Epidemiology of Parkinson's Disease in Rural Gujarat, India

The protective effects of repetitive transcranial magnetic stimulation with different high frequencies on motor functions in MPTP/probenecid induced Parkinsonism mouse models

BACKGROUND

High-frequency repeated transcranial magnetic stimulation (rTMS) stimulating the primary motor cortex (M1) is an alternative, adjunctive therapy for improving the motor symptoms of Parkinson's disease (PD). However, whether the high frequency of rTMS positively correlates to the improvement of motor symptoms of PD is still undecided. By controlling for other parameters, a disease animal model may be useful to compare the neuroprotective effects of different high frequencies of rTMS.

OBJECTIVE

The current exploratory study was designed to compare the protective effects of four common high frequencies of rTMS (5, 10, 15, and 20 Hz) and iTBS (a special form of high-frequency rTMS) and explore the optimal high-frequency rTMS on an animal PD model.

METHODS

Following high frequencies of rTMS application (twice a week for 5 weeks) in a MPTP/probenecid-induced chronic PD model, the effects of the five protocols on motor behavior as well as dopaminergic neuron degeneration levels were identified. The underlying molecular mechanisms were further explored.

RESULTS

We found that all the high frequencies of rTMS had protective effects on the motor functions of PD models to varying degrees. Among them, the 10, 15, and 20 Hz rTMS interventions induced comparable preservation of motor function through the protection of nigrostriatal dopamine neurons. The enhancement of brain-derived neurotrophic factor (BDNF), dopamine transporter (DAT), and vesicular monoamine transporter 2 (VMAT-2) and the suppression of TNF-α and IL-1β in the nigrostriatum were involved in the process. The efficacy of iTBS was inferior to that of the above three protocols. The effect of 5 Hz rTMS protocol was weakest.

CONCLUSIONS

Combined with the results of the present study and the possible side effects induced by rTMS, we concluded that 10 Hz might be the optimal stimulation frequency for preserving the motor functions of PD models using rTMS treatment.

Global prevalence and incidence of Young Onset Parkinson's disease: A systematic review and meta-analysis

BACKGROUND

There is a lack of enough evidence regarding the epidemiology of Young-onset Parkinson's disease (YOPD) which is needed by clinicians and healthcare policymakers.

AIM

Herein, in this systematic review and meta-analysis, we aimed to estimate the global prevalence and incidence rates of YOPD.

METHODS

We searched the literature in PubMed, Scopus, and Web of Science in May 2022. We included retrospective, prospective, cross-sectional observational population-based studies that reported the prevalence or incidence of PD in individuals younger than 40 years with known diagnostic criteria.

RESULTS

After two-step screening, 50 studies were eligible to be included in our study. The age-standardized prevalence of YOPD was 10.2 per 100,000 persons globally while it was 14.7 per 100,000 population in European countries. Age-standardized prevalence estimates for 5-year age bands showed that the YOPD prevalence estimates varied from 6.1 per 100,000 population in the group aged 20-24 to 16.1 per 100,000 population in the group aged 35-39. Also, the age-standardized incidence of YOPD was 1.3 per 100,000 person-years population worldwide and 1.2 per 100,000 person-years in the European population.

CONCLUSION

Based on this systematic review and meta-analysis, the overall prevalence of YOPD is 10.2 per 100,000 population, although estimates of the prevalence and incidence in low-income countries remain scarce. To improve monitoring and certain diagnoses of YOPD, healthcare providers and policymakers should be aware that much more effective tools are required.

Physiotherapy Approach to a Stage V Parkinson's Disease Patient: A Case Report

Parkinson's disease (PD) is a neurodegenerative disorder caused due to decreased dopamine, a neurotransmitter, advancing to a range of motor and non-motor attributes. There is a death of dopamine-producing neurons (dopaminergic neurons) in the Substantia Nigra. Bradykinesia, postural instability, resting tremor, and rigidity are four main symptoms in this patient. A variety of other symptoms, like hypomimia, micrographia, freezing gait, decreased movement amplitude, constipation, cognitive impairments, etc., can be seen in this patient. In this paper, we report a 62-year-old female with stage 5 PD with chief complaints of uncoordinated movements, weakness, and difficulty in daily activities. She was treated with strengthening, stretching, Lee Silverman Voice Treatment (LSVT) BIG, bed mobility, gait training with auditory cueing, balance training, etc. LSVT-BIG enhances motor function by incorporating high amplitude motions of high intensity, consisting of numerous repetitions and progressive complexity. At the end of three weeks, the patient had improved strength, static and dynamic balance, gait, and quality of life.

Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease

BACKGROUND AND OBJECTIVES

Parkinson's Disease (PD) is a devastating chronic neurological condition. Machine learning (ML) techniques have been used in the early prediction of PD progression. Fusion of heterogeneous data modalities proved its capability to improve the performance of ML models. Time series data fusion supports the tracking of the disease over time. In addition, the trustworthiness of the resulting models is improved by adding model explainability features. The literature on PD has not sufficiently explored these three points.

