Preventive Effects of SGLT2 Inhibitors on Cisplatin-Induced Nephrotoxicity in Patients with Solid Tumors and Diabetes: A Multicenter, Retrospective Cohort Study

Preventive Effects of SGLT2 Inhibitors on Cisplatin-Induced Nephrotoxicity in Patients with Solid Tumors and Diabetes: A Multicenter, Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preventive Effects of SGLT2 Inhibitors on Cisplatin-Induced Nephrotoxicity in Patients with Solid Tumors and Diabetes: A Multicenter, Retrospective Cohort Study Masaya Kanda, Toshinobu Hayashi, Mitsuhiro Goda, Kenji Kawasumi, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9380086/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background Cisplatin (CDDP)-induced nephrotoxicity (CIN) is a major dose-limiting toxicity. Preclinical models suggest that sodium-glucose cotransporter 2 (SGLT2) inhibitors, used in the management of diabetes, activate multidrug and toxin extrusion (MATE) transporters by altering luminal sodium and hydrogen ion gradients via the sodium-hydrogen exchanger 3 (NHE3), thereby promoting cisplatin efflux from proximal tubular cells. This study aimed to investigate the clinical preventive effect of SGLT2 inhibitors on CIN in patients with solid tumors and concurrent type 2 diabetes. Methods A multicenter, retrospective cohort study was conducted across 16 institutions in Japan. Eligible patients were aged ≥ 20 years with solid tumors and type 2 diabetes who received high-dose CDDP (≥ 50 mg/m 2 )-based chemotherapy. We compared patients receiving SGLT2 inhibitors with those receiving metformin. Patients receiving dipeptidyl peptidase-4 (DPP-4) inhibitors were excluded. To minimize confounding from baseline patient characteristics, inverse probability of treatment weighting (IPTW) using the stabilized average treatment effect (sATE) was applied. Results After sATE-IPTW adjustment, the incidence of CIN was significantly lower in the SGLT2 inhibitor group than in the metformin group (17.9% vs. 45.2%, P = 0.034). The decline in renal function was significantly mitigated in the SGLT2 inhibitor group, as evidenced by better preservation of serum creatinine, creatinine clearance, and estimated glomerular filtration rate (all P < 0.05). A multivariate logistic regression analysis identified the use of SGLT2 inhibitors as an independent protective factor against CIN (odds ratio [OR] 0.247, P = 0.001), whereas preexisting cardiac disease was identified as an independent risk factor (OR 3.107, P = 0.042). Conclusions SGLT2 inhibitors significantly and robustly reduce the risk of CIN in patients with solid tumors and type 2 diabetes. The concomitant use of SGLT2 inhibitors represents a promising renoprotective strategy to safely maintain the dose intensity of CDDP-based chemotherapy in clinical practice. Sodium-glucose co-transporter 2 inhibitors Cisplatin Nephrotoxicity Acute kidney injury Multidrug and toxic compound extrusion transporter Figures Figure 1 BACKGROUND Cisplatin (CDDP), a platinum-based chemotherapeutic agent, serves as a cornerstone in the treatment of various solid tumors [1]. Despite its proven clinical efficacy, nephrotoxicity represents a major dose-limiting factor, frequently necessitating dose reduction or treatment discontinuation. Standard renoprotective measures, such as aggressive hydration, diuretic administration, and magnesium supplementation, are routinely implemented in clinical practice. Despite these preventive interventions, acute kidney injury occurs in approximately 20–40% of patients receiving CDDP-based chemotherapy [2–5]. To address this substantial incidence of CDDP-induced nephrotoxicity (CIN), the development of novel preventive strategies is imperative. The mechanisms underlying CIN have been investigated extensively using experimental models and cell culture systems. The primary pathophysiological pathway involves CDDP accumulation within proximal tubular epithelial cells [6], which triggers a cascade of deleterious events, including increased oxidative stress mediated by reactive oxygen species [6, 7], promotion of apoptosis [6, 8], and propagation of intrarenal inflammatory responses [9]. Building on these mechanistic insights, numerous therapeutic candidates possessing antioxidant, anti-inflammatory, and anti-apoptotic properties have been evaluated in CIN animal models, with many demonstrating apparent renoprotective effects [6]. However, clinical translation of these strategies has been limited. When tested in human subjects, most candidates exhibit substantially reduced efficacy or complete ineffectiveness, and none have achieved clinical application. These translational failures suggest that therapeutic strategies focused solely on ameliorating cellular damage and downstream inflammatory cascades are insufficient. Accordingly, alternative approaches targeting CDDP accumulation itself merit investigation. Renal handling of CDDP is mediated by multiple transporters expressed in proximal tubular cells. In particular, CDDP is taken up from the blood across the basolateral membrane primarily via organic cation transporter 2 (OCT2) and is subsequently extruded into the tubular lumen through multidrug and toxin extrusion proteins (MATE1 and MATE2-K) located on the apical membrane [10, 11]. We have previously used a combination of a FAERS (FDA Adverse Event Reporting System)-based pharmacovigilance analysis, hospital electronic medical records, and preclinical studies to demonstrate that altered intrarenal CDDP accumulation is closely linked to nephrotoxicity in both experimental and clinical settings [12]. These observations raise the possibility that enhancing MATE-mediated efflux from proximal tubular cells may mitigate CIN. Because MATE transport is driven by a transmembrane proton gradient, changes in luminal sodium–hydrogen handling could potentially influence its activity. Sodium–glucose cotransporter 2 (SGLT2) and sodium–hydrogen exchanger 3 (NHE3) are both expressed on the apical membrane of proximal tubular cells and play central roles in glucose and sodium handling [13, 14]. Based on their functional interplay, SGLT2 inhibition may modify the proton gradient that drives MATE-dependent transport. Ongoing translational work by our group has generated complementary preclinical evidence supporting this concept. In mouse models of CIN, SGLT2 inhibitor (SGLT2i) attenuated renal injury in association with reduced proximal tubular CDDP accumulation. In parallel, in vitro studies suggested a functional interaction between MATE1 and NHE3. Given the current indications for SGLT2i, a feasible approach to evaluate their renoprotective effects is to investigate these agents in patients with cancer and diabetes. We conducted a multicenter, retrospective observational study to evaluate whether SGLT2i use mitigates CIN in patients with solid tumors and concomitant type 2 diabetes mellitus. METHODS Patients This multicenter retrospective study was conducted at 16 hospitals in Japan. Patients were eligible if they were aged 20 years or older with solid cancers and type 2 diabetes and received high-dose CDDP (≥ 50 mg/m 2 )-based chemotherapy for the first time. The study included patients who were administered SGLT2i (canagliflozin, dapagliflozin empagliflozin, ipragliflozin, luseogliflozin, and tofogliflozin) or metformin for 1 week before and after CDDP administration. Concomitant use of other antidiabetic medications was permitted; however, patients taking dipeptidyl peptidase-4 (DPP-4) inhibitors were excluded. Data collection The following information was retrospectively extracted from medical records at each institution: patient characteristics (age, sex, height, weight, body mass index [BMI], primary cancer, and history of chemotherapy, cardiac disease, and hypertension), laboratory data (alanine aminotransferase [ALT], aspartate aminotransferase [AST], serum creatinine [SCr], estimated glomerular filtration rate [eGFR], hemoglobin A1c [HbA1c], potassium, and albumin), and treatment-related factors (CDDP dose, chemotherapy cycle, chemotherapy regimens, diabetic medications, renal protection measures [diuretics, type of hydration, and magnesium supplementation], prophylactic antiemetics, and concomitant medications that cause nephrotoxicity or MATE-inhibitor). Nephrotoxicity was evaluated according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5. Specifically, for increased SCr, Grade 1 was defined as >upper limit of normal (ULN) − 1.5×ULN, Grade 2 as > 1.5–3.0×ULN, Grade 3 as > 3.0–6.0×ULN, and Grade 4 as > 6.0×ULN. The SCr value measured immediately before the first CDDP administration was used as the baseline. The worst creatinine level was defined as the peak creatinine value recorded within the chemotherapy treatment period from the index date. In addition, the percent changes in SCr levels, creatinine clearance (CCr), and eGFR from baseline were assessed. Statistical analysis The primary endpoint was the incidence of CIN during the CDDP administration period, which was compared between the metformin and SGLT2i groups. To minimize the impact of confounding factors, we used the stabilized average treatment effect (sATE) of the inverse probability treatment weighting (IPTW) model