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Ghofrani-Jahromi M, Poudel GR, Razi A, Abeyasinghe PM, Paulsen JS, Tabrizi SJ, Saha S, Georgiou-Karistianis N. Prognostic enrichment for early-stage Huntington's disease: An explainable machine learning approach for clinical trial. Neuroimage Clin 2024; 43:103650. [PMID: 39142216 DOI: 10.1016/j.nicl.2024.103650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/11/2024] [Accepted: 07/31/2024] [Indexed: 08/16/2024]
Abstract
BACKGROUND In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process. OBJECTIVES To improve stratification of Huntington's disease individuals for clinical trials. METHODS We used data from 451 gene positive individuals with Huntington's disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement. RESULTS The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %). CONCLUSIONS This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.
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Affiliation(s)
| | - Govinda R Poudel
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne VIC3000, Australia
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Clayton VIC3800, Australia
| | - Pubu M Abeyasinghe
- Turner Institute for Brain and Mental Health, Monash University, Clayton VIC3800, Australia
| | - Jane S Paulsen
- Department of Neurology, University of Wisconsin-Madison, 1685 Highland Avenue, Madison, WI, USA
| | - Sarah J Tabrizi
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, UK Dementia Research Institute, Department of Neurodegenerative Diseases, University College London, London, UK
| | - Susmita Saha
- Turner Institute for Brain and Mental Health, Monash University, Clayton VIC3800, Australia
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Mills JA, Long JD, Vaidya JG, Gantman EC, Sathe S, Tabrizi SJ, Sampaio C. Time to Functional Loss as an Endpoint in Huntington's Disease Trials: Enrichment and Sample Size. Mov Disord 2024. [PMID: 39101272 DOI: 10.1002/mds.29963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/13/2024] [Accepted: 07/18/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND Clinical trial scenarios can be modeled using data from observational studies, providing critical information for design of real-world trials. The Huntington's Disease Integrated Staging System (HD-ISS) characterizes disease progression over an individual's lifespan and allows for flexibility in the design of trials with the goal of delaying progression. Enrichment methods can be applied to the HD-ISS to identify subgroups requiring smaller estimated sample sizes. OBJECTIVE Investigate time to the event of functional decline (HD-ISS Stage 3) as an endpoint for trials in HD and present sample size estimates after enrichment. METHODS We classified individuals from observational studies according to the HD-ISS. We assessed the ability of the prognostic index normed (PIN) and its components to predict time to HD-ISS Stage 3. For enrichment, we formed groups from deciles of the baseline PIN distribution for HD-ISS Stage 2 participants. We selected enrichment subgroups closer to Stage 3 transition and estimated sample sizes, using delay in the transition time as the effect size. RESULTS In predicting time to HD-ISS Stage 3, PIN outperforms its components. Survival curves for each PIN decile show that groups with PIN from 1.48 to 2.74 have median time to Stage 3 of approximately 2 years and these are combined to create enrichment subgroups. Sample size estimates are presented by enrichment subgroup. CONCLUSIONS PIN is predictive of functional decline. A delay of 9 months or more in the transition to Stage 3 for an enriched sample yields feasible sample size estimates, demonstrating that this approach can aid in planning future trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- James A Mills
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Jeffrey D Long
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
- Department of Biostatistics, University of Iowa, Iowa City, IA, USA
| | - Jatin G Vaidya
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | | | | | - Sarah J Tabrizi
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, UK Dementia Research Institute, Department of Neurodegenerative Diseases, University College London, London, UK
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Saade J, Mestre TA. Huntington's Disease: Latest Frontiers in Therapeutics. Curr Neurol Neurosci Rep 2024; 24:255-264. [PMID: 38861215 DOI: 10.1007/s11910-024-01345-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 06/12/2024]
Abstract
PURPOSE OF REVIEW Huntington's disease (HD) is an autosomal-dominant disorder caused by a pathological expansion of a trinucleotide repeat (CAG) on exon 1 of the huntingtin (HTT) gene. HD is characterized by the presence of chorea, alongside other hyperkinesia, parkinsonism and a combination of cognitive and behavioural features. Currently, there are no disease-modifying therapies (DMTs) for HD, and the only intervention(s) with approved indication target the treatment of chorea. This article reviews recent research on the clinical development of DMTs and newly developed tools that enhance clinical trial design towards a successful DMT in the future. RECENT FINDINGS HD is living in an era of target-specific drug development with emphasis on the mechanisms related to mutant Huntingtin (HTT) protein. Examples include antisense oligonucleotides (ASO), splicing modifiers and microRNA molecules that aim to reduce the levels of mutant HTT protein. After initial negative results with ASO molecules Tominersen and WVE-120101/ WVE-120102, the therapeutic landscape continues to expand, with various trials currently under development to document proof-of-concept and safety/tolerability. Immune-targeted therapies have also been evaluated in early-phase clinical trials, with promising preliminary findings. The possibility of quantifying mHTT in CSF, along with the development of an integrated biological staging system in HD are important innovations applicable to clinical trial design that enhance the drug development process. Although a future in HD with DMTs remains a hope for those living with HD, care partners and care providers, the therapeutic landscape is promising, with various drug development programs underway following a targeted approach supported by disease-specific biomarkers and staging frameworks.
