Multimodal Magnetic Resonance Imaging in Diabetic Kidney Disease: From Pathophysiological Insights to Clinical Applications

Background: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Conventional clinical markers of renal function lack sufficient sensitivity for early diagnosis, whereas renal biopsy is unsuitable for routine monitoring because of its invasiveness. Objective: This narrative review aimed to evaluate recent advances in novel, non-invasive multimodal magnetic resonance imaging (MRI) biomarkers for the assessment of renal pathological alterations in DKD. Recent findings: Recent studies have demonstrated that multimodal MRI can non-invasively characterize several key pathological features of DKD, including renal hypoxia, microvascular dysfunction, ectopic fat deposition, and interstitial fibrosis. Furthermore, emerging evidence suggests that these imaging biomarkers may enhance risk stratification, monitor disease progression, and assess treatment efficacy, particularly in the presence of comorbidities and the advent of emerging therapies. Conclusions: Multimodal MRI shows considerable promise in translating advanced imaging biomarkers into clinical practice, facilitating the personalized management of DKD. However, future research must focus on establishing standardized imaging acquisition and analytical protocols, conducting prospective cohort studies to validate the association between imaging biomarkers and hard clinical endpoints, integrating artificial intelligence for automated analysis, and developing molecular imaging probes targeted at early disease pathways.

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Publication Details

Journal
Diagnostics
Published
2026-05-29
DOI
https://doi.org/10.3390/diagnostics16111676
Primary Topic
MRI in cancer diagnosis
Type
article
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article

Multimodal Magnetic Resonance Imaging in Diabetic Kidney Disease: From Pathophysiological Insights to Clinical Applications

Huang Bingcang, Mengdan Ni
Diagnostics
MRI in cancer diagnosis
article

Multimodal Magnetic Resonance Imaging in Diabetic Kidney Disease: From Pathophysiological Insights to Clinical Applications

Huang Bingcang, Mengdan Ni
article en

Abstract

Background: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Conventional clinical markers of renal function lack sufficient sensitivity for early diagnosis, whereas renal biopsy is unsuitable for routine monitoring because of its invasiveness. Objective: This narrative review aimed to evaluate recent advances in novel, non-invasive multimodal magnetic resonance imaging (MRI) biomarkers for the assessment of renal pathological alterations in DKD. Recent findings: Recent studies have demonstrated that multimodal MRI can non-invasively characterize several key pathological features of DKD, including renal hypoxia, microvascular dysfunction, ectopic fat deposition, and interstitial fibrosis. Furthermore, emerging evidence suggests that these imaging biomarkers may enhance risk stratification, monitor disease progression, and assess treatment efficacy, particularly in the presence of comorbidities and the advent of emerging therapies. Conclusions: Multimodal MRI shows considerable promise in translating advanced imaging biomarkers into clinical practice, facilitating the personalized management of DKD. However, future research must focus on establishing standardized imaging acquisition and analytical protocols, conducting prospective cohort studies to validate the association between imaging biomarkers and hard clinical endpoints, integrating artificial intelligence for automated analysis, and developing molecular imaging probes targeted at early disease pathways.

DiagnosticsVol. 16(11)
University of Shanghai for Science and Technology (CN), Second Military Medical University (CN), Shanghai Pudong New Area Gongli Hospital (CN)
Openalex Percentile: Top 5%
MRI in cancer diagnosis
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Multimodal Magnetic Resonance Imaging in Diabetic Kidney Disease: From Pathophysiological Insights to Clinical Applications — Huang Bingcang, Mengdan Ni · Diagnostics (2026) | TGRS Research Map | TGRS