The temporal event-based model: Learning event timelines in progressive diseases.

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80% with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.

Standardizing the CAP Score in Huntington's Disease by Predicting Age-at-Onset.

BACKGROUND: Huntington's disease (HD) is an autosomal dominant, neurological disease caused by an expanded CAG repeat near the N-terminus of the huntingtin (HTT) gene. A leading theory concerning the etiology of HD is that both onset and progression are driven by cumulative exposure to the effects of mutant (or CAG expanded) huntingtin (mHTT). The CAG-Age-Product (CAP) score (i.e., the product of excess CAG length and age) is a commonly used measure of this cumulative exposure. CAP score has been widely used as a predictor of a variety of disease state variables in HD. The utility of the CAP score has been somewhat diminished, however, by a lack of agreement on its precise definition. The most commonly used forms of the CAP score are highly correlated so that, for purposes of prediction, it makes little difference which is used. However, reported values of CAP scores, based on commonly used definitions, differ substantially in magnitude when applied to the same data. This complicates the process of inter-study comparison. OBJECTIVE: In this paper, we propose a standardized definition for the CAP score which will resolve this difficulty. Our standardization is chosen so that CAP = 100 at the expected age of diagnosis. METHODS: Statistical methods include novel survival analysis methodology applied to the 13 disease landmarks taken from the Enroll-HD database (PDS 5) and comparisons with the existing, gold standard, onset model. RESULTS: Useful by-products of our work include up-to-date, age-at-onset (AO) results and a refined AO model suitable for use in other contexts, a discussion of several useful properties of the CAP score that have not previously been noted in the literature and the introduction of the concept of a toxicity onset model. CONCLUSION: We suggest that taking L = 30 and K = 6.49 provides a useful standardization of the CAP score, suitable for use in the routine modeling of clinical data in HD.

Estimating the causal effects of modifiable, non-genetic factors on Huntington disease progression using propensity score weighting.

INTRODUCTION: Despite being genetically inherited, it is unclear how non-genetic factors (e.g., substance use, employment) might contribute to the progression and severity of Huntington's disease (HD). METHODS: We used propensity score (PS) weighting in a large (n = 2914) longitudinal dataset (Enroll-HD) to examine the impact of education, employment status, and use of tobacco, alcohol, and recreational and therapeutic drugs on HD progression. Each factor was investigated in isolation while controlling for 19 other factors to ensure that groups were balanced at baseline on potential confounders using PS weights. Outcomes were compared several years later using doubly robust models. RESULTS: Our results highlighted cases where modifiable (non-genetic) factors - namely light and moderate alcohol use and employment - would have been associated with HD progression in models that did not use PS weights to control for baseline imbalances. These associations did not hold once we applied PS weights to balance baseline groups. We also found potential evidence of a protective effect of substance use (primarily marijuana use), and that those who needed antidepressant treatment were likely to progress faster than non-users. CONCLUSIONS: Our study is the first to examine the effect of non-genetic factors on HD using a novel application of PS weighting. We show that previously-reported associated factors - including light and moderate alcohol use - are reduced and no longer significantly linked to HD progression after PS weighting. This indicates the potential value of PS weighting in examining non-genetic factors contributing to HD as well as in addressing the known biases that occur with observational data.

Huntington's Disease Progression: A Population Modeling Approach to Characterization Using Clinical Rating Scales.

Development of effective therapeutics that slow Huntington's disease progression is a research priority that requires an understanding of natural disease progression. We applied a population-modeling approach to describe the progression of 2 routinely used rating scales - the total motor score and the total functional capacity score. Models were fitted to data from research participants aged ≥ 18 years with Huntington's disease stage I or II at study entry (total functional capacity score ≥ 7), from a controlled clinical trial (CARE-HD) and 2 observational studies (COHORT and Registry). A logistic model without shape factors was selected as the base model based on placebo data from CARE-HD and validated using data from the CARE-HD active-treatment arms. Albeit with a smaller progression rate constant than was found in CARE-HD, the proposed models provided reasonable predictions for both rating scales in the pooled data from COHORT and Registry and were considered suitable for use in clinical trial simulations. Results also showed that disease burden score (a product of age and expanded CAG length) is a significant covariate on both the progression rate constant and the baseline score in the total motor score model. These findings suggest that total motor score and total functional capacity progress fastest near their half-maximal score, implying that the efficiency of clinical trials evaluating disease-modifying therapeutics for Huntington's disease could be enhanced by enrolling patients with faster disease progression or evaluating treatment effect near their half-maximal score, provided that the evaluated therapy is expected to be efficacious at this disease stage.

Resting-state connectivity stratifies premanifest Huntington's disease by longitudinal cognitive decline rate.

