Kidney Res Clin Pract > Epub ahead of print
Lee, Jung, Kim, Lee, Lee, Baek, Kwon, Shin, Kim, Shin, Park, Park, and Kim: Association of metformin with cardiovascular and graft outcomes in kidney transplant recipients with posttransplantation diabetes mellitus

Abstract

Background

Posttransplantation diabetes mellitus (PTDM) is a crucial problem after kidney transplantation. We aimed to determine whether metformin affects cardiovascular and graft outcomes in patients with PTDM.

Methods

This retrospective cohort study included 1,663 kidney transplant recipients without preexisting diabetes mellitus. The patients were divided into metformin and non-metformin groups, with matched propensity scores. We also estimated metformin’s effect on percutaneous coronary intervention (PCI), major adverse cardiovascular events (MACEs), acute rejection, and graft failure.

Results

Of 634 recipients with PTDM, 406 recipients were treated with metformin. The incidence of PCI was 2.4% and 7.1% in the metformin and non-metformin groups, respectively (p = 0.04). The metformin group exhibited a lower risk of PCI in Cox regression analyses (hazard ratio [HR], 0.27; 95% confidence interval [CI], 0.10–0.77; p = 0.014), especially in subgroups with male sex, age over 49 years (median), long-term metformin use (mean of ≥1,729 days), and simultaneous tacrolimus administration. Long-term metformin use was also associated with lower incidence of MACEs (HR, 0.09; 95% CI, 0.01–0.67; p = 0.02). Incidence of graft failure was 9.9% and 17.0% in the metformin and non-metformin groups, respectively (p = 0.046). Both long-term use and higher dose of metformin, as well as tacrolimus administration with metformin, were associated with a lower risk of graft failure (HR, 0.29; 95% CI, 0.11–0.75; p = 0.01; HR, 0.39; 95% CI, 0.18–0.85; p = 0.02; and HR, 0.39; 95% CI, 0.19–0.79; p = 0.009, respectively).

Conclusion

Metformin use is associated with a decreased risk of developing coronary artery disease and better graft outcomes in PTDM.

Introduction

Posttransplantation diabetes mellitus (PTDM) is a common metabolic complication associated with kidney transplantation (KT) [1] and is provoked by immunosuppressive drugs (glucocorticoids, calcineurin inhibitors, and mammalian target of rapamycin inhibitors) [2]. According to recent studies, the incidence of PTDM ranges from 7% to 30% [3,4].
PTDM presents a significant clinical challenge because it negatively affects cardiovascular (CV) events, graft outcomes, and mortality in patients undergoing KT [5]. Several studies have consistently shown that PTDM is associated with impaired long-term patient survival compared to nondiabetic recipients of kidney transplants [68] and identified PTDM as an independent risk factor for graft loss [9]. Furthermore, patients with PTDM had an approximately three-fold risk of major cardiac events compared with nondiabetic patients [10], and the overall incidence of CV events was significantly higher in impaired fasting glucose by 23% or PTDM by 11%, compared to those with euglycemia [11].
Despite these risks, optimal treatment strategies for PTDM have not yet been established. Insulin is the preferred agent for treating hyperglycemia immediately after transplantation because of its related protection of β cells [12]. However, there is much more uncertainty regarding the long-term management of PTDM, which necessitates an appropriate selection and use of oral hypoglycemic agents [3]. Metformin is the drug of choice for the general population with diabetes, owing to its established anti-hyperglycemic properties and related improvements in insulin resistance, lipid profile, and obesity via fat redistribution, as well as anti-inflammatory effects relating to CV protection [1315]. However, the clinical outcomes and safety of metformin have not been sufficiently established in patients undergoing KT.
Some studies have investigated the relationship between metformin use and patient mortality and graft outcomes in KT recipients with diabetes mellitus (DM) [16,17]. However, their study populations consisted of KT recipients who had DM before undergoing transplantation; therefore, there were limitations in evaluating metformin use in patients with PTDM.
In this study, we aimed to determine the effectiveness of metformin on CV and graft outcomes in KT recipients with PTDM in Korea. We also compared the biochemical changes between the patients receiving metformin and other DM medications.