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Affiliation(s)
- Joseph Saade
- The Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Tiago A Mestre
- The Ottawa Hospital Research Institute, Ottawa, ON, Canada.
- The University of Ottawa Brain and Research Institute, Ottawa, ON, Canada.
- Parkinson's Disease and Movement Disorder Clinic, Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, ON, Canada.
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Sprenger GP, van Zwet EW, Bakels HS, Achterberg WP, Roos RA, de Bot ST. Prevalence and burden of pain across the entire spectrum of Huntington's disease. J Neurol Neurosurg Psychiatry 2024; 95:647-655. [PMID: 38290837 DOI: 10.1136/jnnp-2023-332992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 01/09/2024] [Indexed: 02/01/2024]
Abstract
BACKGROUND Pain is an important symptom in Huntington's disease (HD), however, not systematically studied and understood. The objective of the current study is to assess the prevalence of pain, pain interference in daily activities, painful conditions, analgesic use and the severity of the pain burden across different disease stages and 'Age at symptom Onset' groups. Additionally, the association between pain and disease burden was investigated. METHODS A cross-sectional analysis was conducted within two large data sets, which included different types of pain scales. Multivariable logistic regression analyses and analyses of variance were performed to compare the pain levels with those in the general population. The analyses were adjusted for sex and age. Locally Estimated Scatterplot Smoothing was used to test the association between pain and the HD pathology score: a measure of disease burden. RESULTS The mean prevalence of pain in the HD population was 40% and for pain interference around 35% in both data sets. Patients in the early, middle and late stage of HD experience more pain burden compared with what is reported in patients with chronic pain (p<0.01). A positive and significant association was demonstrated between pain and disease burden. Patients in late stage HD with pain use significantly less analgesics compared with the general population (5% vs 13%, respectively (p<0.01)). CONCLUSIONS Pain is a prevalent and important symptom in HD. Severe pain burden in the HD population is present and positively associated with disease burden. Risk for undertreatment with analgesics is nevertheless present. Awareness of pain in HD needs to be increased, both clinically and scientifically.
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Affiliation(s)
- Gregory P Sprenger
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
- Huntington Center, Amstelring, Amsterdam, The Netherlands
| | - Erik W van Zwet
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Hannah S Bakels
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Wilco P Achterberg
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
- Huntington Center overduin, Topaz, Leiden, The Netherlands
| | - Raymund A Roos
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Susanne T de Bot
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
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Parkin GM, Culbert B, Churchill E, Gilbert PE, Corey-Bloom J. Exploring bradyphrenia in Huntington's disease using the computerized test of information processing (CTiP). Clin Park Relat Disord 2024; 10:100243. [PMID: 38425474 PMCID: PMC10901849 DOI: 10.1016/j.prdoa.2024.100243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/03/2023] [Accepted: 02/20/2024] [Indexed: 03/02/2024] Open
Abstract
Background Bradyphrenia, best thought of as the mental equivalent of bradykinesia, has been described in several disorders of the brain including Parkinson's disease and schizophrenia; however, little is known about this phenomenon in Huntington's Disease (HD). Objective The aim of this study was to investigate the presence of bradyphrenia in HD using the Computerized Test of Information Processing (CTiP), an easy to administer and objective task that assesses cognitive processing speed with increasing task complexity. Methods This study included 211 participants: Huntington's Disease Integrated Staging System (HD-ISS) Stage 0 [n = 28], Stage 1 [n = 30], Stage 2 [n = 48] and Stage 3 [n = 48], and healthy controls (HC) [n = 57]. The CTiP incorporates three subtests: Simple Reaction Time (SRT), which assesses baseline motor function; Choice Reaction Time (CRT), with an added decisional component; and Semantic Search Reaction Time (SSRT), with an added conceptual component. SRT scores were subtracted from CRT and SSRT scores to establish a motor-corrected measure of central conduction time, which was used to operationalize bradyphrenia. Results HD-ISS and HC within-group reaction times differed significantly when comparing motor-corrected CRT vs SSRT (all ps < 0.0001). Furthermore, the magnitude of these differences increased with HD disease stage (p < 0.0001). An ROC analysis determined that motor-corrected within-subject differences significantly distinguished Stage 2 + 3 from Stage 0 + 1 (AUC = 0.72, p < 0.0001). Conclusions We report evidence of bradyphrenia in HD that increases with disease progression. This processing deficit, which can be quantified using the CTiP, has the potential to greatly impact HD daily life and warrants additional research.