Patient stratification is critical for the sensitivity of clinical trials at early stages of neurodegenerative disorders. In Huntington's disease (HD), genetic tests make cognitive, motor and brain imaging measurements possible before symptom manifestation (pre-HD). We evaluated pre-HD stratification models based on single visit resting-state functional MRI (rs-fMRI) data that assess observed longitudinal motor and cognitive change rates from the multisite Track-On HD cohort (74 pre-HD, 79 control participants). We computed longitudinal performance change on 10 tasks (including visits from the preceding TRACK-HD study when available), as well as functional connectivity density (FCD) maps in single rs-fMRI visits, which showed high test-retest reliability. We assigned pre-HD subjects to subgroups of fast, intermediate, and slow change along single tasks or combinations of them, correcting for expectations based on aging; and trained FCD-based classifiers to distinguish fast- from slow-progressing individuals. For robustness, models were validated across imaging sites. Stratification models distinguished fast- from slow-changing participants and provided continuous assessments of decline applicable to the whole pre-HD population, relying on previously-neglected white matter functional signals. These results suggest novel correlates of early deterioration and a robust stratification strategy where a single MRI measurement provides an estimate of multiple ongoing longitudinal changes.

PET Molecular Imaging of Phosphodiesterase 10A: An Early Biomarker of Huntington's Disease Progression.

BACKGROUND: Changes in phosphodiesterase 10A enzyme levels may be a suitable biomarker of disease progression in Huntington's disease. OBJECTIVES: To evaluate phosphodiesterase 10A PET imaging as a biomarker of HD progression using the radioligand, [18 F]MNI-659. METHODS: The cross-sectional study (NCT02061722) included 45 Huntington's disease gene-expansion carriers stratified into four disease stages (early and late premanifest and Huntington's disease stages 1 and 2) and 45 age- and sex-matched healthy controls. The primary analysis compared striatal and pallidal phosphodiesterase 10A availability between Huntington's disease gene-expansion carriers and healthy controls as assessed by [18 F]MNI-659 binding. We assessed changes in phosphodiesterase 10A expression using several PET methodologies and compared with previously proposed measures of Huntington's disease progression (PET imaging of D2/3 receptors and anatomical volume loss on MRI). The longitudinal follow-up study (NCT02956148) continued evaluation of phosphodiesterase 10A availability in 35 Huntington's disease gene-expansion carriers at a mean of 18 months from baseline of the cross-sectional study. RESULTS: Primary analyses revealed that phosphodiesterase 10A availability in caudate, putamen, and globus pallidus was significantly lower in Huntington's disease gene-expansion carriers versus healthy controls across all stages. Striatal and pallidal phosphodiesterase 10A availability progressively declined in the premanifest stages and appeared to plateau between stages 1 and 2. The percentage decline of phosphodiesterase 10A availability measured cross-sectionally between Huntington's disease gene-expansion carriers and healthy controls was greater than that demonstrated by D2/3 receptor availability or volumetric changes. Annualized rates of phosphodiesterase 10A change showed a statistically significant decline between the cross-sectional study and follow-up. CONCLUSIONS: [18 F]MNI-659 PET imaging is a biologically plausible biomarker of Huntington's disease progression that is more sensitive than the dopamine-receptor and volumetric methods currently used. © 2020 International Parkinson and Movement Disorder Society.

Baseline multimodal information predicts future motor impairment in premanifest Huntington's disease.

In Huntington's disease (HD), accurate estimates of expected future motor impairments are key for clinical trials. Individual prognosis is only partially explained by genetics. However, studies so far have focused on predicting the time to clinical diagnosis based on fixed impairment levels, as opposed to predicting impairment in time windows comparable to the duration of a clinical trial. Here we evaluate an approach to both detect atrophy patterns associated with early degeneration and provide a prognosis of motor impairment within 3 years, using data from the TRACK-HD study on 80 premanifest HD (pre-HD) individuals and 85 age- and sex-matched healthy controls. We integrate anatomical MRI information from gray matter concentrations (estimated via voxel-based morphometry) together with baseline data from demographic, genetic and motor domains to distinguish individuals at high risk of developing pronounced future motor impairment from those at low risk. We evaluate the ability of models to distinguish between these two groups solely using baseline imaging data, as well as in combination with longitudinal imaging or non-imaging data. Our models show improved performance for motor prognosis through the incorporation of imaging features to non-imaging data, reaching 88% cross-validated accuracy when using baseline non-longitudinal information, and detect informative correlates in the caudate nucleus and the thalamus both for motor prognosis and early atrophy detection. These results show the plausibility of using baseline imaging and basic demographic/genetic measures for early detection of individuals at high risk of severe future motor impairment in relatively short timeframes.

