LMTM is being developed as a treatment for AD based on inhibition of tau aggregation.
To examine the efficacy of LMTM as monotherapy in non-randomized cohort analyses as modified primary outcomes in ...an 18-month Phase III trial in mild AD.
Mild AD patients (n = 800) were randomly assigned to 100 mg twice a day or 4 mg twice a day. Prior to unblinding, the Statistical Analysis Plan was revised to compare the 100 mg twice a day as monotherapy subgroup (n = 79) versus 4 mg twice a day as randomized (n = 396), and 4 mg twice a day as monotherapy (n = 76) versus 4 mg twice a day as add-on therapy (n = 297), with strong control of family-wise type I error.
The revised analyses were statistically significant at the required threshold of p < 0.025 in both comparisons for change in ADAS-cog, ADCS-ADL, MRI atrophy, and glucose uptake. The brain atrophy rate was initially typical of mild AD in both add-on and monotherapy groups, but after 9 months of treatment, the rate in monotherapy patients declined significantly to that reported for normal elderly controls. Differences in severity or diagnosis at baseline between monotherapy and add-on patients did not account for significant differences in favor of monotherapy.
The results are consistent with earlier studies in supporting the hypothesis that LMTM might be effective as monotherapy and that 4 mg twice a day may serve as well as higher doses. A further suitably randomized trial is required to test this hypothesis.
Abstract Objective To determine the frequency and stability over time of the subgroup characterization of the tremor dominant (TD) versus postural instability gait disorder dominant (PIGD) ...Parkinson's disease (PD) in de novo patients. Background There is a substantial body of literature on the clinical sub classification of PD into TD versus PIGD subtype. However, there are limited data on the stability of this classification especially in early disease. Methods Parkinson's Progression Markers Initiative (PPMI) is a longitudinal case control study of de novo, untreated PD participants at enrollment. Participants undergo a number of assessments including the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). TD versus PIGD subtype was defined based on the previously published formula. We report one-year analysis data. Results 320 of 423 PD recruited subjects had data on subtype classification at year 1 and were included in the analysis. 228 (71%) were classified as TD, 56 (18%) as PIGD and 36 (11%) as indeterminate at baseline. At 12 months, 39% PIGD and 18% TD shifted subtypes: 29% PIGD shifted to TD and 11% to Indeterminate; 10% TD shifted to PIGD and 8% to Indeterminate. The classification was not affected by the dopaminergic treatment (p = 0.59). Conclusions TD versus PIGD subtype classification has substantial variability over first year in PD de novo cohort specifically for PIGD subtype. Dopaminergic therapy does not impact the change of the PD subtype. This instability has to be taken into consideration specifically when establishing correlations with the biomarkers and for long term prognostication.
Summary Background Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to ...create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. Methods We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). Findings In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900–0·946) with high sensitivity (0·834, 95% CI 0·711–0·883) and specificity (0·903, 95% CI 0·824–0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867–0·921) in the PDBP cohort, 0·998 (0·992–1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896–0·962) in LABS-PD, and 0·939 (0·891–0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003). Interpretation Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions. Funding National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.
The novel tau-PET tracer 18FPI-2620 detects the 3/4-repeat-(R)-tauopathy Alzheimer’s disease (AD) and the 4R-tauopathies corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP). We ...determined whether 18FPI-2620 binding characteristics deriving from non-invasive reference tissue modelling differentiate 3/4R- and 4R-tauopathies. Ten patients with a 3/4R tauopathy (AD continuum) and 29 patients with a 4R tauopathy (CBS, PSP) were evaluated. 18FPI-2620 PET scans were acquired 0-60 min p.i. and the distribution volume ratio (DVR) was calculated. 18FPI-2620-positive clusters (DVR ≥ 2.5 SD vs. 11 healthy controls) were evaluated by non-invasive kinetic modelling. R1 (delivery), k2 & k2a (efflux), DVR, 30-60 min standardized-uptake-value-ratios (SUVR30-60) and the linear slope of post-perfusion phase SUVR (9-60 min p.i.) were compared between 3/4R- and 4R-tauopathies. Cortical clusters of 4R-tau cases indicated higher delivery (R1SRTM: 0.92 ± 0.21 vs. 0.83 ± 0.10, p = 0.0007), higher efflux (k2SRTM: 0.17/min ±0.21/min vs. 0.06/min ± 0.07/min, p < 0.0001), lower DVR (1.1 ± 0.1 vs. 1.4 ± 0.2, p < 0.0001), lower SUVR30-60 (1.3 ± 0.2 vs. 1.8 ± 0.3, p < 0.0001) and flatter slopes of the post-perfusion phase (slope9-60: 0.006/min ± 0.007/min vs. 0.016/min ± 0.008/min, p < 0.0001) when compared to 3/4R-tau cases. 18FPI-2620 binding characteristics in cortical regions differentiate 3/4R- and 4R-tauopathies. Higher tracer clearance indicates less stable binding in 4R tauopathies when compared to 3/4R-tauopathies.
Objective
To identify plasma‐based biomarkers for Parkinson disease (PD) risk.
