{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T23:24:47Z","timestamp":1784676287068,"version":"3.55.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T00:00:00Z","timestamp":1716854400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T00:00:00Z","timestamp":1716854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Fraunhofer-Institut f\u00fcr Algorithmen und Wissenschaftliches Rechnen SCAI"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EPMA Journal"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Huntington\u2019s disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient\u2019s quality of life. Despite this clear genetic course, high variability of HD patients\u2019 symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI\/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Results demonstrate two distinct subtypes, one large cluster (<jats:italic>n<\/jats:italic>\u2009=\u20097122) showing a relative stable disease progression and a second, smaller cluster (<jats:italic>n<\/jats:italic>\u2009=\u2009411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients\u2019 first visit only.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients\u2019 disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals\u2019 treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. This greatly aligns with the goal of shifting towards 3P medicine.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s13167-024-00368-2","type":"journal-article","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T19:01:32Z","timestamp":1716922892000},"page":"275-287","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Unraveling progression subtypes in people with Huntington\u2019s disease"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2332-6137","authenticated-orcid":false,"given":"Tamara","family":"Raschka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zexin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2037-9460","authenticated-orcid":false,"given":"Heiko","family":"Ga\u00dfner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zacharias","family":"Kohl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jelena","family":"Jukic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5187-4344","authenticated-orcid":false,"given":"Franz","family":"Marxreiter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5328-1243","authenticated-orcid":false,"given":"Holger","family":"Fr\u00f6hlich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"368_CR1","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1016\/0092-8674(93)90585-E","volume":"72","author":"ME MacDonald","year":"1993","unstructured":"MacDonald ME, et al. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington\u2019s disease chromosomes. Cell. 1993;72:971\u201383. https:\/\/doi.org\/10.1016\/0092-8674(93)90585-E.","journal-title":"Cell"},{"key":"368_CR2","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1016\/j.cell.2015.07.003","volume":"162","author":"J-M Lee","year":"2015","unstructured":"Lee J-M, et al. Identification of genetic factors that modify clinical onset of Huntington\u2019s disease. Cell. 2015;162:516\u201326. https:\/\/doi.org\/10.1016\/j.cell.2015.07.003.","journal-title":"Cell"},{"key":"368_CR3","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.1399-0004.2004.00241.x","volume":"65","author":"DR Langbehn","year":"2004","unstructured":"Langbehn DR, et al. A new model for prediction of the age of onset and penetrance for Huntington\u2019s disease based on CAG length. Clin Genet. 