Huntington disease: natural history, biomarkers and prospects for therapeutics.

Huntington disease (HD) can be seen as a model neurodegenerative disorder, in that it is caused by a single genetic mutation and is amenable to predictive genetic testing, with estimation of years to predicted onset, enabling the entire range of disease natural history to be studied. Structural neuroimaging biomarkers show that progressive regional brain atrophy begins many years before the emergence of diagnosable signs and symptoms of HD, and continues steadily during the symptomatic or 'manifest' period. The continued development of functional, neurochemical and other biomarkers raises hopes that these biomarkers might be useful for future trials of disease-modifying therapeutics to delay the onset and slow the progression of HD. Such advances could herald a new era of personalized preventive therapeutics. We describe the natural history of HD, including the timing of emergence of motor, cognitive and emotional impairments, and the techniques that are used to assess these features. Building on this information, we review recent progress in the development of biomarkers for HD, and potential future roles of these biomarkers in clinical trials.

Interventional differences among Huntington's disease patients by disease progression in commercial and medicaid populations.

BACKGROUND: Huntington's disease (HD) is a progressive neurodegenerative disease that spans distinct disease stages over 15-20 years. Various interventions are available which may allow patients to live outside of a nursing home for a longer time. However, little is known about use of these interventions by disease stage and by insurance type. OBJECTIVE: We compared use of interventions among early, middle and late stages of HD in commercial (C) and Medicaid (M) health insurance populations. METHODS: HD patients (ICD-9-CM 333.4) were identified from Thomson Reuters' MarketScan C and M database (2002-2009) and hierarchically grouped into disease stages based upon the presence of defining clinical markers. RESULTS: A total of 1,272 HD patients (752/520 C/M) were identified. While stage distribution was nearly uniform in the C database - 34.0/35.5/34.0% (early/middle/late stage) - in the M population the majority were late stage (74.0%). Overall mean age was similar between C and M populations. Among late-stage patients, more M patients resided in a nursing home (M:73.8% v. C:40.6%) and received hospice care (M:18.4% v. C:11.3%). Physical therapy (PT) and home assistance were the most frequent interventions used by middle-stage patients, however more C patients received PT (C:64.0% v. M:37.1%) while more M patients received home assistance (M:75.3% v. C:53.2%). Among late-stage patients, PT was also higher in the C population (56.3% v. 48.3%). More M patients had assistive devices at home in both middle (M:25.8% v. C:9.7%) and late stages (M:35.6% v.C:23.4%). CONCLUSIONS: Apparent interventional differences emerged which varied by disease stage and insurance type.

The direct medical costs of Huntington's disease by stage. A retrospective commercial and Medicaid claims data analysis.

OBJECTIVE: This study quantified the direct healthcare costs and major cost drivers among patients with Huntington's disease (HD), by disease stage in commercial and Medicaid databases. METHODS: This retrospective database analysis used healthcare utilization/cost data for HD patients (ICD-9-CM 333.4) from Thomson Reuters' MarketScan Commercial and Medicaid 2002-2009 databases. Patients were classified by disease stage (Early/Middle/Late) by a hierarchical assessment of markers of disease severity, confirmed by literature review and key opinion leader input. Costs were measured over the follow-up time of each patient with total costs per patient per stage annualized using a patient-year cost approach. RESULTS: Among 1272 HD patients, the mean age was similar in commercial (752 patients) and Medicaid (520 patients) populations (48.5 years (SD = 13.3) and 49.3 years (SD = 17.2), respectively). Commercial patients were evenly distributed by stage (30.5%/35.5%/34.0%; Early/Middle/Late). However, most (74.0%) Medicaid HD patients were classified as Late stage. The mean total annualized cost per patient increased by stage (commercial: $4947 (SD = $6040)-$22,582 (SD = $39,028); Medicaid: $3257 (SD = $5670)-$37,495 (SD = $27,111). Outpatient costs were the primary healthcare cost component. The vast majority (73.8%) of Medicaid Late stage patients received nursing home care and the majority (54.6%) of Medicaid Late stage costs were associated with nursing home care. In comparison, only 40.6% of commercial Late stage patients received nursing home care, which contributed to only 4.6% of commercial Late stage costs. CONCLUSIONS: The annual direct economic burden of HD is substantial and increased with disease progression. More late stage Medicaid HD patients were in nursing homes and for a longer time than their commercial counterparts, reflected by their higher costs (suggesting greater disease severity). Key limitations include the classification of patients into a single stage, as well as a lack of visibility into full long-term care/nursing home-related costs for commercial patients.

Assessment of Day-to-Day Functioning in Prodromal and Early Huntington Disease.

