Age‐related changes in blood pressure and heart rates of patients with Parkinson's disease

Mechanobiological insight into brain diseases based on mechanosensitive channels: Common mechanisms and clinical potential

BACKGROUND

As physical signals, mechanical cues regulate the neural cells in the brain. The mechanosensitive channels (MSCs) perceive the mechanical cues and transduce them by permeating specific ions or molecules across the plasma membrane, and finally trigger a series of intracellular bioelectrical and biochemical signals. Emerging evidence supports that wide-distributed, high-expressed MSCs like Piezo1 play important roles in several neurophysiological processes and neurological disorders.

AIMS

To systematically conclude the functions of MSCs in the brain and provide a novel mechanobiological perspective for brain diseases.

METHOD

We summarized the mechanical cues and MSCs detected in the brain and the research progress on the functional roles of MSCs in physiological conditions. We then concluded the pathological activation and downstream pathways triggered by MSCs in two categories of brain diseases, neurodegenerative diseases and place-occupying damages. Finally, we outlined the methods for manipulating MSCs and discussed their medical potential with some crucial outstanding issues.

RESULTS

The MSCs present underlying common mechanisms in different brain diseases by acting as the "transportation hubs" to transduce the distinct signal patterns: the upstream mechanical cues and the downstream intracellular pathways. Manipulating the MSCs is feasible to alter the complicated downstream processes, providing them promising targets for clinical treatment.

CONCLUSIONS

Recent research on MSCs provides a novel insight into brain diseases. The common mechanisms mediated by MSCs inspire a wide range of therapeutic potentials targeted on MSCs in different brain diseases.

Prediction of Parkinson's Disease Using Machine Learning Methods

The detection of Parkinson's disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk. In our study, we first grouped PD-associated factors based on their cost and accessibility, and then gradually incorporated them into risk predictions, which were built using eight commonly used machine learning models to allow for comprehensive assessment. Finally, the Shapley Additive Explanations (SHAP) method was used to investigate the contributions of each factor. We found that models built with demographic variables, hospital admission examinations, clinical assessment, and polygenic risk score achieved the best prediction performance, and the inclusion of invasive biomarkers could not further enhance its accuracy. Among the eight machine learning models considered, penalized logistic regression and XGBoost were the most accurate algorithms for assessing PD risk, with penalized logistic regression achieving an area under the curve of 0.94 and a Brier score of 0.08. Olfactory function and polygenic risk scores were the most important predictors for PD risk. Our research has offered a practical framework for PD risk assessment, where necessary information and efficient machine learning tools were highlighted.

X-linked hydrocephalus genes: Their proximity to telomeres and high A + T content compared to Parkinson's disease

Proximity to telomeres (i) and high adenine and thymine (A + T) content (ii) are two factors associated with high mutation rates in human chromosomes. We have previously shown that >100 human genes when mutated to cause congenital hydrocephalus (CH) meet either factor (i) or (ii) at 91% matching, while two factors are poorly satisfied in human genes associated with familial Parkinson's disease (fPD) at 59%. Using the sets of mouse, rat, and human chromosomes, we found that 7 genes associated with CH were located on the X chromosome of mice, rats, and humans. However, genes associated with fPD were in different autosomes depending on species. While the contribution of proximity to telomeres in the autosome was comparable in CH and fPD, high A + T content played a pivotal contribution in X-linked CH (43% in all three species) than in fPD (6% in rodents or 13% in humans). Low A + T content found in fPD cases suggests that PARK family genes harbor roughly 3 times higher chances of methylations in CpG sites or epigenetic changes than X-linked genes.

Atrial fibrillation risk on Parkinson's disease - a systematic review and meta-analysis

The association of Parkinson's Disease (PD) with atrial fibrillation (AF) is not well established and previous studies' results were heterogeneous. This review aimed to evaluate if patients with PD are at increased risk of having AF. MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science, were searched from inception May 2021. Two reviewers independently selected observational studies with data allowing to estimate the risk of atrial fibrillation in PD patients compared with no-PD controls. Pooled estimates Odds Ratio (OR) and 95% confidence intervals (CIs) were derived through meta-analysis. Heterogeneity was assessed using I² test. The risk of bias of individual studies was evaluated using the ROBINS-I tool. The study protocol was registered at PROSPERO: CRD42020216572. Seven studies were included: five case-control studies and two cohort studies. Three of the studies included were a population-based study. No significant difference was detected between PD and controls regarding atrial fibrillation (OR 1.10, 95% CI 0.81 to 1.49). Early PD present a significant higher risk of AF (OR 1.55, 95% CI 1.00 to 2.40, I² 98%). The overall risk of bias was serious, with only two studies being considered as having moderate risk. The best evidence available do not support that there is an increased risk of AF in PD patients. Further studies are needed to better conclude if there is a relation between AF and PD.

Baroreflex dysfunction in Parkinson's disease: integration of central and peripheral mechanisms

The incidence of Parkinson's disease (PD) is increasing worldwide. Although the PD hallmark is the motor impairments, nonmotor dysfunctions are now becoming more recognized. Recently, studies have suggested that baroreflex dysfunction is one of the underlying mechanisms of cardiovascular dysregulation observed in patients with PD. However, the large body of literature on baroreflex function in PD is unclear. The baroreflex system plays a major role in the autonomic, and ultimately blood pressure and heart rate, adjustments that accompany acute cardiovascular stressors on a daily basis. Therefore, impaired baroreflex function (i.e., decreased sensitivity or gain) can lead to altered neural cardiovascular responses. Since PD affects parasympathetic and sympathetic branches of the autonomic nervous system and both are orchestrated by the baroreflex system, understanding of this crucial mechanism in PD is necessary. In the present review, we summarize the potential altered central and peripheral mechanisms affecting the feedback-controlled loops that comprise the reflex arc in patients with PD. Major factors including arterial stiffness, reduced number of C1 and activation of non-C1 neurons, presence of central α-synuclein aggregation, cardiac sympathetic denervation, attenuated muscle sympathetic nerve activity, and lower norepinephrine release could compromise baroreflex function in PD. Results from patients with PD and from animal models of PD provide the reader with a clearer picture of baroreflex function in this clinical condition. By doing so, our intent is to stimulate future studies to evaluate several unanswered questions in this research area.