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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.
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Abstract titles should be brief and reflect the content of the abstract.
There remains an unmet clinical need for robust prognostic markers of renal outcomes in diabetic nephropathy (DN), a major complication of diabetes mellitus (DM) and a leading cause of hemodialysis. Oval fat bodies (OFBs) are cells containing abundant lipid droplets observed in urine sediment and are findings suggesting nephrotic syndrome, including DN with marked proteinuria. Although previous studies have shown an association between OFBs and proteinuria in glomerular disease and with nonselective proteinuria in nephrotic syndrome, the clinical usefulness of OFBs for predicting kidney prognosis remains unclear. Therefore, we investigated the association between OFB count and the rate of eGFR decline to evaluate its clinical utility as a prognostic marker.
67 Japanese subjects clinically diagnosed with chronic kidney disease with DM who showed OFBs across the whole microscopic field on urine sediment were enrolled. Three analyses were performed as follows:
1) Subjects were divided at the median OFB count/Cr (OFB count normalized to urinary creatinine) into high and low groups. The eGFR measurement on the same day as the index date was defined as baseline. The eGFR values were summarized as the median every 3 months over a 5-year observation; a linear mixed-effects model (LMM) compared eGFR slopes, and Kaplan-Meier analysis assessed hemodialysis initiation.
2) Annual eGFR decline (eGFR slope) was calculated, and receiver-operating characteristic (ROC) analysis compared discrimination of OFB count/Cr versus urine protein-to-creatinine ratio (U-TP/Cr) for higher versus lower decline by areas under the ROC curve (AUROC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI).
3) Multiple machine learning models using blood and urinary biomarkers were built, and the performance and incremental value were evaluated.
1 1) The baseline eGFRs (mean±SD) of the two groups were 26.56±18.05 and 22.30±10.71 ml/min/1.73m^2. The high OFB count/Cr group (≥0.245/Whole Field/gCr) showed a significantly greater eGFR decline (-60.6%) than the low group (-41.4%) by LMM. The high OFB count/Cr group also showed a significantly higher incidence of hemodialysis initiation in Kaplan-Meier analysis. (log-rank test, P < 0.05).
2) Although no significant difference was observed in AUROCs between U-TP/Cr (AUROC 0.677 [95%CI 0.546 – 0.807]) and OFB count/Cr (AUROC 0.715 [0.591 – 0.839]), NRI and IDI favored OFB count, including over a combined U-TP/Cr plus OFB model.
3) The best-fit machine learning model was linear regression (R2 = 0.712), in which OFB count/Cr contributed most to predicting eGFR slope.
Higher OFB count/Cr predicts faster eGFR decline in DN and improves the prediction model of renal outcomes, including risk of hemodialysis initiation. Combining OFB count/Cr with proteinuria enhances risk stratification and provides incremental prognostic value.
