TY - JOUR
T1 - Baseline urinary metabolites predict albuminuria response to spironolactone in type 2 diabetes
AU - Mulder, Skander
AU - Perco, Paul
AU - Oxlund, Christina
AU - Mehdi, Uzma F.
AU - Hankemeier, Thomas
AU - Jacobsen, Ib A.
AU - Toto, Robert
AU - Heerspink, Hiddo J.L.
AU - Pena, Michelle J.
N1 - Funding Information:
Funding: The work leading to this paper received funding from the European Community's Seventh Framework Programme under grant agreement no. HEALTH–F2–2009–241544 (SysKID consortium) and BEAt-DKD. The BEAt-DKD project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115974. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA with JDRF . HJ Lambers Heerspink is supported by a VIDI grant from the Netherlands Organisation for Scientific Research.
Funding Information:
Authors? contributions:, Bioinformatic Analysis: Paul Perco, Statistical Analysis: Skander Mulder, Michelle J. Pena, Hiddo J.L. Heerspink, Writing: Skander Mulder, Paul Perco, Michelle J. Pena, Hiddo J.L. Heerspink, Metabolite measurements: Thomas Hankemeier, Data collection Christine Oxlund, Ib A. Jacobsen, Uzma F. Mehdi, Robert Toto, All authors revised the article and approved the submitted version and have read the journal's authorship agreement and policy on disclosure of potential conflicts of interest. Conflict of Interest: SM, PP, CO, UFM, TH, IAJ, and MJP report no conflicts. RDT has received consulting fees from Amgen, AstraZeneca, Bayer, Boehringer-Ingelheim, Novo Nordisk, Reata, Relypsa, and ZS Pharma. HJLH reports grants and other from AbbVie, AstraZeneca, Boehringer Ingelheim, and Janssen; and consultancy fees from CSL Pharma, Gilead, Merck, MundiPharm, Mitsubishi Tanabe, and Retrophin. Funding: The work leading to this paper received funding from the European Community's Seventh Framework Programme under grant agreement no. HEALTH?F2?2009?241544 (SysKID consortium) and BEAt-DKD. The BEAt-DKD project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115974. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA with JDRF. HJ Lambers Heerspink is supported by a VIDI grant from the Netherlands Organisation for Scientific Research.
Publisher Copyright:
© 2020 The Author(s)
PY - 2020/8
Y1 - 2020/8
N2 - The mineralocorticoid receptor antagonist spironolactone significantly reduces albuminuria in subjects with diabetic kidney disease, albeit with a large variability between individuals. Identifying novel biomarkers that predict response to therapy may help to tailor spironolactone therapy. We aimed to identify a set of metabolites for prediction of albuminuria response to spironolactone in subjects with type 2 diabetes. Systems biology molecular process analysis was performed a priori to identify metabolites linked to molecular disease processes and drug mechanism of action. Individual subject data and urine samples were used from 2 randomized placebo controlled double blind clinical trials (NCT01062763, NCT00381134). A urinary metabolite score was developed to predict albuminuria response to spironolactone therapy using penalized ridge regression with leave-one-out cross validation. Bioinformatic analysis identified a set of 18 metabolites linked to a diabetic kidney disease molecular model and potentially affected by spironolactone mechanism of action. Spironolactone reduced UACR relative to placebo by median −42% (25th to 75% percentile −65 to 6) and −29% (25th to 75% percentile −37 to −1) in the test and replication cohorts, respectively. In the test cohort, UACR reduction was higher in the lowest tertile of the baseline urinary metabolite score compared with middle and upper tertiles −58% (25th to 75% percentile −78 to 33), −28% (25th to 75% percentile −46 to 8), −40% (25th to 75% percentile −52% to 31), respectively, P = 0.001 for trend). In the replication cohort, UACR reduction was −54% (25th to 75% percentile −65 to −50), −41 (25th to 75% percentile −46% to 30), and −17% (25th to 75% percentile −36 to 5), respectively, P = 0.010 for trend). We identified a set of 18 urinary metabolites through systems biology to predict albuminuria response to spironolactone in type 2 diabetes. These data suggest that urinary metabolites may be used as a tool to tailor optimal therapy and move in the direction of personalized medicine.
AB - The mineralocorticoid receptor antagonist spironolactone significantly reduces albuminuria in subjects with diabetic kidney disease, albeit with a large variability between individuals. Identifying novel biomarkers that predict response to therapy may help to tailor spironolactone therapy. We aimed to identify a set of metabolites for prediction of albuminuria response to spironolactone in subjects with type 2 diabetes. Systems biology molecular process analysis was performed a priori to identify metabolites linked to molecular disease processes and drug mechanism of action. Individual subject data and urine samples were used from 2 randomized placebo controlled double blind clinical trials (NCT01062763, NCT00381134). A urinary metabolite score was developed to predict albuminuria response to spironolactone therapy using penalized ridge regression with leave-one-out cross validation. Bioinformatic analysis identified a set of 18 metabolites linked to a diabetic kidney disease molecular model and potentially affected by spironolactone mechanism of action. Spironolactone reduced UACR relative to placebo by median −42% (25th to 75% percentile −65 to 6) and −29% (25th to 75% percentile −37 to −1) in the test and replication cohorts, respectively. In the test cohort, UACR reduction was higher in the lowest tertile of the baseline urinary metabolite score compared with middle and upper tertiles −58% (25th to 75% percentile −78 to 33), −28% (25th to 75% percentile −46 to 8), −40% (25th to 75% percentile −52% to 31), respectively, P = 0.001 for trend). In the replication cohort, UACR reduction was −54% (25th to 75% percentile −65 to −50), −41 (25th to 75% percentile −46% to 30), and −17% (25th to 75% percentile −36 to 5), respectively, P = 0.010 for trend). We identified a set of 18 urinary metabolites through systems biology to predict albuminuria response to spironolactone in type 2 diabetes. These data suggest that urinary metabolites may be used as a tool to tailor optimal therapy and move in the direction of personalized medicine.
KW - Albuminuria
KW - Metabolomics
KW - Response
KW - Spironolactone
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U2 - 10.1016/j.trsl.2020.04.010
DO - 10.1016/j.trsl.2020.04.010
M3 - Article
C2 - 32438071
AN - SCOPUS:85086033093
SN - 1931-5244
VL - 222
SP - 17
EP - 27
JO - Translational Research
JF - Translational Research
ER -