{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T12:53:17Z","timestamp":1791204797775,"version":"4.1.0"},"reference-count":70,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In Parkinson\u2019s disease, non-motor symptoms precede the characteristic motor manifestations by up to 20 years. However, predicting the disease risk remains challenging.<\/jats:p>\n                  <jats:p>We applied machine learning to 2937 plasma proteins assayed in UK Biobank participants with Parkinson\u2019s disease to predict disease outcome up to 14 years before diagnosis.<\/jats:p>\n                  <jats:p>Of the 446 proteins found to be dysregulated in incident cases compared with controls, a subset of 23 predicted disease with an area under the curve (AUC) value of 0.78 in incident and 0.795 in prevalent cases. The utility of the identified proteomic signatures was validated in an independent cohort, based on 16 proteins shared across datasets, leading to a validation AUC of up to 0.76.<\/jats:p>\n                  <jats:p>Parkinson\u2019s disease-related pathways, including neuron death and amyloid-\u03b2 clearance, were enriched up to 9 years before diagnosis. Furthermore, a co-expression network analysis revealed protein modules associated with disease risk and time-to-diagnosis. Our findings underscore the potential of applying machine learning to large-scale proteomics data from pre-symptomatic patients for early disease prediction.<\/jats:p>","DOI":"10.1093\/brain\/awaf303","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T11:49:37Z","timestamp":1755085777000},"page":"1254-1267","source":"Crossref","is-referenced-by-count":4,"title":["Leveraging machine learning to predict Parkinson's disease using pre-symptomatic proteomics data"],"prefix":"10.1093","volume":"149","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2329-4879","authenticated-orcid":false,"given":"Shirley","family":"Nieves-Rodriguez","sequence":"first","affiliation":[{"name":"Population Analytics & Insights, DPDS, Data Science & Digital Health, Johnson & Johnson , Spring House, PA 19002 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Hou","sequence":"additional","affiliation":[{"name":"Population Analytics & Insights, DPDS, Data Science & Digital Health, Johnson & Johnson , Spring House, PA 19002 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher D","family":"Whelan","sequence":"additional","affiliation":[{"name":"Neuroscience Data Science, Johnson & Johnson , Cambridge, MA 02142 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuwei","family":"Li","sequence":"additional","affiliation":[{"name":"Population Analytics & Insights, DPDS, Data Science & Digital Health, Johnson & Johnson , Spring House, PA 19002 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abolfazl","family":"Doostparast Torshizi","sequence":"additional","affiliation":[{"name":"Population Analytics & Insights, DPDS, Data Science & Digital Health, Johnson & Johnson , Spring House, PA 19002 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"issue":"Pt 8","key":"2026040804055914300_awaf303-B1","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1093\/brain\/122.8.1437","article-title":"The substantia nigra of the human brain. 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