{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T07:03:36Z","timestamp":1789715016550,"version":"4.0.1"},"reference-count":37,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,5,19]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Although peripheral artery disease (PAD) is an important diabetes complication, a substantial proportion of cases occur among individuals without diabetes. This study aimed to assess the predictive value of plasma proteomics in the long\u2010term risk of PAD among individuals initially free of diabetes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">Included were 46\u2009508 participants (6046 with prediabetes) without diabetes or major cardiovascular disease at recruitment of the UK Biobank. Using multivariable Cox regression models, a total of 2923 unique plasma proteins were assessed for the associations with incident PAD. Significant proteins were subsequently processed by a trained light gradient boosting machine classifier to determine important proteins. Using receiver operating characteristic analyses, the performance of these important proteins in predicting incident PAD were evaluated, in the whole sample and by glycemic status (normoglycemia and prediabetes).<\/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 a median follow\u2010up of 12.7\u2009years, 461 participants developed PAD. There were 107 proteins associated with incident PAD, with 103 positive associations. The LGBM approach identified 9 proteins (eg, WFDC2 [WAP 4\u2010disulfide core domain protein 2], MMP12 [macrophage metalloelastase], and GDF15 [growth differentiation factor 15]) as the top\u2010ranked proteins based on their importance ordering. Whereas glycated hemoglobin showed very modest predictive accuracy, a panel incorporating these top proteins showed good performance in the prediction of PAD risk (area under the curve 0.820), and it significantly enhanced the prediction beyond traditional risk factors (raising area under the curve from 0.803 to 0.837, DeLong test\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      =5.21\u00d710\n                      <jats:sup>\u22123<\/jats:sup>\n                      ). These observations were consistent for participants with normoglycemia or prediabetes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">Plasma protein biomarkers enhance the prediction of long\u2010term risk for PAD among individuals without diabetes, regardless of glycemic status.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.125.047797","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T09:10:02Z","timestamp":1778058602000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Prediction of Peripheral Artery Disease Using Plasma Proteomics Among Individuals Without 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