2004;65:267\u201377. https:\/\/doi.org\/10.1111\/j.1399-0004.2004.00241.x.","journal-title":"Clin Genet"},{"key":"368_CR4","doi-asserted-by":"publisher","first-page":"2327","DOI":"10.1002\/mds.29228","volume":"37","author":"A Medina","year":"2022","unstructured":"Medina A, Mahjoub Y, Shaver L, Pringsheim T. Prevalence and incidence of Huntington\u2019s disease: an updated systematic review and meta-analysis. Mov Disord Off J Mov Disord Soc. 2022;37:2327\u201335. https:\/\/doi.org\/10.1002\/mds.29228.","journal-title":"Mov Disord Off J Mov Disord Soc"},{"key":"368_CR5","doi-asserted-by":"publisher","first-page":"5408","DOI":"10.1007\/s00415-023-11804-0","volume":"270","author":"A Horta-Barba","year":"2023","unstructured":"Horta-Barba A, et al. Measuring cognitive impairment and monitoring cognitive decline in Huntington\u2019s disease: a comparison of assessment instruments. J Neurol. 2023;270:5408\u201317. https:\/\/doi.org\/10.1007\/s00415-023-11804-0.","journal-title":"J Neurol"},{"key":"368_CR6","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1093\/arclin\/acx086","volume":"32","author":"JS Snowden","year":"2017","unstructured":"Snowden JS. The neuropsychology of Huntington\u2019s disease. Arch Clin Neuropsychol. 2017;32:876\u201387. https:\/\/doi.org\/10.1093\/arclin\/acx086.","journal-title":"Arch Clin Neuropsychol"},{"key":"368_CR7","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1002\/mds.22412","volume":"24","author":"AK Ho","year":"2009","unstructured":"Ho AK, Gilbert AS, Mason SL, Goodman AO, Barker RA. Health-related quality of life in Huntington\u2019s disease: which factors matter most? Mov Disord. 2009;24:574\u20138. https:\/\/doi.org\/10.1002\/mds.22412.","journal-title":"Mov Disord"},{"key":"368_CR8","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1007\/s00702-012-0787-x","volume":"119","author":"K Banaszkiewicz","year":"2012","unstructured":"Banaszkiewicz K, et al. Huntington\u2019s disease from the patient, caregiver and physician\u2019s perspectives: three sides of the same coin? J Neural Transm. 2012;119:1361\u20135. https:\/\/doi.org\/10.1007\/s00702-012-0787-x.","journal-title":"J Neural Transm"},{"key":"368_CR9","doi-asserted-by":"publisher","first-page":"159","DOI":"10.3233\/JHD-130051","volume":"2","author":"J Read","year":"2013","unstructured":"Read J, et al. Quality of life in Huntington\u2019s disease: a comparative study investigating the impact for those with pre-manifest and early manifest disease, and their partners. J Huntingt Dis. 2013;2:159\u201375. https:\/\/doi.org\/10.3233\/JHD-130051.","journal-title":"J Huntingt Dis"},{"key":"368_CR10","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s12955-016-0575-7","volume":"15","author":"MR Van Walsem","year":"2017","unstructured":"Van Walsem MR, Howe EI, Ruud GA, Frich JC, Andelic N. Health-related quality of life and unmet healthcare needs in Huntington\u2019s disease. Health Qual Life Outcomes. 2017;15:6. https:\/\/doi.org\/10.1186\/s12955-016-0575-7.","journal-title":"Health Qual Life Outcomes"},{"key":"368_CR11","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1002\/mds.26012","volume":"29","author":"CA Ross","year":"2014","unstructured":"Ross CA, Pantelyat A, Kogan J, Brandt J. Determinants of functional disability in Huntington\u2019s disease: role of cognitive and motor dysfunction. Mov Disord Off J Mov Disord Soc. 2014;29:1351\u20138. https:\/\/doi.org\/10.1002\/mds.26012.","journal-title":"Mov Disord Off J Mov Disord Soc"},{"key":"368_CR12","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1038\/nrneurol.2014.24","volume":"10","author":"CA Ross","year":"2014","unstructured":"Ross CA, et al. Huntington disease: natural history, biomarkers and prospects for therapeutics. Nat Rev Neurol. 2014;10:204\u201316. https:\/\/doi.org\/10.1038\/nrneurol.2014.24.","journal-title":"Nat Rev Neurol"},{"key":"368_CR13","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1002\/ana.410410521","volume":"41","author":"JB Penney","year":"1997","unstructured":"Penney JB, Vonsattel J, Macdonald ME, Gusella JF, Myers RH. CAG repeat number governs the development rate of pathology in Huntington\u2019s disease. Ann Neurol. 