derived from a logistic regression model to balance observable characteristics (covariates: age, female sex, BMI, eGFR, HbA1c, cardiac disease, hypertension, albumin level, CDDP dose, type of hydration, type of diuretic, Mg supplementation, angiotensin-converting enzyme inhibitor, angiotensin II receptor blocker, MATE-inhibitor [irinotecan, diltiazem, diphenhydramine, famotidine, and trimethoprim], non-steroidal anti-inflammatory drugs, proton pump inhibitor, and cycle of chemotherapy). Variables were included in the model based on existing knowledge of risk factors for nephrotoxicity and the literature [5, 15–18]. Categorical variables were analyzed using the chi-square test, and continuous variables were analyzed using the Student’s t -test or Wilcoxon rank-sum test. The rates of change in SCr, CCr, and eGFR were calculated using the following formula: (worst value - baseline value) × 100/baseline value. A logistic regression analysis was performed to extract risk factors for the development of nephrotoxicity, adjusting for potential confounding by age, sex, cardiac disease, hypertension, Mg supplementation, CDDP dose, and use of SGLT2i. Variables were selected a priori based on the literature and their clinical relevance. A sensitivity analysis was performed using the weighted average treatment effect on the treated (ATT) and average treatment effect on the controls (ATC). Furthermore, a stratified analysis was conducted within the SGLT2i group according to the presence or absence of concomitant metformin use. All statistical tests were two-sided, and P -values of < 0.05 indicated statistical significance. All analyses were performed using JMP 14.3 (SAS Institute, Cary, NC, USA). RESULTS Baseline characteristics of patients A total of 156 patient records were collected. The effective sample sizes, adjusted by sATE weighting, were 94 in the metformin group and 56.7 in the SGLT2i group. After adjustment using sATE weighting, baseline patient characteristics were well balanced between the groups, with the exception of ALT/AST levels and certain antidiabetic medications (Table 1 ). Supplementary Table S1 summarizes the cancer types and chemotherapy regimens. Table 1 Patient Characteristics Unadjusted sATE weighting Metformin SGLT2i SMD Metformin SGLT2i SMD n = 94 n = 62 ESS = 94 ESS = 56.7 SEX Female, n (%) 18 (19.2) 12 (19.4) 0.005 19.2 19.4 0.005 Age Mean (SD) 65.4 (8.8) 65.4 (8.1) < 0.001 65.4 (8.8) 64.7 (9.9) 0.075 Median (IQR) 67.5 (61, 71) 67.5 (61, 71) BMI Mean (SD) 23.9 (4.1) 24.7 (3.8) 0.183 23.9 (4.3) 24.1 (3.6) 0.049 Median (IQR) 23.0 (21.3, 25.6) 24.5 (21.9, 26.1) Cardiac disease Yes, n (%) 12 (12.8) 7 (11.3) 0.045 12.8 12.9 0.006 Hypertension Yes, n (%) 44 (46.8) 39 (62.9) 0.328 46.8 39.6 0.146 Chemotherapy history Yes, n (%) 22 (23.4) 6 (9.7) 0.376 23.4 23.3 0.002 eGFR, mL/min Mean (SD) 78.3 (20.7) 77.5 (20.4) 0.040 78.3 (17.9) 79.8 (19.0) 0.070 Median (IQR) 74.7 (64.4, 87.3) 76.1 (61.6, 89.9) Alb level, g/dL Mean (SD) 4.0 (0.6) 4.0 (0.5) 0.044 3.9 (0.6) 4.0 (0.5) 0.082 Median (IQR) 4.0 (3.6, 4.4) 4.0 (3.8, 4.3) ALT, U/L Mean (SD) 25.8 (19.4) 20.7 (17.0) 0.239 25.8 (17.9) 20.8 (16.4) 0.257 Median (IQR) 21.5 (14.0, 30.0) 16.0 (13.0, 22.0) AST, U/L Mean (SD) 24.9 (17.5) 21.3 (12.4) 0.232 24.9 (14.2) 20.0 (10.3) 0.320 Median (IQR) 20.0 (16.0, 26.6) 18.0 (15.0, 25.0) HbA1c, % Mean (SD) 7.1 (1.2) 6.8 (0.9) 0.268 7.1 (1.2) 7.0 (1.0) 0.083 Median (IQR) 6.8 (6.2, 7.8) 6.6 (6.2, 7.2) K level, mEq/L Mean (SD) 4.3 (0.4) 4.2 (0.3) 0.194 4.3 (0.4) 4.3 (0.3) 0.029 Median (IQR) 4.2 (4.0, 4.5) 4.2 (4.0, 4.3) Antidiabetic drugs αGI, n (%) 11 (11.7) 2 (3.2) 0.327 11.7 1.6 0.414 GLP1 RA, n (%) 6 (6.4) 9 (14.5) 0.268 6.4 16.5 0.321 Insulin, n (%) 16 (17.0) 9 (14.5) 0.069 17.0 20.8 0.097 SU or Glinides, n (%) 17 (18.1) 8 (12.9) 0.144 18.1 10.6 0.215 Thiazolidine, n (%) 4 (4.3) 0 (0.0) 0.298 4.3 0 0.300 CDDP dose, mg/m 2 Mean (SD) 72.2 (10.1) 73.0 (10.5) 0.077 72.2 (10.1) 71.8 (11.5) 0.032 Median (IQR) 75.0 (64.8, 80.0) 77.5 (62.5, 80.0) Chemotherapy cycle Mean (SD) 2.7 (1.5) 2.7 (1.7) 0.005 2.7 (1.5) 2.9 (1.8) 0.107 Median (IQR) 2.0 (2.0, 3.8) 2.0 (2.0, 4.0) Short hydration Yes, n (%) 22 (23.4) 17 (27.4) 0.092 23.4 24.1 0.016 Diuretic Yes, n (%) 82 (87.2) 59 (95.2) 0.285 87.2 89.2 0.062 Mg supplementation Yes, n (%) 66 (70.2) 46 (74.2) 0.089 70.2 67.4 0.060 Concomitant drugs ACE inhibitor, n (%) 4 (4.3) 1 (1.6) 0.157 4.3 4.2 0.005 ARB, n (%) 19 (20.2) 31 (50.0) 0.657 20.2 23.3 0.075 MATE inhibitor, n (%) 7 (7.5) 4 (6.5) 0.039 7.5 5.4 0.082 NSAIDs, n (%) 11 (11.7) 12 (19.4) 0.212 11.7 14.1 0.072 PPI, n (%) 17 (18.1) 23 (37.1) 0.435 18.1 20.8 0.066 ACE, angiotensin-converting enzyme; αGI, α-glucosidase inhibitor; ALT, alanine aminotransferase; Alb, albumin; ARB, angiotensin II receptor blocker; AST, aspartate aminotransferase; BMI, body mass index; CDDP, cisplatin; eGFR, estimated glomerular filtration rate; ESS, effective sample size; GLP1 RA, glucagon-like peptide-1 receptor agonist; HbA1c, hemoglobin A1c; IQR, interquartile range; MATE, multidrug and toxin extrusion; PPI, proton pump inhibitor; sATE, stabilized average treatment effect; SD, standard deviation; SGLT2i, sodium/glucose cotransporter 2 inhibitor; SMD, standardized mean difference; SU, Sulfonylurea; NSAIDs, non-steroidal anti-inflammatory drugs MATE inhibitors in this study population include the following: irinotecan, diltiazem, diphenhydramine, famotidine, and trimethoprim. Incidence of nephrotoxicity The overall incidence of nephrotoxicity was 45.2% in the metformin group and 17.9% in the SGLT2i group ( P < 0.001). As shown in Table 2 , the incidence of CTCAE Grade 1 nephrotoxicity was 42.7% in the metformin group and 11.7% in the SGLT2i group, while those of Grade 2 were 2.5% and 6.2%, respectively. The difference in the incidence of nephrotoxicity between the two groups was significant ( P = 0.034). Table 2 Incidence of nephrotoxicity (CTCAE ver.5) Metformin SGLT2i P -value n % n % WNL 49 54.8 47 82.1 0.034 Grade 1 38 42.7 7 11.7 Grade 2 2 2.5 3 6.2 CTCAE, Common Terminology Criteria for Adverse Events; WNL, within normal limits Rate of change in SCr, CCr, and eGFR Significant differences were found in the median rates of change in SCr, CCr, and eGFR between the two groups in all subsequent cycles ( P = 0.002, P = 0.002, and P = 0.002, respectively) (Fig. 1 ). Risk factors for nephrotoxicity In univariate and multivariate analyses, cardiac disease was identified as a significant independent risk factor for nephrotoxicity (odds ratio [OR] = 3.107, 95% confidence interval [CI] 1.042 to 9.262, P = 0.042). Conversely, receiving an SGLT2i was an independent protective factor against the development of nephrotoxicity (OR = 0.247, 95% CI 0.107 to 0.568, P = 0.001) (Table 3 ). Table 3 Risk factors for nephrotoxicity Univariate Multivariate OR 95%CI P -value OR 95%CI P -value Age 0.964 0.923 1.008 0.098 0.962 0.915 1.011 0.112 Female 1.227 0.485 3.107 0.666 1.347 0.475 3.818 0.575 Hypertension 1.543 0.75 3.173 0.236 1.65 0.765 3.556 0.201 Cardiac disease 2.337 0.829 6.589 0.106 3.107 1.042 9.262 0.042 Mg supplementation 1.015 0.458 2.248 0.97 0.824 0.336 2.021 0.672 CDDP dose 1.01 0.975 1.047 0.579 1.002 0.964 1.042 0.913 SGLT2i group 0.375 0.181 0.781 0.007 0.247 0.107 0.568 0.001 CDDP, cisplatin; CI, confidence interval; OR, odds ratio; SGLT2i, sodium-glucose cotransporter 2 inhibitor Sensitivity analysis Supplementary Table S2 shows the patient characteristics for the ATT and ATC weighting analysis. Using ATT and ATC weighting, the incidence of nephrotoxicity (Supplementary Table S3) and rates of change in SCr, CCr, and eGFR (Supplementary Table S4) were all significantly better in the SGLT2i group, consistent with the results of the primary analysis. Furthermore, in both analyses, the use of SGLT2i was associated with a reduced incidence of nephrotoxicity (Supplementary Table S5). In the stratified analysis of the SGLT2i group according to the presence or absence of concomitant metformin use, these parameters did not differ with respect to metformin use (Supplementary Tables S6–8). DISCUSSION In this multicenter retrospective cohort study, concomitant use of SGLT2i was associated with a significantly lower incidence of CIN than that for metformin use in patients with solid tumors and type 2 diabetes receiving high-dose CDDP-based chemotherapy. After adjustment using IPTW (sATE weighting), the incidence of nephrotoxicity was 17.9% in the SGLT2i group, which was significantly lower than that in the metformin group (i.e., 45.2%). Furthermore, the rates of change in SCr, CCr, and eGFR were significantly better preserved in the SGLT2i group. A multivariate analysis further showed that SGLT2i use was independently associated with lower odds of CIN, whereas preexisting cardiac disease was associated with higher odds. Consistent results were obtained in sensitivity analyses using ATT and ATC weighting. Furthermore, the renoprotective effects of SGLT2i were observed regardless of concomitant metformin use. Consequently, the findings from both the primary and sensitivity analyses suggest that the clinical utility of SGLT2i for the prevention of CIN is robust. An important strength of the present study is that it was not purely exploratory