Prodromal Markers of Parkinson's Disease in Patients With Essential Tremor

Distinguishing essential tremor from Parkinson's disease: clinical and experimental tools

INTRODUCTION

Essential tremor (ET) and Parkinson's disease (PD) are the most common causes of tremor and the most prevalent movement disorders, with overlapping clinical features that can lead to diagnostic challenges, especially in the early stages.

AREAS COVERED

In the present paper, the authors review the clinical and experimental studies and emphasized the major aspects to differentiate between ET and PD, with particular attention to cardinal phenomenological features of these two conditions. Ancillary and experimental techniques, including neurophysiology, neuroimaging, fluid biomarker evaluation, and innovative methods, are also discussed for their role in differential diagnosis between ET and PD. Special attention is given to investigations and tools applicable in the early stages of the diseases, when the differential diagnosis between the two conditions is more challenging. Furthermore, the authors discuss knowledge gaps and unsolved issues in the field.

EXPERT OPINION

Distinguishing ET and PD is crucial for prognostic purposes and appropriate treatment. Additionally, accurate diagnosis is critical for optimizing clinical and experimental research on pathophysiology and innovative therapies. In a few years, integrated technologies could enable accurate, reliable diagnosis from early disease stages or prodromal stages in at-risk populations, but further research combining different techniques is needed.

Eye movement study in essential tremor patients and its clinical correlates

Essential tremor (ET) encompasses a wide spectrum of motor and non-motor features. Eye movement abnormalities were first reported two decades ago as an atypical finding in ET. Today, a growing number of publications about eye movement abnormalities in neurodegenerative diseases have helped understand their pathophysiology and the basis of their phenotypic variability. Thus, addressing such aspect in ET may disentangle, based on the oculomotor network abnormalities, the dysfunctional brain pathways in ET. In this study, we aimed to describe neurophysiological eye movement abnormalities in ET and their clinical correlates in terms of cognition and other associated clinical signs. We conducted a cross-sectional study in a tertiary neurology referral center including consecutive ET patients and cognitively normal healthy controls (HC) matched for age and sex. The study protocol included the assessment of voluntary horizontal saccades, smooth pursuit, anti-saccades and saccadic intrusions. We assessed the associated motor signs, cognitive functions and the presence of rapid eye movement disorder (RBD). Sixty-two ET patients and 66 HC were enrolled in the study. Eye movement examination showed significant abnormalities in comparison with HC (46.7% vs 20%, p = 0.002). Prolonged saccadic latency (38.7%, p = 0.033) and altered smooth pursuit (38.7%, p = 0.033) were the most common abnormalities in ET patients. Anti-saccadic errors (16% vs 0% in HC, p = 0.034) correlated with the presence of rigidity (p = 0.046), bradykinesia (p = 0.001), cognitive dysfunction (p = 0.006), executive dysfunction (p = 0.0002), apraxia (p = 0.0001), altered verbal fluency (p = 0.013) and altered backward digit span (p = 0.045) along with the presence of RBD (p = 0.035). Square-wave jerks (11.5% vs 0% in HC, p = 0.0024) correlated with rest tremor. A distinctive phenotype of ET could emerge out of this study characterized by anti-saccadic errors and a sub-cortical cognitive profile, consecutive to the disruption of the cerebello-thalamo-cortical loop. Patients with anti-saccadic errors could be cognitively vulnerable and in need of a close monitoring of their cognitive efficiency during the disease's progression. They may as well convert to Parkinson disease if they present with parkinsonism, RBD and square-wave jerks and require, consequently, a close observation of their motor progression.

Identifying and distinguishing of essential tremor and Parkinson's disease with grouped stability analysis based on searchlight-based MVPA

BACKGROUND

Since both essential tremor (ET) and Parkinson's disease (PD) are movement disorders and share similar clinical symptoms, it is very difficult to recognize the differences in the presentation, course, and treatment of ET and PD, which leads to misdiagnosed commonly.

PURPOSE

Although neuroimaging biomarker of ET and PD has been investigated based on statistical analysis, it is unable to assist the clinical diagnosis of ET and PD and ensure the efficiency of these biomarkers. The aim of the study was to identify the neuroimaging biomarkers of ET and PD based on structural magnetic resonance imaging (MRI). Moreover, the study also distinguished ET from PD via these biomarkers to validate their classification performance.

