{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T12:36:03Z","timestamp":1768307763456,"version":"3.49.0"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"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>Background<\/jats:title>\n                    <jats:p>To investigate the association between the total bilirubin-to-prothrombin time ratio index (TBPTRI) and major chronic complications of type 2 diabetes mellitus (T2DM), including diabetic nephropathy (DN), diabetic retinopathy (DR), diabetic peripheral neuropathy (DPN), and diabetic foot (DF), and to evaluate its predictive value for multiple complications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cross-sectional study analyzed 15,695 hospitalized T2DM patients at the Central Hospital of Shaoyang from January 2019 to December 2024, with 5,117 eligible patients ultimately included. Missing data were handled using multiple imputation by chained equations (MICE). Logistic regression was employed to assess the independent correlation between TBPTRI and various complications, while restricted cubic spline (RCS) analysis was used to evaluate nonlinear relationships. To enhance model stability and robustness, subgroup and sensitivity analyses were conducted. Feature variables were further screened using the Boruta algorithm, LASSO regression, and Random Forest (RF) to construct a TBPTRI-based complication risk prediction model. A nomogram was developed, and model performance was evaluated using receiver operating characteristic (ROC) curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multivariate logistic regression revealed that TBPTRI was significantly inversely associated with the risks of diabetic nephropathy (OR = 0.61, 95% CI: 0.48\u20130.79), retinopathy (OR = 0.53, 95% CI: 0.37\u20130.76), peripheral neuropathy (OR = 0.82, 95% CI: 0.70\u20130.95), and foot disease (OR = 0.52, 95% CI: 0.40\u20130.68) (all P &amp;lt; 0.01). RCS analysis indicated nonlinear relationships between TBPTRI and complication risks in diabetic nephropathy and foot disease (P-nonlinear &amp;lt; 0.05). Subgroup analysis demonstrated that the protective effect of TBPTRI remained relatively stable across populations with different clinical characteristics, with significant interactions observed in patients with hypertension or coronary heart disease (CHD). Furthermore, the nomogram constructed using core variables selected by LASSO regression and RF exhibited strong predictive performance, with area under the curve (AUC) values in the test set of 0.871 for nephropathy, 0.647 for retinopathy, 0.735 for peripheral neuropathy, and 0.855 for foot disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>TBPTRI was inversely associated with major chronic complications of T2DM and demonstrated high predictive value for patients with multiple complications. The TBPTRI-based model exhibited robust performance, supporting its utility in early detection, risk stratification, and precision prevention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2025.1682680","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T05:39:51Z","timestamp":1768196391000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Association between the total bilirubin to prothrombin time ratio index and diabetic retinopathy, nephropathy, peripheral neuropathy, and foot disease: a retrospective study and risk prediction model 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