Mediators of the effects of canagliflozin on kidney protection in patients with type 2 diabetes
Authors: JingWei Li, Bruce Neal, Vlado Perkovic, Dick de Zeeuw, Brendon L. Neuen, Clare Arnott, Roger Simpson, Richard Oh, Kenneth W. Mahaffey, Hiddo J.L. Heerspink
Journal: Kidney International (2020)

Canagliflozin reduced kidney disease progression in participants with type 2 diabetes in the CANagliflozin cardioVascular Assessment Study (CANVAS) Program. This analysis explored potential mediators of the effects of canagliflozin on kidney outcomes. The percent mediating effect of 18 biomarkers indicative of disease was determined by comparing the hazard ratios for the effect of randomized treatment from an unadjusted model and from a model adjusting for the average post-randomization level of each biomarker. Multivariable analyses assessed the joint effects of biomarkers that mediated most strongly in univariable analyses. The kidney outcome was defined as a composite of 40% estimated glomerular filtration rate decline, end-stage kidney disease, or death due to kidney disease. Nine biomarkers (systolic blood pressure [8.9% of effect explained], urinary albumin:creatinine ratio [UACR; 23.9%], gamma glutamyltransferase [4.1%], hematocrit [51.1%], hemoglobin [41.3%], serum albumin [19.5%], erythrocytes [56.7%], serum urate [35.4%], and urine pH [7.5%]) individually mediated the effect of canagliflozin on the kidney outcome. In a parsimonious multivariable model, erythrocyte concentration, serum urate, and systolic blood pressure maximized cumulative mediation (115%). Mediating effects of UACR, but not other mediators, were highly dependent upon the baseline level of UACR: UACR mediated 42% and 7% of the effect in those with baseline UACR 30 mg/g or more and under 30 mg/g, respectively. The identified mediators support existing hypothesized mechanisms for the prevention of kidney outcomes with sodium glucose co-transporter 2 inhibitors. Thus, the disparity in mediating effects across baseline UACR subgroups suggests that the mechanism for kidney protection with canagliflozin may vary across patient subgroups. Canagliflozin reduced kidney disease progression in participants with type 2 diabetes in the CANagliflozin cardioVascular Assessment Study (CANVAS) Program. This analysis explored potential mediators of the effects of canagliflozin on kidney outcomes. The percent mediating effect of 18 biomarkers indicative of disease was determined by comparing the hazard ratios for the effect of randomized treatment from an unadjusted model and from a model adjusting for the average post-randomization level of each biomarker. Multivariable analyses assessed the joint effects of biomarkers that mediated most strongly in univariable analyses. The kidney outcome was defined as a composite of 40% estimated glomerular filtration rate decline, end-stage kidney disease, or death due to kidney disease. Nine biomarkers (systolic blood pressure [8.9% of effect explained], urinary albumin:creatinine ratio [UACR; 23.9%], gamma glutamyltransferase [4.1%], hematocrit [51.1%], hemoglobin [41.3%], serum albumin [19.5%], erythrocytes [56.7%], serum urate [35.4%], and urine pH [7.5%]) individually mediated the effect of canagliflozin on the kidney outcome. In a parsimonious multivariable model, erythrocyte concentration, serum urate, and systolic blood pressure maximized cumulative mediation (115%). Mediating effects of UACR, but not other mediators, were highly dependent upon the baseline level of UACR: UACR mediated 42% and 7% of the effect in those with baseline UACR 30 mg/g or more and under 30 mg/g, respectively. The identified mediators support existing hypothesized mechanisms for the prevention of kidney outcomes with sodium glucose co-transporter 2 inhibitors. Thus, the disparity in mediating effects across baseline UACR subgroups suggests that the mechanism for kidney protection with canagliflozin may vary across patient subgroups. The CANagliflozin cardioVascular Assessment Study (CANVAS) Program reported that the sodium glucose cotransporter 2 (SGLT2) inhibitor canagliflozin reduces the risk of clinically important kidney outcomes in patients with type 2 diabetes with established cardiovascular disease or those at high cardiovascular risk.1Zelniker T.A. Wiviott S.D. Raz I. et al.SGLT2 inhibitors for primary and secondary prevention of cardiovascular and renal outcomes in type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials.Lancet. 