{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T13:24:19Z","timestamp":1792157059518,"version":"4.4.0"},"reference-count":82,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001826","name":"ZonMw","doi-asserted-by":"publisher","award":["733050516"],"award-info":[{"award-number":["733050516"]}],"id":[{"id":"10.13039\/501100001826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Parkinsons Dis."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Cognitive impairment is a debilitating symptom in Parkinson\u2019s disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson\u2019s Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic\/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic\/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.<\/jats:p>","DOI":"10.1038\/s41531-022-00409-5","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T02:02:42Z","timestamp":1667786562000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Machine learning-based prediction of cognitive outcomes in de novo Parkinson\u2019s disease"],"prefix":"10.1038","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6423-9983","authenticated-orcid":false,"given":"Joshua","family":"Harvey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7599-0385","authenticated-orcid":false,"given":"Rick A.","family":"Reijnders","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3796-1687","authenticated-orcid":false,"given":"Rachel","family":"Cavill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0279-1806","authenticated-orcid":false,"given":"Annelien","family":"Duits","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"K\u00f6hler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6473-2839","authenticated-orcid":false,"given":"Lars","family":"Eijssen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bart P. F.","family":"Rutten","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gemma","family":"Shireby","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Torkamani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6490-6037","authenticated-orcid":false,"given":"Byron","family":"Creese","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert F. G.","family":"Leentjens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katie","family":"Lunnon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8964-0682","authenticated-orcid":false,"given":"Ehsan","family":"Pishva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"409_CR1","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/S1474-4422(12)70152-7","volume":"11","author":"P Svenningsson","year":"2012","unstructured":"Svenningsson, P., Westman, E., Ballard, C. & Aarsland, D. Cognitive impairment in patients with Parkinson\u2019s disease: diagnosis, biomarkers, and treatment. Lancet Neurol. 11, 697\u2013707 (2012).","journal-title":"Lancet Neurol."},{"key":"409_CR2","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1002\/mds.20527","volume":"20","author":"D Aarsland","year":"2005","unstructured":"Aarsland, D., Zaccai, J. & Brayne, C. A systematic review of prevalence studies of dementia in Parkinson\u2019s disease. Mov. Disord. 20, 1255\u20131263 (2005).","journal-title":"Mov. Disord."},{"key":"409_CR3","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1212\/01.wnl.0000338632.00552.cb","volume":"72","author":"D Aarsland","year":"2009","unstructured":"Aarsland, D. et al. Cognitive impairment in incident, untreated Parkinson disease The Norwegian ParkWest Study. Neurology 72, 1121\u20131126 (2009).","journal-title":"Neurology"},{"key":"409_CR4","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1038\/nrneurol.2017.27","volume":"13","author":"D Aarsland","year":"2017","unstructured":"Aarsland, D. et al. Cognitive decline in Parkinson disease. Nat. Rev. Neurol. 13, 217\u2013231 (2017).","journal-title":"Nat. Rev. Neurol."},{"key":"409_CR5","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1136\/jnnp-2013-305277","volume":"84","author":"CH Williams-Gray","year":"2013","unstructured":"Williams-Gray, C. H. et al. The CamPaIGN study of Parkinson\u2019s disease: 10-year outlook in an incident population-based cohort. J. Neurol. Neurosurg. Psychiatry 84, 1258\u20131264 (2013).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"409_CR6","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1177\/0891988717701001","volume":"30","author":"KA Wyman-Chick","year":"2017","unstructured":"Wyman-Chick, K. A., Martin, P. K., Barrett, M. J., Manning, C. A. & Sperling, S. A. Diagnostic accuracy and confidence in the clinical detection of cognitive impairment in early-stage Parkinson disease. J. Geriatr. Psychiatry Neurol. 