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April 22, 2026Insights into Imaging0 citationsOpen Access

Clinical utility of BOLD-MRI in accurate diagnosis and prognostic evaluation of diabetic nephropathy: a prospective renal biopsy-based cohort study

QWQian WangSZShaopeng ZhouYNYue Niu

Key Points

  • Validate BOLD-MRI for distinguishing diabetic nephropathy from non-diabetic renal disease and predicting end-stage renal disease.
  • Biopsy-proven cohort of 133 diabetic kidney disease patients underwent BOLD-MRI.
  • Analyses used semi-automated concentric-objects method and Cox regression for prognostic markers.
  • Logistic regression and machine learning techniques identified key diagnostic variables.
  • 15.5% of patients progressed to end-stage renal disease over 21.8 months.
  • Renal medullary R2* greater than 24 1/s reduced the risk of end-stage renal disease by 52%.
  • Random forest model achieved an AUC of 0.901 for distinguishing diabetic nephropathy.

Abstract

Abstract Objectives To validate blood oxygen level-dependent MRI (BOLD-MRI) for non-invasive discrimination of diabetic nephropathy (DN) vs non-diabetic renal disease (NDRD) and prediction of end-stage renal disease (ESRD) in diabetic kidney disease (DKD). Materials and methods A prospective cohort of 133 biopsy-proven DKD patients underwent BOLD-MRI. The semi-automated 12-layer concentric-objects method was used to analyze BOLD-MRI variables. Prognostic markers for ESRD were identified using univariate and multivariate Cox regression. Feature importance was used to select key diagnostic variables and establish logistic regression and machine-learning differential diagnosis models. Results Among 133 patients (44 DN, 55 NDRD, 34 combined), 20 (15.5%) progressed to ESRD over a mean of 21.8 months. Higher renal medullary R2* (MR2*) (> 24 1/s) reduced ESRD risk by 52% (HR, 0.48) in DKD. Prognostic models integrating pathological grouping, hemoglobin levels, and cysC levels achieved a c -index of 0.90. For the DN and combined groups, MR2*, glomerular grading, interstitial lesions, interstitial fibrosis, and tubular atrophy were predictive of ESRD, with a c -index of 0.91. For differential diagnosis, the random forest (RF) model achieved an AUC of 0.901, with diabetic retinopathy, diabetes duration, albumin, blood urea nitrogen, MR2*, hypertension, and glycosylated hemoglobin as the most contributing factors. For the combined group classified as DN, the AUC of the RF model was 0.791; when classified as NDRD, the AUC was 0.856. Conclusion MR2* shows potential value as a non-invasive diagnostic and prognostic tool in the assessment of DKD. However, BOLD-MRI remains a promising yet exploratory technique that requires external validation and interventional studies before clinical implementation. Critical relevance statement Blood oxygen level-dependent-MRI-derived renal medullary R2* robustly predicts ESRD risk and distinguishes DN without biopsy, offering an immediately translatable, non-invasive biomarker for the precision management of DKD in routine nephrology practice. Trial registration ClinicalTrials.gov, NCT03865914. Key Points Blood oxygen level-dependent-MRI medullary R2*(MR2*) > 24 s − 1 halves DKD ESRD risk (HR 0.48). MR2* integrated with clinical variables drives c -index to 0.90 for ESRD prognosis. RF leveraging MR2* and clinical traits attains an AUC of 0.901 for diagnosing DN. Graphical Abstract

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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/69e865476e0dea528dde9db9https://doi.org/10.1186/s13244-026-02274-9
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