{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T22:30:28Z","timestamp":1783636228770,"version":"3.55.0"},"reference-count":45,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2022J01212"],"award-info":[{"award-number":["2022J01212"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017686","name":"Fujian Provincial Health Technology Project","doi-asserted-by":"publisher","award":["2021CXA018"],"award-info":[{"award-number":["2021CXA018"]}],"id":[{"id":"10.13039\/501100017686","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Diabetes Invest"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To explore the association between serum fibrinogen (FIB) and clinicopathological features and renal prognosis in type 2 diabetic nephropathy (T2DN), and develop a web\u2010based dynamic model to predict renal progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This paper retrospectively enrolled 173 biopsy\u2010proven T2DN patients and stratified them by the optimal FIB cutoff. Renal progression was defined as a &gt;50% decline in eGFR, doubling of creatinine, or onset of end\u2010stage renal disease (ESRD). Cox regression analysis was used to screen out the independent predictors of T2DN progression; a web\u2010based dynamic prediction model was established and evaluated using time\u2010dependent receiver operating characteristic (Time\u2010ROC) curves, calibration curves, and decision curve analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of the 173 patients, 81 (46.82%) experienced the renal endpoint event. Multifactorial Cox regression analysis showed that eGFR, hemoglobin, FIB, parathyroid hormone, and interstitial fibrosis and tubular atrophy (IFTA) scores were independent risk factors for T2DN progression (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05). Among the three models constructed based on these factors, model 3 had the highest areas under the curve (AUC) (90.42; 95% CI, 85.80\u201395.04). The AUC of the risk prediction model constructed by the nomogram was 0.846, 0.752, and 0.754 at 1, 3, and 5\u2009years, showing good discrimination, and the Web\u2010based dynamic nomogram demonstrated good calibration.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>FIB is an independent predictor of T2DN progression. A web\u2010based dynamic model was developed to predict renal progression risk in T2DN.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jdi.70230","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T14:12:01Z","timestamp":1767795121000},"page":"448-459","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Construction of a prognostic assessment model for diabetic nephropathy based on serum fibrinogen and renal tissue\n                    <scp>IFTA<\/scp>\n                    score"],"prefix":"10.1111","volume":"17","author":[{"given":"Lin","family":"Ning","sequence":"first","affiliation":[{"name":"Department of Nephrology, Blood Purification Research Center, The First Affiliated Hospital Fujian Medical University  Fuzhou China"},{"name":"Fujian Clinical Research Center for Metabolic Chronic Kidney Disease, The First Affiliated Hospital Fujian Medical University  Fuzhou 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