2019; 393: 31-39Abstract Full Text Full Text PDF PubMed Scopus (1137) Google Scholar SGLT2 inhibitors prevent glucose and sodium from reabsorbing in the proximal tubule,2Ferrannini E. DeFronzo R.A. Impact of glucose-lowering drugs on cardiovascular disease in type 2 diabetes.Eur Heart J. 2015; 36: 2288-2296Crossref PubMed Scopus (180) Google Scholar which promotes glycosuria and natriuresis and results in improved glycemia, weight loss, blood pressure, and albuminuria.3DeFronzo R.A. Norton L. Abdul-Ghani M. Renal, metabolic and cardiovascular considerations of SGLT2 inhibition.Nat Rev Nephrol. 2017; 13: 11-26Crossref PubMed Scopus (244) Google Scholar These effects may explain individually or jointly why canagliflozin confers long-term kidney benefits, as observed in the CANVAS Program and subsequently confirmed in the Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE) trial.4Wanner C. Inzucchi S.E. Lachin J.M. et al.Empagliflozin and progression of kidney disease in type 2 diabetes.N Engl J Med. 2016; 375: 323-334Crossref PubMed Scopus (1694) Google Scholar, 5Mosenzon O. Wiviott S.D. Cahn A. et al.Effects of dapagliflozin on development and progression of kidney disease in patients with type 2 diabetes: an analysis from the DECLARE–TIMI 58 randomised trial.Lancet Diabetes Endocrinol. 2019; 7: 606-617Abstract Full Text Full Text PDF PubMed Scopus (276) Google Scholar, 6Perkovic V. Jardine M.J. Neal B. et al.Canagliflozin and renal outcomes in type 2 diabetes and nephropathy.N Engl J Med. 2019; 380: 2295-2306Crossref PubMed Scopus (1772) Google Scholar The mechanisms of action for kidney protection with canagliflozin is an area of great interest. In epidemiologic research, mediation analyses are used to investigate the mechanism that underlies an observed effect of an intervention on an outcome.7MacKinnon D.P. Fairchild A.J. Fritz M.S. Mediation analysis.Annu Rev Psychol. 2007; 58: 593-614Crossref PubMed Scopus (3576) Google Scholar The standard methodology quantifies the degree of attenuation of the treatment effect on the outcome of interest after inclusion of the mediator (i.e., biomarker) in the efficacy estimation model. Selection of biomarkers for investigation as potential mediators may be based on prior hypotheses, by systematically testing all available biomarkers, or by some combination of the two. To qualify as a potential mediator, a biomarker must be affected both by the drug under investigation and associated with the outcome of interest. However, even when these criteria are met, the change in the biomarker does not always infer a causal association.8Fleming T.R. DeMets D.L. Surrogate end points in clinical trials: are we being misled?.Ann Intern Med. 1996; 125: 605-613Crossref PubMed Scopus (1297) Google Scholar A recent mediation analysis of the effects of canagliflozin suggested that uric acid, albuminuria, and markers of plasma volume and hematopoiesis were the most important mediators for effects on heart failure.9Li J. Woodward M. Perkovic V. et al.Mediators of the effects of canagliflozin on heart failure in patients with type 2 diabetes.JACC Heart Fail. 2020; 8: 57-66Crossref PubMed Scopus (44) Google Scholar Whether the same or other mediators underlie the long-term kidney protective effects of canagliflozin is unknown. The objective of the present analyses therefore was to explore potential mediators of the beneficial effects of canagliflozin on major kidney outcomes in the CANVAS Program. The potential mediating effects of 21 biomarkers were assessed, 18 of which were available for the entire CANVAS Program and 3 of which were available for participants in the CANVAS trial but not in CANVAS-Renal. For assessments of average biomarker levels, the mean number of