1997;41:689\u201392. https:\/\/doi.org\/10.1002\/ana.410410521.","journal-title":"Ann Neurol"},{"key":"368_CR14","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1002\/ajmg.b.31232","volume":"156","author":"Y Zhang","year":"2011","unstructured":"Zhang Y, et al. Indexing disease progression at study entry with individuals at-risk for Huntington disease. Am J Med Genet B Neuropsychiatr Genet. 2011;156:751\u201363. https:\/\/doi.org\/10.1002\/ajmg.b.31232.","journal-title":"Am J Med Genet B Neuropsychiatr Genet"},{"key":"368_CR15","doi-asserted-by":"publisher","first-page":"RRN1184","DOI":"10.1371\/currents.RRN1184","volume":"2","author":"M Orth","year":"2011","unstructured":"Orth M, et al. Observing Huntington\u2019s disease: the European Huntington\u2019s Disease Network\u2019s REGISTRY. PLoS Curr. 2011;2:RRN1184. https:\/\/doi.org\/10.1371\/currents.RRN1184.","journal-title":"PLoS Curr."},{"key":"368_CR16","doi-asserted-by":"publisher","first-page":"e29522","DOI":"10.1371\/journal.pone.0029522","volume":"7","author":"ER Dorsey","year":"2012","unstructured":"Dorsey ER, the Huntington Study Group COHORT Investigators. Characterization of a large group of individuals with Huntington disease and their relatives enrolled in the COHORT study. PLoS ONE. 2012;7:e29522. https:\/\/doi.org\/10.1371\/journal.pone.0029522.","journal-title":"PLoS ONE"},{"key":"368_CR17","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1002\/mds.20332","volume":"20","author":"P Hogarth","year":"2005","unstructured":"Hogarth P, et al. Interrater agreement in the assessment of motor manifestations of Huntington\u2019s disease. Mov Disord. 2005;20:293\u20137. https:\/\/doi.org\/10.1002\/mds.20332.","journal-title":"Mov Disord"},{"key":"368_CR18","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1002\/mds.26011","volume":"29","author":"R Reilmann","year":"2014","unstructured":"Reilmann R, Leavitt BR, Ross CA. Diagnostic criteria for Huntington\u2019s disease based on natural history. Mov Disord. 2014;29:1335\u201341. https:\/\/doi.org\/10.1002\/mds.26011.","journal-title":"Mov Disord"},{"key":"368_CR19","doi-asserted-by":"publisher","unstructured":"Mandel S, Korczyn AD. In Neurodegener. Dis. Integr. PPPM Approach Med. Future (ed. Mandel, S.) 2013;2:95\u2013140. Springer Netherlands. https:\/\/doi.org\/10.1007\/978-94-007-5866-7_6","DOI":"10.1007\/978-94-007-5866-7_6"},{"key":"368_CR20","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1016\/S1474-4422(09)70170-X","volume":"8","author":"SJ Tabrizi","year":"2009","unstructured":"Tabrizi SJ, et al. Biological and clinical manifestations of Huntington\u2019s disease in the longitudinal TRACK-HD study: cross-sectional analysis of baseline data. Lancet Neurol. 2009;8:791\u2013801. https:\/\/doi.org\/10.1016\/S1474-4422(09)70170-X.","journal-title":"Lancet Neurol"},{"key":"368_CR21","doi-asserted-by":"publisher","first-page":"2282","DOI":"10.1002\/mds.28650","volume":"36","author":"PM Abeyasinghe","year":"2021","unstructured":"Abeyasinghe PM, et al. Tracking Huntington\u02bcs disease progression using motor, functional, cognitive, and imaging markers. Mov Disord. 2021;36:2282\u201392. https:\/\/doi.org\/10.1002\/mds.28650.","journal-title":"Mov Disord"},{"key":"368_CR22","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1016\/S1474-4422(22)00120-X","volume":"21","author":"SJ Tabrizi","year":"2022","unstructured":"Tabrizi SJ, et al. A biological classification of Huntington\u2019s disease: the Integrated Staging System. Lancet Neurol. 2022;21:632\u201344. https:\/\/doi.org\/10.1016\/S1474-4422(22)00120-X.","journal-title":"Lancet Neurol"},{"key":"368_CR23","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1002\/mds.28866","volume":"37","author":"A Mohan","year":"2022","unstructured":"Mohan A, et al. A machine-learning derived Huntington\u2019s disease progression model: insights for clinical trial design. Mov Disord. 