Abstract
Aims
Combination therapy with sodium-glucose cotransporter inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1Ras) is now of interest in clinical practice. The present study evaluated the effects of the preceding drug type on the renal outcome in clinical practice.
Methods
We retrospectively extracted type 2 diabetes mellitus patients who had received both SGLT2i and GLP1Ra treatment for at least 1 year. A total of 331 patients in the GLP1Ra-preceding group and 312 patients in the SGLT2i-preceding group were ultimately analyzed. Either progression of the albuminuria status and/or a ≥30% decrease in the eGFR was set as the primary renal composite outcome. The analysis using propensity score with inverse probability weighting was performed for the outcome.
Results
The incidences of the renal composite outcome in the SGLT2i- and GLP1Ra-preceding groups were 28% and 25%, respectively, with an odds ratio [95% confidence interval] of 1.14 [0.75, 1.73] (p = .54). A logistic regression analysis showed that the mean arterial pressure (MAP) at baseline, the logarithmic value of the urine albumin-to-creatinine ratio at baseline, and the change in MAP were independent factors influencing the renal composite outcome.
Conclusion
With combination therapy of SGLT2i and GLP1Ra, the preceding drug did not affect the renal outcome.
Keywords: Sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 receptor agonist, renal outcome, combination treatment, preceding drug
Key message
• The renal outcome on the combination treatment of SGLT2i and GLP1Ra was compared.
• The preceding drug (SGLT2i or GLP1Ra) did not affect the renal outcome.
• The larger decrease in body weight was observed in the GLP1Ra-preceding group.
Introduction
Based on the cardiovascular or renal evidence of sodium-glucose cotransporter inhibitors (SGLT2is)1–4 and Glucagon-like peptide-1 receptor agonists (GLP1Ras),5–8 the executive summary of the KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease recommends metformin and an SGLT2i as first-line treatment for patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). 9 GLP1Ra as additional combination therapy, was recommended for patients with failed glycemic control despite using metformin and SGLT2i or those unable to use these drugs or required intentional weight loss.
SGLT2i treatment is superior over GLP1Ra in terms of renoprotective effects, especially with the annual estimated glomerular filtration rate (eGFR) slope, in clinical practice in Japan. 10 Furthermore, in our long-term observational GLP1Ra study, SGLT2is were administered to 52% of patients, and a renoprotective effect was observed only in GLP1Ra-treated patients with concomitant SGLT2i use. 11 This study aimed to evaluate the renoprotective effects of combination treatment with SGLT2is and GLP-1Ra in patients with T2DM, according to their preceding medication (RECAP study).
Materials and methods
Study subjects and data collection
We conducted a retrospective survey of patients with T2DM treated with a combination of SGLT2is and GLP1Ra (Supplementary Figure S1). The inclusion criteria were patients with T2DM, who were (i) treated with both SGLT2i and GLP1Ra from April 2010 to December 2021 and for whom (ii) the period of the preceding medication was ≥6 months, (iii) the period of concomitant medication was ≥12 months, and (iv) clinical data at baseline, the time of addition, and the final observation time were available. This includes the age*, gender*, height, body weight (BW), systolic blood pressure (SBP), diastolic blood pressure (DBP), eGFR, glycated hemoglobin A1c (HbA1c), urinary data (urine albumin-to-creatinine ratio (ACR) or qualitative proteinuria), aspartate aminotransferase (AST), alanine aminotransferase (ALT), platelet count, and concomitant medications* (hypoglycemic drugs, antihypertensive drugs, and statins) (where “*” indicates essential data). We calculated the eGFR using the following formula: eGFR (mL/min/1.73 m2) = 194 × age−0.287 × serum creatinine−1.094 × (0.739 for women). 12 Qualitative proteinuria values were converted to albuminuria values using the formula reported by Sumida et al. 13 Patients who opted out of the study were also excluded. A schematic of the study participants is presented in Supplementary Figure S2. Finally, 643 patients (312 with preceding SGLT2i treatment [SGLT2i-preceding group] and 331 with preceding GLP1Ra treatment [GLP1Ra-preceding group]) were included in the full analysis set (FAS).
Outcomes
Either progression of the ACR status and/or a ≥30% decrease in the eGFR was set as the primary renal composite outcome.
Statistical analyses
IBM SPSS Statistics software (version 25.0; IBM Inc., Armonk, NY, USA) was used for the statistical analyses, and a p-value <.05 was considered significant.
The missing value analysis
To account for missing data, we used the multiple imputation (MI) method. 14 We replaced each missing value with a set of substituted plausible values by creating 100 filled-in complete datasets using MI with the chained equations method.15-17 A breakdown of the missing data is shown in Supplementary Figure S3.