The Functional Rating Scale Taskforce for pre-Huntington Disease (FuRST-pHD) is a multinational, multidisciplinary initiative with the goal of developing a data-driven, comprehensive, psychometrically sound, rating scale for assessing symptoms and functional ability in prodromal and early Huntington disease (HD) gene expansion carriers. The process involves input from numerous sources to identify relevant symptom domains, including HD individuals, caregivers, and experts from a variety of fields, as well as knowledge gained from the analysis of data from ongoing large-scale studies in HD using existing clinical scales. This is an iterative process in which an ongoing series of field tests in prodromal (prHD) and early HD individuals provides the team with data on which to make decisions regarding which questions should undergo further development or testing and which should be excluded. We report here the development and assessment of the first iteration of interview questions aimed to assess functional impact in day-to-day activities in prHD and early HD individuals.

Indexing disease progression at study entry with individuals at-risk for Huntington disease.

The identification of clinical and biological markers of disease in persons at risk for Huntington disease (HD) has increased in efforts to better quantify and characterize the epoch of prodrome prior to clinical diagnosis. Such efforts are critical in the design and implementation of clinical trials for HD so that interventions can occur at a time most likely to increase neuronal survival and maximize daily functioning. A prime consideration in the examination of prodromal individuals is their proximity to diagnosis. It is necessary to quantify proximity so that individual differences in key marker variables can be properly interpreted. We take a data-driven approach to develop an index that can be viewed as a proxy for time to HD diagnosis known as the CAG-Age Product Scaled or CAP(S) . CAP(S) is an observed utility variable computed for all genetically at-risk individuals based on age at study entry and CAG repeat length. Results of a longitudinal receiver operating characteristic (ROC) analysis showed that CAP(S) had a relatively strong ability to predict individuals who became diagnosed, especially in the first 2 years. Bootstrap validation provided evidence that CAP(S) computed on a new sample from the same population could have similar discriminatory power. Cutoffs for the empirical CAP(S) distribution can be used to create a classification for mutation-positive individuals (Low-Med-High), which is, useful for comparison with the naturally occurring mutation-negative Control group. The classification is an improvement over the one currently in use as it is based on observed data rather than model-based estimated values. © 2011 Wiley-Liss, Inc.

Assessment of motor symptoms and functional impact in prodromal and early huntington disease.

The Functional Rating Scale Taskforce for pre-Huntington Disease (FuRST-pHD) is a multinational, multidisciplinary initiative with the goal of developing a data-driven, comprehensive, psychometrically sound, rating scale for assessing symptoms and functional ability in prodromal and early Huntington disease (HD) gene expansion carriers. The process involves input from numerous sources to identify relevant symptom domains, including HD individuals, caregivers, and experts from a variety of fields, as well as knowledge gained from the analysis of data from ongoing large-scale studies in HD using existing clinical scales. This is an iterative process in which an ongoing series of field tests in prodromal (prHD) and early HD individuals provides the team with data on which to make decisions regarding which questions should undergo further development or testing and which should be excluded. We report here the development and assessment of the first iteration of interview questions aimed to assess functional impact of motor manifestations in prHD and early HD individuals.

Adaptive regression modeling of biomarkers of potential harm in a population of U.S. adult cigarette smokers and nonsmokers.

BACKGROUND: This article describes the data mining analysis of a clinical exposure study of 3585 adult smokers and 1077 nonsmokers. The analysis focused on developing models for four biomarkers of potential harm (BOPH): white blood cell count (WBC), 24 h urine 8-epi-prostaglandin F2alpha (EPI8), 24 h urine 11-dehydro-thromboxane B2 (DEH11), and high-density lipoprotein cholesterol (HDL). METHODS: Random Forest was used for initial variable selection and Multivariate Adaptive Regression Spline was used for developing the final statistical models RESULTS: The analysis resulted in the generation of models that predict each of the BOPH as function of selected variables from the smokers and nonsmokers. The statistically significant variables in the models were: platelet count, hemoglobin, C-reactive protein, triglycerides, race and biomarkers of exposure to cigarette smoke for WBC (R-squared = 0.29); creatinine clearance, liver enzymes, weight, vitamin use and biomarkers of exposure for EPI8 (R-squared = 0.41); creatinine clearance, urine creatinine excretion, liver enzymes, use of Non-steroidal antiinflammatory drugs, vitamins and biomarkers of exposure for DEH11 (R-squared = 0.29); and triglycerides, weight, age, sex, alcohol consumption and biomarkers of exposure for HDL (R-squared = 0.39). CONCLUSIONS: Levels of WBC, EPI8, DEH11 and HDL were statistically associated with biomarkers of exposure to cigarette smoking and demographics and life style factors. All of the predictors together explain 29%-41% of the variability in the BOPH.