Methods

Study population

We retrospectively collected data from 4,372 adult patients who underwent KT at Asan Medical Center and Dongguk University Ilsan Hospital in Korea between 2000 and 2018. The study protocol was approved by the Ethics Committee of the Institutional Review Board of the Asan Medical Center (No. 2021-0862) and Dongguk University Ilsan Hospital (No. DUIH 2022-04-003-004), and informed consent was waived because the data were collected retrospectively. The work was conducted in accordance with the Helsinki Declaration of 1975, as revised in 2013.
Patients were defined as having PTDM when all the following criteria were met: 1) prescription of initial diabetes medication, including insulin and oral hypoglycemic agents after KT, 2) diagnosis of DM according to the American Diabetes Association (ADA) diagnostic criteria [18] and 3) prescription of diabetes medication for over 90 days to prevent overestimation of transient hyperglycemia (Fig. 1).

Baseline characteristics

The following demographic and clinical data were collected: sex, age, primary cause of kidney failure, comorbidity (hypertension, heart failure, coronary artery disease, cerebrovascular disease, hepatitis C virus infection, liver cirrhosis), other medications (lipid-lowering agent, blood pressure-lowering agent, antiplatelet agent) before KT, body mass index (BMI) at the time of PTDM diagnosis. In addition, kidney donor type, ABO incompatible, human leukocyte antigen incompatible, donor-specific antibodies before transplantation, pretransplantation induction therapy, and warm ischemic time were collected.

Laboratory findings

Baseline laboratory findings, including hemoglobin A1c (HbA1c), eGFR, hemoglobin, albumin, and total cholesterol were obtained at the time of PTDM diagnosis. We also collected HbA1c, total cholesterol, and low-density lipoprotein (LDL) of each patient during the follow-up period after the first DM medication and used the average lab data every 3 months. Lactic acidosis was defined as a plasma lactate level of ≥5 mmol/L and pH of ≤7.35 [19].

Metformin exposure

The prescription of metformin for more than 90 days has been defined as the use of metformin. We obtained the daily dose, frequency, and prescription period of metformin during the follow-up period. Additionally, we calculated the cumulative defined daily dose (cDDD) to investigate the relationship between metformin dosage and outcome. It was based on the World Health Organization’s defined daily dose methodology [20].

Outcomes

We investigated the occurrence of elective and emergent percutaneous coronary intervention (PCI), major adverse CV events (MACEs), acute rejection, and graft failure after KT. Patients with normal PCI results were excluded from the total number of PCI events. MACEs were comprised of primary or secondary outcomes of previous studies, including myocardial infarction, cerebrovascular disease, angina, and heart failure [21,22]. We collected all hospitalization and outpatient records of transplanted patients after KT and we found MACE by reviewing the record. Acute rejection was confirmed by kidney biopsy and included both antibody-mediated rejection and T-cell–mediated rejection. Graft failure was defined as a condition requiring renal replacement therapy such as dialysis or transplantation.

Statistical analyses

The propensity score matching (PSM) method was used to adjust the imbalance of measured covariates between the metformin and non-metformin groups by sharing similar propensity scores [23]. We used 1:1 nearest-neighbor matching in the logistic regression adjusted for sex, age, BMI, estimated glomerular filtration rate (eGFR) at the start of diabetic medication, use of induction therapy before KT, and types of immunosuppressant, lipid-lowering, and blood pressure-lowering agents. The statistical difference test of covariates between groups was performed using the unpaired Student t tests for continuous variables and the chi-square test for categorical variables. After PSM, we compared the characteristics between the metformin and non-metformin groups by estimating the survival probability of outcomes using the Kaplan-Meier curve. In addition, multivariate Cox proportional hazard models adjusted for sex, age, BMI, eGFR, hemoglobin, albumin, and total cholesterol were used to assess the risk of PCI, MACE, acute rejection, and graft failure in patients with PTDM; both in the metformin and non-metformin group satisfying the proportional hazard assumption by Schoenfeld residual. Years of follow-up were calculated from the first DM medication prescription date to the occurrence date of the outcome for event cases or December 31, 2021, for censored cases.
Stratified analyses were conducted to investigate potential confounders by sex, age (<median, ≥median), duration of metformin prescription (<mean, ≥mean), and type of immunosuppressant (tacrolimus and cyclosporine). We defined the difference between the first prescription date of metformin after KT and the last prescription date of metformin by considering the dosing period as the medication duration. In addition, we observed a temporal trend of HbA1c as well as total cholesterol, LDL, and BMI after starting the first DM medication until the end of the follow-up. All results were presented as hazard ratios (HR) and 95% confidence intervals (CI) of the metformin usage group compared with the non-metformin group and performed. Data were analyzed using R version 4.1.1. (R Foundation for Statistical Computing).

