{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:33:19Z","timestamp":1790620399968,"version":"4.1.0"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>As a common neurodegenerative disorder, Parkinson\u2019s disease (PD) primarily affects dopaminergic neurons, leading to progressive motor disabilities along with a spectrum of non-motor complications. The early identification of Parkinson\u2019s disease, as well as the exploration of biomarkers related to its associated comorbidities, remains an important focus of current research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, a metabolomics approach combined with machine learning techniques was applied to explore potential biomarkers for PD and its related comorbid conditions. Using liquid chromatography\u2013tandem mass spectrometry (LC\u2013MS\/MS), blood plasma samples were analyzed from individuals with PD, PD with rapid eye movement sleep behavior disorder (PD+RBD), PD with insomnia (PD + insomnia), and healthy controls, resulting in the detection of 2,601 metabolites. Multivariate statistical methods\u2014including the unsupervised principal component analysis (PCA) and the supervised techniques of partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA)\u2014were employed to investigate intergroup metabolic variations. Machine learning algorithms, such as recursive feature elimination in conjunction with logistic regression, random forest, and support vector machines, were used to assist in selecting discriminative metabolites and constructing classification models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>These models showed strong internal performance in distinguishing PD from healthy individuals and in characterizing PD patients with non-motor comorbidities such as RBD and insomnia. Overall, the results suggest that metabolic biomarkers may provide valuable insights into disease-related and symptom-associated metabolic alterations in Parkinson\u2019s disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This study provides a basis for future investigations aimed at validating these findings and further exploring their potential relevance in clinical research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2025.1753016","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T06:35:09Z","timestamp":1768286109000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning-guided analysis of metabolomic alterations in Parkinson\u2019s disease with comorbid symptoms"],"prefix":"10.3389","volume":"17","author":[{"given":"Ran","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanli","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghui","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingrao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinbiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongping","family":"Lai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1186\/s40001-024-01779-7","article-title":"Role of GABA pathway in motor and non-motor symptoms in Parkinson's disease: a bidirectional circuit","volume":"29","author":"Alharbi","year":"2024","journal-title":"Eur. J. Med. Res."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1080752","DOI":"10.3389\/fneur.2023.1080752","article-title":"Toward objective monitoring of Parkinson's disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor\u00ae","volume":"14","author":"Antonini","year":"2023","journal-title":"Front. Neurol."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"3767","DOI":"10.1007\/s11042-023-15738-7","article-title":"A deep learning framework for early diagnosis of Alzheimer\u2019s disease on MRI images","volume":"83","author":"Arafa","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40708-023-00195-7","article-title":"A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer\u2019s disease","volume":"10","author":"Arya","year":"2023","journal-title":"Brain Inform"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"101830","DOI":"10.1016\/j.media.2020.101830","article-title":"A novel CERNNE approach for predicting Parkinson\u2019s disease-associated genes and brain regions based on multimodal imaging genetics data","volume":"67","author":"Bi","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1111\/jnc.15516","article-title":"The role of neuroimaging in Parkinson\u2019s disease","volume":"159","author":"Bidesi","year":"2021","journal-title":"J. Neurochem."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"29603","DOI":"10.1038\/s41598-025-13475-5","article-title":"Machine learning-assisted quantitative metabolomics of west African patients with advanced breast cancer","volume":"15","author":"Bissan","year":"2025","journal-title":"Sci. Rep."