimum, first decile, first quartile, and median values of SDNN and CVRR. The area under the receiver operating characteristic curve (AUC) for each HRV parameter was calculated to differentiate PD from the disease controls. The minimum values of SDNN and CVRR

BACKGROUND: Differentiating early-stage Parkinson's disease (PD) from essential tremor (ET) is challenging since they have some overlapping clinical features. Since early-stage PD may present with slight gait impairment and ET generally does not, gait analysis could be used to differentiate PD from ET using machine learning. OBJECTIVE: To differentiate early-stage PD from ET via machine learning using gait and postural transition parameters calculated using the raw kinematic signal captured from inertial measurement unit (IMU) sensors. METHODS: Gait and postural transition parameters were collected from 84 early-stage PD and 80 ET subjects during the Time Up and Go (TUG) test. We randomly split our data into training and test data. Within the training data, we separated the TUG test into four components: standing, straight walk, turning, and sitting to build weighted average ensemble classification models. The four components' weight indices were trained using logistic regression. Seve

BACKGROUND: Early diagnosis is crucial for ensuring that patients with Parkinson disease (PD) receive timely treatment, which can improve their quality of life and prolong lifespan. Wearable sensors have emerged as promising tools for early PD diagnosis, offering noninvasive, continuous symptom monitoring. OBJECTIVE: This review aimed to evaluate how wearable sensors have been applied in early diagnosis of PD over the past decade, focusing on sensor types, methods, findings, and limitations. METHODS: The systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were sourced from PubMed, IEEE Xplore, Scopus, and Web of Science and screened based on predefined criteria. The inclusion criteria were as follows: (1) the study was observational or experimental, (2) wearable sensors were applied for the early diagnosis of PD, (3) participants were diagnosed with early-stage or prodromal PD, (4) the study inclu

Pharmacogenetics (PGx) research over the past 2 decades has produced extensive evidence for the influence of genetic factors on the efficacy and tolerability of antipsychotic treatment. However, the application of these findings to optimize treatment outcomes for patients in clinical practice has been limited. This paper presents a meta-review of key PGx findings related to antipsychotic response and common adverse effects, including antipsychotic-induced weight gain, tardive dyskinesia (TD), and clozapine-induced agranulocytosis (CIAG), and highlights advances and challenges in clinical implementation. Most robust findings from candidate gene and genomewide association studies were reported for associations between polymorphisms in CYP2D6 and exposure and response to specific antipsychotics. As a result, product labels and guidelines from various PGx expert groups have provided selection and dosing recommendations based on CYP2D6 metabolizer phenotypes for commonly prescribed antipsyc

PURPOSE: The World Health Organization (WHO) reported in 2016 that 81% of adolescents aged 11 to 17 years have insufficient physical activity (PA). This decline, coupled with poor nutrition and sedentary lifestyles, has emerged as a global concern. Regular PA is linked to better academic performance, motor skills, sleep, and stronger bones and muscles. Although most interventions to enhance PA in children are school-based, these have not effectively increased the overall daily PA. This review assesses the effectiveness of new devices such as electronic watches and smartphones in evaluating PA in older schoolchildren. METHODS: This review followed the Joanna Briggs Institute methodology. MEDLINE (PubMed) and Scopus were searched for articles. After removing duplicates, two reviewers independently screened the titles, abstracts, and full texts. Methodological quality was assessed using standardized tools, and data were extracted systematically. RESULTS: The search identified 2,259 articl

The dopamine D2 receptor (DRD2) represents a pivotal target for therapeutic intervention in the treatment of neuropsychiatric disorders, including schizophrenia, bipolar disorder, and Parkinson's disease. The successful discovery of numerous effective DRD2 inhibitors has led to their clinical application and ongoing evaluation in various clinical trials. This review explores the synthetic approaches and clinical applications of prototypical small-molecule DRD2 inhibitors that have received approval or are currently undergoing clinical trials, highlighting their therapeutic potential and challenges. The synthesis of these inhibitors employs various chemical strategies, including modifications of phenothiazine and butyrophenone structures, which have yielded significant antipsychotic agents like chlorpromazine and haloperidol. Additionally, newer classes of inhibitors, such as aripiprazole, exhibit partial agonist activity at DRD2, offering a unique therapeutic profile. Clinically, DRD2