Phenotype Characterization of HD Intermediate Alleles in PREDICT-HD

BACKGROUND: Huntington disease (HD) is a neurodegenerative disease caused by a CAG repeat expansion on chromosome 4. Pathology is associated with CAG repeat length. Prior studies examining people in the intermediate allele (IA) range found subtle differences in motor, cognitive, and behavioral domains compared to controls. OBJECTIVE: The purpose of this study was to examine baseline and longitudinal differences in motor, cognitive, behavioral, functional, and imaging outcomes between persons with CAG repeats in three ranges: normal (≤26), intermediate (27-35), and reduced penetrance (36-39). METHODS: We examined longitudinal data from 389 participants in three allele groups: 280 normal controls (NC), 21 intermediate allele [IA], and 88 reduced penetrance [RP]. We used linear mixed models to identify differences in baseline and longitudinal outcomes between groups. Three models were tested: 1) no baseline or longitudinal differences; 2) baseline differences but no longitudinal differences; and 3) baseline and longitudinal differences. RESULTS: Model 1 was the best fitting model for most outcome variables. Models 2 and 3 were best fitting for some of the variables. We found baseline and longitudinal trends of declining performance across increasing CAG repeat length groups, but no significant differences between the NC and IA groups. CONCLUSION: We did not find evidence to support differences in the IA group compared to the NC group. These findings are limited by a small IA sample size

Performance of the 12-item WHODAS 2.0 in prodromal Huntington disease

ACKNOWLEDGEMENTS We thank the PREDICT-HD sites, the study participants, the National Research Roster for Huntington Disease Patients and Families, the Huntington’s Disease Society of America and the Huntington Study Group. This publication was supported by the National Center for Advancing Translational Sciences, and the National Institutes of Health (NIH), through Grant 2 UL1 TR000442-06. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This research is supported by the National Institutes of Health, National Institute of Neurological Disorders and Stroke (5R01NS040068) awarded to Dr Paulsen, CHDI Foundation, Inc (A3917) awarded to Dr Paulsen, Cognitive and Functional Brain Changes in Preclinical Huntington’s Disease (HD) (5R01NS054893) awarded to Dr Paulsen, 4D Shape Analysis for Modeling Spatiotemporal Change Trajectories in Huntington’s (1U01NS082086), Functional Connectivity in Premanifest Huntington’s Disease (1U01NS082083), and Basal Ganglia Shape Analysis and Circuitry in Huntington’s Disease (1U01NS082085).Peer reviewedPublisher PD

Metformin reverses early cortical network dysfunction and behavior changes in Huntington's disease

Catching primal functional changes in early, 'very far from disease onset' (VFDO) stages of Huntington's disease is likely to be the key to a successful therapy. Focusing on VFDO stages, we assessed neuronal microcircuits in premanifest Hdh150 knock-in mice. Employing in vivo two-photon Ca(2+) imaging, we revealed an early pattern of circuit dysregulation in the visual cortex- one of the first regions affected in premanifest Huntington's disease - characterized by an increase in activity, an enhanced synchronicity and hyperactive neurons. These findings are accompanied by aberrations in animal behavior. We furthermore show that the anti-diabetic drug metformin diminishes aberrant Huntingtin protein load and fully restores both, early network activity patterns and behavioral aberrations. This network-centered approach reveals a critical window of vulnerability far before clinical manifestation and establishes metformin as a promising candidate for a chronic therapy starting early in premanifest Huntington's disease pathogenesis long before the onset of clinical symptoms

Exome sequencing of individuals with Huntington’s disease implicates FAN1 nuclease activity in slowing CAG expansion and disease onset

The age at onset of motor symptoms in Huntington’s disease (HD) is driven by HTT CAG repeat length but modified by other genes. In this study, we used exome sequencing of 683 patients with HD with extremes of onset or phenotype relative to CAG length to identify rare variants associated with clinical effect. We discovered damaging coding variants in candidate modifier genes identified in previous genome-wide association studies associated with altered HD onset or severity. Variants in FAN1 clustered in its DNA-binding and nuclease domains and were associated predominantly with earlier-onset HD. Nuclease activities of purified variants in vitro correlated with residual age at motor onset of HD. Mutating endogenous FAN1 to a nuclease-inactive form in an induced pluripotent stem cell model of HD led to rates of CAG expansion similar to those observed with complete FAN1 knockout. Together, these data implicate FAN1 nuclease activity in slowing somatic repeat expansion and hence onset of HD

