{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T06:18:46Z","timestamp":1787811526563,"version":"build-2784847793"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"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. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify plasma protein biomarkers associated with incident non-fatal major adverse cardiovascular events (MACE) in diabetic kidney disease (DKD) patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Research design and methods<\/jats:title>\n                    <jats:p>We analyzed 317 DKD patients from the UK Biobank. Plasma proteomics and clinical data (demographics, metabolism, renal function) were integrated. In an exploratory discovery phase, three sequential Cox regression models (crude, socio-demographic-adjusted, socio-demographic-metabolic adjusted) screened non-fatal MACE-associated proteins. To prevent information leakage, the cohort was then randomly split into training (70%) and testing (30%) sets; machine-learning feature selection, hyperparameter optimization, and final model development were performed exclusively within the training set. The associated proteins were input into the four-step machine-learning pipeline (LASSO-Cox, random survival forest, Boruta, XGBoost-Cox). Predictive performance was validated using Kaplan-Meier survival analyses, longitudinal trajectory modeling, and ROC benchmarking. An interactive web application was deployed for clinical implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 1,463 plasma proteins, 561 were associated with non-fatal MACE across Cox models, with 14 overlapping proteins. Nine core proteins (ANG, IL1R1, CXCL14, ESAM, PTGDS, HAVCR1, FGFR2, IGSF8, CCL3) were validated: ANG showed the strongest non-fatal MACE association (HR\u00a0=\u00a03.88, 95%CI 2.33-6.48, p&amp;lt;0.001), and all high-expression groups had elevated non-fatal MACE risk. GO\/KEGG enrichment highlighted inflammatory-immune pathways like positive regulation of MAPK cascade, Cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway as key mechanisms. The model integrating proteins, demographic factors, and clinical variables achieved the highest predictive performance across non-fatal MACE (AUC\u00a0=\u00a00.768), myocardial infarction (MI) (0.808), and stroke (0.816) outcomes, with superior stability in cross-validation. CoxBoost + Elastic Net framework was selected as the optimal framework via benchmarking of 101 algorithms. The model demonstrated favorable calibration in high-risk patients and yielded positive net clinical benefit across decision thresholds of 5% to 45%. The web tool (\n                      <jats:ext-link>https:\/\/jiangli2941.github.io\/MACE-prediction-v2\/<\/jats:ext-link>\n                      ) enables input of 28 variables, outputs non-fatal MACE risk status, risk probability, and highlights abnormal indicators.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Plasma proteomics combined with machine learning identifies robust non-fatal MACE predictors in DKD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1883523","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T05:39:45Z","timestamp":1787809185000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease"],"prefix":"10.3389","volume":"17","author":[{"given":"Li","family":"Jiang","sequence":"first","affiliation":[{"name":"Diabetes Department of integrated Chinese and Western medicine; China National Center for Integrated Traditional Chinese and Western Medicine, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chieh","family":"Chien","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Mental Health Center of Dongcheng District","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianli","family":"Li","sequence":"additional","affiliation":[{"name":"National Integrated Traditional and Western Medicine Center for Cardiovascular Disease, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haojun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Lab for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beijing Key Lab for Immune-Mediated Inflammatory Diseases, Institute of Clinical Medical Sciences, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiai","family":"Wu","sequence":"additional","affiliation":[{"name":"Diabetes Department of integrated Chinese and Western medicine; China National Center for Integrated Traditional Chinese and Western Medicine, China-Japan Friendship Hospital","place":["Beijing, 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