The propensity score analysis using inverse probability weighting
Propensity score (PS) was used to minimize the influence of confounding factors. In each dataset built using MI, the PS for the GLP1Ra-preceding group was calculated via logistic analysis using the following covariates: age, sex, height, BW, BMI, SBP, DBP, HbA1c, eGFR, LnACR at baseline, history of DM, use of concomitant medications at baseline, duration of treatment with the preceding drug, and combination treatment. The inverse probability weighting (IPW) method using PS was used to analyze the primary outcome. We selected the model using stabilized average treatment effect (ATE) weighting with trimming (patients with 0.05 > PS or PS >0.95 are excluded from further analyses because this model showed the lowest standardized differences in the covariates (Supplementary Figure S4).
The sensitivity analysis
PS matching with the following algorithm was performed for sensitivity analysis: 1:1 nearest neighbor match with a caliper value of 0.047, calculated as 0.2-fold the SD of PS 18 with no replacement. Odds ratios (ORs) for the outcomes were calculated using Cox regression analysis.
Multivariable logistic regression analysis
A multivariable logistic regression analysis to evaluate independent predictors of the renal composite outcome associated with potential predictors was performed on the complete case set (CCA) of 418 patients (227 in the SGLT2i-preceding group and 191 in the GLP1Ra-preceding group).
This study was approved by the Institutional Review Board for Clinical Research, Tokai University, Japan (approval on December 6, 2021).
Results
PS-IPW model
The baseline data are presented in Table 1. At the final observation time, the types of administered drugs were ipragliflozin (n = 67, 10%), dapagliflozin (n = 158, 25%), tofogliflozin (n = 69, 11%), luseogliflozin (n = 32), canagliflozin (n = 67, 10%), and empaglifozin (n = 147, 23%) for SGLT2i, and liraglutide (n = 214, 33%), dulaglutide (n = 246, 38%), lixisenatide (n = 9, 1%), and exenatide (8, 1%) for GLP1Ra. The number of patients who changed drug type was 103 (16%) for SGLT2is and 166 (26%) for GLP1Ras.
Table 1.
Clinical characteristics at baseline.
| Unadjusted | PS-IPW; stabilized ATE with trimming | PS-matching | |||||||
|---|---|---|---|---|---|---|---|---|---|
| GLP1Ra-preceding group, N = 331 | SGLT2i-preceding group, N = 312 | p-value | GLP1Ra-preceding group, N = 327 b | SGLT2i-preceding group, N = 293 b | Standardized difference | GLP1Ra-preceding group, N = 203 | SGLT2i-preceding group, N = 203 | Standardized difference | |
| Age (year-old) | 55.7 ± 13.5 | 56.5 ± 12.7 | 0.10 | 56.3 ± 13.9 | 56.8 ± 12.5 | 0.04 | 57.1 ± 13.6 | 57.0 ± 13.2 | 0.007 |
| Sex (female (%)) | 152 (46%) | 130 (42%) | 0.27 a | 148 (45%) | 131 (45%) | 0.01 | 89 (44%) | 87 (43%) | 0.02 |
| A history of DM >10 years (%) | 281 (85%) | 237 (76%) | 0.006 a | 260 (80%) | 233 (80%) | <0.001 | 165 (81%) | 159 (78%) | 0.07 |
| BW (kg) | 79.5 ± 20.1 | 79.4±18.1 | 0.95 | 79.2 ± 19.1 | 78.7 ± 18.0 | 0.03 | 78.7 ± 18.5 | 78.8 ± 17.0 | 0.006 |
