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Abstract: TH-PO174

Identification of Circulating Metabolites to Predict Cardiorenal Outcomes in Patients with Type 2 Diabetes (T2D)

Session Information

Category: Diabetic Kidney Disease

  • 702 Diabetic Kidney Disease: Clinical

Authors

  • Hansen, Michael K., Janssen Research & Development, Spring House, Pennsylvania, United States
  • Darshi, Manjula, Janssen Research & Development, Boston, Massachusetts, United States
  • Kohler, Kristen, Janssen Research & Development, La Jolla, California, United States
  • Tang, Owen, Charles Perkins Centre, The University of Sydney; Department of Cardiology, Royal North Shore Hospital; Cardiovascular Discovery Group, Kolling Institute of Medical Research, The University of Sydney; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
  • Figtree, Gemma, Charles Perkins Centre, The University of Sydney; Department of Cardiology, Royal North Shore Hospital; Cardiovascular Discovery Group, Kolling Institute of Medical Research, The University of Sydney; Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
Background

Individuals with type 2 diabetes mellitus (T2D) are at elevated risk of adverse cardiorenal outcomes. Additional biomarkers are needed to better identify patients at highest risk of adverse outcomes and to better understand pathways associated with risk. We examined if baseline plasma metabolites predict renal and cardiovascular (CV) outcomes in the Canagliflozin Cardiovascular Assessment Study (CANVAS) participants with T2D at high cardiovascular risk.

Methods

Plasma metabolites were assayed from a subset of the CANVAS study participants by HPLC (HILIC & AMIDE)-mass spectrometry using targeted assays. Cox proportional hazard regression analysis was used to examine the association of 105 baseline metabolites with the renal outcome (40% eGFR decline, end-stage kidney disease, or renal death), and CV outcomes including heart failure, heart failure and CV death, and MACE. The predicted hazard ratio (HR) for a 1-unit increase in the log metabolite values, 95% confidence intervals, and p-values were calculated. Results were calculated overall and broken out by treatment when a significant treatment interaction was observed.

Results

We included 934 (22%) of the 4,330 CANVAS participants. The Figure below shows a summary of the metabolites across the renal and CV outcomes where a 1-unit increase in the log metabolite was associated with either increased or decreased risk in a fully adjusted model, (age, gender, race, BMI, HbA1C, cholesterol, blood pressure, history of heart failure, baseline ACR and eGFR).

Conclusion

A number of metabolites were identified and associated with higher or lower risk of cardiorenal outcomes. Further research is required to better understand and validate these findings.

Funding

  • Commercial Support – Janssen Research & Development, LLC.