METHODS

In this work, we proposed an ML pipeline for predicting the progression of PD that is both accurate and explainable. We explore the fusion of different combinations of five time series modalities from the Parkinson's Progression Markers Initiative (PPMI) real-world dataset, including patient characteristics, biosamples, medication history, motor, and non-motor function data. Each patient has six visits. The problem has been formulated in two ways: ❶ a three-class based progression prediction with 953 patients in each time series modality, and ❷ a four-class based progression prediction with 1,060 patients in each time series modality. The statistical features of these six visits were calculated from each modality and diverse feature selection methods were applied to select the most informative feature sets. The extracted features were used to train a set of well-known ML models including Support vector machines (SVM), random forests (RF), extra tree classifier (ETC), light gradient boosting machines (LGBM), and stochastic gradient descent (SGD). We examined a number of data-balancing strategies in the pipeline with different combinations of modalities. ML models have been optimized using the Bayesian optimizer. A comprehensive evaluation of various ML methods has been conducted, and the best models have been extended to provide different explainability features.

RESULTS

We compare the performance of ML models before and after optimization and using and without using feature selection. In the three-class experiment and with various modality fusions, the LGBM model produced the most accurate results with a 10-fold cross-validation (10-CV) accuracy of 90.73% using non-motor function modality. RF produced the best results in the four-class experiment with various modality fusions with a 10-CV accuracy of 94.57% using non-motor modality. With the fused dataset of non-motor and motor function modalities, the LGBM model outperformed the other ML models in both the 3-class and 4-class experiments (i.e., 10-CV accuracy of 94.89% and 93.73%, respectively). Using the Shapely Additive Explanations (SHAP) framework, we employed global and instance-based explanations to explain the behavior of each ML classifier. Moreover, we extended the explainability by implementing the LIME and SHAPASH local explainers. The consistency of these explainers has been explored. The resultant classifiers were accurate, explainable, and thus medically more relevant and applicable.

CONCLUSIONS

The select modalities and feature sets were confirmed by the literature and medical experts. The various explainers suggest that the bradykinesia (NP3BRADY) feature was the most dominant and consistent. By providing thorough insights into the influence of multiple modalities on the disease risk, the suggested approach is expected to help improve the clinical knowledge of PD progression processes.

Apoptotic Factors and Mitochondrial Complexes Assist Determination of Strain-Specific Susceptibility of Mice to Parkinsonian Neurotoxin MPTP

Identification of genetic mutations in Parkinson's disease (PD) promulgates the genetic nature of disease susceptibility. Resilience-associated genes being unknown till date, the normal genetic makeup of an individual may be determinative too. Our earlier studies comparing the substantia nigra (SN) and striatum of C57BL/6J, CD-1 mice, and their F1-crossbreds demonstrated the neuroprotective role of admixing against the neurotoxin MPTP. Furthermore, the differences in levels of mitochondrial fission/fusion proteins in the SN of parent strains imply effects on mitochondrial biogenesis. Our present investigations suggest that the baseline levels of apoptotic factors Bcl-2, Bax, and AIF differ across the three strains and are differentially altered in SN following MPTP administration. The reduction in complex-I levels exclusively in MPTP-injected C57BL/6J reiterates mitochondrial involvement in PD pathogenesis. The MPTP-induced increase in complex-IV, in the nigra of both parent strains, may be compensatory in nature. The ultrastructural evaluation showed fairly preserved mitochondria in the dopaminergic neurons of CD-1 and F1-crossbreds. However, in CD-1, the endoplasmic reticulum demonstrated distinct luminal enlargement, bordering onto ballooning, suggesting proteinopathy as a possible initial trigger.The increase in α-synuclein in the pars reticulata of crossbreds suggests a supportive role for this output nucleus in compensating for the lost function of pars compacta. Alternatively, since α-synuclein over-expression occurs in different brain regions in PD, the α-synuclein increase here may suggest a similar pathogenic outcome. Further understanding is required to resolve this biological contraption. Nevertheless, admixing reduces the risk to MPTP by favoring anti-apoptotic consequences. Similar neuroprotection may be envisaged in the admixed populace of Anglo-Indians.

Expanding Arsenal against Neurodegenerative Diseases Using Quercetin Based Nanoformulations: Breakthroughs and Bottlenecks

Quercetin (Qu), a dietary flavonoid, is obtained from many fruits and vegetables such as coriander, broccoli, capers, asparagus, onion, figs, radish leaves, cranberry, walnuts, and citrus fruits. It has proven its role as a nutraceutical owing to numerous pharmacological effects against various diseases in preclinical studies. Despite these facts, Qu and its nanoparticles are less explored in clinical research as a nutraceutical. The present review covers various neuroprotective actions of Qu against various neurodegenerative diseases (NDs) such as Alzheimer's, Parkinson's, Huntington's, and Amyotrophic lateral sclerosis. A literature search was conducted to systematically review the various mechanistic pathways through which Qu elicits its neuroprotective actions and the challenges associated with raw Qu that compromise therapeutic efficacy. The nanoformulations developed to enhance Qu's therapeutic efficacy are also covered. Various ongoing/completed clinical trials related to Qu in treating various diseases, including NDs, are also tabulated. Despite these many successes, the exploration of research on Qu-loaded nanoformulations is limited mostly to preclinical studies, probably due to poor drug loading and stability of the formulation, time-consuming steps involved in the formulation, and their poor scale-up capacity. Hence, future efforts are required in this area to reach Qu nanoformulations to the clinical level.