| BMI | 29.8 ± 6.3 | 29.5±5.6 | 0.51 | 29.6 ± 5.8 | 29.5 ± 5.6 | 0.02 | 29.4 ± 5.5 | 29.2 ± 5.3 | 0.04 |
| SBP (mmHg) | 132.0 ± 18.4 | 135.4 ± 18.9 | 0.02 | 132.9 ± 18.4 | 133.7 ± 18.4 | 0.04 | 133.1 ± 19.1 | 134.7 ± 19.4 | 0.08 |
| DBP (mmHg) | 76.6 ± 12.3 | 78.7 ± 13.6 | 0.04 | 77.2 ± 12.3 | 77.4 ± 13.1 | 0.02 | 76.7 ± 12.4 | 78.2 ± 13.5 | 0.12 |
| MAP (mmHg) | 95.0 ± 12.7 | 97.6 ± 13.6 | 0.02 | 95.7 ± 12.6 | 96.2 ± 13.1 | 0.04 | 95.5 ± 13.0 | 97.0 ± 13.9 | 0.11 |
| HbA1c (mmol/mol (%)) | 73.6 ± 18.6 (8.9 ± 1.7) | 71.0 ± 17.3 (8.6 ± 1.6) | 0.07 | 72.8 ± 18.1 (8.8 ± 1.7) | 73.2 ± 18.9 (8.8 ± 1.7) | 0.02 | 72.8 ± 17.8 (8.7 ± 11.6) | 71.9 ± 18.2 (8.7 ± 1.7) | 0.05 |
| eGFR (mL/min/1.73 m2) | 78.8 ± 28.7 | 78.2 ± 26.0 | 0.79 | 79.1 ± 27.9 | 78.7 ± 26.5 | 0.02 | 76.6 ± 26.7 | 77.7 ± 26.9 | 0.04 |
| ACR (mg/gCr) | 36.6 [10.4, 11.9] | 34.1 [11.9, 131.3] | 37.8 [11.3, 152.9] | 35.7 [11.9, 131.3] | 39.2 [11.3, 141.2] | 35.7 [11.6, 142.0] | |||
| LnACR | 3.75 ± 1.91 | 3.76 ± 1.97 | 0.91 | 3.77 ± 1.86 | 3.77 ± 1.88 | <0.001 | 3.72±1.90 | 3.77 ±1.95 | 0.003 |
| Duration of the preceding treatment (month) | 31.8 ± 23.1 | 23.9 ± 14.0 | <0.001 | 26.2 ± 20.0 | 24.8 ± 14.4 | 0.08 | 25.1±18.3 | 24.7 ± 14.5 | 0.03 |
| Duration of the combination treatment (month) | 38.8 ± 18.6 | 28.5 ± 13.5 | <0.001 | 33.3±17.1 | 32.1 ± 15.2 | 0.08 | 31.6±15.0 | 31.9 ± 14.0 | 0.02 |
| Total duration of the study (month) | 70.6 ± 27.0 | 52.4 ± 15.7 | <0.001 | 59.5 ± 24.4 | 56.9 ± 16.1 | 0.13 | 56.7 ± 19.4 | 56.6 ± 14.7 | 0.006 |
| Concomitant medications | |||||||||
| Sulphonyl urea | 108 (33%) | 91 (29%) | 0.34 a | 100 (31%) | 85 (29%) | 0.03 | 58 (29%) | 64 (32%) | 0.06 |
| Metforimin | 169 (51%) | 190 (61%) | 0.01 a | 187 (57%) | 170 (58%) | 0.02 | 115 (57%) | 114 (56%) | 0.01 |
| Insulin | 141 (43%) | 140 (45%) | 0.56 a | 140 (43%) | 131 (45%) | 0.04 | 95 (47%) | 90 (44%) | 0.05 |
| Pioglitazon | 35 (11%) | 51 (16%) | 0.03 a | 43 (13%) | 41 (14%) | 0.02 | 29 (14%) | 29 (14%) | 0 |
| αGI | 40 (12%) | 48 (15%) | 0.22 a | 42 (13%) | 41 (14%) | 0.03 | 30 (15%) | 29 (14%) | 0.01 |
| Glinide | 14 (4.2%) | 14 (4.5%) | 0.87 a | 15 (5%) | 14 (5%) | 0.01 | 11 (5%) | 11 (5%) | 0 |
| RAS inhibitor | 166 (50%) | 160 (51%) | 0.77 a | 165 (50%) | 155 (53%) | 0.05 | 108 (53%) | 96 (47%) | 0.12 |
| CCB | 128 (39%) | 110 (35%) | 0.37 a | 126 (39%) | 115 (39%) | 0.01 | 83 (41%) | 83 (41%) | 0 |
| Β blocker | 53 (16%) | 49 (16%) | 0.92 a | 49 (15%) | 44 (15%) | 0.001 | 33 (16%) | 33 (16%) | 0 |
| MRB | 14 (4%) | 12 (%) | 0.81 a | 14 (4%) | 13 (4%) | 0.01 | 10 (5%) | 9 (4%) | 0.02 |
| Thiazide | 29 (9%) | 16 (5%) | 0.07 a | 22 (7%) | 19 (6%) | 0.01 | 13 (6%) | 14 (7%) | 0.02 |
| Loop | 24 (7%) | 14 (5%) | 0.14 a | 18 (6%) | 14 (5%) | 0.03 | 10 (5%) | 11 (5%) | 0.02 |
| Statin | 160 (48%) | 160 (51%) | 0.46 a | 157 (48%) | 147 (50%) | 0.04 | 109 (54%) | 98 (45%) | 0.11 |
Values are mean ± SD or n/total n (%). p values by unpaired t test or
achi-square test.
bCalculated number of subjects after weighting.