Results

Baseline characteristics

We identified a 38.1% incidence of PTDM (n = 634) in 1,663 KT recipients between 2000 and 2018 and found 406 recipients (65.7%) who received a metformin-based regimen over 90 days. We also identified 16 patients who had received metformin for less than 90 days, which were excluded from further analysis. Of the remaining 618 PTDM patients, 76.9% were diagnosed with PTDM within the first year after KT. The mean duration of metformin prescription was 1,729 days (Supplementary Fig. 1, available online), and the average dose of metformin was 452.6 cDDD before matching. The median and mean periods from KT to metformin administration were 258 and 1,278 days, respectively.
Table 1 shows the baseline characteristics of the patients with PTDM before and after PSM. Before matching, the metformin group was more likely to be older and have a higher mean BMI and eGFR than the non-metformin group. More than half of the PTDM patients were male, and the most common primary causes of renal failure and its comorbidities were other/unknown and hypertension, respectively, in both groups. In addition, basiliximab was the most commonly used induction therapy before KT in both metformin (79.8%) and non-metformin (64.6%) groups. All patients used steroids, followed by tacrolimus (73.2% metformin and 68.4% non-metformin), cyclosporine (23.9% metformin and 27.4% non-metformin), and sirolimus (2.5% metformin and 3.3% non-metformin). Once-daily tacrolimus was used by 4.9% in the metformin group and 2.1% in the non-metformin group. The most frequently used diabetic medications except metformin were dipeptidyl peptidase-4 inhibitors (96.6%) and sulfonylureas (48.5%) in the metformin group and insulin (68.4%) and sulfonylureas (61.8%) in the non-metformin group. After PSM, the significant intergroup differences in age, distribution of transplantation year, BMI, basiliximab use, and eGFR disappeared.

Incidence of cardiovascular and graft outcomes

Table 2 shows the outcomes in the metformin and non-metformin groups before and after PSM. After matching, the incidence of PCI was significantly lower in the metformin group than in the non-metformin group (2.4% vs. 7.1%, p = 0.039). Similarly, a significantly lower incidence of graft failure was observed in the metformin group compared with the non-metformin group (9.9% vs. 17.0%, p = 0.046). There were no significant differences in the incidence of MACE and acute rejection between the two groups.

Association of metformin usage with cardiovascular and graft outcomes (including subgroup analysis)

As shown in Fig. 2, the cumulative number of PCI events at 5 and 10 years was four and 10 for the non-metformin group and one and five for the metformin group, respectively. Kaplan-Meier analysis showed that the occurrence rates of PCI and graft failure events were higher in the non-metformin group (for log-rank test, p = 0.02 and p = 0.04, respectively). There was no significant difference between the two groups in MACE or acute rejection on the Kaplan-Meier plot.
Table 3 shows the effects of metformin compared with non-metformin on PCI, MACE, acute rejection, and graft failure in the overall patients and patient subgroups. Overall, there was a tendency to have a 72.7% lower risk for PCI in the metformin group compared with the non-metformin group (HR, 0.27; 95% CI, 0.10–0.77; p = 0.01) for an average follow-up of 10.01 years. Also, the metformin group tended to have a 42.4% reduced risk of graft failure, but the association was relatively weak (HR, 0.58; 95% CI, 0.33–1.00; p = 0.05). The adjusted HR for other outcomes showed no significant differences between the two groups: MACE (HR, 0.58; 95% CI, 0.29–1.17; p = 0.13) and acute rejection (HR, 0.92; 95% CI, 0.62–1.37; p = 0.69). Subgroup analyses revealed that metformin was associated with an 83.0% risk reduction in PCI events (HR, 0.17; 95% CI, 0.03–0.85; p = 0.03) for males, but not females (HR, 0.46; 95% CI, 0.11–1.93; p = 0.29). Those aged over the median value of 49 years in the metformin group tended to have a lower risk of PCI (HR, 0.28; 95% CI, 0.09–0.93; p = 0.04), whereas no association was estimated in patients aged <49 years. The long-term use of metformin (≥1,729 days, mean value) was associated with lower risks of PCI (HR, 0.11; 95% CI, 0.01–0.89; p = 0.04), MACE (HR, 0.09; 95% CI, 0.01–0.67; p = 0.02), and graft failure (HR, 0.29; 95% CI, 0.11–0.75; p = 0.01). Based on the average dose of metformin (337.4 cDDD) after matching, the higher dose of metformin was associated with lower risks of acute rejection by 43.4% (HR, 0.57; 95% CI, 0.32–0.99; p = 0.045) and graft failure by 61.0% (HR, 0.39; 95% CI, 0.18–0.85; p = 0.02) compared to the non-metformin group. We observed that subgroups that were administered tacrolimus as maintenance immunosuppressive medicine had a beneficial association with metformin with a reduction in PCI (HR, 0.28; 95% CI, 0.09–0.89; p = 0.03) and graft failure (HR, 0.39; 95% CI, 0.19–0.79; p = 0.009), whereas no significant effects were found in patients receiving metformin and cyclosporine.