but rather was designed as a translational clinical investigation grounded in our group’s parallel preclinical work using CIN mouse models and transporter-based in vitro systems. Our ongoing translational studies have suggested that SGLT2i attenuates renal injury in association with reduced proximal tubular CDDP accumulation and that a functional interaction between MATE1 and NHE3 may underlie this effect. The present clinical findings are consistent with this biological framework and thus extend our bench-side observations into a clinically relevant population. Although the current study does not provide direct mechanistic proof in humans, it offers translational support for the hypothesis that modulation of proximal tubular transporter function represents a novel strategy for CIN prevention. Both animal experiments and clinical studies have suggested that DPP-4 inhibitors exert renoprotective effects and prevent acute kidney injury through their anti-inflammatory properties [19], attenuation of oxidative stress [20], and inhibition of apoptosis [20]. Accordingly, to evaluate the clinical association of SGLT2i with CIN risk, patients receiving DPP-4 inhibitors were excluded. Instead, we selected metformin, a biguanide, as the control group, reflecting contemporary prescribing patterns and patient characteristics in Japan [21]. Although the renoprotective efficacy of metformin against CIN has not been established, metformin acts as a substrate for MATE1 and MATE2-K, potentially causing competitive inhibition of the transport of other substrates, including creatinine [22, 23]. Furthermore, metformin has been shown to mitigate renal functional decline in diabetic nephropathy via AMPK activation and the promotion of autophagy [24]. These diverse pharmacological mechanisms may introduce confounding bias into the evaluation of renal function. To address this potential source of bias, we conducted a stratified analysis according to concomitant metformin use within the SGLT2i-treated population, and the findings remained consistent with those of the primary analysis. Taken together, these results suggest that the observed association was unlikely to be explained solely by metformin-related confounding. Ishigami et al. [25] reported no significant association between SGLT2i use and the development of CIN. However, their single-center, retrospective study differed from the present investigation in several key methodological aspects. The diagnostic criteria for nephrotoxicity and observation period differed between the cohorts. Additionally, rigorous covariate adjustment techniques, such as IPTW, were not employed in this previous study, and a substantial baseline imbalance in CDDP dosage was observed between the two studies. Furthermore, approximately 60% of participants received concomitant DPP-4 inhibitors, and several baseline characteristics were dissimilar, specifically regarding metformin use, magnesium supplementation, and the prevalence of hypertension. These methodological discrepancies and variations in baseline clinical characteristics likely account for the divergent findings between the two studies. Recent experimental models and in vitro studies have reported that SGLT2i use mitigates CIN [26–29]. SGLT2i protect renal tissue by decreasing oxidative stress markers (such as MDA) [28], restoring antioxidant enzymes (e.g., SOD and catalase), and suppressing inflammatory cytokines (e.g., TNF-α and IL-1β) [28]. Mechanistically, they reduce the cellular uptake of CDDP in the proximal tubules and inhibit apoptosis [27]. The current study supports for value of modulating proximal tubular transporter function as a novel strategy for CIN prevention. The present results are biologically plausible and broadly consistent with previous experimental studies showing renoprotective effects of SGLT2i in CIN models. CIN is primarily driven by its accumulation within proximal tubular cells, caused by an imbalance between OCT2-mediated uptake and MATE-mediated efflux [6, 27]. Alterations in luminal Na+ dynamics induced by SGLT2 inhibition may influence the activity of NHE3 and MATE transporters, thereby promoting the efflux of CDDP and reducing its intracellular accumulation [10, 11, 30]. Our group’s ongoing preclinical work using mouse models and transporter-based in vitro systems supports the biological plausibility of this mechanism. Furthermore, the identification of cardiac disease as an independent risk factor in the multivariate analysis is clinically sound [15, 31, 32]. From the perspective of cardiorenal crosstalk, hemodynamic vulnerability due to impaired cardiac function [31, 32] exacerbates renal ischemia and tubular damage caused by CDDP. On the other hand, the reliance on SCr for renal function assessment in this study necessitates careful interpretation from a pharmacokinetic perspective. The renal elimination of creatinine depends not solely on glomerular filtration but also, to a certain extent (approximately 20% to 30%), on active secretion mediated by the proximal tubules [33]. Specifically, creatinine is taken up into tubular epithelial cells via OCT2 on the basolateral membrane and is subsequently excreted into the tubular lumen through MATE1 and MATE2-K on the apical membrane [6, 10]. If our hypothesis holds true in the clinical setting, it is plausible that the tubular secretion of creatinine—which, like CDDP, serves as a substrate for MATE transporters—may be concurrently upregulated. Theoretically, we cannot exclude the possibility that the observed attenuation of SCr elevation and mitigation of CCr/eGFR decline partially reflect an artifactual enhancement in creatinine clearance secondary to MATE activation, rather than the prevention of CDDP-induced glomerular injury. Consequently, reliance on SCr alone may be inadequate to rigorously establish whether SGLT2i exerts a true GFR-preserving effect in the context of CIN. Future prospective studies must incorporate evaluations utilizing cystatin C, an alternative biomarker that reflects the true glomerular filtration rate independently of MATE-mediated tubular secretion [33, 34]. This approach will more accurately delineate the genuine renoprotective efficacy of SGLT2i and help elucidate their underlying molecular mechanisms. This study has some limitations. First, owing to its retrospective observational design, the influence of unmeasured confounders cannot be eliminated. However, the application of the IPTW to the multicenter cohort data allowed us to minimize the influence of confounders—including age, renal function, comorbidities, and concurrent medications—in the inter-group comparisons. This approach improved the internal validity of our findings. Second, the study population was limited to Japanese patients with diabetes, and the effective sample size was limited; therefore, caution should be exercised when generalizing the findings to other racial or ethnic populations and to patients without diabetes. Third, individual quantifiable data for heart disease (e.g., cardiac output and ejection fraction) were not available; therefore, we defined heart disorders based only on the medical history of cardiac diseases, such as angina or myocardial infarction. Fourth, since urine dipstick data for hematuria/proteinuria were not available, these could not be considered in this study. Finally, because this was a clinical data-based study, it does not provide direct molecular evidence of reduced intracellular CDDP accumulation or the functional activation of MATE transporters in the human proximal tubule. CONCLUSION Among patients with solid tumors and type 2 diabetes receiving high-dose CDDP-based chemotherapy, concomitant SGLT2i use was associated with a substantially lower risk of CIN and better preservation of renal function than those for metformin use. In patients with type 2 diabetes scheduled for CDDP-based chemotherapy, SGLT2i represents a compelling therapeutic option not only for glycemic control but also as a robust renoprotective strategy to maintain chemotherapy dose intensity. To establish this approach as a novel supportive care strategy in cancer pharmacotherapy, large-scale prospective trials in diverse patient populations, incorporating evaluations based on cystatin C, are highly warranted. Furthermore, translational research elucidating detailed transporter dynamics is warranted. Abbreviations ALT alanine aminotransferase AST aspartate aminotransferase ATC average treatment effect on the controls ATT average treatment effect on the treated BMI body mass index CCr creatinine clearance CIN Cisplatin-induced nephrotoxicity CDDP Cisplatin DPP-4 dipeptidyl peptidase-4 CTCAE Common Terminology Criteria for Adverse Events eGFR estimated glomerular filtration rate HbA1c hemoglobin A1c IPTW inverse probability of treatment weighting MATE multidrug and toxic compound extrusion NHE3 sodium–hydrogen exchanger 3 sATE stabilized average treatment effect SCr serum creatinine SGLT2 sodium–glucose cotransporter 2 ULN upper limit of normal Declarations Ethics Approval and Consent to Participate: The study was approved by the Institutional Review Boards of all participating institutions and by the Ethics Committee of Tokushima