Disease Progression in Multiple System Atrophy-Novel Modeling Framework and Predictive Factors.

BACKGROUND: Multiple system atrophy (MSA) is a rare and aggressive neurodegenerative disease that typically leads to death 6 to 10 years after symptom onset. The rapid evolution renders it crucial to understand the general disease progression and factors affecting the disease course. OBJECTIVES: The aims of this study were to develop a novel disease-progression model to estimate a population-level MSA progression trajectory and predict patient-specific continuous disease stages describing the degree of progress into the disease. METHODS: The disease-progression model estimated a population-level progression trajectory of subscales of the Unified MSA Rating Scale and the Unified Parkinson's Disease Rating Scale using patients in the European MSA natural history study. The predicted disease continuum was validated via multiple analyses based on reported anchor points, and the effect of MSA subtype on the rate of disease progression was evaluated. RESULTS: The predicted disease continuum spanned approximately 6 years, with an estimated average duration of 51 months for a patient with global disability score 0 to reach the highest level of 4. The predicted continuous disease stages were shown to be correlated with time of symptom onset and predictive of survival time. MSA motor subtype was found to significantly affect disease progression, with MSA-parkinsonian (MSA-P) type patients having an accelerated rate of progression. CONCLUSIONS: The proposed modeling framework introduces a new method of analyzing and interpreting the progression of MSA. It can provide new insights and opportunities for investigating covariate effects on the rate of progression and provide well-founded predictions of patient-level future progressions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Personalized prediction of progression in pre-dementia patients based on individual biomarker profile: A development and validation study.

INTRODUCTION: The prognosis of patients at the pre-dementia stage is difficult to define. The aim of this study is to develop and validate a biomarker-based continuous model for predicting the individual cognitive level at any future moment. In addition to personalized prognosis, such a model could reduce trial sample size requirements by allowing inclusion of a homogenous patient population. METHODS: Disease-progression modeling of longitudinal cognitive scores of pre-dementia patients (baseline Clinical Dementia Rating ≤ 0.5) was used to derive a biomarker profile that was predictive of patient's cognitive progression along the dementia continuum. The biomarker profile model was developed and validated in the MEMENTO cohort and externally validated in the Alzheimer's Disease Neuroimaging Initiative. RESULTS: Of nine candidate biomarkers in the development analysis, three cerebrospinal fluid and two magnetic resonance imaging measures were selected to form the final biomarker profile. The model-based prognosis of individual future cognitive deficit was shown to significantly improve when incorporating biomarker information on top of cognition and demographic data. In trial power calculations, adjusting the primary analysis for the baseline biomarker profile reduced sample size requirements by ≈10%. Compared to conventional cognitive cut-offs, inclusion criteria based on biomarker-profile cut-offs resulted in up to 28% reduced sample size requirements due to increased homogeneity in progression patterns. DISCUSSION: The biomarker profile allows prediction of personalized trajectories of future cognitive progression. This enables accurate personalized prognosis in clinical care and better selection of patient populations for clinical trials. A web-based application for prediction of patients' future cognitive progression is available online.

Simultaneous modeling of Alzheimer's disease progression via multiple cognitive scales.

Analyzing the progression of Alzheimer's disease (AD) is challenging due to lacking sensitivity in currently available measures. AD stages are typically defined based on cognitive cut-offs, but this results in heterogeneous patient groups. More accurate modeling of the continuous progression of the disease would enable more accurate patient prognosis. To address these issues, we propose a new multivariate continuous-time disease progression (MCDP) model. The model is formulated as a nonlinear mixed-effects model that aligns patients based on their predicted disease progression along a continuous latent disease timeline. The model is evaluated using long-term follow-up data from 2152 participants in the Alzheimer's Disease Neuroimaging Initiative. The MCDP model was used to simultaneously model three cognitive scales; the Alzheimer's Disease Assessment Scale-cognitive subscale, the Mini-Mental State Examination, and the Clinical Dementia Rating scale-sum of boxes. Compared with univariate modeling and previously proposed multivariate disease progression models, the MCDP model showed superior ability to predict future patient trajectories. Finally, based on the multivariate disease timeline estimated using the MCDP model, the sensitivity of the individual items of the cognitive scales along the different stages of disease was analyzed. The analysis showed that delayed memory recall items had the highest sensitivity in the early stages of disease, whereas language and attention items were sensitive later in disease.

Utility of plasma neurofilament light and total tau for clinical trials in Alzheimer's disease.

INTRODUCTION: Several blood-based biomarkers are associated with neuronal injury, but their utility in interventional clinical trials is unclear. This study retrospectively evaluated the utility of plasma neurofilament light (NfL) and total tau (t-tau) in an 18-month trial in mild Alzheimer's disease (AD). METHODS: Correlation and conditional independence analyses and Gaussian graphical models were used to investigate cross-sectional and longitudinal relations between NfL, t-tau, and clinical scales. RESULTS: NfL had a stronger association than t-tau with clinical scales; t-tau did not hold additional information to that given by NfL (P > 0.05 at all time points). NfL held independent information about shorter-term (3- to 6-month) progression beyond patient age and clinical scores. However, no meaningful gain in power was found when adjusting a longitudinal analysis of cognitive scores for baseline NfL. DISCUSSION: Plasma NfL is superior to t-tau in mild AD. The ability of NfL to detect changes before clinical manifestations makes it a promising biomarker of drug response in trials of disease-modifying drugs.

The influence of inflammation and hematocrit on clot strength in canine thromboelastographic hypercoagulability.

OBJECTIVE: To investigate parameters causing canine thromboelastographic hypercoagulability and to investigate whether thromboelastography (TEG) with Cytochalasin D (Cyt D) added is related to parameters of platelet activity. DESIGN: Prospective observational study on hemostatic and inflammatory parameters. Data were collected between November 2012 and July 2013. SETTING: University teaching hospital. ANIMALS: Twenty-eight dogs suffering from diseases predisposing to thrombosis and 19 clinically healthy dogs. Diseased dogs were enrolled if they fulfilled inclusion criteria regarding age, size, informed client consent, and obtained a diagnosis of a disease that has been associated with thrombosis or hypercoagulability. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Parameters of coagulation and anticoagulation, fibrinolysis, and antifibrinolysis, platelet activity, inflammation, platelet count, and hematocrit were measured using CBC, TEG, platelet aggregation on multiplate, platelet activity on flow cytometry, and hemostatic and inflammatory markers on plasma and serum analyses. ANOVA and multilinear regression analyses indicated that especially hematocrit and the inflammatory parameters C-reactive protein and interleukin-8 showed best association with overall clot strength in diseased dogs with hypercoagulable TEG tracings. Ratios presumed to reflect platelet contribution to the TEG tracing obtained in TEG analyses with Cyt D were related especially with hematocrit and P-selectin expression of platelets measured after γ-Thrombin activation on flow cytometry. CONCLUSION: Overall clot strength in TEG analyses of the hypercoagulable dogs included in the present study appears to be primarily associated with inflammation as well as hematocrit. Furthermore, the ratio between standard TEG analyses and TEG analyses with Cyt D may reflect some degree of platelet activity.