30, 178\u2013183 (2017).","journal-title":"J. Geriatr. Psychiatry Neurol."},{"key":"409_CR7","first-page":"91","volume":"1","author":"HM Kim","year":"2019","unstructured":"Kim, H. M. et al. Prediction of cognitive progression in Parkinson\u2019s disease using three cognitive screening measures. Clin. Park Relat. Disord. 1, 91\u201397 (2019).","journal-title":"Clin. Park Relat. Disord."},{"key":"409_CR8","doi-asserted-by":"publisher","first-page":"1784","DOI":"10.1212\/WNL.0000000000000425","volume":"82","author":"G Alves","year":"2014","unstructured":"Alves, G. et al. CSF Abeta42 predicts early-onset dementia in Parkinson disease. Neurology 82, 1784\u20131790 (2014).","journal-title":"Neurology"},{"key":"409_CR9","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1001\/archneurol.2011.17","volume":"68","author":"N Seto-Salvia","year":"2011","unstructured":"Seto-Salvia, N. et al. Dementia risk in Parkinson disease: disentangling the role of MAPT haplotypes. Arch. Neurol. 68, 359\u2013364 (2011).","journal-title":"Arch. Neurol."},{"key":"409_CR10","doi-asserted-by":"crossref","unstructured":"Smith, N. et al. Predicting future cognitive impairment in de novo Parkinson\u2019s disease using clinical data and structural MRI. medRxiv, https:\/\/www.medrxiv.org\/content\/10.1101\/2021.08.13.21261662v1 (2021).","DOI":"10.1101\/2021.08.13.21261662"},{"key":"409_CR11","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1111\/ejn.14094","volume":"49","author":"JC Greenland","year":"2019","unstructured":"Greenland, J. C., Williams-Gray, C. H. & Barker, R. A. The clinical heterogeneity of Parkinson\u2019s disease and its therapeutic implications. Eur. J. Neurosci. 49, 328\u2013338 (2019).","journal-title":"Eur. J. Neurosci."},{"key":"409_CR12","doi-asserted-by":"publisher","first-page":"e2136553","DOI":"10.1001\/jamanetworkopen.2021.36553","volume":"4","author":"C James","year":"2021","unstructured":"James, C., Ranson, J. M., Everson, R. & Llewellyn, D. J. Performance of machine learning algorithms for predicting progression to dementia in memory clinic patients. JAMA Netw. Open 4, e2136553 (2021).","journal-title":"JAMA Netw. Open"},{"key":"409_CR13","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1016\/S1474-4422(17)30122-9","volume":"16","author":"G Liu","year":"2017","unstructured":"Liu, G. et al. Prediction of cognition in Parkinson\u2019s disease with a clinical-genetic score: a longitudinal analysis of nine cohorts. Lancet Neurol. 16, 620\u2013629 (2017).","journal-title":"Lancet Neurol."},{"key":"409_CR14","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/s41531-020-00121-2","volume":"6","author":"T Phongpreecha","year":"2020","unstructured":"Phongpreecha, T. et al. Multivariate prediction of dementia in Parkinson\u2019s disease. npj Parkinsons Dis. 6, 20 (2020).","journal-title":"npj Parkinsons Dis."},{"key":"409_CR15","doi-asserted-by":"publisher","first-page":"633752","DOI":"10.3389\/fnagi.2021.633752","volume":"13","author":"J Mei","year":"2021","unstructured":"Mei, J., Desrosiers, C. & Frasnelli, J. Machine learning for the diagnosis of Parkinson\u2019s disease: a review of literature. Front. Aging Neurosci. 13, 633752 (2021).","journal-title":"Front. Aging Neurosci."},{"key":"409_CR16","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41531-020-00127-w","volume":"6","author":"C Su","year":"2020","unstructured":"Su, C., Tong, J. & Wang, F. Mining genetic and transcriptomic data using machine learning approaches in Parkinson\u2019s disease. npj Parkinsons Dis. 6, 24 (2020).","journal-title":"npj Parkinsons Dis."},{"key":"409_CR17","doi-asserted-by":"publisher","first-page":"104142","DOI":"10.1016\/j.compbiomed.2020.104142","volume":"129","author":"MR Salmanpour","year":"2021","unstructured":"Salmanpour, M. R. et al. Robust identification of Parkinsonas disease subtypes using radiomics and hybrid machine learning. Computers Biol. Med. 129, 104142 (2021).","journal-title":"Computers Biol. Med."},{"key":"409_CR18","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1038\/s41598-018-37545-z","volume":"9","author":"X Zhang","year":"2019","unstructured":"Zhang, X. et al. Data-driven subtyping of parkinson\u2019s disease using longitudinal clinical records: a cohort study. Sci. Rep. 9, 797 (2019).","journal-title":"Sci. Rep."