University Hospital (approval No. 4609-3). All participants were treated in accordance with the principles outlined in the Declaration of Helsinki. The Ethics Committee of Tokushima University Hospital, the coordinating center, waived the requirement for informed consent owing to the retrospective nature of the study. Data were used after allowing patients to refuse to participate using an opt-out form. Consent for Publication: Not applicable. Competing Interests : The authors declare that the study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding: The present work received no financial support. Author Contribution Conception and design: Toshinobu Hayashi, Masaya Kanda, Mitsuhiro Goda.Collection and assembly of data: Toshinobu Hayashi, Kenji Kawasumi, Naomi Matsuo, Shohei Nishida, Shimon Takahashi, Yurino Nishijima, Miki Yamashita, Yuko Nagata, Yoshimichi Koutake, Shingo Kawano, Tomoki Sugiyama, Shinya Takada, Ryuji Yamaguchi, Kengo Umehara, Yoko Toriyama, Keisuke Matsufuji, Hiroshi Bando. Data analysis and interpretation: Toshinobu Hayashi, Masaya Kanda, Mitsuhiro Goda, Kei Kawada, Keisuke Ishizawa. Manuscript writing: all authors.Final approval of manuscript: all authors. Accountable for all aspects of the work: all authors. Acknowledgement We are grateful to the physicians, nurses, and pharmacists for assisting and caring for the patients. Furthermore, we extend our sincere appreciation to Hirotoshi Iihara, Keisuke Matsuo, Makiko Go, and Kazuyoshi Kawakami for their invaluable support throughout this multicenter observational study. We also acknowledge Editage (Cactus Communications) for their English Language editing of the final draft. Following these services, the authors thoroughly reviewed and edited the manuscript and assumed full responsibility for the content of this publication. Data Availability The data that support the findings of this study are available from the study group. However, restrictions apply to the availability of these data, as they were used under a license for the current study and are therefore not publicly available. The data are, nevertheless, available from the corresponding author, Toshinobu Hayashi. References Go RS, Adjei AA. Review of the comparative pharmacology and clinical activity of cisplatin and carboplatin. J Clin Oncol. 1999;17:409–22. https://doi.org/10.1200/jco.1999.17.1.409 Saito Y, Sakamoto T, Takekuma Y, Kobayashi M, Okamoto K, Shinagawa N, et al. Diabetes mellitus degenerates cisplatin-induced nephrotoxicity in short hydration method: A propensity score-matching analysis. Sci Rep. 2022;12:21819. https://doi.org/10.1038/s41598-022-26454-x Wensing KU, Ciarimboli G. Saving ears and kidneys from cisplatin. Anticancer Res. 2013;33:4183–8 Harimitsu Y, Hayashi T, Shimokawa M, Miyoshi T, Yamashita A, Uchiyama M, et al. Mannitol versus furosemide for prevention of cisplatin-induced nephrotoxicity in a multicenter retrospective cohort study. Sci Rep. 2025;15:41537. https://doi.org/10.1038/s41598-025-25510-6 Miyoshi T, Hayashi T, Uoi M, Omura F, Tsumagari K, Maesaki S, et al. Preventive effect of 20 mEq and 8 mEq magnesium supplementation on cisplatin-induced nephrotoxicity: A propensity score–matched analysis. Support Care Cancer. 2022;30:3345–51. https://doi.org/10.1007/s00520-021-06790-w Pabla N, Dong Z. Cisplatin nephrotoxicity: Mechanisms and renoprotective strategies. Kidney Int. 2008;73:994–1007. https://doi.org/10.1038/sj.ki.5002786 Miller RP, Tadagavadi RK, Ramesh G, Reeves WB. Mechanisms of cisplatin nephrotoxicity. Toxins. 2010;2:2490–518. https://doi.org/10.3390/toxins2112490 Stewart JD, Bolt HM. Cisplatin-induced nephrotoxicity. Arch Toxicol. 2012;86:1155–6. https://doi.org/10.1007/s00204-012-0887-2 Oh GS, Kim HJ, Shen A, Lee SB, Khadka D, Pandit A, et al. Cisplatin-induced kidney dysfunction and perspectives on improving treatment strategies. Electrolyte Blood Press. 2014;12:55–65. https://doi.org/10.5049/EBP.2014.12.2.55 Li Q, Guo D, Dong Z, Zhang W, Zhang L, Huang SM, et al. Ondansetron can enhance cisplatin-induced nephrotoxicity via inhibition of multiple toxin and extrusion proteins (MATEs). Toxicol Appl Pharmacol. 2013;273:100–9. https://doi.org/10.1016/j.taap.2013.08.024 Yonezawa A, Inui KI. Organic cation transporter OCT/SLC22A and H(+)/organic cation antiporter MATE/SLC47A are key molecules for nephrotoxicity of platinum agents. 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Antioxidants (Basel). 2021;10:246. https://doi.org/10.3390/antiox10020246 Uchida T, Oda T, Matsubara H, Watanabe A, Takechi H, Oshima N, et al. Renoprotective effects of a dipeptidyl peptidase 4 inhibitor in a mouse model of progressive renal fibrosis. Ren Fail. 2017;39:340–9. https://doi.org/10.1080/0886022X.2017.1279553 Bouchi R, Sugiyama T, Goto A, Imai K, Ihana-Sugiyama N, Ohsugi M, et al. Retrospective nationwide study on the trends in first-line antidiabetic medication for patients with type 2 diabetes in Japan. J Diabetes Investig. 2022;13:280–91. https://doi.org/10.1111/jdi.13636 Grün B, Kiessling MK, Burhenne J, Riedel KD, Weiss J, Rauch G, et al. Trimethoprim-metformin interaction and its genetic modulation by OCT2 and MATE1 transporters. Br J Clin Pharmacol. 2013;76:787–96. https://doi.org/10.1111/bcp.12079 Yonezawa A, Inui K. Importance of the multidrug and toxin extrusion MATE/SLC47A family to pharmacokinetics, pharmacodynamics/toxicodynamics and pharmacogenomics. Br J Pharmacol. 2011;164:1817–25. https://doi.org/10.1111/j.1476-5381.2011.01394.x Ravindran S, Kuruvilla V, Wilbur K, Munusamy S. Nephroprotective effects of metformin in diabetic nephropathy. J Cell Physiol. 2017;232:731–42. https://doi.org/10.1002/jcp.25598 Ishigami Y, Takahashi M, Nakatsukasa H, Nishiura H, Kohara Y, Nakamura Y. Efficacy of SGLT-2 inhibitors in preventing cisplatin-induced kidney injury in patients with diabetes. Anticancer Res. 2025;45:5185–90. https://doi.org/10.21873/anticanres.17858 Park CH, Lee B, Han M, Rhee WJ, Kwak MS, Yoo TH, et al. Canagliflozin protects against cisplatin-induced acute kidney injury by AMPK-mediated autophagy in renal proximal tubular cells. Cell Death Discov. 2022;8:12. https://doi.org/10.1038/s41420-021-00801-9 Song Z, Zhu J, Wei Q, Dong G, Dong Z. Canagliflozin reduces cisplatin uptake and activates Akt to protect against cisplatin-induced nephrotoxicity. Am J Physiol Renal Physiol. 2020;318:F1041–F52. https://doi.org/10.1152/ajprenal.00512.2019 Farrokh-Eslamlou N, Momtaz S, Niknejad A, Hosseini Y, Mahdaviani P, Ghasemnejad-Berenji M, et al. Empagliflozin protective effects against cisplatin-induced acute nephrotoxicity by interfering with oxidative stress and inflammation in Wistar rats. Naunyn Schmiedebergs Arch Pharmacol. 2024;397:7061–70. https://doi.org/10.1007/s00210-024-03088-6 Abdelrahman AM, Al Suleimani Y, Shalaby A, Ashique M, Manoj P, Nemmar A, et al. Effect of canagliflozin, a sodium glucose co-transporter 2 inhibitor, on cisplatin-induced nephrotoxicity in mice. Naunyn Schmiedebergs Arch Pharmacol. 2019;392:45–53. https://doi.org/10.1007/s00210-018-1564-7 Nakamura T, Yonezawa A, Hashimoto S, Katsura T, Inui KI. Disruption of multidrug and toxin extrusion MATE1 potentiates cisplatin-induced nephrotoxicity. Biochem Pharmacol. 2010;80:1762–7. https://doi.org/10.1016/j.bcp.2010.08.019 Ronco C, Haapio M, House AA, Anavekar N, Bellomo R. Cardiorenal syndrome. J Am Coll Cardiol. 2008;52:1527–39. https://doi.org/10.1016/j.jacc.2008.07.051 Rangaswami J, Bhalla V, Blair JEA, Chang TI, Costa S, Lentine KL, et al. Cardiorenal syndrome: Classification, pathophysiology, diagnosis, and treatment strategies: A scientific statement from the American Heart Association. Circulation. 2019;139:e840–e78. https://doi.org/10.1161/CIR.0000000000000664 Imamura Y, Murayama N, Okudaira N, Kurihara A, Okazaki O, Izumi T, et al. Prediction of fluoroquinolone-induced elevation in serum creatinine levels: A case of drug-endogenous substance interaction involving the inhibition of renal secretion. Clin Pharmacol Ther. 2011;89:81–8. https://doi.org/10.1038/clpt.2010.232 Chen Z, Dong Q, Dokos C, Boland J, Fuhr U, Taubert M. A joint pharmacometric model of iohexol and creatinine administered through a meat meal to assess GFR and renal OCT2/MATE activity. Clin Pharmacol Ther. 2025;118:510–9. https://doi.org/10.1002/cpt.3612 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 13 May, 2026 Reviews received at journal 13 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 10 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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CCr, and eGFR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median (IQR) rates of change in (a) SCr, (b) CCr, and (c) eGFR in the metformin and SGLT2i groups are shown.