Abbreviation; αGI, alpha glucosidase inhibitor; ATE, average treatment effect; BMI, body mass index; BW, body weight; DBP, diastolic blood pressure; CCB, calcium channel blocker; DM, diabetes mellitus; eGFR, estimated glomerular filtration; FAS, full analysis set; GLP1Ra, glucagon-like peptide 1 receptor agonist; HbA1c, glycated hemoglobin A1c; IPW, inverse provability weighting; LNACR, logarithmic value of urine albumin-to- creatinine ratio; MAP, mean arterial pressure; MI, multiple imputation; MRB, mineral corticoid receptor blocker; PS, propensity score; RAS, renin-angiotensin system inhibitor; SBP, systolic blood pressure; SGLT2i, sodium-glucose co-transporter inhibitor.
Table 2 presents the results of the PS-IPW analysis based on the generalized linear model. During the observation period, the incidence of renal composite outcomes in the SGLT2i- and GLP1Ra-preceding groups was 28% and 25%, respectively, with an OR (95% confidence interval [CI]) of 1.14 (0.75, 1.73) (p = .54). The decrease in BW in the GLP1Ra-preceding group was significantly larger than that in the SGLT2i-preceding group by 1.9 kg (95% CI, 0.5, 3.2) (p = .006).
Table 2.
Renal outcomes and clinical characteristics after combination treatment.
| Unadjusted | PS-IPW; Stabilized ATE with trimming | PS-matching | |||||||
|---|---|---|---|---|---|---|---|---|---|
| GLP1Ra-preceding group, N = 331 | SGLT2i-preceding group, N = 312 | p-value | GLP1Ra-preceding group, N = 327 a | SGLT2i-preceding group, N = 293 a | GLM b | GLP1Ra-preceding group, N = 203 | SGLT2i-preceding group, N = 203 | p-value c | |
| Renal outcomes and function | |||||||||
| a) Incidence of renal composite outcome | 88 (27%) | 81 (26%) | 0.79 d | 82 (25%) | 81 (28%) | 1.14 [0.75, 1.74], p = .54 | 54 (27%) | 58 (29%) | p = .61 |
| ≥30% decrease in the eGFR | 42 (13%) | 26 (8%) | 0.10 d | 36 (11%) | 27 (9%) | 0.83 [0.46, 1.49], p = .53 | 24 (12%) | 17 (8%) | p = .32 |
| Progression of ACR status | 57 (17%) | 60 (19%) | 0.54 d | 55 (17%) | 60 (20%) | 1.26 [0.78, 2.05], p = .35 | 36 (18%) | 43 (21%) | p = .37 |
| Progression to microalbuminuria | 22 (10%) | 19 (10%) | 0.58 | 23 (11%) | 15 (9%) | 0.81 [0.40, 1.64], p = .81 | 15 (12%) | 11 (9%) | 0.21 |
| Progression to macroalbuminuria | 13 (6%) | 15 (8%) | 0.40 | 11 (5%) | 18 (10%) | 2.21 [0.93, 5.25], p = .07 | 5 (4%) | 11 (9%) | 0.54 |
| b) Changes in eGFR | |||||||||
| Change rate in the eGFR (%) | −10.1% ± 20.9 | −7.5 ± 21.5 | 0.12 e | −9.8 ± 19.7 | −8.1 ± 21.9 | 1.8 [-1.8, 5.3], p = .33 | −9.4 ± 19.3 | −7.6 ± 22.7 | 0.37 |
| Annual changes in the eGFR (mL/min/1.73 m2/year) | −1.7 ± 3.4 | −1.7 ± 4.1 | 0.90 e | −2.0 ± 3.8 | −1.6 ± 3.8 | 0.3 [-0.3, 1.0], p = .35 | −1.8 ± 3.6 | −1.5 ± 3.6 | 0.37 |
