<b>Methylglyoxal adducts are prognostic biomarkers for diabetic kidney disease in patients with type 1 diabetes</b>

Abstract

Over 30% of patients with type 1 diabetes develop diabetic kidney disease (DKD), significantly increasing mortality risk. The Diabetes Control and Complications Trial (DCCT) and follow-up study Epidemiology of Diabetes Interventions and Complications (EDIC) established that glycemic control measured by HbA1c predicts DKD risk. However, the continued high incidence of DKD reinforces the urgent need for additional biomarkers to supplement HbA1c. Here, we assessed biomarkers induced by methylglyoxal (MG), a metabolic by-product that forms covalent adducts on DNA, RNA, and protein called MG-adducts. Urinary MG-adducts were measured from patients with type 1 diabetes enrolled in DCCT/EDIC who did (cases, n=90) or did not (controls, n=117) develop DKD. Univariate and multivariable analysis revealed that measurements of MG-adducts independently predict DKD, even before established DKD biomarkers such as glomerular filtration rate and albumin excretion rate. Elevated levels of MG-adducts bestowed the greatest risk of developing DKD in a multivariable model that included HbA1c and other clinical covariates. Our work establishes a novel class of biomarkers to predict DKD risk and suggests that inclusion of MG-adducts may be a valuable tool to improve existing predictors of complications like DKD prior to overt disease to aid in identifying at-risk individuals and personalized risk management.

Article Highlights

· Diabetic kidney disease (DKD) is a significant source of mortality in patients with type 1 diabetes, and there is an unmet need to identify novel biomarkers to predict DKD.

· We sought to elucidate if methylglyoxal adducts, which are a marker of altered metabolism, have predictive utility for DKD in patients with type 1 diabetes.

· We found that methylglyoxal adducts predict the risk of DKD at least 16 years pre-diagnosis.

· Our data presents a novel class of biomarkers with potential utility for predicting DKD risk to supplement existing tools such as HbA1c, AER, and GFR.