\u003c/p\u003e\n\u003cp\u003eCCr, creatinine clearance; eGFR, estimated glomerular filtration rate; IQR, interquartile range; SCr, serum creatinine; SGLT2i, sodium-glucose cotransporter 2 inhibitor\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9380086/v1/c68272bf8cff55a66803a391.jpg"},{"id":108183781,"identity":"2fdd415c-00e4-496e-b409-9b44fe633ca2","added_by":"auto","created_at":"2026-04-30 09:02:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":650870,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9380086/v1/e492d49a-927c-46ef-b153-56e3f1251812.pdf"},{"id":108071362,"identity":"24cedf6e-4f88-462f-9acb-55ea9a7112b3","added_by":"auto","created_at":"2026-04-29 06:08:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":50494,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-9380086/v1/c44c9fa220bb21adfa82e39c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preventive Effects of SGLT2 Inhibitors on Cisplatin-Induced Nephrotoxicity in Patients with Solid Tumors and Diabetes: A Multicenter, Retrospective Cohort Study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eCisplatin (CDDP), a platinum-based chemotherapeutic agent, serves as a cornerstone in the treatment of various solid tumors [1]. Despite its proven clinical efficacy, nephrotoxicity represents a major dose-limiting factor, frequently necessitating dose reduction or treatment discontinuation. Standard renoprotective measures, such as aggressive hydration, diuretic administration, and magnesium supplementation, are routinely implemented in clinical practice. Despite these preventive interventions, acute kidney injury occurs in approximately 20\u0026ndash;40% of patients receiving CDDP-based chemotherapy [2\u0026ndash;5]. To address this substantial incidence of CDDP-induced nephrotoxicity (CIN), the development of novel preventive strategies is imperative.\u003c/p\u003e \u003cp\u003eThe mechanisms underlying CIN have been investigated extensively using experimental models and cell culture systems. The primary pathophysiological pathway involves CDDP accumulation within proximal tubular epithelial cells [6], which triggers a cascade of deleterious events, including increased oxidative stress mediated by reactive oxygen species [6, 7], promotion of apoptosis [6, 8], and propagation of intrarenal inflammatory responses [9]. Building on these mechanistic insights, numerous therapeutic candidates possessing antioxidant, anti-inflammatory, and anti-apoptotic properties have been evaluated in CIN animal models, with many demonstrating apparent renoprotective effects [6]. However, clinical translation of these strategies has been limited. When tested in human subjects, most candidates exhibit substantially reduced efficacy or complete ineffectiveness, and none have achieved clinical application. These translational failures suggest that therapeutic strategies focused solely on ameliorating cellular damage and downstream inflammatory cascades are insufficient. Accordingly, alternative approaches targeting CDDP accumulation itself merit investigation.\u003c/p\u003e \u003cp\u003eRenal handling of CDDP is mediated by multiple transporters expressed in proximal tubular cells. In particular, CDDP is taken up from the blood across the basolateral membrane primarily via organic cation transporter 2 (OCT2) and is subsequently extruded into the tubular lumen through multidrug and toxin extrusion proteins (MATE1 and MATE2-K) located on the apical membrane [10, 11]. We have previously used a combination of a FAERS (FDA Adverse Event Reporting System)-based pharmacovigilance analysis, hospital electronic medical records, and preclinical studies to demonstrate that altered intrarenal CDDP accumulation is closely linked to nephrotoxicity in both experimental and clinical settings [12]. These observations raise the possibility that enhancing MATE-mediated efflux from proximal tubular cells may mitigate CIN. Because MATE transport is driven by a transmembrane proton gradient, changes in luminal sodium\u0026ndash;hydrogen handling could potentially influence its activity.\u003c/p\u003e \u003cp\u003eSodium\u0026ndash;glucose cotransporter 2 (SGLT2) and sodium\u0026ndash;hydrogen exchanger 3 (NHE3) are both expressed on the apical membrane of proximal tubular cells and play central roles in glucose and sodium handling [13, 14]. Based on their functional interplay, SGLT2 inhibition may modify the proton gradient that drives MATE-dependent transport. Ongoing translational work by our group has generated complementary preclinical evidence supporting this concept. In mouse models of CIN, SGLT2 inhibitor (SGLT2i) attenuated renal injury in association with reduced proximal tubular CDDP accumulation. In parallel, in vitro studies suggested a functional interaction between MATE1 and NHE3. Given the current indications for SGLT2i, a feasible approach to evaluate their renoprotective effects is to investigate these agents in patients with cancer and diabetes.\u003c/p\u003e \u003cp\u003eWe conducted a multicenter, retrospective observational study to evaluate whether SGLT2i use mitigates CIN in patients with solid tumors and concomitant type 2 diabetes mellitus.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThis multicenter retrospective study was conducted at 16 hospitals in Japan. Patients were eligible if they were aged 20 years or older with solid cancers and type 2 diabetes and received high-dose CDDP (\u0026ge;\u0026thinsp;50 mg/m\u003csup\u003e2\u003c/sup\u003e)-based chemotherapy for the first time. The study included patients who were administered SGLT2i (canagliflozin, dapagliflozin empagliflozin, ipragliflozin, luseogliflozin, and tofogliflozin) or metformin for 1 week before and after CDDP administration. Concomitant use of other antidiabetic medications was permitted; however, patients taking dipeptidyl peptidase-4 (DPP-4) inhibitors were excluded.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe following information was retrospectively extracted from medical records at each institution: patient characteristics (age, sex, height, weight, body mass index [BMI], primary cancer, and history of chemotherapy, cardiac disease, and hypertension), laboratory data (alanine aminotransferase [ALT], aspartate aminotransferase [AST], serum creatinine [SCr], estimated glomerular filtration rate [eGFR], hemoglobin A1c [HbA1c], potassium, and albumin), and treatment-related factors (CDDP dose, chemotherapy cycle, chemotherapy regimens, diabetic medications, renal protection measures [diuretics, type of hydration, and magnesium supplementation], prophylactic antiemetics, and concomitant medications that cause nephrotoxicity or MATE-inhibitor). Nephrotoxicity was evaluated according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5. Specifically, for increased SCr, Grade 1 was defined as \u0026gt;upper limit of normal (ULN)\u0026thinsp;\u0026minus;\u0026thinsp;1.5\u0026times;ULN, Grade 2 as \u0026gt;\u0026thinsp;1.5\u0026ndash;3.0\u0026times;ULN, Grade 3 as \u0026gt;\u0026thinsp;3.0\u0026ndash;6.0\u0026times;ULN, and Grade 4 as \u0026gt;\u0026thinsp;6.0\u0026times;ULN. The SCr value measured immediately before the first CDDP administration was used as the baseline. The worst creatinine level was defined as the peak creatinine value recorded within the chemotherapy treatment period from the index date. In addition, the percent changes in SCr levels, creatinine clearance (CCr), and eGFR from baseline were assessed.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe primary endpoint was the incidence of CIN during the CDDP administration period, which was compared between the metformin and SGLT2i groups. To minimize the impact of confounding factors, we used the stabilized average treatment effect (sATE) of the inverse probability treatment weighting (IPTW) model derived from a logistic regression model to balance observable characteristics (covariates: age, female sex, BMI, eGFR, HbA1c, cardiac disease, hypertension, albumin level, CDDP dose, type of hydration, type of diuretic, Mg supplementation, angiotensin-converting enzyme inhibitor, angiotensin II receptor blocker, MATE-inhibitor [irinotecan, diltiazem, diphenhydramine, famotidine, and trimethoprim], non-steroidal anti-inflammatory drugs, proton pump inhibitor, and cycle of chemotherapy). Variables were included in the model based on existing knowledge of risk factors for nephrotoxicity and the literature [5, 15\u0026ndash;18]. Categorical variables were analyzed using the chi-square test, and continuous variables were analyzed using the Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or Wilcoxon rank-sum test.\u003c/p\u003e \u003cp\u003eThe rates of change in SCr, CCr, and eGFR were calculated using the following formula: (worst value - baseline value) \u0026times; 100/baseline value.\u003c/p\u003e \u003cp\u003eA logistic regression analysis was performed to extract risk factors for the development of nephrotoxicity, adjusting for potential confounding by age, sex, cardiac disease, hypertension, Mg supplementation, CDDP dose, and use of SGLT2i. Variables were selected a priori based on the literature and their clinical relevance.