Metformin usage and trends of metabolic parameters

When monitoring the temporal trends of other metabolic parameters, BMI slightly decreased over time in the metformin group (p < 0.001); however, there was no significant change in the non-metformin group (p = 0.46). The BMI difference in the time trend between the two groups was significant (p = 0.03) (Fig. 3D). During the follow-up period, there were no significant differences between the two groups regarding changes in HbA1c (p = 0.15), total cholesterol (p = 0.77), or LDL (p = 0.14) over time (Fig. 3AC).

Metformin-associated lactic acidosis

Finally, only three patients in the metformin group had metabolic acidosis throughout the study period. However, all metabolic acidosis cases were associated with septic shock and did not overlap with the period of metformin administration.

Discussion

In this retrospective study, we analyzed a cohort of patients with PTDM over the past decade and found that metformin had a beneficial association with developing coronary arterial disease (CAD) represented by PCI events and graft failure. The significant negative correlation of metformin with CAD was stronger in males and older adults. In particular, the risks of CAD and graft failure tended to decrease in those who were consistently receiving metformin over a long-term period, and those who were taking tacrolimus. We also found that metformin was significantly associated with a decreasing temporal tendency of BMI, representing a metabolic benefit in patients with PTDM. Adverse outcomes, such as metformin-associated lactic acidosis (MALA), were not identified.
Our results are consistent with those of previous studies reporting the positive effects of metformin on CV outcomes in general patients with diabetes. In a representative study of patients with type 2 DM (T2DM) in the UK Protective Diabetes Study (UKPDS), metformin use was associated with a 36% decreased risk of all-cause mortality and a 39% lower risk of myocardial infarction [14]. In addition, a subsequent study of the UKPDS found that metformin use in overweight patients reduced myocardial infarction and all-cause mortality by 33% and 27%, respectively [13]. The literature indicates that metformin can improve CV outcomes by alleviating hyperglycemia, insulin resistance, and other CV risk factors, such as obesity, dyslipidemia, and carotid intima-media thickness [24]. However, the results of previous studies on KT recipients did not fully demonstrate the benefit of metformin on CV outcomes. A US study of 14,144 patients undergoing KT with T2DM between 2007 and 2015 showed insignificant effects on CV mortality associated with metformin use in the first year after KT (adjusted HR, 0.52; 95% CI, 0.23–1.18) [17]. Furthermore, another small prospective study investigating the association between metformin use and impaired glucose tolerance after KT could not estimate the effect of metformin on CV outcomes in KT recipients with PTDM because there were no cardiac events during the first-year follow-up [25]. As a consequence, the inconsistency of previous studies might have resulted from differences in the study population and follow-up period, affecting the occurrence of CV outcomes.
We found a significantly lower incidence and higher survival probability of CAD associated with metformin after PSM, whereas significant differences in the incidence and survival probability of MACE between the metformin and non-metformin groups were not found. Among the general population, metformin had been significantly associated with a lower risk of MACE and all-cause mortality compared to other diabetes medications, but there was a lack of study in renal transplant population. This study recorded a small number of deaths (2.5%); therefore, CV deaths were not considered in the analysis of MACE. Additional large-scale studies are needed to identify the correlation between metformin and MACE in PTDM patients, including CV death.
We observed a lower risk of graft failure in patients receiving long-term metformin for over 1,192 days compared with the non-metformin group. Recently, Kwon et al. [26] reported that metformin usage was associated with a reduced risk of death-censored graft failure. And higher dose of metformin was correlated with a lower risk of death-censored graft failure and biopsy-proven acute rejection. It is consistent with our results. The possible mechanism for lowering the risk of graft failure and CV outcomes could be attributed to the anti-inflammatory effect of metformin. It included the inhibition of nuclear factor kappa-B (NF-κB) by activating AMP-activated protein kinase and forming advanced glycation end products [27]. One animal study showed that metformin inhibited NF-κB activation and decreased serum C-reactive protein (CRP) [28]. The anti-inflammatory effect of metformin was also confirmed in a clinical study showing a significant reduction in CRP levels in patients who are overweight with T2DM when metformin was prescribed over a 24-week period [29]. Therefore, decreasing subclinical inflammation in KT recipients may decrease the likelihood of the development of cardiometabolic complications such as metabolic syndrome and CV disease as well as tubular atrophy and interstitial fibrosis [30].