measurements made is shown in Supplementary Table S1. The overall average number of biomarker measurements was 14, and was least for hematocrit, hemoglobin, erythrocytes, urine pH, and ketones (mean, 8 measurements during the follow-up period). The most biomarker measures were acquired for blood pressure, pulse rate, weight, and body mass index (mean, 19 measurements during the follow-up period). There were clear effects of canagliflozin, compared with placebo, on multiple potential mediators of effect (Table 16Perkovic V. Jardine M.J. Neal B. et al.Canagliflozin and renal outcomes in type 2 diabetes and nephropathy.N Engl J Med. 2019; 380: 2295-2306Crossref PubMed Scopus (1772) Google Scholar and Supplementary Tables S2, S3, and S4): hemoglobin A1c, fasting plasma glucose, systolic blood pressure (SBP), diastolic blood pressure, weight, body mass index, urine pH, serum bicarbonate, serum urate, serum gamma glutamyltransferase (GGT), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, total cholesterol, hematocrit, hemoglobin, erythrocyte concentration, serum albumin, ketonuria, and urinary albumin:creatinine ratio (UACR).Table 1Effects of canagliflozin on biomarkers that might mediate the effect of canagliflozin on kidney outcomesBiomarkerMean (SE) at baselineAverage difference (SE) at follow-up evaluationaMixed-model with repeated-measures analysis using all data available before completion in patients who had baseline and follow-up measurements for the respective outcome. The model adjusted for region, baseline HbA1c, estimated glomerular filtration rate, BMI, baseline of the outcome, treatment, visit, and study subgroup (CANagliflozin cardioVascular Assessment Study [CANVAS]/CANagliflozin cardioVascular Assessment Study–Renal [CANVAS-R]).PlaceboCanagliflozinGlycemia HbA1c, %8.25 (0.01)8.25 (0.01)–0.52 (0.02)Vascular tone SBP, mm Hg136.90 (0.24)136.44 (0.21)–3.91 (0.19) DBP, mm Hg77.81 (0.15)77.62 (0.13)–1.33 (0.11) Pulse rate, bpm72.49 (0.16)72.64 (0.14)–0.22 (0.12)Lipids, mmol/l LDL-CbFasting test results.2.30 (0.01)2.29 (0.01)0.11 (0.01) HDL-CbFasting test results.1.18 (0.005)1.18 (0.004)0.05 (0.003) TriglyceridesbFasting test results.2.03 (0.02)2.02 (0.02)0.02 (0.02) Total cholesterolbFasting test results.4.37 (0.02)4.35 (0.02)0.16 (0.01)Kidney UACR, mg/gcBaseline data are the geometric mean (geometric coefficient of variation); differences are the percentage change from the adjusted geometric mean ratio (95% confidence interval) obtained from a mixed-model with repeated-measures analysis applied on log-transformed data.Bold indicates a significant effect at P < 0.05.Reprinted in part from Li J, Woodward M, Perkovic V, et al. Mediators of the effects of canagliflozin on heart failure in patients with type 2 diabetes. JACC Heart Fail. 2020;8:57–66.9 © 2020 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).20.49 (315.41)19.87 (288.54)–19% (–21%, –17%)Adiposity Weight, kg90.01 (0.31)90.28 (0.27)–2.20 (0.07) BMI, kg/m231.97 (0.09)31.95 (0.08)–0.79 (0.02) GGT, U/l37.74 (0.64)38.37 (0.58)–4.34 (0.57)Volume status and hematopoiesis Hematocrit, %41.96 (0.06)42.00 (0.05)2.53 (0.05) Hemoglobin, g/l139.09 (0.22)139.54 (0.19)7.65 (0.18) Serum albumin, g/l41.40 (0.05)41.32 (0.04)0.56 (0.04) Erythrocytes, ×1012/l4.68 (0.01)4.71 (0.01)0.27 (0.01)Indicators of acidosis/alkalosis Serum bicarbonate, mmol/l23.37 (0.04)23.33 (0.03)–0.34 (0.03)Other Serum urate, μmol/l349.78 (1.47)348.21 (1.24)–23.49 (1.02)BMI, body mass index; bpm, beats per minute; DBP, diastolic blood pressure; GGT, gamma glutamyltransferase; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; UACR, urinary albumin:creatinine ratio.a Mixed-model with repeated-measures analysis using all data available before completion in patients who had baseline and follow-up measurements for the respective outcome. The model adjusted for region, baseline HbA1c, estimated glomerular filtration rate, BMI, baseline of the outcome, treatment, visit, and study subgroup (CANagliflozin cardioVascular Assessment Study [CANVAS]/CANagliflozin cardioVascular Assessment Study–Renal [CANVAS-R]).b Fasting test results.c