Abstract: SA-PO412
Metabolites Associated to Renal Function in the CKD and General Population
Session Information
- CKD: Estimating Equations, Incidence, Prevalence, Special Populations
November 04, 2017 | Location: Hall H, Morial Convention Center
Abstract Time: 10:00 AM - 10:00 AM
Category: Chronic Kidney Disease (Non-Dialysis)
- 302 CKD: Estimating Equations, Incidence, Prevalence, Special Populations
Authors
- Titan, Silvia M., Faculty of Medicine, Sao Paulo University, Sao Paulo, Sao Paulo, Brazil
- Venturini, Gabriela, Incor, Hospital das Clínicas, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil
- Padilha, Kallyandra, Incor, Hospital das Clínicas, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil
- Tavares, Gesiane Fernandes, Faculty of Medicine, Sao Paulo University, Sao Paulo, Sao Paulo, Brazil
- Bensenor, Isabela M., Universitary Hospital, Sao Paulo University, Sao Paulo, Sao Paulo, Brazil
- Lotufo, Paulo, Universitary Hospital, Sao Paulo University, Sao Paulo, Sao Paulo, Brazil
- Rhee, Eugene P., Massachusetts General Hospital, Boston, Massachusetts, United States
- Thadhani, Ravi I., Massachusetts General Hospital, Boston, Massachusetts, United States
- Pereira, Alexandre Costa, Incor, Hospital das Clínicas, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil
Background
Metabolomics is a novel tool to identify biomarkers and pathways involved in diseases. In CKD, there is a growing interest in metabolites that are related to renal function that could be used for assessing GFR. However, data is scarce and diverse. Objective: To evaluate metabolites related to eGFR in a CKD Study. Replication was tested in 2 other studies using the same metabolomics platform.
Methods
Derivation study was the Progredir Study (PS; n=454 class 3 and 4 CKD). Validation studies were Diabetic Nephropathy Study (DNS; n=56 macroalbuminuric DN) and the Baependi Study (BS; n=1145p from the general population). Metabolomics was performed by GC and Mass Spectrometry. Metabolites were identified using Agilent Fiehn GC/MS Metabolomics and NIST libraries (Agilent MassHunter Work-station Quantitative Analysis, version B.06.00). Adjusted linear regression models on eGFR-CKDEPI were built. FDR<0,05 was used in the derivation study.
Results
In the PS, 135 metabolites where related to eGFR, after adjustments for batch, sex, age, DM, smoking and SBP. Of those, 24 were also related to eGFR in the BS and 17 in the DNS. However, only 6 metabolites were significantly related to eGFR in all 3 studies: D-threitol, myo-inositol, ribitol, 4-deoxierythronic acid, galactonic acid and galacturonic acid. While correlation to eGFR was high in the 2 CKD studies, it was moderate in the general population (Table 1).
Conclusion
Our results demonstrate that metabolites are potential markers of renal function. Further investigation is needed to determine their performance against otherwise gold-standard methods, most notably among those with normal eGFR.
Table 1. Spearman’s correlation coefficients and p values for metabolites and eGFR in the 3 studies.
| Metabolite (log2) | PS | DNS | BS |
| Replicated in the 3 studies | |||
| D-threitol | -0.71 / 8.7E-70 | -0.80 / 9.6E-14 | -0.28 / 1.6E-21 |
| Myo-inositol | -0.70 / 4.9E-66 | -0.81 / 5.4E-14 | -0.19 / 2.0E-11 |
| Galactonic acid | -0.61 / 5.7E-19 | -0.47 / 2.8E-04 | -0.23 / 2.8E-05 |
| Galacturonic acid | -0.47 / 6.1E-15 | -0.58 / 9.3E-06 | -0.23 / 9.0E-11 |
| 4-Deoxyerythronic acid | -0.38 / 7.9E-17 | -0.56 / 2.5E-05 | -0.02 / 0.51 |
| Ribitol | -0.36 / 3.0E-8 | -0.81 / 7.7E-13 | -0.28 / 8.0E-22 |
| Replicated only in the CKD studies | |||
| Pseudo uridine | -0.71 / 1.26E-65 | -0.86 / 3.8E-17 | -0.08 / 0.01 |
| Butyric acid | -0.67 / 8.26E-53 | -0.60 / 1.6E-05 | - |
Funding
- Government Support - Non-U.S.