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. 2025 Jul 21:18:4011-4026.
doi: 10.2147/IJGM.S517683. eCollection 2025.

Non-Invasive Diagnosis and Monitoring of Diabetic Nephropathy: Assessment of Renal Function and Fibrosis by Diffusion Kurtosis Imaging

Jian-Lei Yuan #  1   2 , Li-Ao Hu #  1   2 , Xin-Yu Wang #  2   3 , Zhao-Yu Shi  1   2 , Ting Chen  1   2 , Xin-Zhong Huang  1   2 , Yu Wu  1   2 , Qiu-Jie Cai  1   2 , Zi-Xu Yang  1   2 , Xin-Yi Chen  4 , Li Yuan  1   2 , Yuan Zhang  1   2
Affiliations

Non-Invasive Diagnosis and Monitoring of Diabetic Nephropathy: Assessment of Renal Function and Fibrosis by Diffusion Kurtosis Imaging

Jian-Lei Yuan et al. Int J Gen Med. .

Abstract

Background: This monocentric, cross-sectional study explored the use of diffusion kurtosis imaging (DKI) as a non-invasive means to diagnose and monitor diabetic nephropathy (DN).

Methods: Patients with diabetes mellitus (DM, n = 11), mild DN (N = 14), and severe DN (n = 29) were recruited. Eight DKI metrics (MK, MD, Da, Dr, Ka, Kr, FA, FAk) were determined from the imaging results, and their correlations with routine laboratory results were analyzed. The receiver operating characteristic (ROC) curves were plotted, and the diagnostic value of the DKI metrics was analyzed. In addition, renal biopsy was carried out for ten DN patients who had appropriate indications. Their interstitial fibrosis and tubular atrophy (IFTA) score and the fibrosis ratio of cortical area (F%) were analyzed in combination with the DKI metrics.

Results: The progression of DN, reflected by the estimated glomerular filtration rate (eGFR), was accompanied by rising mean kurtosis (MK) and axial kurtosis (Ka) along with decreasing mean diffusivity (MD), axial diffusivity (Da), and radial diffusivity (Dr). Whereas MK was correlated negatively with hemoglobin (Hb) and eGFR and positively with neutrophil gelatinase-associated lipocalin (NGAL), cystatin C (CysC), and serum creatinine (Scr), MD, Da, and Dr were positively correlated with Hb and eGFR and negatively correlated with CysC and Scr. For the biopsied patients, MK was positively correlated with IFTA, and fractional anisotropy of kurtosis (FAk) was negatively correlated with F% and IFTA. Among the DKI indicators, MK had the highest AUC (0.922, 95% CI: 0.843-1.000).

Conclusion: The noninvasive monitoring of DN was feasible with DKI, and MK could indicate the renal function and fibrosis of DN patients. Changes in MK may also serve as a biomarker to assess treatment response (eg, microstructural improvement) after therapeutic interventions (eg, drug therapy for diabetic nephropathy, anti-fibrotic therapy).

Keywords: diabetes mellitus; diabetic nephropathy; diffusion kurtosis imaging; magnetic resonance imaging; mean kurtosis.

Plain language summary

Diabetic nephropathy (DN) is a serious kidney complication of diabetes and the leading cause of end-stage renal disease. While imaging tools like ultrasound, CT, and standard MRI are widely used to evaluate kidney structure and blood flow, they play a limited role in diagnosing or monitoring DN. There is a growing need for non-invasive methods that can detect early changes and track disease progression more precisely. Diffusional Kurtosis Imaging (DKI) is an advanced MRI technique that captures the complexity of water movement in tissues, offering insights into kidney microstructure. The present study analyzed, in DN patients, eight DKI metrics—mean kurtosis and diffusivity (MK and MD), axial and radial diffusivity and kurtosis (Da, Dr, Ka, Kr,), fractional anisotropy (FA), and fractional anisotropy in kurtosis (FAk)—and compared them to standard lab markers and biopsy results. Among all DKI measures, MK showed the strongest correlation with kidney function and fibrosis. Specifically, higher MK values were associated with worse kidney function (lower hemoglobin and eGFR, higher creatinine, NGAL, and cystatin C) and more severe tissue damage (higher fibrosis scores on biopsy). MK also achieved the highest diagnostic accuracy (AUC = 0.922) in distinguishing disease severity. These findings suggest that DKI, and MK in particular, can serve as a powerful, non-invasive biomarker for assessing kidney damage and fibrosis in DN patients. MK may also help track treatment response, offering a way to monitor improvements in kidney microstructure following therapies.

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Conflict of interest statement

The authors declare no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Representative renal MRI images. (A) Patient diagnosed with diabetes (Group DM). (B) Patient diagnosed with diabetic nephropathy and eGFR > 60 mL/min/1.73 m2 (Group Mild). (C) Patient diagnosed with diabetic nephropathy and eGFR ≤ 60 mL/min/1.73 m2 (Group Severe).
Figure 2
Figure 2
Comparisons of DKI metrics between groups. Group DM, patients diagnosed with diabetes; Group Mild, patients diagnosed with diabetic nephropathy with eGFR > 60 mL/min/1.73 m2; Group Severe, patients diagnosed with diabetic nephropathy and eGFR ≤ 60 mL/min/1.73 m2. * P < 0.05 vs Group DM. # P < 0.05 vs Group Mild.
Figure 3
Figure 3
Correlation between MK and laboratory test results.
Figure 4
Figure 4
Correlation between MD and laboratory test results.
Figure 5
Figure 5
Correlation between Da and laboratory test results.
Figure 6
Figure 6
Correlation between Dr and laboratory test results.
Figure 7
Figure 7
Pathological images (PAS staining) of renal biopsy. (A) Patient diagnosed with diabetes (Group DM). (B) Patient diagnosed with diabetic nephropathy and eGFR > 60 mL/min/1.73 m2 (Group Mild). (C) Patient diagnosed with diabetic nephropathy and eGFR ≤ 60 mL/min/1.73 m2 (Group Severe).
Figure 8
Figure 8
ROC curves of different DKI metrics in identifying patients with severe diabetic nephropathy.
See this image and copyright information in PMC

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