Baseline data are the geometric mean (geometric coefficient of variation); differences are the percentage change from the adjusted geometric mean ratio (95% confidence interval) obtained from a mixed-model with repeated-measures analysis applied on log-transformed data.Bold indicates a significant effect at P < 0.05.Reprinted in part from Li J, Woodward M, Perkovic V, et al. Mediators of the effects of canagliflozin on heart failure in patients with type 2 diabetes. JACC Heart Fail. 2020;8:57–66.9Li J. Woodward M. Perkovic V. et al.Mediators of the effects of canagliflozin on heart failure in patients with type 2 diabetes.JACC Heart Fail. 2020; 8: 57-66Crossref PubMed Scopus (44) Google Scholar © 2020 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). Open table in a new tab BMI, body mass index; bpm, beats per minute; DBP, diastolic blood pressure; GGT, gamma glutamyltransferase; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; UACR, urinary albumin:creatinine ratio. For 10 of the 18 potential mediators (SBP, triglycerides, UACR, GGT, hematocrit, hemoglobin, serum albumin, erythrocytes, serum bicarbonate, and serum urate) assessed in the overall CANVAS Program, there was a significant association of the average postrandomization level with the risk of kidney outcomes in the Cox proportional hazard regression models (all P < 0.05) (Table 2). The supplementary analyses of the biomarkers measured only in CANVAS identified additional significant associations of the average follow-up levels of urine pH with the risk of kidney outcomes (Supplementary Table S2).Table 2Observational associations of potential mediators and the risk of the composite kidney outcomesEffect of a 1-unit increaseHR95% CIP valueGlycemia HbA1c, %1.040.94–1.150.449Vascular tone SBP, mm Hg1.021.01–1.03<0.001 DBP, mm Hg1.000.98–1.010.496 Pulse rate, bpm1.000.99–1.010.884Lipids, mmol/l LDL-C1.040.92–1.190.510 HDL-C0.690.46–1.040.074 Triglycerides1.331.05–1.690.017 Total cholesterol1.060.96–1.170.269Kidney UACR, mg/g1.811.69–1.93<0.001Adiposity Weight, kg1.011.00–1.010.131 BMI, kg/m21.021.00–1.040.073 GGT, U/l1.341.13–1.58<0.001Volume status and hematopoiesis Hematocrit, %0.850.83–0.88<0.001 Hemoglobin, g/l0.960.95–0.97<0.001 Serum albumin, g/l0.810.79–0.83<0.001 Erythrocytes, ×1012/l0.220.17–0.28<0.001Indicators of acidosis/alkalosis Serum bicarbonate, mmol/l0.920.88–0.96<0.001Others Serum urate, μmol/l1.011.01–1.01<0.001BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; GGT, gamma glutamyltransferase; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; UACR, urinary albumin:creatinine ratio.Bold indicates a significant effect at P < 0.05. Open table in a new tab BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; GGT, gamma glutamyltransferase; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; UACR, urinary albumin:creatinine ratio. Bold indicates a significant effect at P < 0.05. For all 10 (1 in CANVAS alone) biomarkers modified by canagliflozin versus placebo, there also were associations of the biomarker changes with the subsequent risk of the kidney outcome (SBP, triglycerides, UACR, GGT, hematocrit, hemoglobin, serum albumin, erythrocytes, serum bicarbonate, serum urate, and urine pH). Nine of these biomarkers were identified as individually significant mediators of the effect of canagliflozin on kidney outcome when the average postrandomization levels were assessed in the primary models (SBP, UACR, GGT, hematocrit, hemoglobin, serum albumin, erythrocytes, serum urate, and urine pH). The 3 with the largest magnitude of mediating effect were erythrocyte concentration (57%), hematocrit (51%), and hemoglobin (41%) (Figure 1). Assessment of joint effects in a multivariable model of the strongest 3 mediators representing different modes of action resulted in the inclusion of erythrocyte concentration, serum urate, and SBP, and an estimated cumulative mediation of 115% (95% confidence interval [CI], 95–159) of the effects of canagliflozin on kidney outcome (Figure 2).Figure 2Effects of multivariable adjustment for potential mediators of the effect of canagliflozin