METHODS

This study has developed and implemented a three-level machine learning framework to identify and distinguish ET and PD. First of all, at the model-level assessment, the searchlight-based machine learning method has been used to identify the group differences of patients (ET/PD) with normal controls (NCs). And then, at the feature-level assessment, the stability of group differences has been tested based on structural brain atlas separately using the permutation test to identify the robust neuroimaging biomarkers. Furthermore, the identified biomarkers of ET and PD have been applied to classify ET from PD based on machine learning techniques. Finally, the identified biomarkers have been compared with the previous findings of the biology-level assessment.

RESULTS

According to the biomarkers identified by machine learning, this study has found widespread alterations of gray matter (GM) for ET and large overlap between ET and PD and achieved superior classification performance (PCA + SVM, accuracy = 100%).

CONCLUSIONS

This study has demonstrated the significance of a machine learning framework to identify and distinguish ET and PD. Future studies using a large data set are needed to confirm the potential clinical application of machine learning techniques to discern between PD and ET.

The interplay of epilepsy with impaired mitophagy and autophagy linked dementia (MAD): A review of therapeutic approaches

The duration and, age of dementia have been linked to a higher risk of seizures. The exact mechanism that drives epileptogenesis in impaired mitophagy and autophagy linked dementia (MAD) is fully defined after reviewing the Scopus, Publon, and Pubmed databases. The epileptogenesis in patients with Alzheimer's disease dementia (ADD) and Parkinson's disease dementia (PDD) is due to involvement of amyloid plaques (Aβ), phosphorylated tau (pTau), Parkin, NF-kB and NLRP3 inflammasome. Microglia, the prime protective and inflammatory cells in the brain exert crosstalk between mitophagy and inflammation. Several researchers believed that the inflammatory brain cells microglia could be a therapeutic target for the treatment of a MAD associated epilepsy. There are conventional antiepileptic drugs such as gabapentin, lamotrigine, phenytoin sodium, carbamazepine, oxcarbazepine, felbamate, lamotrigine, valproate sodium, and topiramate are prescribed by a psychiatrist to suppress seizure frequency. Also, the conventional drugs generate serious adverse effects and synergises dementia characteristics. The adverse effect of carbamazepine is neurotoxic and also, damages haemopoietic system and respiratory tract. The phenytoin treatment causes cerebellar defect and anemia. Dementia and epilepsy have a complicated relationship, thus targeting mitophagy for cure of epileptic dementia makes sense. Complementary and alternative medicine (CAM) is one of the rising strategies by many patients of the world, not only to suppress seizure frequency but also to mitigate dementia characteristics of patients. Therefore our present review focus on the interplay between epilepsy and MAD and their treatment with CAM approaches.

Antioxidant and Neuroprotective Effects of Carnosine: Therapeutic Implications in Neurodegenerative Diseases

Neurodegenerative diseases (NDs) constitute a global challenge to human health and an important social and economic burden worldwide, mainly due to their growing prevalence in an aging population and to their associated disabilities. Despite their differences at the clinical level, NDs share fundamental pathological mechanisms such as abnormal protein deposition, intracellular Ca²⁺ overload, mitochondrial dysfunction, redox homeostasis imbalance and neuroinflammation. Although important progress is being made in deciphering the mechanisms underlying NDs, the availability of effective therapies is still scarce. Carnosine is a natural endogenous molecule that has been extensively studied during the last years due to its promising beneficial effects for human health. It presents multimodal mechanisms of action, being able to exert antioxidant, anti-inflammatory and anti-aggregate activities, among others. Interestingly, most NDs exhibit oxidative and nitrosative stress, protein aggregation and inflammation as molecular hallmarks. In this review, we discuss the neuroprotective functions of carnosine and its implications as a therapeutic strategy in different NDs. We summarize the existing works that study alterations in carnosine metabolism in Alzheimer’s disease and Parkinson’s disease, the two most common NDs. In addition, we review the beneficial effect that carnosine supplementation presents in models of such diseases as well as in aging-related neurodegeneration.