Alcohol and Parkinson's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

A substantial body of research has examined the relationship between alcohol consumption and risk of Parkinson's disease (PD).

OBJECTIVE

To provide an updated systematic review and meta-analysis of observational studies examining the relationship between alcohol consumption and risk of PD.

METHODS

Eligible studies comparing PD risk in ever vs. never alcohol drinkers were sourced from six databases. Outcomes were pooled using standard meta-analysis techniques. Separate female and male estimates were generated from studies reporting sex-specific data. Additionally, cohort studies stratifying participants by quantity of alcohol intake were integrated in a dose-response analysis.

RESULTS

52 studies were included, totaling 63,707 PD patients and 9,817,924 controls. Our meta-analysis supported a statistically significant overrepresentation of never drinkers among PD subjects; odds ratio (OR) for ever drinking alcohol 0.84 (95% confidence interval (CI) 0.76 - 0.92). A subgroup analysis revealed similar effect estimates in females and males. A further synthesis of seven cohort studies suggested a negative, dose-dependent association between alcohol and risk of PD.

CONCLUSION

In the absence of a known neuroprotective pathway, there may be reason to doubt a true biological effect. The role of survivor bias, selection and recall bias, misclassification, and residual confounding requires consideration. Alternatively, observations might be attributable to reverse causation if those predestined for PD alter their alcohol habits during the preclinical phase. Major limitations of our study include high between-study heterogeneity (I2 = 93.2%) and lack of adjustment for key confounders, namely smoking status.

Lower Blood Lipid Level Is Associated with the Occurrence of Parkinson's Disease: A Meta-Analysis and Systematic Review

BACKGROUND

The changes of blood lipid levels in patients with Parkinson's disease (PD) and its clinical relevance remain unclear. We aimed to evaluate the potential association of blood lipid and the occurrence of PD, to provide evidence to the clinical treatment and nursing care of PD.

METHODS

We searched PubMed, Medline, Web of Science, Cochrane Library, Wanfang Database, Weipu Database, and China National Knowledge Infrastructure for studies related to the blood lipid levels and PD until November 30, 2021. Two researchers independently screened the literature and extricated the data including the levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of included studies. RevMan5.3 and Stata 12.0 software were used for statistical processing and analysis.

RESULTS

A total of 15 cohort studies with 9740 participants involving 2032 PD patients and 7708 controls were included. Meta-analysis indicated that TC (SMD = -0.29, 95% CI -0.55∼-0.03, P=0.04), TG (SMD = -16.83, 95% CI -20.71∼-12.95, P < 0.001), HDL-C (SMD = -0.14, 95% CI -0.26∼-0.02, P < 0.001) and LDL-C (SMD = -0.26, 95% CI -0.50∼-0.01, P=0.04) level in the PD patients was significantly lower than that of health controls. Sensitivity analysis indicated that the results were stable. No significant publication bias was found between the synthesized outcomes.

CONCLUSIONS

Lower blood TC, TG, HDL-C, and LDL-C level are associated with the occurrence of PD. Limited by sample size and study population, further high-quality, large-sample clinical trials in different areas are needed to further determine the relationship between blood lipids and PD in the future.

Recalling the pathology of Parkinson's disease; lacking exact figure of prevalence and genetic evidence in Asia with an alarming outcome: A time to step-up

Parkinson's disease (PD) is the second most common and progressive neurodegenerative disease globally, with major symptoms like bradykinesia, impaired posture, and tremor. Several genetic and environmental factors have been identified but elucidating the main factors have been challenging due to the disease's complex nature. Diagnosis, prognosis, and management of such diseases are challenging and require effective targeted attention in developing countries. Recently, PD is growing rapidly in many crowded Asian countries as an alarming threat with inadequate knowledge of its prevalence, genetic architecture, and geographic distribution. This study gave an in-depth overview of the prevalence, incidence and genomic/genetics studies published so far in the Asian population. To the best of our knowledge, PD has increased significantly in several Asian countries, including China, South Korea, Japan, Thailand, and Israel over the past few years, requiring a greater level of care and attention. Genetic screening of families with PD at national levels and establishing an official database of PD cases are essential to get a comprehensive and conclusive view of the exact prevalence and genetic diversity of PD in the Asian population to properly manage and treat the disease.