on the composite kidney outcome. Bold indicates a significant effect at P < 0.05. BMI, body mass index; CI, confidence interval; HR, hazard ratio; SBP, systolic blood pressure; UACR, urinary albumin:creatinine ratio.View Large Image Figure ViewerDownload Hi-res image Download (PPT) For the early change in levels of potential mediators, there were significant associations between potential mediators and kidney outcome for 9 of the 18 biomarkers (Table 3 and Supplementary Table S3). The same biomarkers identified as possible mediators in the analyses of the average postrandomization levels, with the exception of serum albumin, were potential mediators in analyses of early change in the biomarker of interest, although the magnitude of the mediating effect in the early change analyses was somewhat smaller (Table 3).Table 3Mediation analysis for potential mediators of the effect of canagliflozin on the composite kidney outcome when using changes measured early after randomizationBiomarkerCanagliflozin on mediators: acute changeaExpressed as mean (SE).,bMixed-model with repeated-measures analysis using all data available before completion in patients who had baseline and follow-up measurements for the respective outcome. The model adjusted for region, baseline HbA1c, estimated glomerular filtration rate, BMI, baseline of the outcome, treatment, visit, and study subgroup (CANagliflozin cardioVascular Assessment Study [CANVAS]/CANagliflozin cardioVascular Assessment Study–Renal [CANVAS-R]).Association with kidney eventsMediation effect after adjustment in modelHR95% CIP valueEvents/patientsHR (95% CI)Mediation, %95% CIGlycemia HbA1c, %–0.62 (0.02)1.231.06–1.420.005244/98680.64 (0.48–0.84)9.7–6.4 to 40.4Vascular tone SBP, mm Hg–3.58 (0.26)1.021.01–1.030.003249/10,0020.64 (0.50–0.82)9.52.2–24.9 DBP, mm Hg–1.52 (0.15)1.021.00–1.030.080249/10,0020.61 (0.48–0.79)3.1–2.3 to 11.0 Pulse rate, bpm0.06 (0.16)1.000.99–1.020.829249/10,0010.60 (0.47–0.77)–0.2–1.8 to 0.8Lipids, mmol/l LDL-CcFasting test results.0.09 (0.01)1.020.84–1.240.825228/94600.55 (0.42–0.72)–0.4–3.0 to 2.0 HDL-CcFasting test results.0.04 (0.004)0.550.27–1.140.109228/94650.56 (0.43–0.73)2.1–2.5 to 7.7 TriglyceridescFasting test results.–0.06 (0.02)1.450.99–2.120.060228/94630.56 (0.43–0.73)1.5–0.6 to 4.7 Total cholesterolcFasting test results.0.11 (0.02)1.040.88–1.210.671228/94640.55 (0.42–0.72)–0.4–3.2 to 2.4Kidney UACR, mg/gdBaseline data are the geometric mean (geometric coefficient of variation); differences are the adjusted geometric mean ratio (95% CI) obtained from mixed-model with repeated-measures analysis applied on log-transformed data.–17% (–19%, –15%)1.581.35–1.85<0.001236/95550.65 (0.50–0.85)18.23.2–47.6Adiposity Weight, kg–1.33 (0.04)1.010.95–1.070.718248/99940.59 (0.45–0.76)–4.5–21.1 to 17.0 BMI, kg/m2–0.47 (0.02)1.050.89–1.250.558248/99810.59 (0.45–0.77)–3.3–20.9 to 19.2 GGT, U/l–3.89 (0.50)2.031.39–2.95<0.001249/99840.64 (0.49–0.82)9.31.4–23.5Volume status and hematopoiesis Hematocrit, %2.34 (0.07)0.870.84–0.90<0.001222/90540.73 (0.56–0.97)40.719.0–92.4 Hemoglobin, g/l6.63 (0.22)0.960.95–0.97<0.001223/91460.68 (0.52–0.89)29.113.0–62.6 Serum albumin, g/l0.59 (0.04)0.880.83–0.93<0.001249/99840.63 (0.49–0.82)8.3–3.3 to 24.9 Erythrocytes, ×1012/l0.26 (0.01)0.280.20–0.39<0.001223/91460.73 (0.55–0.97)40.517.9–93.3Indicators of acidosis/alkalosis Serum bicarbonate, mmol/l–0.42 (0.05)1.010.96–1.060.789249/99810.60 (0.47–0.77)–0.1–5.0 to 4.7Other Serum urate, μmol/l–23.21 (1.16)1.011.00–1.01<0.001249/99840.68 (0.52–0.87)19.09.5–46.7BMI, body mass index; bpm, beats per minute; CI, confidence interval; DBP, diastolic blood pressure; GGT, gamma glutamyltransferase; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; UACR, urinary albumin:creatinine ratio.Bold indicates a significant effect at P < 0.05.a Expressed as mean (SE).b Mixed-model with repeated-measures analysis using all data available before completion in patients who had baseline and follow-up measurements for the respective outcome. The model adjusted for region, baseline HbA1c, estimated glomerular filtration rate, BMI, baseline of