Abstract
Background/Objectives: Methylglyoxal-derived hydroimidazolone-1 (MG-H1), an advanced glycation end product, has implications in the pathogenesis of diabetic kidney disease (DKD). Although liquid chromatography–mass spectrometry is the current gold standard for quantifying MG-H1, its overall complexity limits its utility. We developed an ELISA to measure MG-H1 using a specific monoclonal antibody. Methods: Competitive ELISA was used to quantify total, high-molecular-weight (HMW), and low-molecular-weight (LMW) MG-H1 in serum. The assay’s specificity was validated against structurally related compounds. Spike-and-recovery experiments were conducted to assess accuracy and precision. Serum samples from healthy controls, diabetic patients without kidney disease, and patients with DKD were analyzed (n = 10, 23, and 19, respectively). MG-H1’s correlation with renal biomarkers and diagnostic performance was assessed using receiver operating characteristic analyses. Results: The ELISA exhibited preferential reactivity toward MG-H1 compared with structurally related compounds. Spike-and-recovery experiments resulted in recovery rates ranging 108–119%. MG-H1 levels were increased in patients with DKD, although the magnitude of the changes varied among the MG-H1 forms. All MG-H1 forms correlated positively with serum creatinine and blood urea nitrogen, and negatively with estimated glomerular filtration rate. No significant correlations were observed with glycoalbumin, and only a modest association was observed between LMW MG-H1 and HbA1c. Exploratory ROC analyses suggested that all MG-H1 forms could discriminate DKD from DM, with total and HMW MG-H1 showing performance comparable to that of conventional renal function markers. Conclusions: This competitive ELISA enables high-throughput quantification of MG-H1 in serum and demonstrates analytical feasibility; further multicenter validation is required before clinical implementation.
| Original language | English |
|---|---|
| Article number | 2593 |
| Journal | Diagnostics |
| Volume | 16 |
| Issue number | 16 |
| DOIs | |
| Publication status | Published - 08-2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- Internal Medicine
- Clinical Biochemistry
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