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Background and hypothesis <p>Diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM) presents heterogeneously, complicating risk assessment. This study evaluated microRNAs and other biomarkers for DKD phenotyping and predicting kidney function in clinical practice.</p> Methods <p>Data from 79 patients with T2DM were analyzed. DKD phenotypes were defined based on eGFR and UACR:</p> <p><?indent??>• F1 (albuminuric): eGFR &lt; 60 mL/min/1.73&#xa0;m², UACR ≥ 300&#xa0;mg/g.</p> <p><?indent??>• F2 (non-albuminuric, preserved filtration): eGFR ≥ 60 mL/min/1.73&#xa0;m², UACR ≤ 30&#xa0;mg/g.</p> <p><?indent??>• F3 (non-albuminuric, reduced filtration): eGFR &lt; 60 mL/min/1.73&#xa0;m², UACR ≤ 30&#xa0;mg/g.</p> <p><?indent??>• F4 (moderately increased albuminuria): UACR &gt; 30 and &lt; 300&#xa0;mg/g was shown for completeness but excluded from primary analyses.</p> <p>Multiple regression and partial least squares structural equation modeling (PLS-SEM) were applied to identify predictors of eGFR. Discriminant analysis (including age, ACE, uric acid, and AIP) was used for phenotype classification. Serum levels of hsa-miR-126-3p and hsa-miR-423-5p were compared between phenotypes.</p> Results <p>Independent predictors of eGFR included ACE (β = − 0.478; <i>p</i> &lt; 0.001), age (β = − 0.336), AIP (β = − 0.245), and hsa-miR-423-5p (β = 0.138; <i>p</i> = 0.033) (R² = 0.619). In the PLS-SEM model, ACE, AIP, and hsa-miR-423-5p had significant direct effects on eGFR, while ACE was modulated by hsa-miR-126-3p, age, and BMI. Discriminant analysis correctly classified 87.5% of patients (Wilks’ Lambda, <i>p</i> &lt; 0.05). F1 exhibited the highest ACE and hsa-miR-126-3p levels, lowest HDL-C, and most microvascular complications. F2 had the best renal function, lowest ACE and miR-126-3p expression, and the highest proportion of women. F3 patients were the oldest, with elevated uric acid and hsa-miR-423-5p levels. Coronary heart disease was most common in F1 and F3, while stroke occurred only in F1 and F2.</p> Conclusions <p>ACE, AIP, and the miRNAs hsa-miR-126-3p and hsa-miR-423-5p may support DKD phenotyping and kidney function prediction. Incorporating these markers into clinical models could enable the implementation of individualized nephroprotective strategies in patients with T2DM.</p>

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The “Picasso faces” of diabetic kidney disease – how the art of phenotyping and molecular biomarkers is transforming clinical nephrology: an observational study in patients with type 2 diabetes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Małgorzata Rodzoń-Norwicz,
  • Natalia Potocka,
  • Marzena Skrzypa,
  • Izabela Zawlik,
  • Katarzyna Milian–Ciesielska,
  • Patryk Kogut,
  • Krzysztof Gargasz,
  • Michael Maes,
  • Magdalena Sowa-Kućma,
  • Agnieszka Gala-Błądzińska

摘要

Background and hypothesis

Diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM) presents heterogeneously, complicating risk assessment. This study evaluated microRNAs and other biomarkers for DKD phenotyping and predicting kidney function in clinical practice.

Methods

Data from 79 patients with T2DM were analyzed. DKD phenotypes were defined based on eGFR and UACR:

• F1 (albuminuric): eGFR < 60 mL/min/1.73 m², UACR ≥ 300 mg/g.

• F2 (non-albuminuric, preserved filtration): eGFR ≥ 60 mL/min/1.73 m², UACR ≤ 30 mg/g.

• F3 (non-albuminuric, reduced filtration): eGFR < 60 mL/min/1.73 m², UACR ≤ 30 mg/g.

• F4 (moderately increased albuminuria): UACR > 30 and < 300 mg/g was shown for completeness but excluded from primary analyses.

Multiple regression and partial least squares structural equation modeling (PLS-SEM) were applied to identify predictors of eGFR. Discriminant analysis (including age, ACE, uric acid, and AIP) was used for phenotype classification. Serum levels of hsa-miR-126-3p and hsa-miR-423-5p were compared between phenotypes.

Results

Independent predictors of eGFR included ACE (β = − 0.478; p < 0.001), age (β = − 0.336), AIP (β = − 0.245), and hsa-miR-423-5p (β = 0.138; p = 0.033) (R² = 0.619). In the PLS-SEM model, ACE, AIP, and hsa-miR-423-5p had significant direct effects on eGFR, while ACE was modulated by hsa-miR-126-3p, age, and BMI. Discriminant analysis correctly classified 87.5% of patients (Wilks’ Lambda, p < 0.05). F1 exhibited the highest ACE and hsa-miR-126-3p levels, lowest HDL-C, and most microvascular complications. F2 had the best renal function, lowest ACE and miR-126-3p expression, and the highest proportion of women. F3 patients were the oldest, with elevated uric acid and hsa-miR-423-5p levels. Coronary heart disease was most common in F1 and F3, while stroke occurred only in F1 and F2.

Conclusions

ACE, AIP, and the miRNAs hsa-miR-126-3p and hsa-miR-423-5p may support DKD phenotyping and kidney function prediction. Incorporating these markers into clinical models could enable the implementation of individualized nephroprotective strategies in patients with T2DM.