the outcome, treatment, visit, and study subgroup (CANagliflozin cardioVascular Assessment Study [CANVAS]/CANagliflozin cardioVascular Assessment Study–Renal [CANVAS-R]).c Fasting test results.d Baseline data are the geometric mean (geometric coefficient of variation); differences are the adjusted geometric mean ratio (95% CI) obtained from mixed-model with repeated-measures analysis applied on log-transformed data. Open table in a new tab BMI, body mass index; bpm, beats per minute; CI, confidence interval; DBP, diastolic blood pressure; GGT, gamma glutamyltransferase; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; UACR, urinary albumin:creatinine ratio. Bold indicates a significant effect at P < 0.05. Subsidiary analyses based on the alternative counterfactual framework, which aims to establish causal inferences in epidemiologic research, identified 11 mediators based on average levels (Supplementary Table S4). In the multivariable model, inclusion of the average levels of erythrocyte concentration, serum urate, and UACR resulted in an estimated overall mediation of 96% (95% CI, 92–104). In subgroup analyses, mediating effects of biomarkers generally were similar across patient subgroups, except for UACR. In patients with a baseline UACR less than 30 mg/g, the mediating effect of UACR was 7.4% (95% CI, 2.8–20.1), whereas it was 42.3% (95% CI, 21.1–113.6) in patients with a baseline UACR of 30 mg/g or greater (Figure 3). When mediators were assessed jointly, erythrocytes, serum urate, and UACR were the strongest mediators in patients with a UACR less than 30 mg/g, whereas in patients with a UACR of 30 mg/g erythrocytes or greater, UACR and body mass index were the strongest mediators. We identified a large set of potential mediators of the effect of canagliflozin on kidney outcomes. Some of the identified mediators, such as UACR, were expected given the well-established relationship between UACR changes and kidney outcomes. Weak mediating effects were observed for SBP and GGT, while hematocrit and hemoglobin, which may be markers of volume and/or hematopoiesis, were identified consistently as mediators across all analyses. Measures of glycemic control were not identified as mediators. Mediating effects of all biomarkers were consistent in patient subgroups, except for UACR. UACR showed stronger mediating effects in patients with increased UACR, suggesting that the underlying mechanism of kidney protection from canagliflozin may vary by baseline UACR level. Markers of volume status and erythropoiesis were strong mediators in all models. SGLT2 inhibition causes a modest natriuretic/diuretic effect,10Heerspink H.J.L. Perkins B.A. Fitchett D.H. et al.Sodium glucose cotransporter 2 inhibitors in the treatment of diabetes mellitus: cardiovascular and kidney effects, potential mechanisms, and clinical applications.Circulation. 2016; 134: 752-772Crossref PubMed Scopus (597) Google Scholar which results in modest reductions in plasma volume and extracellular volume, as well as increases in hematocrit and hemoglobin. Markers of volume status, including hematocrit and hemoglobin, have been reported previously to be strong mediators of the beneficial effects of SGLT2 inhibitors on cardiovascular death and heart failure hospitalization.9Li J. Woodward M. Perkovic V. et al.Mediators of the effects of canagliflozin on heart failure in patients with type 2 diabetes.JACC Heart Fail. 2020; 8: 57-66Crossref PubMed Scopus (44) Google Scholar,11Inzucchi S.E. Zinman B. Fitchett D. et al.How does empagliflozin reduce cardiovascular mortality? Insights from a mediation analysis of the EMPA-REG OUTCOME trial.Diabetes Care. 2018; 41: 356-363Crossref PubMed Scopus (350) Google Scholar Fluid overload has been associated with a higher risk of end-stage kidney disease in observational studies.12Faucon A.L. Flamant M. Metzger M. et al.Extracellular fluid volume is associated with incident end-stage kidney disease and mortality in patients with chronic kidney disease.Kidney Int. 2019; 96: 1020-1029Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar,13Tsai Y.-C. Tsai J.-C. Chen S.