John H. Warner

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 CAPS. CAPS 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 CAPS had a relatively strong ability to predict individuals who became diagnosed, especially in the first 2 years. Bootstrap validation provided evidence that CAPS computed on a new sample from the same population could have similar discriminatory power. Cutoffs for the empirical CAPS 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.

8OHdG is not a biomarker for Huntington disease state or progression

Objective: To evaluate plasma 8-hydroxy-deoxy-guanosine (8OHdG) levels as a potential biomarker of premanifest and early Huntington disease (HD). Methods: Personnel from 2 independent laboratories quantified 8OHdG in blinded longitudinal plasma samples taken 24 months apart from 160 TRACK-HD participants, as well as samples containing control plasma with added (“spiked”) 8OHdG. One laboratory used a liquid chromatography–electrochemical array (LCECA) assay, and the other used liquid chromatography–mass spectrometry (LCMS). Results: The LCMS assay was more accurate than the LCECA assay for measurements of “spiked” 8OHdG levels in plasma. Neither assay demonstrated cross-sectional differences in plasma 8OHdG among controls, premanifest HD, and early symptomatic HD. Similarly, neither assay showed longitudinal changes in any disease group over 24 months. Conclusions: Plasma concentration of 8OHdG is not a biomarker of disease state or progression in HD. We recommend that future putative biomarker studies use blinded sample analysis, standard curves, independent analytical methods, and strict quality control of sample collection and storage.

Validation of a prognostic index for Huntington's disease

Characterizing progression in Huntingtons disease is important for study the natural course and selecting appropriate participants for clinical trials.

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.

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

Abstract 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:

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

4947 (SD = 

Computational psychiatry: Advancing predictive modeling of neurodegeneration with neuroimaging of Huntington's disease

6040)–

Huntington disease: natural history, biomarkers and prospects for therapeutics

22,582 (SD = 

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

39,028); Medicaid:

F14 8OHdG is not a biomarker for Huntington's disease; lessons for future biomarker studies

3257 (SD =