2022;37:553\u201362. https:\/\/doi.org\/10.1002\/mds.28866.","journal-title":"Mov Disord"},{"key":"368_CR24","doi-asserted-by":"publisher","unstructured":"EPMA, Golubnitschaja O, Costigliola V. General report & recommendations in predictive, preventive and personalised medicine 2012: white paper of the European Association for Predictive, Preventive and Personalised Medicine. EPMA J. 2012;3:14. https:\/\/doi.org\/10.1186\/1878-5085-3-14","DOI":"10.1186\/1878-5085-3-14"},{"key":"368_CR25","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/1878-5085-5-6","volume":"5","author":"O Golubnitschaja","year":"2014","unstructured":"Golubnitschaja O, Kinkorova J, Costigliola V. Predictive, preventive and personalised medicine as the hardcore of \u2018Horizon 2020\u2019: EPMA position paper. EPMA J. 2014;5:6. https:\/\/doi.org\/10.1186\/1878-5085-5-6.","journal-title":"EPMA J"},{"key":"368_CR26","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1002\/sim.6793","volume":"35","author":"M Wang","year":"2016","unstructured":"Wang M, et al. Statistical methods for studying disease subtype heterogeneity. Stat Med. 2016;35:782\u2013800. https:\/\/doi.org\/10.1002\/sim.6793.","journal-title":"Stat Med"},{"key":"368_CR27","doi-asserted-by":"publisher","first-page":"4216","DOI":"10.1038\/s41598-018-22578-1","volume":"8","author":"A Giannoula","year":"2018","unstructured":"Giannoula A, Gutierrez-Sacrist\u00e1n A, Bravo \u00c1, Sanz F, Furlong LI. Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study. Sci Rep. 2018;8:4216. https:\/\/doi.org\/10.1038\/s41598-018-22578-1.","journal-title":"Sci Rep"},{"key":"368_CR28","doi-asserted-by":"publisher","first-page":"2897","DOI":"10.1038\/s41598-023-30038-8","volume":"13","author":"C Birkenbihl","year":"2023","unstructured":"Birkenbihl C, et al. Artificial intelligence-based clustering and characterization of Parkinson\u2019s disease trajectories. Sci Rep. 2023;13:2897. https:\/\/doi.org\/10.1038\/s41598-023-30038-8.","journal-title":"Sci Rep"},{"key":"368_CR29","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s13167-016-0072-4","volume":"7","author":"O Golubnitschaja","year":"2016","unstructured":"Golubnitschaja O, et al. Medicine in the early twenty-first century: paradigm and anticipation - EPMA position paper 2016. EPMA J. 2016;7:23. https:\/\/doi.org\/10.1186\/s13167-016-0072-4.","journal-title":"EPMA J"},{"key":"368_CR30","doi-asserted-by":"publisher","first-page":"giz134","DOI":"10.1093\/gigascience\/giz134","volume":"8","author":"J de Jong","year":"2019","unstructured":"de Jong J, et al. Deep learning for clustering of multivariate clinical patient trajectories with missing values. GigaScience. 2019;8:giz134. https:\/\/doi.org\/10.1093\/gigascience\/giz134.","journal-title":"GigaScience"},{"key":"368_CR31","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0233296","volume":"15","author":"S Krishnagopal","year":"2020","unstructured":"Krishnagopal S, Coelln RV, Shulman LM, Girvan M. Identifying and predicting Parkinson\u2019s disease subtypes through trajectory clustering via bipartite networks. PLoS ONE. 2020;15: e0233296. https:\/\/doi.org\/10.1371\/journal.pone.0233296.","journal-title":"PLoS ONE"},{"key":"368_CR32","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1212\/WNL.54.2.452","volume":"54","author":"K Marder","year":"2000","unstructured":"Marder K, et al. Rate of functional decline in Huntington\u2019s disease. Neurology. 