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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 A_1c (HbA_1c), 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, HbA_1c, 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 PS¹⁸ 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
HbA_1c (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
HbA_1c (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 stress^23,24 related to its renoprotective effects have been reported. In contrast, in addition to reducing oxidative stress²⁵ 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 - 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

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

Footnotes

Acknowledgements

We are grateful to all participants and acknowledge the support of the members of RECAP study who contributed considerably to data collection.

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

Declaration of conflicting interests

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.

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

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

Supplemental Material

Supplemental material for this article is available online.

Appendix

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