Introduction & Objective: The prevalence of diabetic kidney disease (DKD) is increasing, and biomarkers that can enhance prognosis beyond renal function and albuminuria are rare. This study aimed to evaluate the potential of adenosine, succinate, and their related pathways, including hyaluronic acid (HA), as predictors of DKD progression.
Methods: We examined 230 participants categorized as healthy controls, DKD non-progressors, and rapid progressors. Eligible patients with diabetes had at least five prior urine creatinine measurements during a median of 5.8 years of follow-up. Using previously collected eGFR data, rapid progressors were identified based on a decline in eGFR (ΔeGFR/year ≤−3 mL/min/1.73 m2/year or annual rate of eGFR decline ≥ 3.3%) before enrollment. At the end of the cohort, we measured the concentrations of urinary adenosine, succinate, HA, and serum cluster of differentiation 39 (CD39) and CD73, which are involved in adenosine generation. Associations between eGFR, ΔeGFR, and albuminuria were analyzed after adjusting for DKD progression risk factors.
Results: Urinary succinate and serum CD39 levels were more elevated in rapid progressors compared to controls and non-progressors. This trend persisted when using the percentage criterion (≥40% decline in eGFR) for renal disease progression. Correlation analysis consistently linked urinary succinate and serum CD39 concentrations with eGFR at enrollment, ΔeGFR and albuminuria. However, among the various metabolites studied, multivariate regression analysis identified urinary succinate as an independent metabolite of rapid DKD progression and albuminuria.
Conclusion: Among several potential metabolites, only urinary succinate exhibited associations with rapid DKD progression.
I. Jung: None. S. Nam: Consultant; HieraBio Inc., Korea, Republic of. S. Park: None. D. Lee: None. J. Yu: None. J. Seo: None. D. Lee: None. N. Kim: None.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2023R1A2C2003479) and the Bio & Medical Technology Development Program of the NRF funded by MSIT (NRF-2019M3E5D3073102). This study was also supported by a National IT Industry Promotion Agency (NIPA) grant funded by the MSIT (No. S0252-21-1001, Development of AI Precision Medical Solution (Doctor Answer 2.0)) and a Korea Health Industry Development Institute (KHIDI) grant funded by the Ministry of Health & Welfare of Korea (No. HI16C1997 and No. HI23C0679).