| c) Changes in LnACR | 0.07 ± 1.51 | 0.10 ± 1.63 | 0.81 e | −0.01 ± 1.48 | 0.2 ± 1.64 | 0.20 [-0.06, 0.47], p = .14 | 0.06 ± 1.53 | 0.17 ± 1.60 | 0.47 |
| Clinical characteristics after combination treatment | |||||||||
| eGFR (mL/min/1.73 m2) | 70.1 ± 27.5 | 71.4 ± 26.1 | 0.54 e | 70.8 ± 27.0 | 71.4 ± 26.6 | 69.0 ± 26.4 | 70.8 ± 26.5 | 0.51 | |
| LnACR | 3.82 ± 1.80 | 3.86 ± 1.93 | 0.75 e | 3.76 ± 1.77 | 3.97 ± 2.02 | 3.78 ± 1.78 | 3.94 ± 2.00 | 0.39 | |
| BW (kg) | 74.0 ± 18.4 | 75.9 ± 17.7 | 0.19 e | 73.9 ± 18.2 | 75.2 ± 17.7 | 73.6 ± 18.3 | 75.5 ± 17.2 | 0.27 | |
| SBP (mmHg) | 128.7 ± 16.0 | 128.9 ± 16.4 | 0.83 e | 128.4 ± 16.7 | 129.4 ± 17.3 | 129.3 ± 16.1 | 128.9 ± 17.4 | 0.84 | |
| DBP (mmHg) | 74.5 ± 11.8 | 74.9 ± 13.1 | 0.65 e | 74.2 ± 12.5 | 74.3 ± 12.9 | 74.6 ± 12.3 | 74.6 ± 12.5 | 0.97 | |
| MAP (mmHg) | 92.5 ± 11.7 | 92.9 ± 12.4 | 0.68 e | 92.3 ± 12.5 | 92.7 ± 12.4 | 92.8 ± 12.0 | 92.7 ± 12.3 | 0.91 | |
| HbA1c (mmol/mol (%)) | 63.9 ± 15.7 (8.0 ± 1.4) | 63.4 ± 16.7 (8.0 ± 1.5) | 0.70 e | 62.9 ± 15.3 (7.9 ± 1.4) | 63.5 ± 16.4 (8.0 ± 1.5) | 62.9 ± 15.2 (7.9 ± 1.4) | 62.4 ± 15.0 (7.9 ± 1.4) | 0.75 | |
| Change in the clinical findings | |||||||||
| Change in BW (kg) | −5.5 ± 8.2 | −3.5 ± 6.6 | <0.001 e | −5.3 ± 8.4 | −3.5 ± 6.7 | 1.9 [0.5, 3.2], p = .006 | −5.1 ± 7.6 | −3.3 ± 6.4 | 0.01 |
| Change in SBP (mmHg) | −3.3 ± 20.0 | −6.5 ± 21.0 | 0.05 e | −4.5 ± 20.6 | −4.3 ± 21.6 | 0.20 [-3.6, 4.0], p = .92 | −3.9 ± 20.6 | −5.8 ± 21.8 | 0.36 |
| Change in DBP (mmHg) | −2.1 ± 13.1 | −3.7 ± 13.4 | 0.12 e | −3.0 ± 13.5 | −3.1 ± 13.4 | −0.1 [-2.5, 2.2], p = .91 | −2.1 ± 13.1 | −3.6 ± 13.5 | 0.25 |
| Change in MAP (mmHg) | −2.5 ± 14.0 | −4.6 ± 14.2 | 0.05 e | −3.5 ± 14.4 | −3.5 ± 14.3 | −0.03 [-2.6, 2.5], p = .98 | −2.7 ± 14.2 | −4.3 ± 14.7 | 0.25 |
| Change in HbA1c (mmol/mol (%)) | −9.7 ± 19.9 (−0.9 ± 1.8) | −7.6 ± 20.9 (−0.7 ± 1.8) | 0.20 e | −9.9 ± 20.0 (−0.9 ± 1.8) | −9.6 ± 21.1 (−0.9 ± 1.9) | 0.3 [-3.3, 3.9] (0.03 [-0.3, 0.4]), p = .86 | −9.9 ± 20.0 (−0.9 ± 1.8) | −9.5 ± 20.5 (−0.9 ± 1.9) | 0.83 |
Values are mean ± SD, n/total n (%), or the difference [95% CI] and p-value.
aCalculated number of subjects after weighting.
bData present as the difference [95% CI] and p-value analyzed by GLM.
cMcNemar test,
dchi-square test
eunpaired t test.
Abbreviation; ATE, average treatment effect; BW, body weight; DBP, diastolic blood pressure; CI, confidence interval, eGFR, estimated glomerular filtration; FAS, full analysis set; GLM, generalized linear model, GLP1Ra, glucagon-like peptide 1 receptor agonist; HbA1c, glycated hemoglobin A1c; IPW, inverse provability weighting; LNACR, logarithmic value of urine albumin-to- creatinine ratio; MAP, mean arterial pressure; MI, multiple imputation; PS, propensity score; SBP, systolic blood pressure; SGLT2i, sodium-glucose co-transporter inhibitor.
