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Background <p>Diabetic kidney disease (DKD) is a major microvascular complication of diabetes and a leading cause of end-stage renal disease. Conventional biomarkers such as albuminuria and estimated glomerular filtration rate (eGFR) inadequately predict disease heterogeneity or progression. microRNAs (miRNAs), small non-coding RNAs regulating post-transcriptional gene expression, have emerged as potential non-invasive biomarkers for DKD phenotyping and prognosis.</p> Objectives <p>To systematically evaluate human studies investigating the association of miRNAs with DKD phenotypes, proteinuric (P-DKD) and non-proteinuric (NP-DKD), and their relationship with disease progression (rapid vs. slow progressors).</p> Methods <p>A comprehensive search of major databases (2015–2025) identified 19 observational studies meeting inclusion criteria. Data on study design, miRNA assay methods, and differential expression were extracted. Quality assessment was performed using the Newcastle–Ottawa Scale (NOS). Studies were narratively synthesized according to DKD phenotype and progression pattern.</p> Results <p>Distinct miRNA expression patterns were identified. Upregulation of miR-21, miR-192, miR-194, and miR-615-3p characterized P-DKD and rapid-progressor forms, indicating activation of TGF-β/Smad3-mediated fibrotic signaling. In contrast, preservation or elevation of anti-fibrotic miRNAs such as miR-27b-3p and miR-1228-3p was associated with NP-DKD and slower eGFR decline. Overall, 79% of studies were of moderate-to-high quality.</p> Conclusions <p>Distinct miRNA signatures differentiate DKD phenotypes and predict disease trajectory, supporting their role as diagnostic and prognostic biomarkers. Standardized multicenter validation integrating miRNA panels with clinical and biochemical data, coupled with machine-learning approaches, is essential to enable precision nephrology.</p>

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MicroRNAs as predictors of phenotype and progression in diabetic kidney disease: a systematic review

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Cynthia Amrutha Sukumar,
  • Ajit Singh,
  • Sudha Vidyasagar,
  • Somasish Ghosh Dastidar,
  • Manjunath J. Kulkarni,
  • Ravindra Prabhu A.

摘要

Background

Diabetic kidney disease (DKD) is a major microvascular complication of diabetes and a leading cause of end-stage renal disease. Conventional biomarkers such as albuminuria and estimated glomerular filtration rate (eGFR) inadequately predict disease heterogeneity or progression. microRNAs (miRNAs), small non-coding RNAs regulating post-transcriptional gene expression, have emerged as potential non-invasive biomarkers for DKD phenotyping and prognosis.

Objectives

To systematically evaluate human studies investigating the association of miRNAs with DKD phenotypes, proteinuric (P-DKD) and non-proteinuric (NP-DKD), and their relationship with disease progression (rapid vs. slow progressors).

Methods

A comprehensive search of major databases (2015–2025) identified 19 observational studies meeting inclusion criteria. Data on study design, miRNA assay methods, and differential expression were extracted. Quality assessment was performed using the Newcastle–Ottawa Scale (NOS). Studies were narratively synthesized according to DKD phenotype and progression pattern.

Results

Distinct miRNA expression patterns were identified. Upregulation of miR-21, miR-192, miR-194, and miR-615-3p characterized P-DKD and rapid-progressor forms, indicating activation of TGF-β/Smad3-mediated fibrotic signaling. In contrast, preservation or elevation of anti-fibrotic miRNAs such as miR-27b-3p and miR-1228-3p was associated with NP-DKD and slower eGFR decline. Overall, 79% of studies were of moderate-to-high quality.

Conclusions

Distinct miRNA signatures differentiate DKD phenotypes and predict disease trajectory, supporting their role as diagnostic and prognostic biomarkers. Standardized multicenter validation integrating miRNA panels with clinical and biochemical data, coupled with machine-learning approaches, is essential to enable precision nephrology.