\u003c/p\u003e \u003cp\u003eA sensitivity analysis was performed using the weighted average treatment effect on the treated (ATT) and average treatment effect on the controls (ATC). Furthermore, a stratified analysis was conducted within the SGLT2i group according to the presence or absence of concomitant metformin use. All statistical tests were two-sided, and \u003cem\u003eP\u003c/em\u003e-values of \u0026lt;\u0026thinsp;0.05 indicated statistical significance. All analyses were performed using JMP 14.3 (SAS Institute, Cary, NC, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of patients\u003c/h2\u003e \u003cp\u003eA total of 156 patient records were collected. The effective sample sizes, adjusted by sATE weighting, were 94 in the metformin group and 56.7 in the SGLT2i group. After adjustment using sATE weighting, baseline patient characteristics were well balanced between the groups, with the exception of ALT/AST levels and certain antidiabetic medications (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e summarizes the cancer types and chemotherapy regimens.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c15\" namest=\"c10\"\u003e \u003cp\u003esATE weighting\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSGLT2i\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eSGLT2i\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eESS\u0026thinsp;=\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eESS\u0026thinsp;=\u0026thinsp;56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(61, 71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(61, 71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(21.3, 25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(21.9, 26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e79.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(64.4, 87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(61.6, 89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb level, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.6, 4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3.8, 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(14.0, 30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(13.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(16.0, 26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(15.0, 25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.2, 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6.2, 7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK level, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.0, 4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4.0, 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidiabetic drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eαGI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLP1 RA, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsulin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSU or Glinides, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThiazolidine, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDDP dose, mg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e71.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(64.8, 80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(62.5, 80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.0, 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.0, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort hydration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e87.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg supplementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcomitant drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACE inhibitor, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARB, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMATE inhibitor, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSAIDs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eACE, angiotensin-converting enzyme; αGI, α-glucosidase inhibitor; ALT, alanine aminotransferase; Alb, albumin; ARB, angiotensin II receptor blocker; AST, aspartate aminotransferase; BMI, body mass index; CDDP, cisplatin; eGFR, estimated glomerular filtration rate; ESS, effective sample size; GLP1 RA, glucagon-like peptide-1 receptor agonist; HbA1c, hemoglobin A1c; IQR, interquartile range; MATE, multidrug and toxin extrusion; PPI, proton pump inhibitor; sATE, stabilized average treatment effect; SD, standard deviation; SGLT2i, sodium/glucose cotransporter 2 inhibitor; SMD, standardized mean difference; SU, Sulfonylurea; NSAIDs, non-steroidal anti-inflammatory drugs\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003eMATE inhibitors in this study population include the following: irinotecan, diltiazem, diphenhydramine, famotidine, and trimethoprim.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIncidence of nephrotoxicity\u003c/h2\u003e \u003cp\u003eThe overall incidence of nephrotoxicity was 45.2% in the metformin group and 17.9% in the SGLT2i group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the incidence of CTCAE Grade 1 nephrotoxicity was 42.7% in the metformin group and 11.7% in the SGLT2i group, while those of Grade 2 were 2.5% and 6.2%, respectively. The difference in the incidence of nephrotoxicity between the two groups was significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence of nephrotoxicity (CTCAE ver.5)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSGLT2i\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCTCAE, Common Terminology Criteria for Adverse Events; WNL, within normal limits\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRate of change in SCr, CCr, and eGFR\u003c/h3\u003e\n\u003cp\u003eSignificant differences were found in the median rates of change in SCr, CCr, and eGFR between the two groups in all subsequent cycles (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eRisk factors for nephrotoxicity\u003c/h3\u003e\n\u003cp\u003eIn univariate and multivariate analyses, cardiac disease was identified as a significant independent risk factor for nephrotoxicity (odds ratio [OR]\u0026thinsp;=\u0026thinsp;3.107, 95% confidence interval [CI] 1.042 to 9.262, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). Conversely, receiving an SGLT2i was an independent protective factor against the development of nephrotoxicity (OR\u0026thinsp;=\u0026thinsp;0.247, 95% CI 0.107 to 0.568, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factors for nephrotoxicity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg supplementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDDP dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT2i group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eCDDP, cisplatin; CI, confidence interval; OR, odds ratio; SGLT2i, sodium-glucose cotransporter 2 inhibitor\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eSupplementary Table S2 shows the patient characteristics for the ATT and ATC weighting analysis. Using ATT and ATC weighting, the incidence of nephrotoxicity (Supplementary Table S3) and rates of change in SCr, CCr, and eGFR (Supplementary Table S4) were all significantly better in the SGLT2i group, consistent with the results of the primary analysis. Furthermore, in both analyses, the use of SGLT2i was associated with a reduced incidence of nephrotoxicity (Supplementary Table S5). In the stratified analysis of the SGLT2i group according to the presence or absence of concomitant metformin use, these parameters did not differ with respect to metformin use (Supplementary Tables S6\u0026ndash;8).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this multicenter retrospective cohort study, concomitant use of SGLT2i was associated with a significantly lower incidence of CIN than that for metformin use in patients with solid tumors and type 2 diabetes receiving high-dose CDDP-based chemotherapy. After adjustment using IPTW (sATE weighting), the incidence of nephrotoxicity was 17.9% in the SGLT2i group, which was significantly lower than that in the metformin group (i.e., 45.2%). Furthermore, the rates of change in SCr, CCr, and eGFR were significantly better preserved in the SGLT2i group. A multivariate analysis further showed that SGLT2i use was independently associated with lower odds of CIN, whereas preexisting cardiac disease was associated with higher odds. Consistent results were obtained in sensitivity analyses using ATT and ATC weighting. Furthermore, the renoprotective effects of SGLT2i were observed regardless of concomitant metformin use. Consequently, the findings from both the primary and sensitivity analyses suggest that the clinical utility of SGLT2i for the prevention of CIN is robust.\u003c/p\u003e \u003cp\u003eAn important strength of the present study is that it was not purely exploratory but rather was designed as a translational clinical investigation grounded in our group\u0026rsquo;s parallel preclinical work using CIN mouse models and transporter-based in vitro systems. Our ongoing translational studies have suggested that SGLT2i attenuates renal injury in association with reduced proximal tubular CDDP accumulation and that a functional interaction between MATE1 and NHE3 may underlie this effect. The present clinical findings are consistent with this biological framework and thus extend our bench-side observations into a clinically relevant population. Although the current study does not provide direct mechanistic proof in humans, it offers translational support for the hypothesis that modulation of proximal tubular transporter function represents a novel strategy for CIN prevention.