Sensitivity analyses: PS matching model
Baseline data for the PS-matching model, which included 203 patients in each group, are presented in Table 1. There were no significant differences in the renal composite outcomes between the two groups (Table 2).
Results of a multivariable logistic regression analysis
Logistic regression analysis showed that the mean arterial pressure (MAP) at baseline, LnACR at baseline, and change in MAP were independent factors influencing the renal composite outcome, with ORs (95% CIs) of 1.05 (1.02, 1.07) (p < .001), 1.18 (1.03, 1.34) (p = .02), and 1.02 (1.00, 1.05) (p = .03), respectively.
Discussion
In recent CVOTs using GLP1Ras, the proportion of concomitant treatment with SGLT2i has increased to 7% in the Harmony outcome trials, 7 10.4% in the Pioneer six trials, 19 and 15% in the AMPLITUDE O trials, 8 with more interest in the impact of combination treatment on cardiovascular and renal outcomes as drugs specifically improve the outcomes. It is uncommon for these two drugs to be administered simultaneously; instead, it is more common for one drug to be administered first and the other added later. Therefore, this study aims to determine whether renal outcomes in clinical practice differ depending on the drug administered. However, there were no significant differences in renal outcomes for the preceding drug.
However, the mechanisms underlying the improvement in cardiovascular and renal outcomes after SGLT2i or GLP1Ra treatment remain unclear. SGLT2is and GLP1Ras commonly decrease plasma glucose levels, BW, and BP, leading to the improvement of insulin resistance and beta cell function. 20 However, different mechanisms underlie the exertion of these organ-protective effects. With GLP1Ras, natriuresis through the inhibition of the sodium-hydrogen exchanger three isoform, 21 a direct effect on the renal vascular endothelium, 22 and a decrease in inflammation and oxidative stress23,24 related to its renoprotective effects have been reported. In contrast, in addition to reducing oxidative stress 25 and suppressing fibrosis, 26 the hemodynamic effect of decreasing intraglomerular pressure by dilating the efferent renal artery via the suppression of tubule-glomerular feedback plays a major role in the renoprotective effects induced by SGLT2is. 27 In addition to the common antimetabolic effects, different renoprotective effects are presumed; therefore, further renoprotective effects in combination treatment can be expected.
In our analysis, significantly greater BW loss was observed in GLP1Ra-preceding patients than in SGLT2i-preceding patients. The changes in BW induced by hypoglycemic drugs compared with placebo were previously reported in a network meta-analysis, and both GLP1Ras and SGLT2is were shown to decrease BW by approximately 1-2 kg. 28 In patients who are expected to receive combination treatment with SGLT2i and GLP1Ra, it seems logical to recommend GLP1Ra-preceding treatment to prioritize BW loss, as our results showed that SGLT2i-preceding treatment did not improve renal outcomes.
Study limitations
The retrospective study, limited number of patients who could continue treatment with good adherence, relatively high BMI (nearly 30) of Japanese patients, and small-dose administration of GLP1Ra in clinical practice in Japan are limitations of the present study that should be considered in future surveys.
Conclusion
When administering SGLT2i and GLP1Ra combination therapy, the choice of drug administered first did not affect the renal composite outcome.
Supplemental Material
Supplemental Material for Renoprotective effects of combination treatment with sodium-glucose cotransporter inhibitors and GLP-1 receptor agonists in patients with type 2 diabetes mellitus according to preceding medication by Kazuo Kobayashi, Masao Toyoda, Atsuhito Tone, Daiji Kawanami, Daisuke Suzuki, Daisuke Tsuriya, Hideo Machimura, Hidetoshi Shimura, Hiroshi Takeda, Hisashi Yokomizo, Kei Takeshita, Keiichi Chin, Keizo Kanasaki, Masaaki Miyauchi, Masuo Saburi, Miwa Morita, Miwako Yomota, Moritsugu Kimura, Nobuo Hatori, Shinichi Nakajima, Shun Ito, Shunichiro Tsukamoto, Takashi Murata, Takaya Matsushita, Takayuki Furuki, Takuya Hashimoto, Tomoya Umezono, Yoshimi Muta, Yuichi Takashi, and Kouichi Tamura in Diabetes and Vascular Disease Research
Acknowledgements
We are grateful to all participants and acknowledge the support of the members of RECAP study who contributed considerably to data collection.
Appendix.
