Clinical outcomes after MRI connectivity-guided radiofrequency thalamotomy for tremor

OBJECTIVE: Radiofrequency thalamotomy (RF-T) is an established treatment for refractory tremor. It is unclear whether connectivity-guided targeting strategies could further augment outcomes. The aim of this study was to evaluate the efficacy and safety of MRI connectivity-guided RF-T in severe tremor. METHODS: Twenty-one consecutive patients with severe tremor (14 with essential tremor [ET], 7 with Parkinson's disease [PD]) underwent unilateral RF-T at a single institution between 2017 and 2020. Connectivity-derived thalamic segmentation was used to guide targeting. Changes in the Fahn-Tolosa-Marin Rating Scale (FTMRS) were recorded in treated and nontreated hands as well as procedure-related side effects. RESULTS: Twenty-three thalamotomies were performed (with 2 patients receiving a repeated intervention). The mean postoperative assessment time point was 14.1 months. Treated-hand tremor scores improved by 63.8%, whereas nontreated-hand scores deteriorated by 10.1% (p < 0.01). Total FTMRS scores were significantly better at follow-up compared with baseline (mean 34.7 vs 51.7, p = 0.016). Baseline treated-hand tremor severity (rho = 0.786, p < 0.01) and total FTMRS score (rho = 0.64, p < 0.01) best correlated with tremor improvement. The most reported side effect was mild gait ataxia (n = 11 patients). CONCLUSIONS: RF-T guided by connectivity-derived segmentation is a safe and effective option for severe tremor in both PD and ET

Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models

Parkinson’s disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future

Proximity extension assay testing reveals novel diagnostic biomarkers of atypical parkinsonian syndromes.

OBJECTIVE: The high degree of clinical overlap between atypical parkinsonian syndromes (APS) and Parkinson's disease (PD) makes diagnosis challenging. We aimed to identify novel diagnostic protein biomarkers of APS using multiplex proximity extension assay (PEA) testing. METHODS: Cerebrospinal fluid (CSF) samples from two independent cohorts, each consisting of APS and PD cases, and controls, were analysed for neurofilament light chain (NF-L) and Olink Neurology and Inflammation PEA biomarker panels. Whole-cohort comparisons of biomarker concentrations were made between APS (n=114), PD (n=37) and control (n=34) groups using logistic regression analyses that included gender, age and disease duration as covariates. RESULTS: APS versus controls analyses revealed 11 CSF markers with significantly different levels in cases and controls (p<0.002). Four of these markers also reached significance (p<0.05) in APS versus PD analyses. Disease-specific analyses revealed lower group levels of FGF-5, FGF-19 and SPOCK1 in multiple system atrophy compared with progressive supranuclear palsy and corticobasal syndrome. Receiver operating characteristic curve analyses suggested that the diagnostic accuracy of NF-L was superior to the significant PEA biomarkers in distinguishing APS, PD and controls. The biological processes regulated by the significant proteins include cell differentiation and immune cell migration. Delta and notch-like epidermal growth factor-related receptor (DNER) had the strongest effect size in APS versus controls and APS versus PD analyses. DNER is highly expressed in substantia nigra and is an activator of the NOTCH1 pathway which has been implicated in the aetiology of other neurodegenerative disorders including Alzheimer's disease. CONCLUSIONS: PEA testing has identified potential novel diagnostic biomarkers of APS

Combining biomarkers for prognostic modelling of Parkinson's disease

BACKGROUND: Patients with Parkinson's disease (PD) have variable rates of progression. More accurate prediction of progression could improve selection for clinical trials. Although some variance in clinical progression can be predicted by age at onset and phenotype, we hypothesise that this can be further improved by blood biomarkers. OBJECTIVE: To determine if blood biomarkers (serum neurofilament light (NfL) and genetic status (glucocerebrosidase, GBA and apolipoprotein E (APOE))) are useful in addition to clinical measures for prognostic modelling in PD. METHODS: We evaluated the relationship between serum NfL and baseline and longitudinal clinical measures as well as patients' genetic (GBA and APOE) status. We classified patients as having a favourable or an unfavourable outcome based on a previously validated model, and explored how blood biomarkers compared with clinical variables in distinguishing prognostic phenotypes . RESULTS: 291 patients were assessed in this study. Baseline serum NfL was associated with baseline cognitive status. Nfl predicted a shorter time to dementia, postural instability and death (dementia-HR 2.64; postural instability-HR 1.32; mortality-HR 1.89) whereas APOEe4 status was associated with progression to dementia (dementia-HR 3.12, 95% CI 1.63 to 6.00). NfL levels and genetic variables predicted unfavourable progression to a similar extent as clinical predictors. The combination of clinical, NfL and genetic data produced a stronger prediction of unfavourable outcomes compared with age and gender (area under the curve: 0.74-age/gender vs 0.84-ALL p=0.0103). CONCLUSIONS: Clinical trials of disease-modifying therapies might usefully stratify patients using clinical, genetic and NfL status at the time of recruitment

Protein aggregation and calcium dysregulation are hallmarks of familial Parkinson's disease in midbrain dopaminergic neurons