Clinical and biomarker changes in premanifest Huntington disease show trial feasibility: A decade of the PREDICT-HD study

There is growing consensus that intervention and treatment of Huntington disease (HD) should occur at the earliest stage possible. Various early-intervention methods for this fatal neurodegenerative disease have been identified, but preventive clinical trials for HD are limited by a lack of knowledge of the natural history of the disease and a dearth of appropriate outcome measures. Objectives of the current study are to document the natural history of premanifest HD progression in the largest cohort ever studied and to develop a battery of imaging and clinical markers of premanifest HD progression that can be used as outcome measures in preventive clinical trials. Neurobiological predictors of Huntington’s disease is a 32-site, international, observational study of premanifest HD, with annual examination of 1013 participants with premanifest HD and 301 gene-expansion negative controls between 2001 and 2012. Findings document 39 variables representing imaging, motor, cognitive, functional, and psychiatric domains, showing different rates of decline between premanifest HD and controls. Required sample size and models of premanifest HD are presented to inform future design of clinical and preclinical research. Preventive clinical trials in premanifest HD with participants who have a medium or high probability of motor onset are calculated to be as resource-effective as those conducted in diagnosed HD and could interrupt disease 7–12years earlier. Methods and measures for preventive clinical trials in premanifest HD more than a dozen years from motor onset are also feasible. These findings represent the most thorough documentation of a clinical battery for experimental therapeutics in stages of premanifest HD, the time period for which effective intervention may provide the most positive possible outcome for patients and their families affected by this devastating disease

Multivariate prediction of motor diagnosis in Huntington's disease: 12 years of PREDICT-HD.

It is well known in Huntington's disease that cytosine-adenine-guanine expansion and age at study entry are predictive of the timing of motor diagnosis. The goal of this study was to assess whether additional motor, imaging, cognitive, functional, psychiatric, and demographic variables measured at study entry increased the ability to predict the risk of motor diagnosis over 12 years.One thousand seventy-eight Huntington's disease gene-expanded carriers (64% female) from the Neurobiological Predictors of Huntington's Disease study were followed up for up to 12 y (mean = 5, standard deviation = 3.3) covering 2002 to 2014. No one had a motor diagnosis at study entry, but 225 (21%) carriers prospectively received a motor diagnosis. Analysis was performed with random survival forests, which is a machine learning method for right-censored data.Adding 34 variables along with cytosine-adenine-guanine and age substantially increased predictive accuracy relative to cytosine-adenine-guanine and age alone. Adding six of the common motor and cognitive variables (total motor score, diagnostic confidence level, Symbol Digit Modalities Test, three Stroop tests) resulted in lower predictive accuracy than the full set, but still had twice the 5-y predictive accuracy than when using cytosine-adenine-guanine and age alone. Additional analysis suggested interactions and nonlinear effects that were characterized in a post hoc Cox regression model.Measurement of clinical variables can substantially increase the accuracy of predicting motor diagnosis over and above cytosine-adenine-guanine and age (and their interaction). Estimated probabilities can be used to characterize progression level and aid in future studies' sample selection

Multivariate prediction of motor diagnosis in Huntington's disease: 12 years of PREDICT-HD.

BackgroundIt is well known in Huntington's disease that cytosine-adenine-guanine expansion and age at study entry are predictive of the timing of motor diagnosis. The goal of this study was to assess whether additional motor, imaging, cognitive, functional, psychiatric, and demographic variables measured at study entry increased the ability to predict the risk of motor diagnosis over 12 years.MethodsOne thousand seventy-eight Huntington's disease gene-expanded carriers (64% female) from the Neurobiological Predictors of Huntington's Disease study were followed up for up to 12 y (mean = 5, standard deviation = 3.3) covering 2002 to 2014. No one had a motor diagnosis at study entry, but 225 (21%) carriers prospectively received a motor diagnosis. Analysis was performed with random survival forests, which is a machine learning method for right-censored data.ResultsAdding 34 variables along with cytosine-adenine-guanine and age substantially increased predictive accuracy relative to cytosine-adenine-guanine and age alone. Adding six of the common motor and cognitive variables (total motor score, diagnostic confidence level, Symbol Digit Modalities Test, three Stroop tests) resulted in lower predictive accuracy than the full set, but still had twice the 5-y predictive accuracy than when using cytosine-adenine-guanine and age alone. Additional analysis suggested interactions and nonlinear effects that were characterized in a post hoc Cox regression model.ConclusionsMeasurement of clinical variables can substantially increase the accuracy of predicting motor diagnosis over and above cytosine-adenine-guanine and age (and their interaction). Estimated probabilities can be used to characterize progression level and aid in future studies' sample selection

