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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Diabetes mellitus (DM), especially type 2 diabetes mellitus (T2DM), seriously threatens human health. Diabetic renal injury is divided into diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) by etiology, with significant differences in treatment and prognosis; thus, their differential diagnosis is key for clinical practice. This study aimed to construct a non-invasive, simple, and efficient predictive model for T2DM patients with renal injury before renal biopsy to assist DKD/NDKD differentiation and provide a scientific basis for timely and precise clinical diagnosis and treatment.
Patients with T2DM complicated with renal injury were enrolled. Retrospective analysis was conducted on 117 confirmed T2DM patients from Hospital A (Jan 2017–May 2022), grouped by renal biopsy results. Medical history, physical signs, and laboratory data were collected; predictive factors were screened via univariate and multivariate regression to build the differential diagnostic model. For independent internal/external validation: Validation Cohort 1 (52 cases, Hospital A, Jun 2022–Sep 2023), Validation Cohort 2 (78 cases, Hospital B, Jul–Sep 2023), Validation Cohort 3 (168 cases, Hospital C, May 2018–Oct 2023). Only DKD/NDKD patients were included (low proportion and complex mechanism of DKD+NDKD [Mix] group). Model discriminative ability, calibration, goodness-of-fit, and clinical utility were evaluated via area under ROC curve (AUC), calibration curve, and decision curve analysis (DCA).
Of 415 T2DM patients, 130 (31.33%) were DKD, 233 (56.15%) NDKD, 52 (12.53%) Mix. The RICH model (for DKD/NDKD differentiation in T2DM with renal injury) included variables: RBC, IgA, cystatin C-based estimated glomerular filtration rate (eGFR_2), and HbA1c. AUCs were 0.847 (0.766–0.929) (modeling cohort), 0.755 (0.611–0.899) (Cohort 1), 0.754 (0.654–0.874) (Cohort 2), 0.768 (0.665–0.845) (Cohort 3). Calibration curves showed good consistency between predicted and actual probabilities (all Hosmer-Lemeshow test P>0.05). DCA indicated high clinical net benefit at threshold probability 0.10–0.80, with theoretical 42.05% reduction in renal biopsy rate.
Based on objective laboratory indicators from three centers, the RICH model was constructed and validated for DKD/NDKD differentiation in adult T2DM with renal injury. Higher eGFR_2, IgA, RBC, and lower HbA1c are key for NDKD identification. This model is highly objective, straightforward to operate, and possesses both high accuracy and strong multicentre applicability. It aids in reducing unnecessary renal biopsies and enhances care for patients who cannot undergo the procedure.