In stratified analyses, males and patients aged over 49 years had a greater negative correlation of metformin with CAD compared to the non-metformin group. However, there is no consensus on the utility of metformin according to age and sex. In a study of 78 patients with T2DM, metformin was noted to positively affect cardiac metabolism in women, which was inconsistent with our results. In one study, the effects of metformin according to sex were inconclusive because of the differences in metformin dose and preference [31]. In another study of the long-term effect of metformin in 3,234 participants with impaired glucose tolerance, the subgroup analysis for intervention effects on CV events showed no significant heterogeneity by age and sex [32]. Therefore, further studies are needed to assess the effects of metformin on age and sex to confirm our findings.
In this study, patients receiving metformin with tacrolimus had lower risks of CAD and graft failure, whereas no significant effects were found in patients using metformin with cyclosporine. Calcineurin inhibitors are representative immunosuppressants commonly used after KT and are associated with glucose dysregulation in transplant recipients [6,8,33]. Tacrolimus and cyclosporine, commonly classified as calcineurin inhibitors, are widely known to have adverse effects on insulin sensitivity and direct inhibitory effects on pancreatic beta cells [34]. However, tacrolimus has stronger dose-dependent suppressive effects on insulin secretion and higher diabetogenic effects than cyclosporine [35]. Although a plausible mechanism has not been elucidated, it could be assumed that metformin exhibits a more protective effect in the tacrolimus group, supporting the idea that the main mechanism of metformin is to improve insulin resistance. Additional scientific evidence is required to confirm these results.
Although metformin has several advantages in weight loss, CV protection, and improvement of the metabolic profile, the risk of MALA is a major concern in patients with PTDM. MALA is a fatal event with a very low incidence rate of 3–10 per 100,000 person-years but a mortality rate of up to 50% [36]. Increased plasma lactate levels are associated with the inhibition of mitochondrial respiration in tissues responsible for lactate removal, and MALA is thought to occur in patients with severe renal impairment because lactate levels are highly dependent on metformin concentrations [19]. Accordingly, the Kidney Disease: Improving Global Outcomes guideline recommends reducing the dose of metformin in patients with an eGFR of <45 mL/min/1.73 m2, and metformin has been contraindicated for patients with an eGFR of <30 mL/min/1.73 m2 [37]. Interestingly, a recent study showed that metformin use reduced all-cause mortality and end-stage kidney disease incidence in patients with moderate renal impairment, with an eGFR of 30–44 mL/min/1.73 m2 [38]. In a review article focusing on the management of PTDM in heart transplant recipients, Cehic et al. [39] reported that metformin was an acceptable choice in patients with PTDM with an eGFR of ≥30 mL/min/1.73 m2. Moreover, Kurian et al. [40] reported the long-term safety and efficacy of metformin in 24 KT recipients with no cases of lactic acidosis during a mean duration of 16.4 months. In this study, MALA did not occur in the metformin group during a mean follow-up period of 10 years. Only three patients developed lactic acidosis due to septic shock while not taking metformin.
Our study has several limitations. First, insufficient unmeasured covariates might have led to a selection bias in the retrospective design, although data on baseline characteristics, immunosuppressants, and other medications were collected in this study. Second, the dose and combined type of diabetes medication were not considered in the classification between the metformin and non-metformin groups. As a result, the effects of other DM medications were not completely excluded, as would have been in a randomized control study. Third, we could not estimate the effects of metformin on PCI in patients aged <49 years because CAD was not observed in this group. Fourth, considering the duration of metformin prescription, both the medication duration and the duration after prescription may affect the results. Therefore, further study would be needed in prospective cohort or analysis modeling such as time-varying Cox model and landmark analysis. Lastly, the results of HbA1c, fasting plasma glucose, and random plasma glucose tests were mainly used among the ADA criteria during the selection of patients with PTDM. This may be because each physician used a different main test method for DM diagnosis and the oral glucose tolerance test was not used as a routine screening protocol, although it could be the gold standard for diagnosing PTDM [3].
Despite these limitations, our study has several strengths. First, this is the first study to determine the effects of metformin compared with non-metformin-based regimens on clinical outcomes in patients undergoing KT who have developed PTDM, considering the prescription period in balanced data between the metformin and non-metformin groups using PSM. Second, a follow-up examination of HbA1c, TC, LDL, and BMI over a decade provided an extensive body of data to support the positive effects of metformin. Third, several stratified analyses by sex, age, medication duration, and type of immunosuppressant associated with outcomes were conducted to assess stronger associations. Finally, we investigated the additional adverse outcomes of MALA in patients with PTDM.
Considering the high CV risk and mortality of KT patients with PTDM, proper management of PTDM is essential, and this study could help clinical decisions to consider metformin as a preferential treatment option. Further large-scale prospective studies are required to establish the usefulness of metformin in KT patients with PTDM.

