Candidate composite biomarker to inform drug treatments for diabetic kidney disease - Jones, Roger D.; Abebe, Seyum; Distefano, Veronica; Mayer, Gert; Poli, Irene; Silvestri, Claudio; Slanzi, Debora (Frontiers in Medicine, 2023)
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. 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-learning 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 (∆eGFR) in the estimated glomerular filtration rate (eGFR). The ∆eGFR is estimated for both the RASi-alone treatment and the add-on treatment.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.