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1016\/S0140-6736(21)00218-X","article-title":"Parkinson's disease","volume":"397","author":"Bloem","year":"2021","journal-title":"Lancet"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1038\/s41746-024-01236-z","article-title":"Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson\u2019s disease","volume":"7","author":"Brzenczek","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"e806","DOI":"10.1016\/S2589-7500(22)00169-8","article-title":"A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study","volume":"4","author":"Cheung","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"612","DOI":"10.3390\/biom11040612","article-title":"Treatment options for motor and non-motor symptoms of Parkinson\u2019s disease","volume":"11","author":"Church","year":"2021","journal-title":"Biomolecules"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"e30452","DOI":"10.1016\/j.heliyon.2024.e30452","article-title":"Diagnosing Parkinson\u2019s disease and monitoring its progression: biomarkers from combined GC-TOF MS and LC-MS\/MS untargeted metabolomics","volume":"10","author":"Dahabiyeh","year":"2024","journal-title":"Heliyon"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/s41531-024-00671-9","article-title":"Comprehensive blood metabolomics profiling of Parkinson\u2019s disease reveals coordinated alterations in xanthine metabolism","volume":"10","author":"de Lope","year":"2024","journal-title":"NPJ Parkinsons Dis."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1186\/s13195-022-01047-y","article-title":"Early diagnosis of Alzheimer\u2019s disease using machine learning: a multi-diagnostic, generalizable approach","volume":"14","author":"Diogo","year":"2022","journal-title":"Alzheimer's Res Ther"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"1212","DOI":"10.3390\/brainsci13081212","article-title":"Imaging markers in genetic forms of Parkinson\u2019s disease","volume":"13","author":"Droby","year":"2023","journal-title":"Brain Sci."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"514","DOI":"10.3390\/metabo15080514","article-title":"Machine learning-driven insights in cancer metabolomics: from subtyping to biomarker discovery and prognostic modeling","volume":"15","author":"Elguoshy","year":"2025","journal-title":"Meta"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1017340","DOI":"10.3389\/fgene.2022.1017340","article-title":"Applications of machine learning in metabolomics: disease modeling and classification","volume":"13","author":"Galal","year":"2022","journal-title":"Front. Genet."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"7628","DOI":"10.1021\/acs.jcim.3c01525","article-title":"Evaluating machine learning methods of analyzing multiclass metabolomics","volume":"63","author":"Gong","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1038\/s41531-021-00216-4","article-title":"Prognostic biomarkers of Parkinson\u2019s disease in the Spanish EPIC cohort: a multiplatform metabolomics approach","volume":"7","author":"Gonzalez-Riano","year":"2021","journal-title":"NPJ Parkinsons Dis."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/s13195-021-00900-w","article-title":"Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer\u2019s disease dementia: a systematic review","volume":"13","author":"Grueso","year":"2021","journal-title":"Alzheimer's Res Ther"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1007\/s12559-021-09946-2","article-title":"Deep learning approach for early detection of Alzheimer\u2019s disease","volume":"14","author":"Helaly","year":"2022","journal-title":"Cogn. Comput."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"2501","DOI":"10.1038\/s41467-023-38248-4","article-title":"Gut microbiome dysbiosis across early Parkinson\u2019s disease, REM sleep behavior disorder and their first-degree relatives","volume":"14","author":"Huang","year":"2023","journal-title":"Nat. Commun."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1007\/s12035-021-02657-7","article-title":"Review of metabolomics-based biomarker research for Parkinson\u2019s disease","volume":"59","author":"Li","year":"2022","journal-title":"Mol. Neurobiol."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"4148","DOI":"10.3390\/ijms23084148","article-title":"Biomarker of neuroinflammation in Parkinson\u2019s disease","volume":"23","author":"Liu","year":"2022","journal-title":"Int. J. Mol. Sci."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"113976","DOI":"10.1016\/j.expneurol.2022.113976","article-title":"Etiologies of insomnia in Parkinson's disease\u2013lessons from human studies and animal models","volume":"350","author":"Mizrahi-Kliger","year":"2022","journal-title":"Exp. Neurol."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/S0140-6736(23)01478-2","article-title":"The pathogenesis of Parkinson's disease","volume":"403","author":"Morris","year":"2024","journal-title":"Lancet"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1002\/mds.29500","article-title":"Genetic testing in Parkinson's disease","volume":"38","author":"Pal","year":"2023","journal-title":"Mov. Disord."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/s13024-023-00694-5","article-title":"Untargeted serum metabolomics reveals novel metabolite associations and disruptions in amino acid and lipid metabolism in Parkinson\u2019s disease","volume":"18","author":"Paul","year":"2023","journal-title":"Mol. Neurodegener."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41531-023-00449-5","article-title":"A systematic review and meta-analysis of inflammatory biomarkers in Parkinson\u2019s disease","volume":"9","author":"Qu","year":"2023","journal-title":"NPJ Parkinsons Dis."