{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:44:52Z","timestamp":1791819892725,"version":"4.3.4"},"reference-count":28,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Clin Appl Thromb Hemost"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>We evaluated the association between small dense low-density lipoprotein (sdLDL) and peripheral artery disease (PAD) and developed prediction models using NHANES 1999\u20132004.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This weighted cross-sectional study included 3,415 participants (3,227 without PAD and 188 with PAD). PAD was defined by ankle-brachial index (ABI) &lt;0.90; participants with ABI \u22651.40 or missing ABI were excluded. Survey-weighted logistic regression assessed the association between sdLDL and PAD. For machine learning, complete-case data (n=2,828) were split into training (70%) and test (30%) sets before feature selection and preprocessing. Correlation screening, LASSO, encoding, standardization, and SMOTE were restricted to the training set. Nine algorithms were evaluated using discrimination and calibration metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>sdLDL was not independently associated with PAD in the fully adjusted model (OR=1.01, 95% CI 0.94\u20131.10, P=0.69). LASSO retained eight predictor domains: age, body mass index, C-reactive protein, education, estimated glomerular filtration rate, hypertension, poverty-income ratio, and smoking. In the test set, gradient boosting machine achieved the highest AUC (0.833, 95% CI 0.777\u20130.889), followed by logistic regression (0.819) and XGBoost (0.811). SHAP analysis highlighted age, eGFR, and smoking-related variables as important contributors.<\/jats:p>\n                    <jats:p>Conclusions: sdLDL was not independently associated with PAD. Several models showed acceptable discrimination, with GBM performing numerically best and logistic regression remaining competitive. External validation in contemporary cohorts is required.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10760296261496971","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T20:48:16Z","timestamp":1791492496000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Risk Prediction Models for Peripheral Artery Disease: A Nationally Representative Analysis of NHANES 1999-2004"],"prefix":"10.1177","volume":"32","author":[{"given":"Haijun","family":"Feng","sequence":"first","affiliation":[{"name":"The Central Hospital of Wuhan"},{"name":"Tongji Medical College"},{"name":"Huazhong University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinfeng","family":"Xie","sequence":"additional","affiliation":[{"name":"The Central Hospital of Wuhan"},{"name":"Tongji Medical College"},{"name":"Huazhong University 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