Methods
In a discovery cohort of 152 PD patients, plasma levels of 96 proteins were measured by multiplex immunoassay; ...proteins associated with age at PD onset were identified by linear regression. Findings from discovery screening were then assessed in a second cohort of 187 PD patients, using a different technique. Finally, in a third cohort of at‐risk, asymptomatic individuals enrolled in the Parkinson's Associated Risk Study (PARS, n = 134), plasma levels of the top candidate biomarker were measured, and dopamine transporter (DAT) imaging was performed, to evaluate the association of plasma protein levels with dopaminergic system integrity.
Results
One of the best candidate protein biomarkers to emerge from discovery screening was apolipoprotein A1 (ApoA1; p = 0.001). Low levels of ApoA1 correlated with earlier PD onset, with a 26% decrease in risk of developing PD associated with each tertile increase in ApoA1 (Cox proportional hazards, p < 0.001, hazard ratio = 0.742). The association between plasma ApoA1 levels and age at PD onset was replicated in an independent cohort of PD patients (p < 0.001). Finally, in the PARS cohort of high‐risk, asymptomatic subjects, lower plasma levels of ApoA1 were associated with greater putaminal DAT deficit (p = 0.037).
Interpretation
Lower ApoA1 levels correlate with dopaminergic system vulnerability in symptomatic PD patients and in asymptomatic individuals with physiological reductions in dopamine transporter density consistent with prodromal PD. Plasma ApoA1 may be a new biomarker for PD risk. ANN NEUROL 2013;74:119–127
The pathogenesis and clinical heterogeneity of Parkinson's disease (PD) have been evaluated from molecular, pathophysiological, and clinical perspectives. High-throughput proteomic analysis of ...cerebrospinal fluid (CSF) opened new opportunities for scrutinizing this heterogeneity. To date, this is the most comprehensive CSF-based proteomics profiling study in PD with 569 patients (350 idiopathic patients, 65 GBA + mutation carriers and 154 LRRK2 + mutation carriers), 534 controls, and 4135 proteins analyzed. Combining CSF aptamer-based proteomics with genetics we determined protein quantitative trait loci (pQTLs). Analyses of pQTLs together with summary statistics from the largest PD genome wide association study (GWAS) identified 68 potential causal proteins by Mendelian randomization. The top causal protein, GPNMB, was previously reported to be upregulated in the substantia nigra of PD patients. We also compared the CSF proteomes of patients and controls. Proteome differences between GBA + patients and unaffected GBA + controls suggest degeneration of dopaminergic neurons, altered dopamine metabolism and increased brain inflammation. In the LRRK2 + subcohort we found dysregulated lysosomal degradation, altered alpha-synuclein processing, and neurotransmission. Proteome differences between idiopathic patients and controls suggest increased neuroinflammation, mitochondrial dysfunction/oxidative stress, altered iron metabolism and potential neuroprotection mediated by vasoactive substances. Finally, we used proteomic data to stratify idiopathic patients into "endotypes". The identified endotypes show differences in cognitive and motor disease progression based on previously reported protein-based risk scores.Our findings not only contribute to the identification of new therapeutic targets but also to shape personalized medicine in CNS neurodegeneration.
The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor ...features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care.
18F-AV-1451 is currently the most widely used of several experimental tau PET tracers. The objective of this study was to evaluate 18F-AV-1451 binding with full kinetic analysis using a ...metabolite-corrected arterial input function and to compare parameters derived from kinetic analysis with SUV ratio (SUVR) calculated over different imaging time intervals. Methods:18F-AV-1451 PET brain imaging was completed in 16 subjects: 4 young healthy volunteers (YHV), 4 aged healthy volunteers (AHV), and 8 Alzheimer disease (AD) subjects. Subjects were imaged for 3.5 h, with arterial blood samples obtained throughout. PET data were analyzed using plasma and reference tissue-based methods to estimate the distribution volume, binding potential (BPND), and SUVR. BPND and SUVR were calculated using the cerebellar cortex as a reference region and were compared across the different methods and across the 3 groups (YHV, AHV, and AD). Results: AD demonstrated increased 18F-AV-1451 retention compared with YHV and AHV based on both invasive and noninvasive analyses in cortical regions in which paired helical filament tau accumulation is expected in AD. A correlation of R2 > 0.93 was found between BPND (130 min) and SUVR-1 at all time intervals. Cortical SUVR curves reached a relative plateau around 1.0-1.2 for YHV and AHV by approximately 50 min, but increased in AD by up to approximately 20% at 110-130 min and approximately 30% at 160-180 min relative to 80-100 min. Distribution volume (130 min) was lower by 30%-35% in the YHV than AHV. Conclusion: Our data suggest that although 18F-AV-1451 SUVR curves do not reach a plateau and are still increasing in AD, an SUVR calculated over an imaging window of 80-100 min (as currently used in clinical studies) provides estimates of paired helical filament tau burden in good correlation with BPND, whereas SUVR sensitivity to regional cerebral blood changes needs further investigation.
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