Abstract: TH-PO932
Urine Myo-Inositol, the Novel Prognostic Biomarker for Diabetic Kidney Disease: A Targeted Metabolomics Study Using Nuclear Magnetic Resonance
Session Information
- Diabetic Kidney Disease: Biomarkers, Pathogenesis
November 07, 2019 | Location: Exhibit Hall, Walter E. Washington Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Diabetic Kidney Disease
- 602 Diabetic Kidney Disease: Clinical
Authors
- Kwon, Soie, Seoul national university, Seoul, Korea (the Republic of)
- An, Jung Nam, Seoul National University Boramae Medical Center, Seoul, Korea (the Republic of)
- Lee, Jeonghwan, SMG-SNU Boramae Medical Center, Seoul, Korea (the Republic of)
- Kim, Yong Chul, SNUH, Seoul, Korea (the Republic of)
- Kim, Dong Ki, Seoul National University Hospital, Seoul, Korea (the Republic of)
- Kim, Yon Su, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
- Lee, Jung Pyo, Seoul National University Boramae Medical Center, Seoul, Korea (the Republic of)
Background
Estimated GFR (eGFR) and urine proteinuria are currently the strongest predictive biomarker of CKD regardless of the cause. For more precise prediction according to the cause of CKD and to identify treatment options, metabolomics has been increasingly applied to identifying new biomarkers of diseased specific CKD. We investigated the association between urine metabolites and ESRD progression in diabetic kidney disease (DKD) cohort.
Methods
Based on previous our animal study, targeted metabolomics (n=26) was performed using nuclear magnetic resonance. Prospectively stored urine samples consecutive patients with DKD stage 1 to 5 (n=208) and their healthy controls (n=26) were analyzed. Cross-sectional association between measured metabolites and eGFR were compared. Multivariate cox models were conducted for the risk of ESRD and mortality.
Results
ESRD occurred in 103 (44.0%) patients and the number of death was 65 (27.8%). The median fold change of metabolites compared with control groups, 7 metabolites (glucose, mannose, myoinositol, lactate, succinate, fumarate and choline) revealed a trend according to CKD stages. Linear regression identified myo-inositol is best-associated metabolite with eGFR. The relationship between competitive metabolites and outcomes was investigated by multivariate Cox models after adjusting for the baseline covariates (Table1). Of which, 4 metabolites (myoinositol, glycerol, fumarate, oxoisocaproate) had predictive value for ESRD and only myo-inositol retained predictive significance in mortality (aHR 1.004, 95% CI 1.002–1.006, p-value <0.001).
Conclusion
Our results suggest the myoinositol, previously defined as vitamin B8, can be a predictive biomarker to predict the risk of ESRD progression in DKD. Myoinositol, as a secondary messenger of insulin and confirmed safe compound, further mechanism study is needed.
Table 1. Risk of end-stage renal disease according to the urinary metabolites
| Metabolites/Cr | Unadjusted | Model 1 | Model 2 | ||||||
| HR | CI | P | HR | CI | P | HR | CI | P | |
| Myo-inositol | 1.005 | 1.000-1.010 | <0.001 | 1.003 | 1.001 - 1.005 | 0.001 | 1.003 | 1.001 - 1.005 | 0.011 |
| Glycerol | 1.004 | 1.000-1.010 | 0.009 | 1.004 | 1.001 - 1.008 | 0.007 | 1.005 | 1.001 - 1.009 | 0.022 |
| Fumarate | 1.076 | 0.840-1.380 | 0.022 | 1.093 | 1.007 - 1.187 | 0.033 | 1.105 | 1.014 - 1.204 | 0.023 |
| Oxoisocaproate | 1.374 | 0.750-2.520 | 0.034 | 2.147 | 1.557 - 2.959 | <0.001 | 1.670 | 1.146 - 2.434 | 0.008 |
only 4 significant metabolites were described; Model 1: adjusted for age, sex, HTN, eGFR; Model 2: adjusted for model 1 plus laboratory findings