Supplementary Materials

Supplementary data are available at Kidney Research and Clinical Practice online (https://doi.org/10.23876/j.krcp.23.085).

Notes

Conflicts of interest

All authors have no conflicts of interest to declare.

Data sharing statement

The data underlying this article are available in the article.

Authors’ contributions

Conceptualization: SKP, JYP, HK

Formal analysis, Methodology, Visualization: DL, JJ, JYP, HK

Investigation: DL, JJ, SK

Resources: All authors

Supervision: JYP, HK

Writing–original draft: DL, JJ

Writing–review & editing: DL, JJ, JYP, HK

All authors approved the final version of the manuscript.

Figure 1.

Flow chart of the study population.

j-krcp-23-085f1.jpg
Figure 2.

Comparison of events in metformin vs. non-metformin groups.

Nonoccurrence rates of percutaneous coronary intervention (A), major adverse cardiovascular events (B), acute rejection (C), and graft failure (D) in the Kaplan-Meier plot.
j-krcp-23-085f2.jpg
Figure 3.

Temporal trends after starting diabetes mellitus medication.

(A) Hemoglobin A1c (HbA1c), (B) total cholesterol, (C) low-density lipoprotein (LDL), and (D) body mass index (BMI).
j-krcp-23-085f3.jpg
Table 1.
Baseline characteristics of 618 patients with PTDM before matching and 424 patients with PTDM after matching, from 2000 to 2018
Characteristic Before matching After matching
Metformin group Non-metformin group p-value Metformin group Non-metformin group p-value
No. of patients 406 212 212 212
Age (yr) 49.6 ± 9.9 47.9 ± 11.0 0.048 48.4 ± 10.6 47.9 ± 11.0 0.59
Sex 0.87 0.32
 Male 236 (58.1) 121 (57.1) 132 (62.3) 121 (57.1)
 Female 170 (41.9) 91 (42.9) 80 (37.7) 91 (42.9)
Transplantation year 0.001 0.84
 2000–2005 65 (16.0) 62 (29.2) 54 (25.5) 62 (29.2)
 2006–2010 120 (29.6) 61 (28.8) 62 (29.2) 61 (28.8)
 2011–2015 137 (33.7) 61 (28.8) 66 (31.1) 61 (28.8)
 2016–2018 84 (20.7) 28 (13.2) 30 (14.2) 28 (13.2)
Body mass index (kg/m2) 23.9 ± 3.6 22.7 ± 3.7 <0.001 23.1 ± 3.6 22.7 ± 3.7 0.27
Kidney donor type 0.498 0.64
 Living 325 (80.0) 164 (77.4) 169 (79.7) 164 (77.4)
 Deceased 81 (20.0) 48 (22.6) 43 (20.3) 48 (22.6)
DSA before transplantationa 17 (4.2) 8 (3.8) 0.99 8 (3.8) 8 (3.8) >0.99
ABO incompatible 140 (34.4) 172 (81.1) 0.88 72 (33.9) 71 (33.5) >0.99
HLA incompatible 302 (74.3) 172 (81.1) 0.04 157 (74.1) 172 (81.1) 0.05
Warm ischemic time (min) 30.6 ± 15.5 30.6 ± 24.2 >0.99 28.3 ± 15.8 30.6 ± 24.2 0.35
Primary cause of kidney failure 0.79 0.52
 Hypertension 33 (8.1) 13 (6.1) 19 (9.0) 13 (6.1)
 Glomerulonephritis 79 (19.5) 40 (18.9) 38 (17.9) 40 (18.9)
 Polycystic kidney disease 28 (6.9) 12 (5.7) 14 (6.6) 12 (5.7)
 Malignancy 0 (0) 0 (0) 0 (0) 0 (0)
