The application value of multimodal ultrasound imaging technology in the prediction of early-stage type 2 diabetic kidney disease
Abstract Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. This study examines the diagnostic efficacy of multi-modal ultrasound imaging technology for the early detection of DKD, offering a valuable reference for the prompt diagnosis of affected patients. The clinical d...
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Nature Portfolio
2025-04-01
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| author | Dan Wang Wenping Wang Santing Xiang Caifeng Xia Yuan Zhang Lingling Zhang |
| author_facet | Dan Wang Wenping Wang Santing Xiang Caifeng Xia Yuan Zhang Lingling Zhang |
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| description | Abstract Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. This study examines the diagnostic efficacy of multi-modal ultrasound imaging technology for the early detection of DKD, offering a valuable reference for the prompt diagnosis of affected patients. The clinical data of 88 patients with early-stage type 2 diabetic kidney disease (E-T2DKD group), 82 patients with uncomplicated type 2 diabetes (T2DM group), and 82 healthy individuals (control group) who underwent physical examinations at the outpatient clinic of the Affiliated Jiangning Hospital with Nanjing Medical University, were analyzed. Multimodal ultrasound imaging technology (MUIT) was employed to detect various parameter indicators, and a prediction model was developed using the receiver operating characteristic (ROC) curve. The results indicated no significant differences in age, gender, and BMI among the three patient groups. In comparison to the patients in the T2DM group, those in the E-T2DKD group exhibited significantly higher durations of diabetes and HbA1C levels. Significant differences were observed in the renal function-related indicators assessed across the three groups, including Cystatin C, β2-microglobulin (β2-MG), serum retinol-binding protein (S-RBP), serum creatinine (Scr), plasma urea nitrogen (PUN), estimated glomerular filtration rate (eGFR), urine neutrophil gelatinase-associated lipocalin (U-NGAL), urine retinol-binding protein (U-RBP), urine N-acetyl-β-D-glucosaminidase (U-NAG) and urinary albumin excretion rates (UAER) (p < 0.05), whereas no significant differences were found in eGFR, Scr and PUN levels between the control group and the T2DM group. Notable statistical differences among the three groups were also identified in the MUIT detection parameters, including renal cortex shear wave elastography (SWE), kidney volume index (KVI), interlobar artery (IA) Vsmax, IA Vdmin, IA resistance index (RI), and IA pulsatility index (PI) (p < 0.05). The early SWE, KVI, IA RI, and IA PI in the E-T2DKD group were significantly higher than those in both the T2DM and control groups, while RCT/RMT, IA Vsmax, and IA Vdmin were significantly lower in comparison to the T2DM and control groups (p < 0.05). These indicators were incorporated into a binary logistic regression model, and the joint predictive value was fitted based on the regression coefficients. Further ROC analysis revealed that the prediction area under the curve (AUC) for MUIT and clinical characteristics reached 0.993, indicating a high predictive value for E-T2DKD. |
| format | Article |
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| spelling | doaj-art-5223fec93ef34e05bae5b29a4f83bca92025-08-20T02:28:05ZengNature PortfolioScientific Reports2045-23222025-04-011511910.1038/s41598-025-97151-8The application value of multimodal ultrasound imaging technology in the prediction of early-stage type 2 diabetic kidney diseaseDan Wang0Wenping Wang1Santing Xiang2Caifeng Xia3Yuan Zhang4Lingling Zhang5Department of Ultrasound, The Affiliated Jiangning Hospital with Nanjing Medical UniversityDepartment of Ultrasound, The Affiliated Jiangning Hospital with Nanjing Medical UniversityDepartment of Ultrasound, The Affiliated Jiangning Hospital with Nanjing Medical UniversityDepartment of Ultrasound, The Affiliated Jiangning Hospital with Nanjing Medical UniversityDepartment of Ultrasound, The Affiliated Jiangning Hospital with Nanjing Medical UniversityDepartment of Ultrasound, The