},{"key":"409_CR19","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1016\/S1474-4422(17)30328-9","volume":"16","author":"JC Latourelle","year":"2017","unstructured":"Latourelle, J. C. et al. Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson\u2019s disease: a longitudinal cohort study and validation. Lancet Neurol. 16, 908\u2013916 (2017).","journal-title":"Lancet Neurol."},{"key":"409_CR20","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1002\/mrm.28522","volume":"85","author":"ZY Shu","year":"2021","unstructured":"Shu, Z. Y. et al. Predicting the progression of Parkinson\u2019s disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole-brain white matter. Magn. Reson. Med. 85, 1611\u20131624 (2021).","journal-title":"Magn. Reson. Med."},{"key":"409_CR21","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41531-019-0086-4","volume":"5","author":"DA Rastegar","year":"2019","unstructured":"Rastegar, D. A., Ho, N., Halliday, G. M. & Dzamko, N. Parkinson\u2019s progression prediction using machine learning and serum cytokines. npj Parkinsons Dis. 5, 14 (2019).","journal-title":"npj Parkinsons Dis."},{"key":"409_CR22","doi-asserted-by":"publisher","first-page":"103347","DOI":"10.1016\/j.compbiomed.2019.103347","volume":"111","author":"MR Salmanpour","year":"2019","unstructured":"Salmanpour, M. R. et al. Optimized machine learning methods for prediction of cognitive outcome in Parkinsonas disease. Computers Biol. Med. 111, 103347 (2019).","journal-title":"Computers Biol. Med."},{"key":"409_CR23","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1002\/acn3.644","volume":"5","author":"K Marek","year":"2018","unstructured":"Marek, K. et al. The Parkinson\u2019s progression markers initiative (PPMI)\u2014establishing a PD biomarker cohort. Ann. Clin. Transl. Neurol. 5, 1460\u20131477 (2018).","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"409_CR24","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1002\/mds.26170","volume":"30","author":"D Weintraub","year":"2015","unstructured":"Weintraub, D. et al. Cognitive performance and neuropsychiatric symptoms in early, untreated Parkinson\u2019s disease. Mov. Disord. 30, 919\u2013927 (2015).","journal-title":"Mov. Disord."},{"key":"409_CR25","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.parkreldis.2017.02.017","volume":"38","author":"ME Domellof","year":"2017","unstructured":"Domellof, M. E., Lundin, K. F., Edstrom, M. & Forsgren, L. Olfactory dysfunction and dementia in newly diagnosed patients with Parkinson\u2019s disease. Parkinsonism Relat. Disord. 38, 41\u201347 (2017).","journal-title":"Parkinsonism Relat. Disord."},{"key":"409_CR26","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s00702-019-01996-z","volume":"126","author":"MP Cecchini","year":"2019","unstructured":"Cecchini, M. P. et al. Olfaction and taste in Parkinson\u2019s disease: the association with mild cognitive impairment and the single cognitive domain dysfunction. J. Neural Transm. (Vienna) 126, 585\u2013595 (2019).","journal-title":"J. Neural Transm. (Vienna)"},{"key":"409_CR27","doi-asserted-by":"publisher","first-page":"133","DOI":"10.14802\/jmd.19082","volume":"13","author":"HS Yoo","year":"2020","unstructured":"Yoo, H. S. et al. Association between olfactory deficit and motor and cognitive function in Parkinson\u2019s disease. J. Mov. Disord. 13, 133\u2013141 (2020).","journal-title":"J. Mov. Disord."},{"key":"409_CR28","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.parkreldis.2016.02.013","volume":"25","author":"ME Fullard","year":"2016","unstructured":"Fullard, M. E. et al. Olfactory impairment predicts cognitive decline in early Parkinson\u2019s disease. Parkinsonism Relat. Disord. 25, 45\u201351 (2016).","journal-title":"Parkinsonism Relat. Disord."},{"key":"409_CR29","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1037\/neu0000478","volume":"32","author":"KAE Martens","year":"2018","unstructured":"Martens, K. A. E., Silveira, C. R. A., Intzandt, B. N. & Almeida, Q. J. State anxiety predicts cognitive performance in patients with Parkinson\u2019s disease. Neuropsychology 32, 950\u2013957 (2018).","journal-title":"Neuropsychology"},{"key":"409_CR30","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1016\/j.jagp.2016.05.015","volume":"24","author":"B Gulpers","year":"2016","unstructured":"Gulpers, B. et al. Anxiety as a predictor for cognitive decline and dementia: a systematic review and meta-analysis. Am. J. Geriatr. Psychiatry 24, 823\u2013842 (2016).","journal-title":"Am. J. Geriatr. Psychiatry"},{"key":"409_CR31","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s13059-021-02275-5","volume":"22","author":"MF Nabais","year":"2021","unstructured":"Nabais, M. F. et al. Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders. Genome Biol. 22, 90 (2021).","journal-title":"Genome Biol."},{"key":"409_CR32","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/S1474-4422(16)30328-3","volume":"16","author":"A Schrag","year":"2017","unstructured":"Schrag, A., Siddiqui, U. F., Anastasiou, Z., Weintraub, D. & Schott, J. M. Clinical variables and biomarkers in prediction of cognitive CrossMark impairment in patients with newly diagnosed Parkinson\u2019s disease: a cohort study. Lancet Neurol. 16, 66\u201375 (2017).","journal-title":"Lancet Neurol."},{"key":"409_CR33","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1109\/TNN.2010.2041069","volume":"21","author":"Y Aksu","year":"2010","unstructured":"Aksu, Y., Miller, D. J., Kesidis, G. & Yang, Q. X. Margin-maximizing feature elimination methods for linear and nonlinear Kernel-based discriminant functions. IEEE Trans. Neural Netw. 21, 701\u2013717 (2010).","journal-title":"IEEE Trans. Neural Netw."},{"key":"409_CR34","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389\u2013422 (2002).","journal-title":"Mach. Learn."},{"key":"409_CR35","doi-asserted-by":"publisher","first-page":"103347","DOI":"10.1016\/j.compbiomed.2019.103347","volume":"111","author":"MR Salmanpour","year":"2019","unstructured":"Salmanpour, M. R. et al. Optimized machine learning methods for prediction of cognitive outcome in Parkinson\u2019s disease. Comput Biol. Med. 111, 103347 (2019).","journal-title":"Comput Biol. Med."},{"key":"409_CR36","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1016\/S1474-4422(17)30170-9","volume":"16","author":"D Aarsland","year":"2017","unstructured":"Aarsland, D., Creese, B. & Chaudhuri, K. R. A new tool to identify patients with Parkinson\u2019s disease at increased risk of dementia. Lancet Neurol. 16, 576\u2013578 (2017).","journal-title":"Lancet Neurol."},{"key":"409_CR37","doi-asserted-by":"publisher","first-page":"3735","DOI":"10.1016\/j.csbj.2021.06.030","volume":"19","author":"M Picard","year":"2021","unstructured":"Picard, M., Scott-Boyer, M. P., Bodein, A., Perin, O. & Droit, A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct. Biotechnol. J. 19, 3735\u20133746 (2021).","journal-title":"Comput Struct. Biotechnol. J."},{"key":"409_CR38","doi-asserted-by":"publisher","first-page":"e0175674","DOI":"10.1371\/journal.pone.0175674","volume":"12","author":"C Caspell-Garcia","year":"2017","unstructured":"Caspell-Garcia, C. et al. Multiple modality biomarker prediction of cognitive impairment in prospectively followed de novo Parkinson disease. PLoS ONE 12, e0175674 (2017).","journal-title":"PLoS ONE"},{"key":"409_CR39","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1093\/brain\/awaa461","volume":"144","author":"NP Oxtoby","year":"2021","unstructured":"Oxtoby, N. P. et al. Sequence of clinical and neurodegeneration events in Parkinson\u2019s disease progression. Brain 144, 975\u2013988 (2021).","journal-title":"Brain"},{"key":"409_CR40","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1001\/archneur.62.2.281","volume":"62","author":"C Summerfield","year":"2005","unstructured":"Summerfield, C. et al. Structural brain changes in Parkinson disease with dementia: a voxel-based morphometry study. Arch. Neurol. 62, 281\u2013285 (2005).","journal-title":"Arch. Neurol."},{"key":"409_CR41","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.neulet.2017.08.028","volume":"658","author":"Y Gao","year":"2017","unstructured":"Gao, Y. et al. Changes of brain structure in Parkinson\u2019s disease patients with mild cognitive impairment analyzed via VBM technology. Neurosci. Lett. 658, 121\u2013132 (2017).","journal-title":"Neurosci. Lett."},{"key":"409_CR42","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1002\/hbm.20221","volume":"27","author":"SG Costafreda","year":"2006","unstructured":"Costafreda, S. G. et al. A systematic review and quantitative appraisal of fMRI studies of verbal fluency: role of the left inferior frontal gyrus. Hum. Brain Mapp. 27, 799\u2013810 (2006).","journal-title":"Hum. Brain Mapp."