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Objectives <p>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).</p> Materials and methods <p>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.</p> Results <p>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*) (&gt; 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 <i>c</i>-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 <i>c</i>-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.</p> Conclusion <p>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.</p> Critical relevance statement <p>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.</p> Trial registration <p>ClinicalTrials.gov, NCT03865914.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Blood oxygen level-dependent-MRI medullary R2*(MR2*) &gt; 24 s<sup>−</sup><sup>1</sup> halves DKD ESRD risk (HR 0.48).</p> </ItemContent> <ItemContent> <p>MR2* integrated with clinical variables drives <i>c</i>-index to 0.90 for ESRD prognosis.</p> </ItemContent> <ItemContent> <p>RF leveraging MR2* and clinical traits attains an AUC of 0.901 for diagnosing DN.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Qian Wang,
  • Shaopeng Zhou,
  • Yue Niu,
  • Chaobo Li,
  • Qiang Lyu,
  • Pu Chen,
  • Xiaojing Zhang,
  • Lizhi Xie,
  • Wanjun Shen,
  • Yong Wang,
  • Xueying Cao,
  • Guangyan Cai,
  • Xiangmei Chen,
  • Haiyi Wang,
  • Zheyi Dong

摘要

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