{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T10:07:53Z","timestamp":1789553273282,"version":"build-2803163510"},"reference-count":40,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"12","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,6,16]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Hypertension is a leading risk factor for cardiovascular, cerebrovascular, and renal diseases, significantly worsening prognosis and quality of life. We aimed to develop and validate multimodal machine learning models integrating clinical and proteomic features for early prediction of hypertensive complications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">We analyzed 502\u2009166 participants from the UKB (UK Biobank). Proteomic profiling was performed using the Olink Explore platform. Clinical variables and complication outcomes were obtained from electronic health records. Features were selected using Cox proportional hazards models and light gradient boosting machine classifiers. Multimodal predictive models were constructed using random forest, with Shapley Additive Explanations applied for model interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      During follow\u2010up, 1232, 166, and 549 participants developed heart, brain, and kidney complications, respectively. Among 3244 candidate features, 774, 600, and 1227 were associated with these outcomes. The integrated models achieved an area under the curve of 0.73 (95% CI, 0.68\u20130.77) for heart disease, 0.83 (95% CI, 0.73\u20130.92) for brain disease, and 0.79 (95% CI, 0.73\u20130.85) for kidney disease. Growth\/differentiation factor 15 (hazard ratio [HR], 2.16 [95% CI, 1.93\u20132.42]), adaptor protein 3 complex subunit \u03c3\u20102 (HR, 0.57 [95% CI, 0.42\u20130.78]), and tumor necrosis factor receptor superfamily member 10B (HR, 4.06 [95% CI, 3.40\u20134.85]) were significantly associated with their respective complications, effectively predicting the risk of clinical progression (all\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      &lt;0.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">Multimodal machine learning models combining proteomic and clinical data enable early identification of hypertensive complications. Growth\/differentiation factor 15, adaptor protein 3 complex subunit \u03c3\u20102, and tumor necrosis factor receptor superfamily member 10B may serve as potential biomarkers for risk prediction and early intervention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.125.048151","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T12:15:40Z","timestamp":1781525740000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Machine Learning Integrating Clinical and Proteomic Data for Early Prediction of Hypertensive Complications: A\u00a0UKB Longitudinal Study"],"prefix":"10.1161","volume":"15","author":[{"given":"Yuan","family":"Fei","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University School of Medicine","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2043-2843","authenticated-orcid":false,"given":"Siwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Zunyi Medical University","place":["Zunyi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianlang","family":"Tong","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University School of Medicine","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuemei","family":"Zhang","sequence":"additional","affiliation":[{"name":"The University of Melbourne","place":["Melbourne, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University School of Medicine","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2198-2579","authenticated-orcid":false,"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"Zunyi Medical University","place":["Zunyi, China"]},{"name":"Guizhou Provincial Department of Education","place":["Zunyi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1925-1405","authenticated-orcid":false,"given":"Xiaoqi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University School of Medicine","place":["Shanghai, China"]},{"name":"Shanghai Jiao Tong University School of Medicine","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1161\/hypertensionaha.119.13437"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2022.19590"},{"key":"e_1_3_2_4_2","unstructured":"WHO . 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