 Allograft failure 13 (3.2) 9 (4.2) 4 (1.9) 9 (4.2)
 Others/unknown 253 (62.3) 138 (65.1) 137 (64.6) 138 (65.1)
Comorbidity
 Hypertension 390 (96.1) 196 (92.5) 0.08 204 (96.2) 196 (92.5) 0.14
 Heart failure 14 (3.4) 6 (2.8) 0.86 4 (1.9) 6 (2.8) 0.75
 Coronary artery disease 14 (3.4) 11 (5.2) 0.41 5 (2.4) 11 (5.2) 0.20
 Cerebrovascular disease 8 (2.0) 7 (3.3) 0.46 6 (2.8) 7 (3.3) >0.99
 HCV infection 5 (1.2) 5 (2.4) 0.47 3 (1.4) 5 (2.4) 0.72
 Liver cirrhosis 5 (1.2) 0 (0) 0.25 3 (1.4) 0 (0) 0.25
Induction therapy
 Basiliximab 324 (79.8) 137 (64.6) <0.001 143 (67.5) 137 (64.6) 0.61
 Thymoglobulin 20 (4.9) 15 (7.1) 0.36 13 (6.1) 15 (7.1) 0.85
 Daclizumab 2 (0.5) 5 (2.4) 0.09 1 (0.5) 5 (2.4) 0.22
Type of immunosuppressant 0.59 0.98
 Steroid 406 (100) 212 (100) 212 (100) 212 (100)
 Calcineurin inhibitors
  Tacrolimus 297 (73.2) 145 (68.4) 141 (66.5) 145 (68.4)
  Cyclosporin 97 (23.9) 58 (27.4) 61 (28.8) 58 (27.4)
 mTOR inhibitor
  Sirolimus 10 (2.5) 7 (3.3) 8 (3.8) 7 (3.3)
 Others 2 (0.5) 2 (0.9) 2 (0.9) 2 (0.9)
Laboratory data at first diabetes medication
  eGFR (mL/min/1.73 m2) 74.8 ± 19.1 65.3 ± 25.1 <0.001 68.5 ± 19.1 65.3 ± 25.1 0.14
  HbA1c (%) 7.5 ± 1.6 7.2 ± 1.6 0.05 7.5 ± 1.6 7.2 ± 1.6 0.05
  Hemoglobin 11.2 ± 2.5 11.0 ± 2.6 0.18 11.2 ± 2.5 11.0 ± 2.6 0.25
  Albumin 3.6 ± 0.6 3.5 ± 0.5 0.01 3.6 ± 0.6 3.5 ± 0.5 0.03
  Total cholesterol 176.8 ± 48.2 180.8 ± 54.9 0.37 177.6 ± 50.3 180.8 ± 54.9 0.53
Type of diabetes medication <0.001 <0.001
 Metformin 406 (100) 0 (0) 212 (100) 0
 Insulin 167 (41.1) 145 (68.4) 83 (39.2) 145 (68.4)
 DPP4i 392 (96.6) 111 (52.4) 200 (94.3) 111 (52.4)
 SGLT2i 34 (8.4) 2 (0.9) 11 (5.2) 2 (0.9)
 Sulfonylureas 197 (48.5) 131 (61.8) 106 (50.0) 131 (61.8)
 Others 58 (14.3) 27 (12.7) 28 (13.2) 27 (12.7)
Other medications before KT
 Lipid-lowering agent 130 (32.0) 52 (24.5) 0.07 55 (25.9) 52 (24.5) 0.82
 Blood pressure-lowering agent 395 (97.3) 202 (95.3) 0.28 203 (95.8) 202 (95.3) >0.99
 Antiplatelet agent 120 (29.6) 64 (30.2) 0.94 59 (27.8) 64 (30.2) 0.67
Duration from KT to diagnosis of PTDM (yr)
 <1 310 (76.4) 165 (77.8) 0.76 170 (80.2) 165 (77.8) 0.63
 ≥1 96 (23.6) 47 (22.2) 42 (19.8) 47 (22.2)

Data are expressed as number only, mean ± standard deviation, or number (%).

DSA, donor-specific antibodies; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; HCV, hepatitis C virus; HLA, human leukocyte antigen; KT, kidney transplantation; DPP4i, dipeptidyl peptidase-4 inhibitors; mTOR, mammalian target of rapamycin; PTDM, posttransplantation diabetes mellitus; SGLT2i, sodium-glucose cotransporter-2 inhibitors.

aInformation is missing on 137 (22.2%) for the metformin group and 88 (14.2%) for the non-metformin group.

