Survival End Points for Huntington Disease Trials Prior to a Motor Diagnosis

Prodromal Cognitive Changes as a Prognostic Indicator of Forthcoming Huntington's Disease Severity: A Retrospective Longitudinal Study

BACKGROUND

Cognitive changes in Huntington's disease (HD) precede motor manifestations. ENROLL-HD platform includes four cognitive measures of information processing speed (IPS). Our group is eager to seek clinical markers in the life stage that is as close as possible to the age of onset (ie, the so called prodromal HD phase) because this is the best time for therapeutic interventions.

OBJECTIVES

Our study aimed to test whether cognitive scores in prodromal ENROLL-HD mutation carriers show the potential to predict the severity of motor and behavioral changes once HD became fully manifested.

METHODS

From the global ENROLL-HD cohort of 21,343 participants, we first selected a premanifest Cohort#1 (ie, subjects with Total Motor Score (TMS) <10 and Diagnostic Confidence Level (DCL) <4, N = 1.222). From this cohort, we then focused on a prodromal Cohort#2 of subjects who were ascertained to phenoconvert into manifest HD at follow-up visits (ie, subjects from 6 ≤ TMS≤9 and DCL <4 to TMS≥10 and DCL = 4, n = 206).

RESULTS

The main results of our study showed that low IPS before phenoconversion in Cohort#2 predicted the severity of motor and behavioral manifestations. By combining the four IPS cognitive measures (eg, the Categorical Verbal Fluency Test; Stroop Color Naming Test; Stroop Word Reading; Symbol Digit Modalities Test), we generated a Composite Cognition Score (CCS). The lower the CCS score the higher the TMS and the apathy scores in the same longitudinally followed-up patients after phenoconversion.

CONCLUSIONS

CCS might represent a clinical instrument to predict the prognosis of mutation carriers who are close to manifesting HD.

Applying the Huntington's Disease Integrated Staging System (HD-ISS) to Observational Studies

BACKGROUND

The Huntington's Disease Integrated Staging System (HD-ISS) has four stages that characterize disease progression. Classification is based on CAG length as a marker of Huntington's disease (Stage 0), striatum atrophy as a biomarker of pathogenesis (Stage 1), motor or cognitive deficits as HD signs and symptoms (Stage 2), and functional decline (Stage 3). One issue for implementation is the possibility that not all variables are measured in every study, and another issue is that the stages are broad and may benefit from progression subgrouping.

OBJECTIVE

Impute stages of the HD-ISS for observational studies in which missing data precludes direct stage classification, and then define progression subgroups within stages.

METHODS

A machine learning algorithm was used to impute stages. Agreement of the imputed stages with the observed stages was evaluated using graphical methods and propensity score matching. Subgroups were defined based on descriptive statistics and optimal cut-point analysis.

RESULTS

There was good overall agreement between the observed stages and the imputed stages, but the algorithm tended to over-assign Stage 0 and under-assign Stage 1 for individuals who were early in progression.

CONCLUSION

There is evidence that the imputed stages can be treated similarly to the observed stages for large-scale analyses. When imaging data are not available, imputation can be avoided by collapsing the first two stages using the categories of Stage≤1, Stage 2, and Stage 3. Progression subgroups defined within a stage can help to identify groups of more homogeneous individuals.

Geographic Barriers Drive Disparities in Specialty Center Access for Older Adults with Huntington's Disease

BACKGROUND

Huntington's Disease Society of America Centers of Excellence (HDSA COEs) are primary hubs for Huntington's disease (HD) research opportunities and accessing new treatments. Data on the extent to which HDSA COEs are accessible to individuals with HD, particularly those older or disabled, are lacking.

OBJECTIVE

To describe persons with HD in the U.S. Medicare program and characterize this population by proximity to an HDSA COE.

METHODS

We conducted a cross-sectional study of Medicare beneficiaries ages ≥65 with HD in 2017. We analyzed data on benefit entitlement, demographics, and comorbidities. QGis software and Google Maps Interface were employed to estimate the distance from each patient to the nearest HDSA COE, and the proportion of individuals residing within 100 miles of these COEs at the state level.