Affiliated Jiangning Hospital with Nanjing Medical UniversityAbstract Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. This study examines the diagnostic efficacy of multi-modal ultrasound imaging technology for the early detection of DKD, offering a valuable reference for the prompt diagnosis of affected patients. The clinical data of 88 patients with early-stage type 2 diabetic kidney disease (E-T2DKD group), 82 patients with uncomplicated type 2 diabetes (T2DM group), and 82 healthy individuals (control group) who underwent physical examinations at the outpatient clinic of the Affiliated Jiangning Hospital with Nanjing Medical University, were analyzed. Multimodal ultrasound imaging technology (MUIT) was employed to detect various parameter indicators, and a prediction model was developed using the receiver operating characteristic (ROC) curve. The results indicated no significant differences in age, gender, and BMI among the three patient groups. In comparison to the patients in the T2DM group, those in the E-T2DKD group exhibited significantly higher durations of diabetes and HbA1C levels. Significant differences were observed in the renal function-related indicators assessed across the three groups, including Cystatin C, β2-microglobulin (β2-MG), serum retinol-binding protein (S-RBP), serum creatinine (Scr), plasma urea nitrogen (PUN), estimated glomerular filtration rate (eGFR), urine neutrophil gelatinase-associated lipocalin (U-NGAL), urine retinol-binding protein (U-RBP), urine N-acetyl-β-D-glucosaminidase (U-NAG) and urinary albumin excretion rates (UAER) (p < 0.05), whereas no significant differences were found in eGFR, Scr and PUN levels between the control group and the T2DM group. Notable statistical differences among the three groups were also identified in the MUIT detection parameters, including renal cortex shear wave elastography (SWE), kidney volume index (KVI), interlobar artery (IA) Vsmax, IA Vdmin, IA resistance index (RI), and IA pulsatility index (PI) (p < 0.05). The early SWE, KVI, IA RI, and IA PI in the E-T2DKD group were significantly higher than those in both the T2DM and control groups, while RCT/RMT, IA Vsmax, and IA Vdmin were significantly lower in comparison to the T2DM and control groups (p < 0.05). These indicators were incorporated into a binary logistic regression model, and the joint predictive value was fitted based on the regression coefficients. Further ROC analysis revealed that the prediction area under the curve (AUC) for MUIT and clinical characteristics reached 0.993, indicating a high predictive value for E-T2DKD.https://doi.org/10.1038/s41598-025-97151-8Multimodal ultrasound imaging technologyType 2 diabetesEarly diabetic kidney diseaseEarly diagnosisPredictive value |
| spellingShingle | Dan Wang Wenping Wang Santing Xiang Caifeng Xia Yuan Zhang Lingling Zhang The application value of multimodal ultrasound imaging technology in the prediction of early-stage type 2 diabetic kidney disease Scientific Reports Multimodal ultrasound imaging technology Type 2 diabetes Early diabetic kidney disease Early diagnosis Predictive value |
| title | The application value of multimodal ultrasound imaging technology in the prediction of early-stage type 2 diabetic kidney disease |
| title_full | The application value of multimodal ultrasound imaging technology in the prediction of early-stage type 2 diabetic kidney disease |
| title_fullStr | The application value of multimodal ultrasound imaging technology in the prediction of early-stage type 2 diabetic kidney disease |
| title_full_unstemmed | The application value of multimodal ultrasound imaging technology in the prediction of early-stage type 2 diabetic kidney disease |
| title_short | The application value of multimodal ultrasound imaging technology in the prediction of early-stage type 2 diabetic kidney disease |
| title_sort | application value of multimodal ultrasound imaging technology in the prediction of early stage type 2 diabetic kidney disease |
| topic | Multimodal ultrasound imaging technology Type 2 diabetes Early diabetic kidney disease Early diagnosis Predictive value |
| url | https://doi.org/10.1038/s41598-025-97151-8 |
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