Kidney Week

Abstract: SA-OR055

Metabolites Associated to Mortality and ESRD in a Brazilian CKD Cohort: The Progredir Study

Session Information

• The Slow Burn: CKD Incidence and Progression
  November 04, 2017 | Location: Room 277, Morial Convention Center
  Abstract Time: 05:18 PM - 05:30 PM

Category: Chronic Kidney Disease (Non-Dialysis)

• 301 CKD: Risk Factors for Incidence and Progression

Authors

• Titan, Silvia M., Faculty of Medicine, Sao Paulo University, Sao Paulo, Sao Paulo, Brazil

Silvia M. Titan, MD, PhD



• Venturini, Gabriela, Incor, Hospital das Clínicas, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil

Gabriela Venturini,

• Padilha, Kallyandra, Incor, Hospital das Clínicas, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil

Kallyandra Padilha,

• Lotufo, Paulo, Universitary Hospital, Sao Paulo University, Sao Paulo, Sao Paulo, Brazil

Paulo Lotufo,

• Bensenor, Isabela M., Universitary Hospital, Sao Paulo University, Sao Paulo, Sao Paulo, Brazil

Isabela M. Bensenor,

• Thadhani, Ravi I., Massachusetts General Hospital, Boston, Massachusetts, United States

Ravi I. Thadhani,

• Rhee, Eugene P., Massachusetts General Hospital, Boston, Massachusetts, United States

Eugene P. Rhee,

• Pereira, Alexandre Costa, Incor, Hospital das Clínicas, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil

Alexandre Costa Pereira,

Background

Recent studies have evaluated metabolomics in relation to eGFR and to incident CKD. However, very few studies have evaluated metabolomics biomarkers in the setting of prevalent CKD and hard outcomes. Objective: To evaluate metabolites related to death, ESRD and a composite outcome of both in a CKD population (n=454).

Methods

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). From initial 10940 metabolites, we excluded those present <50% of the samples, leaving 293. We selected candidate metabolites by applying a FDR q value <0.05 in a Cox model on the composite outcome adjusted only for batch. Among the 34 selected metabolites, Cox regression models were built on death (n=93), ESRD (n=36) and composite outcome (n=126). Competing risk analysis was also performed for ESRD.

Results

Mean age was 68(±12)y, mean eGFR-CKDEPI was 38.4 (±14.6) ml/min/1.73m² and 57% were diabetic. After adjustment for batch, sex, age, DM and eGFR, 18 metabolites remained significantly related to the composite outcome, with lactose, D-threitol, docosahexaenoic acid (DHA), butanoic acid and mannitol among the top. For mortality only, 9 metabolites remained significantly associated with death, with D-malic acid (TCA cycle, OR 1.84, 95%CI1.32 – 2.56, p=0.0003), butanoic acid (colon microbiota; 1.59, 95%CI1.17 – 2.15, p=0.003), and DHA (omega3 fatty acid, OR 0.58, 95%CI0.39 – 0.88, p=0.009) among the top 3. For ESRD, 4 metabolites remained significantly associated to its risk: lactose, 2-O-glycerol-α-D-galactopyranoside, D-threitol and tyrosine (Table1), findings confirmed by the competing analysis except for D-threitol.

Conclusion

Our results identify specific metabolites related to hard outcomes in a CKD population. These results require confirmation.

Table1. Cox proportional hazard models on the risk of ESRD after adjustment for batch, sex, age, eGFR and DM.

Funding

• Government Support - Non-U.S.