RESULTS

Among 9,056 Medicare beneficiaries with HD, 54.5% were female, 83.0% were white; 48.5% were ≥65 years, but 64.9% originally qualified for Medicare due to disability. Common comorbidities were dementia (32.4%) and depression (35.9%), and these were more common in HD vs. non-HD patients. Overall, 5,144 (57.1%) lived within 100 miles of a COE. Race/ethnicity, sex, age, and poverty markers were not associated with below-average proximity to HDSA COEs. The proportion of patients living within 100 miles of a center varied from < 10% (16 states) to > 90% (7 states). Most underserved states were in the Mountain and West Central divisions.

CONCLUSION

Older Medicare beneficiaries with HD are frequently disabled and have a distinct comorbidity profile. Geographical, rather than sociodemographic factors, define the HD population with limited access to HDSA COEs.

Cognitive and behavioral associated changes in manifest Huntington disease: A retrospective cross-sectional study

INTRODUCTION

Behavioral and cognitive changes can be observed across all Huntington disease (HD) stages. Our multicenter and retrospective study investigated the association between cognitive and behavioral scale scores in manifest HD, at three different yearly timepoints.

METHODS

We analyzed cognitive and behavioral domains by the Unified Huntington's Disease Rating Scale (UHDRS) and by the Problem Behaviors Assessment Short Form (PBA-s), at three different yearly times of life (t0 or baseline, t1 after one year, t2 after two years), in 97 patients with manifest HD (mean age 48.62 ± 13.1), from three ENROLL-HD Centers. In order to test the disease progression, we also examined patients' motor and functional changes by the UHDRS, overtime.

RESULTS

The severity of apathy and of perseveration/obsession was associated with the severity of the cognitive decline (p < .0001), regardless of the yearly timepoint. The score of irritability significantly and positively correlated with perseveration errors in the verbal fluency test at t0 (r = .34; p = .001), while the psychosis significantly and negatively correlated with the information processing speed at t0 (r = -.21; p = .038) and significantly and positively correlated with perseveration errors in the verbal fluency test at t1 (r = .35; p < .0001). The disease progression was confirmed by the significant worsening of the UHDRS-Total Motor Score (TMS) and of the UHDRS-Total Functional Capacity (TFC) scale score after two-year follow-up (p < .0001).

CONCLUSION

Although the progression of abnormal behavioral manifestations cannot be predicted in HD, the severity of apathy and perseveration/obsessions are significantly associated with the severity of the cognitive function impairment, thus contributing, together, to the disease development and to patients' loss of independence, in addition to the neurological manifestations. This cognitive-behavior pattern determines a common underlying deficit depending on a dysexecutive syndrome.

Predicting the Risk of Huntington's Disease with Multiple Longitudinal Biomarkers

BACKGROUND

Huntington's disease (HD) has gradually become a public health threat, and there is a growing interest in developing prognostic models to predict the time for HD diagnosis.

OBJECTIVE

This study aims to develop a novel prognostic model that leverages multiple longitudinal biomarkers to inform the risk of HD.

METHODS

The multivariate functional principal component analysis was used to summarize the essential information from multiple longitudinal markers and to obtain a set of prognostic scores. The prognostic scores were used as predictors in a Cox model to predict the right-censored time to diagnosis. We used cross-validation to determine the best model in PREDICT-HD (n = 1,039) and ENROLL-HD (n = 1,776); external validation was carried out in ENROLL-HD.

RESULTS

We considered six commonly measured longitudinal biomarkers in PREDICT-HD and ENROLL-HD (Total Motor Score, Symbol Digit Modalities Test, Stroop Word Test, Stroop Color Test, Stroop Interference Test, and Total Functional Capacity). The prognostic model utilizing these longitudinal biomarkers significantly improved the predictive performance over the model with baseline biomarker information. A new prognostic index was computed using the proposed model, and can be dynamically updated over time as new biomarker measurements become available.