Table 2.
PCI, MACE, acute rejection, and graft failure in the metformin and non-metformin groups before and after matching
Variable Before matching
After matching
Metformin group (n = 406) Non-metformin group (n = 212) p-value Metformin group (n = 212) Non-metformin group (n = 212) p-value
Outcome
 PCI 12 (3.0) 15 (7.1) 0.03 5 (2.4) 15 (7.1) 0.04
  Elective 4 (1.0) 10 (4.7) 3 (1.4) 10 (4.7)
  Emergent 8 (2.0) 5 (2.4) 2 (0.9) 5 (2.4)
 MACE 24 (5.9) 21 (9.9) 0.37 13 (6.1) 21 (9.9) 0.39
  Myocardial infarction 6 (25.0) 5 (23.8) 3 (23.1) 5 (23.8)
  Cerebrovascular disease 6 (25.0) 5 (23.8) 4 (30.8) 5 (23.8)
  Angina 9 (37.5) 10 (47.6) 4 (30.8) 10 (47.6)
  Heart failure 3 (12.5) 1 (4.8) 2 (15.4) 1 (4.8)
 Acute rejection 75 (18.5) 54 (25.5) 0.05 49 (23.1) 54 (25.5) 0.65
 Graft failure 33 (8.1) 36 (17.0) 0.001 21 (9.9) 36 (17.0) 0.046

Data are expressed as number (%).

; MACE, major adverse cardiovascular event; PCI, percutaneous coronary intervention.

Table 3.
Multiple Cox regression analysis of PCI, MACE, acute rejection, and graft failure according to metformin use compared with non-metformin use in matched patients and subgroups by sex, age, metformin dosage duration, and type of immunosuppressant
Variable Outcomes
PCI MACE Acute rejection Graft failure
Follow-up (yr) 10.01 ± 5.83 9.6 ± 5.92 8.38 ± 6.01 9.59 ± 5.6
Overall 0.27 (0.10–0.77) 0.58 (0.29–1.17) 0.92 (0.62–1.37) 0.58 (0.33–1.00)
Sex
 Male 0.17 (0.03–0.85) 0.55 (0.23–1.34) 0.87 (0.52–1.46) 0.55 (0.26–1.13)
 Female 0.46 (0.11–1.93) 0.67 (0.20–2.23) 0.91 (0.48–1.74) 0.52 (0.21–1.28)
Age (yr), median
 <49 0.23 (0.02–2.37) 0.53 (0.12–2.28) 0.76 (0.44–1.31) 0.72 (0.33–1.58)
 ≥49 0.28 (0.09–0.93) 0.62 (0.27–1.40) 1.04 (0.57–1.90) 0.48 (0.21–1.08)
Metformin prescription duration (day), mean
 <1,729 0.41 (0.13–1.28) 1.06 (0.51–2.21) 1.18 (0.76–1.83) 0.85 (0.46–1.57)
 ≥1,729 0.11 (0.01–0.89) 0.09 (0.01–0.67) 0.61 (0.34–1.10) 0.29 (0.11–0.75)
Metformin dosage (cDDD), mean
 <337.4 0.32 (0.09–1.18) 0.83 (0.37–1.87) 1.38 (0.88–2.17) 0.83 (0.43–1.59)
 ≥337.4 0.23 (0.05–1.00) 0.35 (0.12–1.04) 0.57 (0.32–0.99) 0.39 (0.18–0.85)
Immunosuppressant
 Tacrolimus 0.27 (0.09–0.89) 0.59 (0.26–1.32) 0.64 (0.38–1.06) 0.39 (0.19–0.79)
 Cyclosporine 0.10 (0.01–1.70) 0.53 (0.09–3.23) 1.94 (0.91–4.14) 1.15 (0.38–3.51)

Data are expressed as mean ± standard deviation or hazard ratio (95% confidence interval).

cDDD, cumulative defined daily dose; MACE, major adverse cardiovascular events; PCI, percutaneous coronary intervention.

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ORCID iDs

Dongyeon Lee
https://orcid.org/0000-0003-4580-6641

Jiyun Jung
https://orcid.org/0000-0002-8235-0316

Sichan Kim
https://orcid.org/0000-0002-8886-5353

Jaeyun Lee
https://orcid.org/0000-0003-4711-7559

Jangwook Lee
https://orcid.org/0000-0003-2181-5850

Chung Hee Baek
https://orcid.org/0000-0001-7611-2373

Hyunwook Kwon
https://orcid.org/0000-0001-5018-5304

Sung Shin
https://orcid.org/0000-0001-7318-4208

Younghoon Kim
https://orcid.org/0000-0003-3840-8426

Sung Joon Shin
https://orcid.org/0000-0002-0777-9278

Su-Kil Park
https://orcid.org/0000-0001-7242-7204

Jae Yoon Park
https://orcid.org/0000-0001-8986-7492

Hyosang Kim
https://orcid.org/0000-0001-8140-9534

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