Biological and clinical changes in premanifest and early stage Huntington's disease in the TRACK-HD study: the 12-month longitudinal analysis

Tabrizi, Sarah J.; Scahill, Rachael I.; Dürr, Alexandra; Roos, Raymund A.C.; Leavitt, Blair R.; Jones, Rebecca; Landwehrmeyer, G. Bernhard; Fox, Nick C.; Johnson, Hans J.; Hicks, Stephen L.; Kennard, Christopher; Craufurd, David; Frost, Chris; Langbehn, Douglas R.; Reilmann, Ralf; Stout, Julie C.

doi:10.1016/s1474-4422(10)70276-3

Cited by 547 publications

(620 citation statements)

References 37 publications

Supporting

Mentioning

577

Contrasting

Unclassified

Order By: Relevance

“…Crucially, when caudate volume was used to retrospectively predict those patients who were within 5 years of clinical diagnosis (Fig 5), this measure was seen to be a robust and individualized identifier of real‐life clinical diagnosis. These findings accord and extend existing work,9, 19, 20, 21 providing additional support for the use of caudate volume as a reliable estimate of disease proximity and making it a potentially useful biomarker.…”

Section: Discussionsupporting

confidence: 88%

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

Mason

Daws

Soreq

et al. 2018

Annals of Neurology

View full text Add to dashboard Cite

ObjectiveHuntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD.MethodA multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy.ResultsClassification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models.InterpretationWe propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532–543

show abstract

Section: Discussionsupporting

confidence: 88%

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

Mason

Daws

Soreq

et al. 2018

Annals of Neurology

View full text Add to dashboard Cite

show abstract

“…Their ordering, however, has not previously been observed: the EBM places the putamen strongly ahead of the caudate and pallidum, even under bootstrapping. Abnormalities in the insula white matter and nonventricular CSF are identified by the EBM as potential mid‐stage biomarkers, the former of which is also reported in the PREDICT‐HD dataset,27 and agrees with observations of white matter abnormality in premanifest and manifest subjects 31, 32, 33, 34…”

Section: Discussionsupporting

confidence: 80%

An image‐based model of brain volume biomarker changes in Huntington's disease

Wijeratne

Young

Oxtoby

et al. 2018

Ann Clin Transl Neurol

View full text Add to dashboard Cite

ObjectiveDetermining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine‐grained model of temporal progression of Huntington's disease from premanifest through to manifest stages.MethodsWe employ a probabilistic event‐based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track‐HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides.ResultsThe model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross‐validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow‐up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers.InterpretationWe used a data‐driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event‐based model, to provide new insight into Huntington's disease progression and to support fine‐grained patient stratification for future precision medicine in Huntington's disease.

show abstract

“…A more homogeneous disease phenotype, clinical endpoints and longitudinal markers of disease progression that are currently being established allow for the assessment of disease-modifying effects of therapeutics in a timeframe of 1-2 years 16 in HD. Effective compounds could then be prioritized for clinical trials in AD patients, whereas drugs that do not show effects in HD patients could be ranked lower in priority.…”

Section: Moving Towards Clinical Trials-the Power Of Predictive Testingmentioning

confidence: 99%

“…To clarify this issue, long-term clinical studies are necessary, and we believe that these could initially be conducted in individuals carrying the mutation causing HD, since this population is well defined and alterations in the course of the disease can be determined at very early stages 16 . HD is the commonest purely genetic neurodegenerative disorders, while AD is the most common neurodegenerative disease overall 1,2 .…”

Section: Introductionmentioning

confidence: 99%

“…HD is the commonest purely genetic neurodegenerative disorders, while AD is the most common neurodegenerative disease overall 1,2 . Since many of the pathways and targets described here are also shared with other diseases such as Parkinson's disease, dementia with Lewy bodies or ataxias, HD could serve as a general model for neurodegeneration, where well-defined animal models are available for preclinical studies and endpoints for human clinical trials are well established 16,17 . The "Enroll-HD" study that is currently underway will provide a registry of HD mutationcarriers worldwide and serve as a resource for recruiting patients to clinical trials (http://www.enroll-hd.org).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Convergent pathogenic pathways in Alzheimer's and Huntington's diseases: shared targets for drug development

Ehrnhoefer

Wong

Hayden

2011

Nat Rev Drug Discov

View full text Add to dashboard Cite

Neurodegenerative diseases exemplified by Alzheimer's and Huntington disease are characterized by the progressive neuropsychiatric dysfunction and loss of specific neuronal subtypes. Even though there are differences in the exact sites of pathology and clinical profiles only partially overlap, considerable similarities in disease mechanisms and pathogenic pathways can be observed. These shared mechanisms raise the possibility of common therapeutic targets for drug development. Huntington disease with a monogenic cause and the possibility to accurately identify pre-manifest mutation carriers could be exploited as a 'model' for Alzheimer's disease to test the efficacy of therapeutic interventions targeting shared pathogenic pathways.

show abstract

Biological and clinical changes in premanifest and early stage Huntington's disease in the TRACK-HD study: the 12-month longitudinal analysis

Cited by 547 publications

References 37 publications

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

An image‐based model of brain volume biomarker changes in Huntington's disease

Convergent pathogenic pathways in Alzheimer's and Huntington's diseases: shared targets for drug development

Contact Info

Product

Resources

About