CONCLUSION

Longitudinal measurements of commonly measured clinical biomarkers substantially improve the risk prediction of Huntington's disease diagnosis. Calculation of the prognostic index informs the patient's risk category and facilitates patient selection in future clinical trials.

Longitudinal Structural MRI in Neurologically Healthy Adults

BACKGROUND

Structural brain MRI measures are frequently examined in both healthy and clinical groups, so an understanding of how these measures vary over time is desirable.

PURPOSE

To test the stability of structural brain MRI measures over time.

POPULATION

In all, 112 healthy volunteers across four sites.

STUDY TYPE

Retrospective analysis of prospectively acquired data.

FIELD STRENGTH/SEQUENCE

3 T, magnetization prepared - rapid gradient echo, and single-shell diffusion sequence.

ASSESSMENT

Diffusion, cortical thickness, and volume data from the sensorimotor network were assessed for stability over time across 3 years. Two sites used a Siemens MRI scanner, two sites a Philips scanner.

STATISTICAL TESTS

The stability of structural measures across timepoints was assessed using intraclass correlation coefficients (ICC) for absolute agreement, cutoff ≥0.80, indicating high reliability. Mixed-factorial analysis of variance (ANOVA) was used to examine between-site and between-scanner type differences in individuals over time.

RESULTS

All cortical thickness and gray matter volume measures in the sensorimotor network, plus all diffusivity measures (fractional anisotropy plus mean, axial and radial diffusivities) for primary and premotor cortices, primary somatosensory thalamic connections, and the cortico-spinal tract met ICC. The majority of measures differed significantly between scanners, with a trend for sites using Siemens scanners to produce larger values for connectivity, cortical thickness, and volume measures than sites using Philips scanners.

DATA CONCLUSION

Levels of reliability over time for all tested structural MRI measures were generally high, indicating that any differences between measurements over time likely reflect underlying biological differences rather than inherent methodological variability.

LEVEL OF EVIDENCE

4.

TECHNICAL EFFICACY STAGE

1.

Prevalence of Huntington Disease in Italy: a systematic review and meta-analysis

Worldwide prevalence of Huntington’s disease (HD) is quite heterogenous. As Italy is characterized by significant genetic heterogeneity, with presumptive differences between Italian regions, this review was undertaken to define available data of HD prevalence in Italy, to assess geographic heterogeneity, and reconcile possible variation in HD prevalence rates with the availability of genetic testing.

Disease Progression in Huntington Disease: An Analysis of Multiple Longitudinal Outcomes

BACKGROUND

Critical to discovering targeted therapies for Huntington disease (HD) are validated methods that more precisely predict when clinical outcomes occur for different patient profiles.

OBJECTIVE

To more precisely predict the probability of when motor diagnosis (diagnostic confidence level 4) on the Unified Huntington's Disease Rating Scale (UHDRS), cognitive impairment (two or more neuropsychological scores on the UHDRS were 1.5 standard deviations below normative means) and Stage II Total Functional Capacity (TFC) first occur by accounting for dependencies between these outcomes.

METHODS

Adult premanifest participants with ≥36 CAG repeats were selected from multi-center, longitudinal, observational studies: Prospective Huntington At Risk Observational Study (PHAROS, n = 346), Neurobiological Predictors of Huntington Disease (PREDICT, n = 909); and Cooperative Huntington Observational Research Trial (COHORT, n = 430). Probabilities were estimated for each study, and pooled using the Joint Progression of Risk Assessment Tool (JPRAT) which accounts for dependencies between outcomes.

RESULTS

All studies had similar probabilities of when motor diagnosis, cognitive impairment, and Stage II TFC first occurred. Probability estimates from JPRAT were 43% less variable than from models that ignored dependencies between outcomes. The probability of experiencing motor-diagnosis, cognitive impairment, and Stage II TFC within 5 years was 10%, 18%, and 7%, respectively for 45-year-olds with 42 CAG repeats, and was 4%, 10% and 5%, respectively, for 40 year olds with 42 CAG repeats.