},{"key":"409_CR43","doi-asserted-by":"publisher","first-page":"793","DOI":"10.3233\/JPD-150623","volume":"5","author":"O Lucas-Jimenez","year":"2015","unstructured":"Lucas-Jimenez, O. et al. Verbal memory in Parkinson\u2019s disease: a combined DTI and fMRI study. J. Parkinsons Dis. 5, 793\u2013804 (2015).","journal-title":"J. Parkinsons Dis."},{"key":"409_CR44","first-page":"10","volume":"11","author":"LHT Fornari","year":"2021","unstructured":"Fornari, L. H. T., da Silva Junior, N., Muratt Carpenedo, C., Hilbig, A. & Rieder, C. R. M. Striatal dopamine correlates to memory and attention in Parkinson\u2019s disease. Am. J. Nucl. Med. Mol. Imaging 11, 10\u201319 (2021).","journal-title":"Am. J. Nucl. Med. Mol. Imaging"},{"key":"409_CR45","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1002\/hbm.22256","volume":"35","author":"Z Zheng","year":"2014","unstructured":"Zheng, Z. et al. DTI correlates of distinct cognitive impairments in Parkinson\u2019s disease. Hum. Brain Mapp. 35, 1325\u20131333 (2014).","journal-title":"Hum. Brain Mapp."},{"key":"409_CR46","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1002\/mds.24893","volume":"27","author":"I Litvan","year":"2012","unstructured":"Litvan, I. et al. Diagnostic criteria for mild cognitive impairment in Parkinson\u2019s disease: movement Disorder Society Task Force guidelines. Mov. Disord. 27, 349\u2013356 (2012).","journal-title":"Mov. Disord."},{"key":"409_CR47","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.1002\/mds.21507","volume":"22","author":"M Emre","year":"2007","unstructured":"Emre, M. et al. Clinical diagnostic criteria for dementia associated with Parkinson\u2019s disease. Mov. Disord. 22, 1689\u20131707 (2007).","journal-title":"Mov. Disord."},{"key":"409_CR48","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1002\/mds.26374","volume":"31","author":"MA Nalls","year":"2016","unstructured":"Nalls, M. A. et al. Baseline genetic associations in the Parkinson\u2019s Progression Markers Initiative (PPMI). Mov. Disord. 31, 79\u201385 (2016).","journal-title":"Mov. Disord."},{"key":"409_CR49","doi-asserted-by":"publisher","first-page":"e1608","DOI":"10.1002\/mpr.1608","volume":"27","author":"AT Marees","year":"2018","unstructured":"Marees, A. T. et al. A tutorial on conducting genome\u2010wide association studies: quality control and statistical analysis. Int. J. methods Psychiatr. Res. 27, e1608 (2018).","journal-title":"Int. J. methods Psychiatr. Res."},{"key":"409_CR50","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/s41588-019-0358-2","volume":"51","author":"BW Kunkle","year":"2019","unstructured":"Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer\u2019s disease identifies new risk loci and implicates A\u03b2, tau, immunity and lipid processing. Nat. Genet. 51, 414\u2013430 (2019).","journal-title":"Nat. Genet."},{"key":"409_CR51","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1001\/jamaneurol.2013.1925","volume":"70","author":"MA Nalls","year":"2013","unstructured":"Nalls, M. A. et al. A multicenter study of glucocerebrosidase mutations in dementia with Lewy bodies. JAMA Neurol. 70, 727\u2013735 (2013).","journal-title":"JAMA Neurol."},{"key":"409_CR52","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1038\/s41588-018-0147-3","volume":"50","author":"JJ Lee","year":"2018","unstructured":"Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112\u20131121 (2018).","journal-title":"Nat. Genet."},{"key":"409_CR53","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/nature13595","volume":"511","author":"C Pantelis","year":"2014","unstructured":"Pantelis, C. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421\u2013427 (2014).","journal-title":"Nature"},{"key":"409_CR54","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1038\/s41588-018-0090-3","volume":"50","author":"NR Wray","year":"2018","unstructured":"Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668\u2013681 (2018).","journal-title":"Nat. Genet."},{"key":"409_CR55","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1161\/CIRCRESAHA.117.312086","volume":"122","author":"P Van Der Harst","year":"2018","unstructured":"Van Der Harst, P. & Verweij, N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ. Res. 122, 433\u2013443 (2018).","journal-title":"Circ. Res."