\u003c/p\u003e \u003cp\u003eBoth animal experiments and clinical studies have suggested that DPP-4 inhibitors exert renoprotective effects and prevent acute kidney injury through their anti-inflammatory properties [19], attenuation of oxidative stress [20], and inhibition of apoptosis [20]. Accordingly, to evaluate the clinical association of SGLT2i with CIN risk, patients receiving DPP-4 inhibitors were excluded. Instead, we selected metformin, a biguanide, as the control group, reflecting contemporary prescribing patterns and patient characteristics in Japan [21]. Although the renoprotective efficacy of metformin against CIN has not been established, metformin acts as a substrate for MATE1 and MATE2-K, potentially causing competitive inhibition of the transport of other substrates, including creatinine [22, 23]. Furthermore, metformin has been shown to mitigate renal functional decline in diabetic nephropathy via AMPK activation and the promotion of autophagy [24]. These diverse pharmacological mechanisms may introduce confounding bias into the evaluation of renal function. To address this potential source of bias, we conducted a stratified analysis according to concomitant metformin use within the SGLT2i-treated population, and the findings remained consistent with those of the primary analysis. Taken together, these results suggest that the observed association was unlikely to be explained solely by metformin-related confounding. Ishigami et al. [25] reported no significant association between SGLT2i use and the development of CIN. However, their single-center, retrospective study differed from the present investigation in several key methodological aspects. The diagnostic criteria for nephrotoxicity and observation period differed between the cohorts. Additionally, rigorous covariate adjustment techniques, such as IPTW, were not employed in this previous study, and a substantial baseline imbalance in CDDP dosage was observed between the two studies. Furthermore, approximately 60% of participants received concomitant DPP-4 inhibitors, and several baseline characteristics were dissimilar, specifically regarding metformin use, magnesium supplementation, and the prevalence of hypertension. These methodological discrepancies and variations in baseline clinical characteristics likely account for the divergent findings between the two studies.\u003c/p\u003e \u003cp\u003eRecent experimental models and in vitro studies have reported that SGLT2i use mitigates CIN [26\u0026ndash;29]. SGLT2i protect renal tissue by decreasing oxidative stress markers (such as MDA) [28], restoring antioxidant enzymes (e.g., SOD and catalase), and suppressing inflammatory cytokines (e.g., TNF-α and IL-1β) [28]. Mechanistically, they reduce the cellular uptake of CDDP in the proximal tubules and inhibit apoptosis [27]. The current study supports for value of modulating proximal tubular transporter function as a novel strategy for CIN prevention. The present results are biologically plausible and broadly consistent with previous experimental studies showing renoprotective effects of SGLT2i in CIN models. CIN is primarily driven by its accumulation within proximal tubular cells, caused by an imbalance between OCT2-mediated uptake and MATE-mediated efflux [6, 27]. Alterations in luminal Na+ dynamics induced by SGLT2 inhibition may influence the activity of NHE3 and MATE transporters, thereby promoting the efflux of CDDP and reducing its intracellular accumulation [10, 11, 30]. Our group\u0026rsquo;s ongoing preclinical work using mouse models and transporter-based in vitro systems supports the biological plausibility of this mechanism. Furthermore, the identification of cardiac disease as an independent risk factor in the multivariate analysis is clinically sound [15, 31, 32]. From the perspective of cardiorenal crosstalk, hemodynamic vulnerability due to impaired cardiac function [31, 32] exacerbates renal ischemia and tubular damage caused by CDDP. On the other hand, the reliance on SCr for renal function assessment in this study necessitates careful interpretation from a pharmacokinetic perspective. The renal elimination of creatinine depends not solely on glomerular filtration but also, to a certain extent (approximately 20% to 30%), on active secretion mediated by the proximal tubules [33]. Specifically, creatinine is taken up into tubular epithelial cells via OCT2 on the basolateral membrane and is subsequently excreted into the tubular lumen through MATE1 and MATE2-K on the apical membrane [6, 10]. If our hypothesis holds true in the clinical setting, it is plausible that the tubular secretion of creatinine\u0026mdash;which, like CDDP, serves as a substrate for MATE transporters\u0026mdash;may be concurrently upregulated. Theoretically, we cannot exclude the possibility that the observed attenuation of SCr elevation and mitigation of CCr/eGFR decline partially reflect an artifactual enhancement in creatinine clearance secondary to MATE activation, rather than the prevention of CDDP-induced glomerular injury. Consequently, reliance on SCr alone may be inadequate to rigorously establish whether SGLT2i exerts a true GFR-preserving effect in the context of CIN. Future prospective studies must incorporate evaluations utilizing cystatin C, an alternative biomarker that reflects the true glomerular filtration rate independently of MATE-mediated tubular secretion [33, 34]. This approach will more accurately delineate the genuine renoprotective efficacy of SGLT2i and help elucidate their underlying molecular mechanisms.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, owing to its retrospective observational design, the influence of unmeasured confounders cannot be eliminated. However, the application of the IPTW to the multicenter cohort data allowed us to minimize the influence of confounders\u0026mdash;including age, renal function, comorbidities, and concurrent medications\u0026mdash;in the inter-group comparisons. This approach improved the internal validity of our findings. Second, the study population was limited to Japanese patients with diabetes, and the effective sample size was limited; therefore, caution should be exercised when generalizing the findings to other racial or ethnic populations and to patients without diabetes. Third, individual quantifiable data for heart disease (e.g., cardiac output and ejection fraction) were not available; therefore, we defined heart disorders based only on the medical history of cardiac diseases, such as angina or myocardial infarction. Fourth, since urine dipstick data for hematuria/proteinuria were not available, these could not be considered in this study. Finally, because this was a clinical data-based study, it does not provide direct molecular evidence of reduced intracellular CDDP accumulation or the functional activation of MATE transporters in the human proximal tubule.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAmong patients with solid tumors and type 2 diabetes receiving high-dose CDDP-based chemotherapy, concomitant SGLT2i use was associated with a substantially lower risk of CIN and better preservation of renal function than those for metformin use. In patients with type 2 diabetes scheduled for CDDP-based chemotherapy, SGLT2i represents a compelling therapeutic option not only for glycemic control but also as a robust renoprotective strategy to maintain chemotherapy dose intensity. To establish this approach as a novel supportive care strategy in cancer pharmacotherapy, large-scale prospective trials in diverse patient populations, incorporating evaluations based on cystatin C, are highly warranted. Furthermore, translational research elucidating detailed transporter dynamics is warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eaverage treatment effect on the controls\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eaverage treatment effect on the treated\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatinine clearance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCisplatin-induced nephrotoxicity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCisplatin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPP-4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edipeptidyl peptidase-4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTCAE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommon Terminology Criteria for Adverse Events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehemoglobin A1c\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIPTW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einverse probability of treatment weighting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMATE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultidrug and toxic compound extrusion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHE3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esodium\u0026ndash;hydrogen exchanger 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esATE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estabilized average treatment effect\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eserum creatinine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSGLT2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esodium\u0026ndash;glucose cotransporter 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eULN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eupper limit of normal\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e \u003cp\u003e The study was approved by the Institutional Review Boards of all participating institutions and by the Ethics Committee of Tokushima University Hospital (approval No. 4609-3). All participants were treated in accordance with the principles outlined in the Declaration of Helsinki. The Ethics Committee of Tokushima University Hospital, the coordinating center, waived the requirement for informed consent owing to the retrospective nature of the study. Data were used after allowing patients to refuse to participate using an opt-out form.