CONCLUSIONS

Improved predictions from JPRAT may benefit treatment studies of rare diseases and is an alternative to composite outcomes when the objective is interpreting individual outcomes within the same model.

Evaluation of mutant huntingtin and neurofilament proteins as potential markers in Huntington's disease

Huntington's disease (HD) is a genetic progressive neurodegenerative disorder, caused by a mutation in the HTT gene, for which there is currently no cure. The identification of sensitive indicators of disease progression and therapeutic outcome could help the development of effective strategies for treating HD. We assessed mutant huntingtin (mHTT) and neurofilament light (NfL) protein concentrations in cerebrospinal fluid (CSF) and blood in parallel with clinical evaluation and magnetic resonance imaging in premanifest and manifest HD mutation carriers. Among HD mutation carriers, NfL concentrations in plasma and CSF correlated with all nonbiofluid measures more closely than did CSF mHTT concentration. Longitudinal analysis over 4 to 8 weeks showed that CSF mHTT, CSF NfL, and plasma NfL concentrations were highly stable within individuals. In our cohort, concentration of CSF mHTT accurately distinguished between controls and HD mutation carriers, whereas NfL concentration, in both CSF and plasma, was able to segregate premanifest from manifest HD. In silico modeling indicated that mHTT and NfL concentrations in biofluids might be among the earliest detectable alterations in HD, and sample size prediction suggested that low participant numbers would be needed to incorporate these measures into clinical trials. These findings provide evidence that biofluid concentrations of mHTT and NfL have potential for early and sensitive detection of alterations in HD and could be integrated into both clinical trials and the clinic.

Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease

BACKGROUND

Joint modeling is appropriate when one wants to predict the time to an event with covariates that are measured longitudinally and are related to the event. An underlying random effects structure links the survival and longitudinal submodels and allows for individual-specific predictions. Multiple time-varying and time-invariant covariates can be included to potentially increase prediction accuracy. The goal of this study was to estimate a multivariate joint model on several longitudinal observational studies of Huntington's disease, examine external validity performance, and compute individual-specific predictions for characterizing disease progression. Emphasis was on the survival submodel for predicting the hazard of motor diagnosis.

METHODS

Data from four observational studies was analyzed: Enroll-HD, PREDICT-HD, REGISTRY, and Track-HD. A Bayesian approach to estimation was adopted, and external validation was performed using a time-varying AUC measure. Individual-specific cumulative hazard predictions were computed based on a simulation approach. The cumulative hazard was used for computing predicted age of motor onset and also for a deviance residual indicating the discrepancy between observed diagnosis status and model-based status.

RESULTS

The joint model trained in a single study had very good performance in discriminating among diagnosed and pre-diagnosed participants in the remaining test studies, with the 5-year mean AUC = .83 (range .77-.90), and the 10-year mean AUC = .86 (range .82-.92). Graphical analysis of the predicted age of motor diagnosis showed an expected strong relationship with the trinucleotide expansion that causes Huntington's disease. Graphical analysis of the deviance-type residual revealed there were individuals who converted to a diagnosis despite having relatively low model-based risk, others who had not yet converted despite having relatively high risk, and the majority falling between the two extremes.

CONCLUSIONS

Joint modeling is an improvement over traditional survival modeling because it considers all the longitudinal observations of covariates that are predictive of an event. Predictions from joint models can have greater accuracy because they are tailored to account for individual variability. These predictions can provide relatively accurate characterizations of individual disease progression, which might be important in the timing of interventions, determining the qualification for appropriate clinical trials, and general genotypic analysis.

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.

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Detection of pre-HD subjects at high risk of impairment is key for clinical trials.

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Prognostic models of motor impairment can aid the detection of this population.

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Genetics only partially explains disease progression (need for other correlates).

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We achieve improved prognosis with baseline imaging, demographics and motor data.