Abbreviation
- ACR; urine albumin-to-creatinine ratio
- ALT; alanine aminotransferase
- AST; aspartate aminotransferase
- ATE; average treatment effect
- BW; body weight
- CCA; complete case analysis set
- CI; confidence interval
- CKD; chronic kidney disease
- CVOT; cardiovascular outcome trial
- DBP; diastolic blood pressure
- eGFR; estimated glomerular filtration rate
- FAS; full analysis set
- FDA; United States Food and Drug Administration
- GLP1Ra; glucagon-like peptide-1 receptor agonist
- HbA1c; glycated hemoglobin A1c
- IPW; inverse probability weighting
- LnACR; logarithmic value of urine albumin-to-creatinine ratio
- MAP; mean arterial pressure
- MI; multiple imputation
- OR; odds ratio
- PS; propensity score
- RCT; randomized control trial
- SGLT2i; sodium-glucose cotransporter inhibitor
- SBP; systolic blood pressure
- SD; standard deviation
- T2DM; type 2 diabetes mellitus
Authors’ contributions: KaK, MT, NH, KoT, and MK made the design of this study.
KaK, MT,AT, DK, DS, DT, HM, HS, HT, HY, KeT, KC, KeK, MaM, MS, MiM, MY, MK, NH, SN, SI, ST, TMu, TMa, TF, TH, TU, YM, YT, and KoT collected the data of the study. KaK, MT, NH, ST, KoT, TMa, DT, DK, YM, MK, KeK, MY, and MiM analyzed the data.
KaK, MT, NH, and ST were major contributors in writing the manuscript.
All authors read and approved the final manuscript
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Supplemental Material: Supplemental material for this article is available online.
Ethics approval and consent to participate
This study was approved by the Institutional Review Board for Clinical Research, Tokai University, Japan (approval on December 6, 2021). This is a retrospective study and informed consent was waived and the optout was set during the study.
ORCID iD
Kazuo Kobayashi https://orcid.org/0000-0002-1284-9728
Data Availability Statement
Data are available from the Tokai University Data Access/Institutional Review Board for Clinical Research, Tokai University, for investigators, bound by confidentiality agreements. Contact details: Masao Toyoda MD/PhD, Division of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Tokai University School of Medicine, Isehara, Japan. Email: m-toyoda@is.icc.u-tokai.ac.jp
References
- 1.Zinman B, Wanner C, Lachin JM, et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med 2015; 373(22): 2117–2128. [DOI] [PubMed] [Google Scholar]
- 2.Neal B, Perkovic V, Mahaffey KW, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med 2017; 377(7): 644–657. [DOI] [PubMed] [Google Scholar]
- 3.Wiviott SD, Raz I, Bonaca MP, et al. Dapagliflozin and cardiovascular outcomes in type 2 diabetes. N Engl J Med 2019; 380(4): 347–357. [DOI] [PubMed] [Google Scholar]
- 4.Perkovic V, Jardine MJ, Neal B, et al. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med 2019; 380(24): 2295–2306. [DOI] [PubMed] [Google Scholar]
- 5.Marso SP, Daniels GH, Brown-Frandsen K, et al. Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med 2016; 375(4): 311–322. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Marso SP, Bain SC, Consoli A, et al. Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med 2016; 375(19): 1834–1844. [DOI] [PubMed] [Google Scholar]
- 7.Hernandez AF, Green JB, Janmohamed S, et al. Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial. Lancet 2018; 392(10157): 1519–1529. [DOI] [PubMed] [Google Scholar]
- 8.Gerstein HC, Sattar N, Rosenstock J, et al. Cardiovascular and renal outcomes with efpeglenatide in type 2 diabetes. N Engl J Med 2021; 385(10): 896–907. [DOI] [PubMed] [Google Scholar]
- 9.Rossing P, Caramori ML, Chan JCN, et al. Executive summary of the KDIGO 2022 clinical practice guideline for diabetes management in chronic kidney disease: an update based on rapidly emerging new evidence. Kidney Int 2022; 102(5): 990–1109. [DOI] [PubMed] [Google Scholar]
- 10.Kobayashi K, Toyoda M, Hatori N, et al. Comparison of the blood pressure management between sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide 1 receptor agonists. Sci Rep 2022; 12(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Kobayashi K, Toyoda M, Hatori N, et al. The concomitant use of sodium-glucose co-transporter 2 inhibitors improved the renal outcome of Japanese patients with type 2 diabetes treated with glucagon-like peptide 1 receptor agonists. Cardiovasc Endocrinol Metab 2023; 12(4): e0292. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Matsuo S, Imai E, Horio M, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 2009; 53(6): 982–992. [DOI] [PubMed] [Google Scholar]