2000;54:452\u2013452. https:\/\/doi.org\/10.1212\/WNL.54.2.452.","journal-title":"Neurology"},{"key":"368_CR33","doi-asserted-by":"publisher","first-page":"3251","DOI":"10.1002\/sim.8932","volume":"40","author":"L K\u00fchnel","year":"2021","unstructured":"K\u00fchnel L, Berger A-K, Markussen B, Raket LL. Simultaneous modeling of Alzheimer\u2019s disease progression via multiple cognitive scales. Stat Med. 2021;40:3251\u201366. https:\/\/doi.org\/10.1002\/sim.8932.","journal-title":"Stat Med"},{"key":"368_CR34","doi-asserted-by":"publisher","first-page":"18928","DOI":"10.1038\/s41598-022-18848-8","volume":"12","author":"I Koval","year":"2022","unstructured":"Koval I, et al. Forecasting individual progression trajectories in Huntington disease enables more powered clinical trials. Sci Rep. 2022;12:18928. https:\/\/doi.org\/10.1038\/s41598-022-18848-8.","journal-title":"Sci Rep"},{"key":"368_CR35","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1177\/0962280217737566","volume":"28","author":"D Li","year":"2019","unstructured":"Li D, Iddi S, Thompson WK, Donohue MC, Alzheimer\u2019s Disease Neuroimaging Initiative. Bayesian latent time joint mixed effect models for multicohort longitudinal data. Stat Methods Med Res. 2019;28:835\u201345. https:\/\/doi.org\/10.1177\/0962280217737566.","journal-title":"Stat Methods Med Res"},{"key":"368_CR36","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3389\/fdata.2020.00024","volume":"3","author":"LL Raket","year":"2020","unstructured":"Raket LL. Statistical disease progression modeling in Alzheimer disease. Front Big Data. 2020;3:24. https:\/\/doi.org\/10.3389\/fdata.2020.00024.","journal-title":"Front Big Data"},{"key":"368_CR37","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1002\/mdc3.12388","volume":"4","author":"GB Landwehrmeyer","year":"2017","unstructured":"Landwehrmeyer GB, et al. Data analytics from Enroll-HD, a global clinical research platform for Huntington\u2019s disease. Mov Disord Clin Pract. 2017;4:212\u201324. https:\/\/doi.org\/10.1002\/mdc3.12388.","journal-title":"Mov Disord Clin Pract"},{"key":"368_CR38","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2021.667420","volume":"12","author":"S Sathe","year":"2021","unstructured":"Sathe S, et al. Enroll-HD: an integrated clinical research platform and worldwide observational study for Huntington\u2019s disease. Front Neurol. 2021;12: 667420. https:\/\/doi.org\/10.3389\/fneur.2021.667420.","journal-title":"Front Neurol"},{"key":"368_CR39","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1002\/mds.870110204","volume":"11","author":"K Kieburtz","year":"1996","unstructured":"Kieburtz K, Penney JB, Como P, Ranen N, Shoulson I. Unified Huntington\u2019s Disease Rating Scale: reliability and consistency. Huntington Study Group. Mov Disord Off J Mov Disord Soc. 1996;11:136\u201342. https:\/\/doi.org\/10.1002\/mds.870110204.","journal-title":"Mov Disord Off J Mov Disord Soc"},{"key":"368_CR40","unstructured":"Raket LL. progmod. 2020. at <https:\/\/github.com\/larslau\/progmod>."},{"key":"368_CR41","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1002\/sim.6052","volume":"33","author":"J Pinheiro","year":"2014","unstructured":"Pinheiro J, Bornkamp B, Glimm E, Bretz F. Model-based dose finding under model uncertainty using general parametric models. Stat Med. 2014;33:1646\u201361. https:\/\/doi.org\/10.1002\/sim.6052.","journal-title":"Stat Med"},{"key":"368_CR42","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324.","journal-title":"Mach Learn"},{"key":"368_CR43","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 785\u2013794 (ACM, 2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"368_CR44","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"368_CR45","doi-asserted-by":"publisher","unstructured":"Lundberg S, Lee S-I. A unified approach to interpreting model predictions. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1705.07874 (2017) https:\/\/doi.org\/10.48550\/arXiv.1705.07874","DOI":"10.48550\/arXiv.1705.07874 10.48550\/arXiv.1705.07874"},{"key":"368_CR46","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1002\/mds.27227","volume":"33","author":"TA Mestre","year":"2018","unstructured":"Mestre TA, et al. Rating scales for cognition in Huntington\u2019s disease: critique and recommendations. Mov Disord Off J Mov Disord Soc. 2018;33:187\u201395. https:\/\/doi.org\/10.1002\/mds.27227.","journal-title":"Mov Disord Off J Mov Disord Soc"},{"key":"368_CR47","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1002\/mdc3.12571","volume":"5","author":"TA Mestre","year":"2018","unstructured":"Mestre TA, et al. Rating scales for motor symptoms and signs in Huntington\u2019s disease: critique and recommendations. Mov Disord Clin Pract. 2018;5:111\u20137. https:\/\/doi.org\/10.1002\/mdc3.12571.","journal-title":"Mov Disord Clin Pract"},{"key":"368_CR48","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.neuro.2017.01.005","volume":"61","author":"T-K Chao","year":"2017","unstructured":"Chao T-K, Hu J, Pringsheim T. Risk factors for the onset and progression of Huntington disease. Neurotoxicology. 2017;61:79\u201399. https:\/\/doi.org\/10.1016\/j.neuro.2017.01.005.","journal-title":"Neurotoxicology"},{"key":"368_CR49","doi-asserted-by":"publisher","first-page":"e2395","DOI":"10.1212\/WNL.0000000000011893","volume":"96","author":"B McAllister","year":"2021","unstructured":"McAllister B, et al. Timing and impact of psychiatric, cognitive, and motor abnormalities in Huntington disease. Neurology. 2021;96:e2395\u2013406. https:\/\/doi.org\/10.1212\/WNL.0000000000011893.","journal-title":"Neurology"},{"key":"368_CR50","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/S1474-4422(13)70088-7","volume":"12","author":"SJ Tabrizi","year":"2013","unstructured":"Tabrizi SJ, et al. Predictors of phenotypic progression and disease onset in premanifest and early-stage Huntington\u2019s disease in the TRACK-HD study: analysis of 36-month observational data. Lancet Neurol. 2013;12:637\u201349. https:\/\/doi.org\/10.1016\/S1474-4422(13)70088-7.","journal-title":"Lancet Neurol"},{"key":"368_CR51","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.neubiorev.2013.01.022","volume":"37","author":"N Georgiou-Karistianis","year":"2013","unstructured":"Georgiou-Karistianis N, Scahill R, Tabrizi SJ, Squitieri F, Aylward E. Structural MRI in Huntington\u2019s disease and recommendations for its potential use in clinical trials. Neurosci Biobehav Rev. 2013;37:480\u201390. https:\/\/doi.org\/10.1016\/j.neubiorev.2013.01.022.","journal-title":"Neurosci Biobehav Rev"},{"key":"368_CR52","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/S1474-4422(17)30124-2","volume":"16","author":"LM Byrne","year":"2017","unstructured":"Byrne LM, et al. Neurofilament light protein in blood as a potential biomarker of neurodegeneration in Huntington\u2019s disease: a retrospective cohort analysis. Lancet Neurol. 2017;16:601\u20139. https:\/\/doi.org\/10.1016\/S1474-4422(17)30124-2.","journal-title":"Lancet Neurol"}],"container-title":["EPMA Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13167-024-00368-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13167-024-00368-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13167-024-00368-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T12:42:00Z","timestamp":1718368920000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13167-024-00368-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,28]]},"references-count":52,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["368"],"URL":"https:\/\/doi.org\/10.1007\/s13167-024-00368-2","relation":{},"ISSN":["1878-5085"],"issn-type":[{"value":"1878-5085","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,28]]},"assertion":[{"value":"4 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Holger Fr\u00f6hlich has received grants from UCB and AbbVie outside this work. All other authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The funders played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclaimer"}}]}}