CAG-repeat length and the age of onset in Huntington disease (HD): A review and validation study of statistical approaches

CAG-repeat length in the gene for HD is inversely correlated with age of onset (AOO). A number of statistical models elucidating the relationship between CAG length and AOO have recently been published. In the present article, we review the published formulae, summarize essential differences in participant sources, statistical methodologies, and predictive results. We argue that unrepresentative sampling and failure to use appropriate survival analysis methodology may have substantially biased much of the literature. We also explain why the survival analysis perspective is necessary if any such model is to undergo prospective validation. We use prospective diagnostic data from the PREDICT-HD longitudinal study of CAG-expanded participants to test conditional predictions derived from two survival models of AOO of HD. A prior model of the relationship of CAG and AOO originally published by Langbehn et al. yields reasonably accurate predictions, while a similar model by Gutierrez and MacDonald substantially overestimates diagnosis risk for all but the highest risk participants in this sample. The Langbehn et al. model appears accurate enough to have substantial utility in various research contexts. We also emphasize remaining caveats, many of which are relevant for any direct application to genetic counseling

Cognitive domains that predict time to diagnosis in prodromal Huntington disease.

BackgroundProdromal Huntington's disease (prHD) is associated with a myriad of cognitive changes but the domains that best predict time to clinical diagnosis have not been studied. This is a notable gap because some domains may be more sensitive to cognitive decline, which would inform clinical trials.ObjectivesThe present study sought to characterise cognitive domains underlying a large test battery and for the first time, evaluate their ability to predict time to diagnosis.MethodsParticipants included gene negative and gene positive prHD participants who were enrolled in the PREDICT-HD study. The CAG-age product (CAP) score was the measure of an individual's genetic signature. A factor analysis of 18 tests was performed to identify sets of measures or latent factors that elucidated core constructs of tests. Factor scores were then fit to a survival model to evaluate their ability to predict time to diagnosis.ResultsSix factors were identified: (1) speed/inhibition, (2) verbal working memory, (3) motor planning/speed, (4) attention-information integration, (5) sensory-perceptual processing and (6) verbal learning/memory. Factor scores were sensitive to worsening of cognitive functioning in prHD, typically more so than performances on individual tests comprising the factors. Only the motor planning/speed and sensory-perceptual processing factors predicted time to diagnosis, after controlling for CAP scores and motor symptoms. Conclusions The results suggest that motor planning/speed and sensory-perceptual processing are important markers of disease prognosis. The findings also have implications for using composite indices of cognition in preventive Huntington's disease trials where they may be more sensitive than individual tests

Multivariate clustering of progression profiles reveals different depression patterns in prodromal Huntington disease.

ObjectiveAlthough Huntington disease (HD) is caused by an autosomal dominant mutation, its phenotypic presentation differs widely. Variability in clinical phenotypes of HD may reflect the existence of disease subtypes. This hypothesis was tested in prodromal participants from the longitudinal Neurobiological Predictors of Huntington Disease (PREDICT-HD) study.MethodWe performed clustering using longitudinal data assessing motor, cognitive, and depression symptoms. Using data from 521 participants with 2,716 data points, we fit growth mixture models (GMM) that identify groups based on multivariate trajectories.ResultsIn various GMM, different phases of disease progression were partitioned by progression trajectories of motor and cognitive signs, and by overall level of depression symptoms. More progressed motor signs were accompanied by more progressed cognitive signs, but not always by higher levels of depressive symptoms. In several models, there were at least 2 groups with similar trajectories for motor and cognitive signs that showed different levels for depression symptoms-one with a very low level of depression and the other with a higher level of depression.ConclusionsFindings indicate that at least intermediate HD progression might be associated with different levels of depression. Depression is one of the few symptoms that is treatable in HD and has implications for clinical care. Identification of potential depression subtypes may also help to select appropriate patients for clinical trials