Variable Effects of PD-Risk Associated SNPs and Variants in Parkinsonism-Associated Genes on Disease Phenotype in a Community-Based Cohort

Genetic risk factors for Parkinson's disease (PD) risk and progression have been identified from genome-wide association studies (GWAS), as well as studies of familial forms of PD, implicating common variants at more than 90 loci and pathogenic or likely pathogenic variants at 16 loci. With the goal of understanding whether genetic variants at these PD-risk loci/genes differentially contribute to individual clinical phenotypic characteristics of PD, we used structured clinical documentation tools within the electronic medical record in an effort to provide a standardized and detailed clinical phenotypic characterization at the point of care in a cohort of 856 PD patients. We analyzed common SNPs identified in previous GWAS studies, as well as low-frequency and rare variants at parkinsonism-associated genes in the MDSgene database for their association with individual clinical characteristics and test scores at baseline assessment in our community-based PD patient cohort: age at onset, disease duration, Unified Parkinson's Disease Rating Scale I-VI, cognitive status, initial and baseline motor and non-motor symptoms, complications of levodopa therapy, comorbidities and family history of neurological disease with one or more than one affected family members. We find that in most cases an individual common PD-risk SNP identified in GWAS is associated with only a single clinical feature or test score, while gene-level tests assessing low-frequency and rare variants reveal genes associated in either a unique or partially overlapping manner with the different clinical features and test scores. Protein-protein interaction network analysis of the identified genes reveals that while some of these genes are members of already identified protein networks others are not. These findings indicate that genetic risk factors for PD differentially affect the phenotypic presentation and that genes associated with PD risk are also differentially associated with individual disease phenotypic characteristics at baseline. These findings raise the intriguing possibility that different SNPs/gene effects impact discrete phenotypic characteristics. Furthermore, they support the hypothesis that different gene and protein-protein interaction networks that underlie PD risk, the PD phenotype, and the neurodegenerative process leading to the disease phenotype, and point to the significance of the genetic background on disease phenotype.

Phenotypic Features of Isolated Essential Tremor, Essential Tremor Plus, and Essential Tremor-Parkinson's Disease in a Movement Disorders Clinic

Background:

Patients with essential tremor were initially considered to have isolated tremor, but additional motor and non-motor features have been increasingly recognized. The term “essential tremor plus” was adopted by the Task Force on Tremor of the International Parkinson and Movement Disorder Society to describe essential tremor patients with additional neurologic signs.

Objectives:

To characterize essential tremor patients and their phenotypes in a movement disorders clinic population in the context of the new tremor classification.

Methods:

Demographic, clinical, historical, treatment, and diagnostic data were retrospectively collected on 300 patients diagnosed by movement disorder experts with essential tremor. Patients were classified as having essential tremor, essential tremor plus, or essential tremor-Parkinson’s disease combination, and features between these groups were compared.

Results:

Of the 300 patients, 20.7% were classified as isolated essential tremor, 53.3% as essential tremor plus, and 26.0% as essential tremor-Parkinson’s disease. There was no significant difference in the duration of tremor symptoms. Essential tremor plus patients were more likely to have dystonia, tandem gait abnormalities, head tremor and greater tremor severity. Essential tremor-Parkinson’s disease patients were more likely to have RBD symptoms. There was no significant difference in cognitive impairment between essential tremor plus and essential tremor-Parkinson’s disease patients.

Conclusions:

Additional motor and non-motor features, including parkinsonism, are common in patients with essential tremor. Further studies are needed to clarify essential tremor phenotypes and to provide insights into possible subtypes.

Highlights:

300 patients with essential tremor from a movement disorders clinic were re-classified based on the Movement Disorder Society Consensus Statement on the Classification of Tremors. Additional motor and non-motor features, including parkinsonism, were common, and only 20.7% of patients remained classified as isolated essential tremor.

Imaging Tremor Quantification for Neurological Disease Diagnosis

In this paper, we introduce a simple method based on image analysis and deep learning that can be used in the objective assessment and measurement of tremors. A tremor is a neurological disorder that causes involuntary and rhythmic movements in a human body part or parts. There are many types of tremors, depending on their amplitude and frequency type. Appropriate treatment is only possible when there is an accurate diagnosis. Thus, a need exists for a technique to analyze tremors. In this paper, we propose a hybrid approach using imaging technology and machine learning techniques for quantification and extraction of the parameters associated with tremors. These extracted parameters are used to classify the tremor for subsequent identification of the disease. In particular, we focus on essential tremor and cerebellar disorders by monitoring the finger–nose–finger test. First of all, test results obtained from both patients and healthy individuals are analyzed using image processing techniques. Next, data were grouped in order to determine classes of typical responses. A machine learning method using a support vector machine is used to perform an unsupervised clustering. Experimental results showed the highest internal evaluation for distribution into three clusters, which could be used to differentiate the responses of healthy subjects, patients with essential tremor and patients with cerebellar disorders.