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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.
To date, clinically diagnosed DKD does not always correspond to biopsy-proven diabetic nephropathy (DN), as non-diabetic nephropathy (NDN) may coexist. Differentiating DN from NDN is crucial since treatment and outcomes differ significantly, but kidney biopsy—the current gold standard—has no uniform indication. Therefore, reliable biomarkers are needed to predict DN and reduce unnecessary biopsies. This study aimed to identify potential proteome in the serum and urine that could aid in diagnosing kidney disease solely attributed to diabetes mellitus, without the need for a kidney biopsy.
A retrospective review was conducted on patients with type 2 diabetes (T2D) who underwent kidney biopsy between 2010 and 2020. The most combined glomerulonephritis were IgA nephropathy (IgAN) and membranous glomerulonephritis (MN). Thus, among enrolled patients with T2D, we selected age-and sex-matched patients with pure DN (n= 11), pure NDN (IgAN, n = 11 & MN, n = 11) for proteomic analysis. The control group was donors for living kidney transplantation (n = 11). Comparative quantitative proteomic analysis was conducted using high-resolution mass spectrometry in combination with nanoflow liquid chromatography. Protein quantification for each group was performed in a label-free manner, followed by statistical analysis to identify potential biomarkers specific to each group.
Among the four groups—DN, IgAN, MN, and healthy controls, there were no significant differences in age and sex. The DN group had higher prevalences of diabetic complications, including nephropathy and retinopathy, as well as a longer duration of diabetes and higher HbA1c levels compared to the NDN groups. In the serum dataset, a total of 1,220 proteins were identified, among which 15 proteins including regenerating islet-derived protein 3-alpha and myosin light polypeptide 6 were found to be distinct in the DN group compared to the NDN and control groups. Random forest analysis effectively differentiated DN from the entire cohort (out-of-bag error of 0, accuracy 100%). Gene ontology analysis revealed that DN-specific serum proteins were predominantly associated with processes such as wound healing response, antibacterial humoral defense, cell development, chemotaxis, and vascular formation. In the urine dataset, 1,579 proteins were identified, among which 15 proteins including keratin, type Ⅰ cytoskeletal 19 and cytoplasmic NADP showed distinct clustering in the DN group relative to the NDN and control groups. Random forest analysis successfully classified DN cases (out-of-bag error 0.159, accuracy 84.1%).
Serum and urine proteome can be useful to diagnose diabetes-related kidney disease without the need for a kidney biopsy. Additionally, it can help determine the appropriate indications for kidney biopsy in T2D patients with kidney damage.