-C. et al.Association of fluid overload with kidney disease progression in advanced CKD: a prospective cohort study.Am J Kidney Dis. 2014; 63: 68-75Abstract Full Text Full Text PDF PubMed Scopus (59) Google Scholar Correction of extracellular volume expansion through natriuresis and diuresis, resulting in amelioration of microcirculation in the kidney secondary to a reduction in venous pressure and interstitial pressure, has some credence as a mechanism by which SGLT2 inhibition may improve kidney outcomes.14Firth J.D. Raine A.E.G. Ratcliffe P.J. et al.Endothelin: an important factor in acute renal failure?.Lancet. 1988; 332: 1179-1182Abstract Scopus (181) Google Scholar In addition to volume reduction, increases in hematocrit and hemoglobin also may reflect increased erythropoiesis. Dapagliflozin has been shown to transiently increase erythropoietin and red cell mass, indicating direct effects on erythropoiesis that may improve kidney tissue oxygenation.15Lambers Heerspink H.J. de Zeeuw D. Wie L. et al.Dapagliflozin a glucose-regulating drug with diuretic properties in subjects with type 2 diabetes.Diabetes Obes Metab. 2013; 15: 853-862Crossref PubMed Scopus (481) Google Scholar Hypoxia in kidney tissues is a powerful predictor of adverse long-term kidney outcomes, and stimulation of erythropoiesis is another plausible potential pathway for kidney protection with SGLT2 inhibition. It also is possible that reducing albuminuria with SGLT2 inhibition is another route through which long-term kidney protection is achieved. The underlying mechanism of the albuminuria-lowering effects of SGLT2 inhibitors are not completely understood, but reduction in intraglomerular pressure secondary to restoration of tubuloglomerular feedback has been proposed to play an important role based on findings in patients with type 1 diabetes.16Dekkers C.C.J. Petrykiv S. Laverman G.D. et al.Effects of the SGLT-2 inhibitor dapagliflozin on glomerular and tubular injury markers.Diabetes Obes Metab. 2018; 20: 1988-1993Crossref PubMed Scopus (96) Google Scholar Whether such findings equally apply to patients with type 2 diabetes is uncertain because more recent data have suggested that acute reductions in the glomerular filtration rate result from efferent arterial vasodilation, possibly through inhibition of prostaglandins.17van Bommel E.J.M. Muskiet M.H.A. van Baar M.J.B. et al.The renal hemodynamic effects of the SGLT2 inhibitor dapagliflozin are caused by post-glomerular vasodilatation rather than pre-glomerular vasoconstriction in metformin-treated patients with type 2 diabetes in the randomized, double-blind RED trial.Kidney Int. 2020; 97: 202-212Abstract Full Text Full Text PDF PubMed Scopus (95) Google Scholar Improvements in endothelial function or endothelial glycocalyx function, which contribute to the charge-selective properties of the glomerular basement membrane, also may explain the albuminuria-lowering effects of SGLT2 inhibitors.18Solini A. Giannini L. Seghieri M. et al.Dapagliflozin acutely improves endothelial dysfunction, reduces aortic stiffness and renal resistive index in type 2 diabetic patients: a pilot study.Cardiovasc Diabetol. 2017; 16: 138Crossref PubMed Scopus (168) Google Scholar Regardless of the underlying mechanism, reductions in albuminuria have been associated consistently with reductions in long-term risk of kidney failure,19Heerspink H.J.L. Greene T. Tighiouart H. et al.Change in albuminuria as a surrogate endpoint for progression of kidney disease: a meta-analysis of treatment effects in randomised clinical trials.Lancet Diabetes Endocrinol. 2019; 7: 128-139Abstract Full Text Full Text PDF PubMed Scopus (107) Google Scholar and decreasing albuminuria with canagliflozin also is likely to contribute to protection against decreases in kidney function. The disparity in mediating effects across subgroups defined by baseline albuminuria suggests that the mechanism of kidney protection may vary in patients with low versus high degrees of albuminuria. The larger percentage of mediation in the subgroup of patients with higher degrees of albuminuria i