Candidate composite biomarker to inform drug treatments for diabetic kidney disease

Roger D Jones; Seyum Abebe; Veronica Distefano; Gert Mayer; Irene Poli; Claudio Silvestri; Debora Slanzi

doi:10.3389/fmed.2023.1271407

Candidate composite biomarker to inform drug treatments for diabetic kidney disease

Front Med (Lausanne). 2023 Nov 1:10:1271407. doi: 10.3389/fmed.2023.1271407. eCollection 2023.

Authors

Roger D Jones^{1

2

3}, Seyum Abebe¹, Veronica Distefano^{1

4}, Gert Mayer⁵, Irene Poli¹, Claudio Silvestri^{1

6}, Debora Slanzi^{1

7}

Affiliations

¹ European Centre for Living Technology, Ca' Foscari University of Venice, Venice, Italy.
² Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
³ Systems Engineering and Research Center, Stevens Institute of Technology, Hoboken, NJ, United States.
⁴ Department of Economic Sciences, Università del Salento, Salento, Italy.
⁵ Internal Medicine IV, Medical University Innsbruck, Innsbruck, Austria.
⁶ Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Venice, Italy.
⁷ Department of Management, Ca' Foscari University of Venice, Venice, Italy.

Abstract

Introduction: Current guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy.

Results: Using data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment.

Discussion: Higher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment.

Keywords: MCRa; RASi; SGLT2i; biomarkers; clinical data; diabetic kidney disease; precision medicine.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 848011. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Innovative Medicines Initiative 2 Joint Undertaking (Grant No. 115974, 2015). Neither the European Union nor the granting authority can be held responsible for them.