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"848","DOI":"10.3390\/biology13110848","article-title":"Integrating molecular perspectives: strategies for comprehensive multi-omics integrative data analysis and machine learning applications in transcriptomics, proteomics, and metabolomics","volume":"13","author":"Sanches","year":"2024","journal-title":"Biology"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"3168","DOI":"10.1021\/acschemneuro.4c00355","article-title":"Metabolomics unveils disrupted pathways in Parkinson\u2019s disease: toward biomarker-based diagnosis","volume":"15","author":"Santos","year":"2024","journal-title":"ACS Chem. Neurosci."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"111875","DOI":"10.1016\/j.mad.2023.111875","article-title":"Exploring the interplay between metabolomics and genetics in Parkinson's disease: insights from ongoing research and future avenues","volume":"216","author":"Santos-Reboucas","year":"2023","journal-title":"Mech. Ageing Dev."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1001\/jamaneurol.2022.1783","article-title":"Six action steps to address global disparities in Parkinson disease: a World Health Organization priority","volume":"79","author":"Schiess","year":"2022","journal-title":"JAMA Neurol."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"e392","DOI":"10.1093\/oncolo\/oyad261","article-title":"Construction of a diagnostic model for small cell lung cancer combining metabolomics and integrated machine learning","volume":"29","author":"Shang","year":"2024","journal-title":"Oncologist"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1038\/s41531-022-00316-9","article-title":"Neuroimaging evidence of glymphatic system dysfunction in possible REM sleep behavior disorder and Parkinson\u2019s disease","volume":"8","author":"Si","year":"2022","journal-title":"NPJ Parkinsons Dis."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"101834","DOI":"10.1016\/j.arr.2022.101834","article-title":"Premotor, nonmotor and motor symptoms of Parkinson's disease: a new clinical state of the art","volume":"84","author":"Silva","year":"2023","journal-title":"Ageing Res. Rev."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"e080952","DOI":"10.1136\/bmj-2024-080952","article-title":"Projections for prevalence of Parkinson\u2019s disease and its driving factors in 195 countries and territories to 2050: modelling study of global burden of disease study 2021","volume":"388","author":"Su","year":"2025","journal-title":"BMJ"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"6045","DOI":"10.1080\/10408398.2024.2435597","article-title":"Emerging perspectives on analytical techniques and machine learning for food metabolomics in the era of industry 4.0: a systematic review","volume":"65","author":"Taheri","year":"2025","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/S1474-4422(21)00030-2","article-title":"Challenges in the diagnosis of Parkinson's disease","volume":"20","author":"Tolosa","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S1474-4422(21)00330-6","article-title":"The neuropsychiatry of Parkinson's disease: advances and challenges","volume":"21","author":"Weintraub","year":"2022","journal-title":"Lancet Neurol."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"2400305","DOI":"10.1002\/smtd.202400305","article-title":"Integrating machine learning in metabolomics: a path to enhanced diagnostics and data interpretation","volume":"8","author":"Xu","year":"2024","journal-title":"Small Methods"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"5661","DOI":"10.1038\/s41467-024-49949-9","article-title":"Neuroimaging and fluid biomarkers in Parkinson\u2019s disease in an era of targeted interventions","volume":"15","author":"Zarkali","year":"2024","journal-title":"Nat. Commun."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1038\/s41531-021-00266-8","article-title":"Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson\u2019s disease","volume":"8","author":"Zhang","year":"2022","journal-title":"NPJ Parkinsons Dis."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1021\/acscentsci.2c01468","article-title":"Interpretable machine learning on metabolomics data reveals biomarkers for Parkinson\u2019s disease","volume":"9","author":"Zhang","year":"2023","journal-title":"ACS Cent. Sci."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"1038636","DOI":"10.3389\/fncom.2023.1038636","article-title":"Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: a review","volume":"17","author":"Zhao","year":"2023","journal-title":"Front. Comput. Neurosci."}],"container-title":["Frontiers in Aging Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2025.1753016\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T06:35:10Z","timestamp":1768286110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2025.1753016\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,13]]},"references-count":45,"alternative-id":["10.3389\/fnagi.2025.1753016"],"URL":"https:\/\/doi.org\/10.3389\/fnagi.2025.1753016","relation":{},"ISSN":["1663-4365"],"issn-type":[{"value":"1663-4365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,13]]},"article-number":"1753016"}}