Mutations in the SNCA gene cause autosomal dominant Parkinson’s disease (PD), with loss of dopaminergic neurons in the substantia nigra, and aggregation of α-synuclein. The sequence of molecular events that proceed from an SNCA mutation during development, to end-stage pathology is unknown. Utilising human-induced pluripotent stem cells (hiPSCs), we resolved the temporal sequence of SNCA-induced pathophysiological events in order to discover early, and likely causative, events. Our small molecule-based protocol generates highly enriched midbrain dopaminergic (mDA) neurons: molecular identity was confirmed using single-cell RNA sequencing and proteomics, and functional identity was established through dopamine synthesis, and measures of electrophysiological activity. At the earliest stage of differentiation, prior to maturation to mDA neurons, we demonstrate the formation of small β-sheet-rich oligomeric aggregates, in SNCA-mutant cultures. Aggregation persists and progresses, ultimately resulting in the accumulation of phosphorylated α-synuclein aggregates. Impaired intracellular calcium signalling, increased basal calcium, and impairments in mitochondrial calcium handling occurred early at day 34–41 post differentiation. Once midbrain identity fully developed, at day 48–62 post differentiation, SNCA-mutant neurons exhibited mitochondrial dysfunction, oxidative stress, lysosomal swelling and increased autophagy. Ultimately these multiple cellular stresses lead to abnormal excitability, altered neuronal activity, and cell death. Our differentiation paradigm generates an efficient model for studying disease mechanisms in PD and highlights that protein misfolding to generate intraneuronal oligomers is one of the earliest critical events driving disease in human neurons, rather than a late-stage hallmark of the disease

Serum and cerebrospinal fluid biomarker profiles in acute SARS-CoV-2-associated neurological syndromes.

Preliminary pathological and biomarker data suggest that SARS-CoV-2 infection can damage the nervous system. To understand what, where and how damage occurs, we collected serum and CSF from patients with COVID-19 and characterized neurological syndromes involving the PNS and CNS (n = 34). We measured biomarkers of neuronal damage and neuroinflammation, and compared these with non-neurological control groups, which included patients with (n = 94) and without (n = 24) COVID-19. We detected increased concentrations of neurofilament light, a dynamic biomarker of neuronal damage, in the CSF of those with CNS inflammation (encephalitis and acute disseminated encephalomyelitis) [14 800 pg/ml (400, 32 400)], compared to those with encephalopathy [1410 pg/ml (756, 1446)], peripheral syndromes (Guillain-Barré syndrome) [740 pg/ml (507, 881)] and controls [872 pg/ml (654, 1200)]. Serum neurofilament light levels were elevated across patients hospitalized with COVID-19, irrespective of neurological manifestations. There was not the usual close correlation between CSF and serum neurofilament light, suggesting serum neurofilament light elevation in the non-neurological patients may reflect peripheral nerve damage in response to severe illness. We did not find significantly elevated levels of serum neurofilament light in community cases of COVID-19 arguing against significant neurological damage. Glial fibrillary acidic protein, a marker of astrocytic activation, was not elevated in the CSF or serum of any group, suggesting astrocytic activation is not a major mediator of neuronal damage in COVID-19

The ongoing pursuit of neuroprotective therapies in Parkinson disease

Many agents developed for neuroprotective treatment of Parkinson disease (PD) have shown great promise in the laboratory, but none have translated to positive results in patients with PD. Potential neuroprotective drugs, such as ubiquinone, creatine and PYM50028, have failed to show any clinical benefits in recent high-profile clinical trials. This 'failure to translate' is likely to be related primarily to our incomplete understanding of the pathogenic mechanisms underlying PD, and excessive reliance on data from toxin-based animal models to judge which agents should be selected for clinical trials. Restricted resources inevitably mean that difficult compromises must be made in terms of trial design, and reliable estimation of efficacy is further hampered by the absence of validated biomarkers of disease progression. Drug development in PD dementia has been mostly unsuccessful; however, emerging biochemical, genetic and pathological evidence suggests a link between tau and amyloid-β deposition and cognitive decline in PD, potentially opening up new possibilities for therapeutic intervention. This Review discusses the most important 'druggable' disease mechanisms in PD, as well as the most-promising drugs that are being evaluated for their potential efficiency in treatment of motor and cognitive impairments in PD

Exenatide and the treatment of Parkinson's disease

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting approximately 1% of people over the age of 65. Despite the best available medical and surgical therapies, there are no known treatments that can slow or alter clinical disease progression or continued neurodegeneration, thus any novel intervention that could delay or slow progression of PD would provide much needed health and socio-economic benefits. The mechanisms underlying PD pathogenesis remain unclear but there is accumulating evidence to suggesting that Type 2 diabetes and PD, both age-related diseases, share similar dysfunctional pathways. Alterations in metabolism, inflammation and mitochondrial function occur in both diseases and growing evidence suggests these pathways may impact on various tissues’ ability to respond to insulin, leading to “insulin resistance” in peripheral tissues and neurones, promoting cell death pathways and leading ultimately to diabetes and neurodegeneration respectively. The common pathways between these two conditions has led some to speculate that “insulin sensitizing” treatments for patients with Type 2 diabetes may be useful as novel treatments in neurodegenerative diseases such as PD. Exenatide is a Glucagon-like peptide-1 (GLP-1) agonist, currently licensed for the treatment for Type 2 diabetes and acts on insulin signalling pathways. It has demonstrated neuroprotective effects across a variety of animal toxin models of PD and also in a proof of concept, open label trial of mid-stage PD patients, conferring persistent motor and cognitive benefits sustained past the period of drug exposure. In this thesis I will present the first data from a fully randomised, placebo-controlled trial of exenatide in PD patients and explore the effects of exenatide on disease progression in PD and its influence on motor and non-motor symptoms. Furthermore, I will explore the pharmacokinetics of exenatide in this population and, utilising data from serum extracellular vesicles (exosomes) enriched for neuronal origin from the Exenatide-PD trial participants, demonstrate that peripherally administered exenatide can engage with neuronal insulin signalling pathways, providing some mechanistic context for the trial results. Taken together, data presented here will provide compelling evidence supporting the links between insulin signalling and PD and that the role of exenatide and other GLP-1 agonists as a novel treatment for PD should be explored further