},{"key":"409_CR56","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giz082","volume":"8","author":"SW Choi","year":"2019","unstructured":"Choi, S. W. & O\u2019Reilly, P. F. PRSice-2: Polygenic Risk Score software for biobank-scale data. Gigascience 8, giz082 (2019).","journal-title":"Gigascience"},{"key":"409_CR57","doi-asserted-by":"publisher","first-page":"2759","DOI":"10.1038\/s41596-020-0353-1","volume":"15","author":"SW Choi","year":"2020","unstructured":"Choi, S. W., Mak, T. S.-H. & O\u2019Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15, 2759\u20132772 (2020).","journal-title":"Nat. Protoc."},{"key":"409_CR58","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.ajhg.2020.08.019","volume":"107","author":"P Garg","year":"2020","unstructured":"Garg, P. et al. A survey of rare epigenetic variation in 23,116 human genomes identifies disease-relevant epivariations and CGG expansions. Am. J. Hum. Genet. 107, 654\u2013669 (2020).","journal-title":"Am. J. Hum. Genet."},{"key":"409_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2164-14-293","volume":"14","author":"R Pidsley","year":"2013","unstructured":"Pidsley, R. et al. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14, 1\u201310 (2013).","journal-title":"BMC Genomics"},{"key":"409_CR60","first-page":"1277","volume":"70","author":"JH Kang","year":"2013","unstructured":"Kang, J. H. et al. Association of cerebrospinal fluid beta-amyloid 1-42, T-tau, P-tau181, and alpha-synuclein levels with clinical features of drug-naive patients with early Parkinson disease. JAMA Neurol. 70, 1277\u20131287 (2013).","journal-title":"JAMA Neurol."},{"key":"409_CR61","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1212\/WNL.0000000000004609","volume":"89","author":"B Mollenhauer","year":"2017","unstructured":"Mollenhauer, B. et al. Longitudinal CSF biomarkers in patients with early Parkinson disease and healthy controls. Neurology 89, 1959\u20131969 (2017).","journal-title":"Neurology"},{"key":"409_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.parkreldis.2020.12.020","volume":"84","author":"C Koros","year":"2021","unstructured":"Koros, C. et al. Serum uric acid level as a putative biomarker in Parkinson? s disease patients carrying GBA1 mutations: 2-Year data from the PPMI study. Parkinsonism Relat. Disord. 84, 1\u20134 (2021).","journal-title":"Parkinsonism Relat. Disord."},{"key":"409_CR63","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1186\/s13041-020-00637-x","volume":"13","author":"ZQ Jiang","year":"2020","unstructured":"Jiang, Z. Q. et al. Characterization of a pathogenic variant in GBA for Parkinsonas disease with mild cognitive impairment patients. Mol. Brain 13, 102 (2020).","journal-title":"Mol. Brain"},{"key":"409_CR64","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1159\/000493103","volume":"18","author":"F Sampedro","year":"2018","unstructured":"Sampedro, F., Marin-Lahoz, J., Martinez-Horta, S., Pagonabarraga, J. & Kulisevsky, J. Cortical thinning associated with age and CSF biomarkers in early Parkinson\u2019s disease is modified by the SNCA rs356181 polymorphism. Neurodegenerative Dis. 18, 233\u2013238 (2018).","journal-title":"Neurodegenerative Dis."},{"key":"409_CR65","doi-asserted-by":"publisher","first-page":"787","DOI":"10.3349\/ymj.2018.59.6.787","volume":"59","author":"Y Seo","year":"2018","unstructured":"Seo, Y. et al. Effect of rs3910105 in the synuclein gene on dopamine transporter availability in healthy subjects. Yonsei Med. J. 59, 787\u2013792 (2018).","journal-title":"Yonsei Med. J."},{"key":"409_CR66","doi-asserted-by":"publisher","first-page":"389","DOI":"10.3233\/JPD-181549","volume":"9","author":"YH Chuang","year":"2019","unstructured":"Chuang, Y. H. et al. Longitudinal epigenome-wide methylation study of cognitive decline and motor progression in Parkinson\u2019s disease. J. Parkinsons Dis. 9, 389\u2013400 (2019).","journal-title":"J. Parkinsons Dis."},{"key":"409_CR67","doi-asserted-by":"publisher","first-page":"303","DOI":"10.18632\/aging.101684","volume":"11","author":"AT Lu","year":"2019","unstructured":"Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303\u2013327 (2019).","journal-title":"Aging"},{"key":"409_CR68","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.18632\/aging.101508","volume":"10","author":"S Horvath","year":"2018","unstructured":"Horvath, S. et al. Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging 10, 1758\u20131775 (2018).","journal-title":"Aging"},{"key":"409_CR69","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.molcel.2012.10.016","volume":"49","author":"G Hannum","year":"2013","unstructured":"Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359\u2013367 (2013).","journal-title":"Mol. Cell"},{"key":"409_CR70","unstructured":"Hastie, T., Tibshirani, R., Narasimhan, B. & Chu, G. impute: Imputation for microarray data. R package version 1.70.0. (2022)."},{"key":"409_CR71","unstructured":"Gill, J. et al. hot.deck: Multiple Hot Deck Imputation_. R package version 1.2, https:\/\/CRAN.R-project.org\/package=hot.deck (2021)."},{"key":"409_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v045.i03","volume":"45","author":"S Van Buuren","year":"2011","unstructured":"Van Buuren, S. & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1\u201367 (2011).","journal-title":"J. Stat. Softw."},{"key":"409_CR73","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3390\/genes10020087","volume":"10","author":"B Mirza","year":"2019","unstructured":"Mirza, B. et al. Machine learning and integrative analysis of biomedical big data. Genes 10, 87 (2019).","journal-title":"Genes"},{"key":"409_CR74","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1038\/msb.2011.70","volume":"7","author":"R Norel","year":"2011","unstructured":"Norel, R., Rice, J. J. & Stolovitzky, G. The self-assessment trap: can we all be better than average?. Mol. Syst. Biol. 7, 537 (2011).","journal-title":"Mol. Syst. Biol."},{"key":"409_CR75","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1007\/s00330-020-07324-4","volume":"31","author":"B Kocak","year":"2021","unstructured":"Kocak, B., Kus, E. A. & Kilickesmez, O. How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts. Eur. Radiol. 31, 1819\u20131830 (2021).","journal-title":"Eur. Radiol."},{"key":"409_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v028.i05","volume":"28","author":"M Kuhn","year":"2008","unstructured":"Kuhn, M. Building predictive models in R using the caret Package. J. Stat. Softw. 28, 1\u201326 (2008).","journal-title":"J. Stat. Softw."},{"key":"409_CR77","unstructured":"Kelleher, J. D., Mac Namee, B. & D\u2019Arcy, A. Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies, pages cm (The MIT Press, 2020)."},{"key":"409_CR78","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1198\/106186006X133933","volume":"15","author":"T Hothorn","year":"2006","unstructured":"Hothorn, T., Hornik, K. & Zeileis, A. Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15, 651\u2013674 (2006).","journal-title":"J. Comput. Graph. Stat."},{"key":"409_CR79","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco, D. & Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21, 6 (2020).","journal-title":"BMC Genomics"},{"key":"409_CR80","first-page":"101903","volume":"59","author":"B Richhariya","year":"2020","unstructured":"Richhariya, B., Tanveer, M., Rashid, A. H. & Initia, A. D. N. Diagnosis of Alzheimeras disease using universum support vector machine based recursive feature elimination (USVM-RFE). Biomed. Signal Process. Control 59, 101903 (2020).","journal-title":"Control"},{"key":"409_CR81","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.eswa.2016.04.020","volume":"59","author":"J Lv","year":"2016","unstructured":"Lv, J., Peng, Q. K., Chen, X. & Sun, Z. A multi-objective heuristic algorithm for gene expression microarray data classification. Expert Syst. Appl. 59, 13\u201319 (2016).","journal-title":"Expert Syst. Appl."},{"key":"409_CR82","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56\u201367 (2020).","journal-title":"Nat. Mach. Intell."}],"container-title":["npj Parkinson's Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41531-022-00409-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41531-022-00409-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41531-022-00409-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T02:05:25Z","timestamp":1668045925000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41531-022-00409-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,7]]},"references-count":82,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["409"],"URL":"https:\/\/doi.org\/10.1038\/s41531-022-00409-5","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.02.02.22270300","asserted-by":"object"}]},"ISSN":["2373-8057"],"issn-type":[{"value":"2373-8057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,7]]},"assertion":[{"value":"2 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"150"}}