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003e \u003cb\u003eCompeting Interests\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors declare that the study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe present work received no financial support.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design: Toshinobu Hayashi, Masaya Kanda, Mitsuhiro Goda.Collection and assembly of data: Toshinobu Hayashi, Kenji Kawasumi, Naomi Matsuo, Shohei Nishida, Shimon Takahashi, Yurino Nishijima, Miki Yamashita, Yuko Nagata, Yoshimichi Koutake, Shingo Kawano, Tomoki Sugiyama, Shinya Takada, Ryuji Yamaguchi, Kengo Umehara, Yoko Toriyama, Keisuke Matsufuji, Hiroshi Bando. Data analysis and interpretation: Toshinobu Hayashi, Masaya Kanda, Mitsuhiro Goda, Kei Kawada, Keisuke Ishizawa. Manuscript writing: all authors.Final approval of manuscript: all authors. Accountable for all aspects of the work: all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to the physicians, nurses, and pharmacists for assisting and caring for the patients. Furthermore, we extend our sincere appreciation to Hirotoshi Iihara, Keisuke Matsuo, Makiko Go, and Kazuyoshi Kawakami for their invaluable support throughout this multicenter observational study. We also acknowledge Editage (Cactus Communications) for their English Language editing of the final draft. Following these services, the authors thoroughly reviewed and edited the manuscript and assumed full responsibility for the content of this publication.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the study group. However, restrictions apply to the availability of these data, as they were used under a license for the current study and are therefore not publicly available. The data are, nevertheless, available from the corresponding author, Toshinobu Hayashi.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGo RS, Adjei AA. Review of the comparative pharmacology and clinical activity of cisplatin and carboplatin. J Clin Oncol. 1999;17:409–22. https://doi.org/10.1200/jco.1999.17.1.409\u003c/li\u003e\n\u003cli\u003eSaito Y, Sakamoto T, Takekuma Y, Kobayashi M, Okamoto K, Shinagawa N, et al. Diabetes mellitus degenerates cisplatin-induced nephrotoxicity in short hydration method: A propensity score-matching analysis. Sci Rep. 2022;12:21819. https://doi.org/10.1038/s41598-022-26454-x\u003c/li\u003e\n\u003cli\u003eWensing KU, Ciarimboli G. Saving ears and kidneys from cisplatin. Anticancer Res. 2013;33:4183–8\u003c/li\u003e\n\u003cli\u003eHarimitsu Y, Hayashi T, Shimokawa M, Miyoshi T, Yamashita A, Uchiyama M, et al. Mannitol versus furosemide for prevention of cisplatin-induced nephrotoxicity in a multicenter retrospective cohort study. Sci Rep. 2025;15:41537. https://doi.org/10.1038/s41598-025-25510-6\u003c/li\u003e\n\u003cli\u003eMiyoshi T, Hayashi T, Uoi M, Omura F, Tsumagari K, Maesaki S, et al. Preventive effect of 20 mEq and 8 mEq magnesium supplementation on cisplatin-induced nephrotoxicity: A propensity score–matched analysis. Support Care Cancer. 2022;30:3345–51. https://doi.org/10.1007/s00520-021-06790-w\u003c/li\u003e\n\u003cli\u003ePabla N, Dong Z. 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Retrospective nationwide study on the trends in first-line antidiabetic medication for patients with type 2 diabetes in Japan. J Diabetes Investig. 2022;13:280–91. https://doi.org/10.1111/jdi.13636\u003c/li\u003e\n\u003cli\u003eGrün B, Kiessling MK, Burhenne J, Riedel KD, Weiss J, Rauch G, et al. Trimethoprim-metformin interaction and its genetic modulation by OCT2 and MATE1 transporters. Br J Clin Pharmacol. 2013;76:787–96. https://doi.org/10.1111/bcp.12079\u003c/li\u003e\n\u003cli\u003eYonezawa A, Inui K. Importance of the multidrug and toxin extrusion MATE/SLC47A family to pharmacokinetics, pharmacodynamics/toxicodynamics and pharmacogenomics. Br J Pharmacol. 2011;164:1817–25. https://doi.org/10.1111/j.1476-5381.2011.01394.x\u003c/li\u003e\n\u003cli\u003eRavindran S, Kuruvilla V, Wilbur K, Munusamy S. Nephroprotective effects of metformin in diabetic nephropathy. 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Clin Pharmacol Ther. 2025;118:510–9. https://doi.org/10.1002/cpt.3612\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sodium-glucose co-transporter 2 inhibitors, Cisplatin, Nephrotoxicity, Acute kidney injury, Multidrug and toxic compound extrusion transporter","lastPublishedDoi":"10.21203/rs.3.rs-9380086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9380086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCisplatin (CDDP)-induced nephrotoxicity (CIN) is a major dose-limiting toxicity. Preclinical models suggest that sodium-glucose cotransporter 2 (SGLT2) inhibitors, used in the management of diabetes, activate multidrug and toxin extrusion (MATE) transporters by altering luminal sodium and hydrogen ion gradients via the sodium-hydrogen exchanger 3 (NHE3), thereby promoting cisplatin efflux from proximal tubular cells. This study aimed to investigate the clinical preventive effect of SGLT2 inhibitors on CIN in patients with solid tumors and concurrent type 2 diabetes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA multicenter, retrospective cohort study was conducted across 16 institutions in Japan. Eligible patients were aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years with solid tumors and type 2 diabetes who received high-dose CDDP (\u0026ge;\u0026thinsp;50 mg/m\u003csup\u003e2\u003c/sup\u003e)-based chemotherapy. We compared patients receiving SGLT2 inhibitors with those receiving metformin. Patients receiving dipeptidyl peptidase-4 (DPP-4) inhibitors were excluded. To minimize confounding from baseline patient characteristics, inverse probability of treatment weighting (IPTW) using the stabilized average treatment effect (sATE) was applied.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter sATE-IPTW adjustment, the incidence of CIN was significantly lower in the SGLT2 inhibitor group than in the metformin group (17.9% vs. 45.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). The decline in renal function was significantly mitigated in the SGLT2 inhibitor group, as evidenced by better preservation of serum creatinine, creatinine clearance, and estimated glomerular filtration rate (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A multivariate logistic regression analysis identified the use of SGLT2 inhibitors as an independent protective factor against CIN (odds ratio [OR] 0.247, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), whereas preexisting cardiac disease was identified as an independent risk factor (OR 3.107, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSGLT2 inhibitors significantly and robustly reduce the risk of CIN in patients with solid tumors and type 2 diabetes. The concomitant use of SGLT2 inhibitors represents a promising renoprotective strategy to safely maintain the dose intensity of CDDP-based chemotherapy in clinical practice.\u003c/p\u003e","manuscriptTitle":"Preventive Effects of SGLT2 Inhibitors on Cisplatin-Induced Nephrotoxicity in Patients with Solid Tumors and Diabetes: A Multicenter, Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 06:08:04","doi":"10.21203/rs.3.rs-9380086/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-14T03:39:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T01:18:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T12:40:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297482923082699269210376382394877472274","date":"2026-04-27T05:29:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66722034363414738117167407021713350108","date":"2026-04-21T00:46:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T05:58:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T05:47:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-13T05:36:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2026-04-10T13:19:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35bd14a2-3bdb-404a-bbf9-fc7ae83167c9","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-14T03:39:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T01:18:04+00:00","index":17,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T12:40:11+00:00","index":16,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T03:53:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 06:08:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9380086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9380086","identity":"rs-9380086","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}