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Affiliation(s)
- Georgia M. Parkin
- Department of Neurosciences, University of California San Diego, San Diego 92093, CA, USA
| | - Braden Culbert
- Department of Neurosciences, University of California San Diego, San Diego 92093, CA, USA
| | - Emma Churchill
- Department of Neurosciences, University of California San Diego, San Diego 92093, CA, USA
| | - Paul E. Gilbert
- Department of Psychology, San Diego State University, San Diego 92182, CA, USA
| | - Jody Corey-Bloom
- Department of Neurosciences, University of California San Diego, San Diego 92093, CA, USA
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Oosterloo M, Touze A, Byrne LM, Achenbach J, Aksoy H, Coleman A, Lammert D, Nance M, Nopoulos P, Reilmann R, Saft C, Santini H, Squitieri F, Tabrizi S, Burgunder JM, Quarrell O. Clinical Review of Juvenile Huntington's Disease. J Huntingtons Dis 2024; 13:149-161. [PMID: 38669553 PMCID: PMC11307030 DOI: 10.3233/jhd-231523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/21/2024] [Indexed: 04/28/2024]
Abstract
Juvenile Huntington's disease (JHD) is rare. In the first decade of life speech difficulties, rigidity, and dystonia are common clinical motor symptoms, whereas onset in the second decade motor symptoms may sometimes resemble adult-onset Huntington's disease (AOHD). Cognitive decline is mostly detected by declining school performances. Behavioral symptoms in general do not differ from AOHD but may be confused with autism spectrum disorder or attention deficit hyperactivity disorder and lead to misdiagnosis and/or diagnostic delay. JHD specific features are epilepsy, ataxia, spasticity, pain, itching, and possibly liver steatosis. Disease progression of JHD is faster compared to AOHD and the disease duration is shorter, particularly in case of higher CAG repeat lengths. The diagnosis is based on clinical judgement in combination with a positive family history and/or DNA analysis after careful consideration. Repeat length in JHD is usually > 55 and caused by anticipation, usually via paternal transmission. There are no pharmacological and multidisciplinary guidelines for JHD treatment. Future perspectives for earlier diagnosis are better diagnostic markers such as qualitative MRI and neurofilament light in serum.
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Affiliation(s)
- Mayke Oosterloo
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Alexiane Touze
- Department of Neurodegenerative Disease, UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Lauren M. Byrne
- Department of Neurodegenerative Disease, UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jannis Achenbach
- Department of Neurology, Huntington Centre NRW, Ruhr-University Bochum, St. Josef-Hospital, Bochum, Germany
| | - Hande Aksoy
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Annabelle Coleman
- Department of Neurodegenerative Disease, UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Dawn Lammert
- Department of Neurology, Division of Child Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Martha Nance
- Struthers Parkinson’s Center, Minneapolis, MN, USA
| | - Peggy Nopoulos
- Departments of Psychiatry, Pediatrics, & Neurology, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Ralf Reilmann
- George-Huntington-Institute & Department of Radiology, University of Muenster, Muenster, Germany
- Department for Neurodegeneration, Hertie Institute for Clinical, Brain Research, University of Tuebingen, Tuebingen, Germany
| | - Carsten Saft
- Department of Neurology, Huntington Centre NRW, Ruhr-University Bochum, St. Josef-Hospital, Bochum, Germany
| | | | - Ferdinando Squitieri
- Centre for Rare Neurological Diseases (CMRN), Italian League for Research on Huntington (LIRH) Foundation, Rome, Italy
- Huntington and Rare Diseases Unit, IRCCS Casa Sollievo Della Sofferenza Research Hospital, San Giovanni Rotondo, Italy
| | - Sarah Tabrizi
- Department of Neurodegenerative Disease, UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jean-Marc Burgunder
- Neurozentrum Siloah and Department of Neurology, Swiss HD Center, University of Bern, Bern, Switzerland
| | - Oliver Quarrell
- Department of Clinical Genetics, Sheffield Children’s Hospital, Sheffield, UK
- Department of Neurosciences University of Sheffield, Sheffield, UK
| | - on behalf of the Pediatric Huntington Disease Working Group of the European Huntington Disease Network
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Neurodegenerative Disease, UCL Huntington’s Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurology, Huntington Centre NRW, Ruhr-University Bochum, St. Josef-Hospital, Bochum, Germany
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
- Department of Neurology, Division of Child Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Struthers Parkinson’s Center, Minneapolis, MN, USA
- Departments of Psychiatry, Pediatrics, & Neurology, University of Iowa Carver College of Medicine, Iowa City, IA, USA
- George-Huntington-Institute & Department of Radiology, University of Muenster, Muenster, Germany
- Department for Neurodegeneration, Hertie Institute for Clinical, Brain Research, University of Tuebingen, Tuebingen, Germany
- Huntington’s Disease Association, England and Wales
- Centre for Rare Neurological Diseases (CMRN), Italian League for Research on Huntington (LIRH) Foundation, Rome, Italy
- Huntington and Rare Diseases Unit, IRCCS Casa Sollievo Della Sofferenza Research Hospital, San Giovanni Rotondo, Italy
- Neurozentrum Siloah and Department of Neurology, Swiss HD Center, University of Bern, Bern, Switzerland
- Department of Clinical Genetics, Sheffield Children’s Hospital, Sheffield, UK
- Department of Neurosciences University of Sheffield, Sheffield, UK
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