- 13.Sumida K, Nadkarni GN, Grams ME, et al. Conversion of urine protein-creatinine ratio or urine dipstick protein to urine albumin-creatinine ratio for use in chronic kidney disease screening and prognosis : an individual participant-based meta-analysis. Ann Intern Med 2020; 173(6): 426–435. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.DBR . Multiple imputation for nonresponse in surveys. John Wiley and Sons. 1987. [Google Scholar]
- 15.Enders CK. Applied missing data analysis. New York: Guilford, 2010. [Google Scholar]
- 16.Aloisio KM, Swanson SA, Micali N, et al. Analysis of partially observed clustered data using generalized estimating equations and multiple imputation. STATA J 2014; 14(4): 863–883. [PMC free article] [PubMed] [Google Scholar]
- 17.Hershberger SL, Fisher DG. A note on determining the number of imputations for missing data. Struct Equ Model: A Multidiscip J 2003; 10(4): 648–650. [Google Scholar]
- 18.Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceut Stat 2011; 10(2): 150–161. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Husain M, Birkenfeld AL, Donsmark M, et al. Oral semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med 2019; 381(9): 841–851. [DOI] [PubMed] [Google Scholar]
- 20.Defronzo RA. Combination therapy with GLP-1 receptor agonist and SGLT2 inhibitor. Diabetes Obes Metabol 2017; 19(10): 1353–1362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Muskiet MHA, Tonneijck L, Smits MM, et al. GLP-1 and the kidney: from physiology to pharmacology and outcomes in diabetes. Nat Rev Nephrol 2017; 13(10): 605–628. [DOI] [PubMed] [Google Scholar]
- 22.Muskiet MH, Tonneijck L, Smits MM, et al. Acute renal haemodynamic effects of glucagon-like peptide-1 receptor agonist exenatide in healthy overweight men. Diabetes Obes Metabol 2016; 18(2): 178–185. [DOI] [PubMed] [Google Scholar]
- 23.Rizzo M, Abate N, Chandalia M, et al. Liraglutide reduces oxidative stress and restores heme oxygenase-1 and ghrelin levels in patients with type 2 diabetes: a prospective pilot study. J Clin Endocrinol Metab 2015; 100(2): 603–606. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Drucker DJ. The cardiovascular biology of glucagon-like peptide-1. Cell Metabol 2016; 24(1): 15–30. [DOI] [PubMed] [Google Scholar]
- 25.Tanaka S, Sugiura Y, Saito H, et al. Sodium-glucose cotransporter 2 inhibition normalizes glucose metabolism and suppresses oxidative stress in the kidneys of diabetic mice. Kidney Int 2018; 94(5): 912–925. [DOI] [PubMed] [Google Scholar]
- 26.Heerspink HJL, Kosiborod M, Inzucchi SE, et al. Renoprotective effects of sodium-glucose cotransporter-2 inhibitors. Kidney Int 2018; 94(1): 26–39. [DOI] [PubMed] [Google Scholar]
- 27.Cherney DZI, Perkins BA, Soleymanlou N, et al. Renal hemodynamic effect of sodium-glucose cotransporter 2 inhibition in patients with type 1 diabetes mellitus. Circulation 2014; 129(5): 587–597. [DOI] [PubMed] [Google Scholar]
- 28.Tsapas A, Karagiannis T, Kakotrichi P, et al. Comparative efficacy of glucose lowering medications on body weight and blood pressure in patients with type 2 diabetes: a systematic review and network meta analysis. Diabetes Obes Metabol 2021; 23(9): 2116–2124. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Material for Renoprotective effects of combination treatment with sodium-glucose cotransporter inhibitors and GLP-1 receptor agonists in patients with type 2 diabetes mellitus according to preceding medication by Kazuo Kobayashi, Masao Toyoda, Atsuhito Tone, Daiji Kawanami, Daisuke Suzuki, Daisuke Tsuriya, Hideo Machimura, Hidetoshi Shimura, Hiroshi Takeda, Hisashi Yokomizo, Kei Takeshita, Keiichi Chin, Keizo Kanasaki, Masaaki Miyauchi, Masuo Saburi, Miwa Morita, Miwako Yomota, Moritsugu Kimura, Nobuo Hatori, Shinichi Nakajima, Shun Ito, Shunichiro Tsukamoto, Takashi Murata, Takaya Matsushita, Takayuki Furuki, Takuya Hashimoto, Tomoya Umezono, Yoshimi Muta, Yuichi Takashi, and Kouichi Tamura in Diabetes and Vascular Disease Research
Data Availability Statement
Data are available from the Tokai University Data Access/Institutional Review Board for Clinical Research, Tokai University, for investigators, bound by confidentiality agreements. Contact details: Masao Toyoda MD/PhD, Division of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Tokai University School of Medicine, Isehara, Japan. Email: m-toyoda@is.icc.u-tokai.ac.jp