Protective Effect of SGLT2i on contrast-induced AKI after Angiography in Patients with Type 2 Diabetes Mellitus and Chronic Coronary Syndrome: A 6-year Ambispective Cohort Study and Meta-analysis

Protective Effect of SGLT2i on contrast-induced AKI after Angiography in Patients with Type 2 Diabetes Mellitus and Chronic Coronary Syndrome: A 6-year Ambispective Cohort Study and Meta-analysis | 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 Protective Effect of SGLT2i on contrast-induced AKI after Angiography in Patients with Type 2 Diabetes Mellitus and Chronic Coronary Syndrome: A 6-year Ambispective Cohort Study and Meta-analysis Zinan ZHAO, Tianqi ZHANG, Yatong ZHANG, Chao TIAN, Chenguang YANG, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5244417/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background SGLT2 inhibitor (SGLT2i) may reduce the risk of contrast-induced acute kidney injury (CI-AKI) in patients with type 2 diabetes mellitus (T2DM) with chronic coronary syndrome (CCS) undergoing angiography. However, the evidence is still inconclusive. We aimed to conduct a real world study and systematically review to provide updated and larger-scale evidence. Study design : Ambispective Cohort Study and Meta-analysis. Setting & population : Patients with T2DM and CCS. Methods The data was obtained from December 2017 to July 2024. Propensity score techniques were applied to enhance between-group comparability. We analyzed CI-AKI ESUR and CI-AKI KDIGO and conducted subgroup analyses based on the types of angiographic procedures, including percutaneous coronary interventions (PCI), coronary arteriography (CAG), and Coronary Computed Tomographic Angiography (CCTA). We retrieved similar cohort studies from the literature to perform a meta-analysis. Results from trials reporting CI-AKI ESUR and/or CI-AKI KDIGO rates among patients randomized to SGLT2i versus placebo were also meta-analysed. Results A total of 2,350 patients receiving dapagliflozin and 16,251 patients did not receiving any SGLT2i were included before PSM. 2,071 SGLT2i users were matched with 2,071 control patients. The incidence of primary outcome 1 and 2 were both significant lower in SGLT2i group than in the control group, which were both confirmed before and after PSM analysis. Subgroup analysis showed that the incidence of CI-AKI in the SGLT2i group was significantly lower after either PCI, CAG or CCTA. The meta-analysis of cohort studies further confirmed this result, that is, the rate of CI-AKI occurrence after angiography in the SGLT2i group was significantly lower than in the control group regardless of which criterion for CI-AKI was used. Limitations Results may be limited by single-center nature, inevitable sample selection bias, etc. and subgroup analysis of angiography operation types was conducted. Conclusion In real-world T2DM patients, SGLT2i was associated with lower CI-AKI risk. Clinical trial registration : Chinese Clinical Trial Registry, identifier: ChiCTR2300076484 Dapagliflozin SGLT2 inhibitors contrast-induced AKI Type 2 diabetes mellitus Chronic coronary syndrome Angiography Figures Figure 1 Figure 2 INTRODUCTION Diabetes mellitus is a common chronic disease, and its complications seriously affect the quality of life and prognosis of patients. Cardiovascular disease is one of the main complications of diabetes, and angiography and interventional therapy are important diagnostic and treatment methods for cardiovascular diseases. However, the contrast agents used in these procedures may lead to contrast-induced acute kidney injury (CI-AKI), especially in diabetic patients, who have a higher risk of developing this condition. 1 – 3 Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are a new class of hypoglycemic drugs 4 that have been proven to have various cardiovascular and renal protective effects. 5 – 9 Numerous studies have shown that SGLT2i can reduce the risk of cardiovascular events, delay the progression of kidney disease, and lower the mortality rate in diabetic patients. 10 – 12 However, the evidence regarding the protective effect of SGLT2i on CI-AKI during angiography is still insufficient. Some studies suggest that SGLT2i may be beneficial in preventing CI-AKI. For example, Hua et al. 13 found that SGLT2i could reduce the risk of CI-AKI in diabetic patients undergoing PCI. Paolisso et al. 14 also demonstrated the potential renal protective effect of SGLT2i in patients with acute myocardial infarction (AMI). However, there are also studies that have raised different opinions on this matter. A randomized controlled trial by Feitosa et al. 15 did not find a significant advantage of SGLT2 inhibitors in preventing CI-AKI. Therefore, we aim to conduct a cohort study to investigate the potential protective effect of SGLT2i against CI-AKI in patients with T2DM undergoing percutaneous coronary interventions (PCI) or coronary arteriography (CAG) or Coronary Computed Tomographic Angiography (CCTA). Furthermore, we plan to perform a meta-analysis to combine the results of relevant studies and provide a more comprehensive assessment of the efficacy of SGLT2i in preventing CI-AKI in this patient population. By doing so, we hope to provide more robust evidence to guide clinical decision-making and optimize the management of T2DM patients undergoing angiography. MATERIALS AND METHODS Study design and data sources This was a cross-sectional analysis of patients with T2DM and CCS undergoing PCI or CAG or CCTA. Data was extracted from the hospital information system (HIS) of Beijing hospital (a tertiary general hospital) from December 1, 2017 to July 31, 2024. The database encompassed comprehensive details on admission and discharge, age, sex, drinking status, medications, procedures, and laboratory test results of the patients. Patient recruitment criteria Eligibility criteria included the following: inpatients with complete data sets, aged 18 years, diagnosis of T2DM with CCS, documentation of dapagliflozin use > 2 days before PCI or CAG or CCTA (study group) or no SGLT2 inhibitors during PCI or CAG or CCTA (control group), normal or mildly impaired liver function, and normal or mildly impaired renal function. For multiple related admissions, each admission data was recorded to avoid any omission. Exclusion criteria were as follows: drug allergies and ketoacidosis that occurred after taking dapagliflozin; serious diseases that affect life span, such as malignant tumors, organ failure caused by various causes, severe immune system diseases, hemodynamic instability, severe anemia, and severe infections; taking drugs with clear nephrotoxicity, incomplete information (illogical data and missing or insufficient data); and lost to follow-up; and any patients with such history was excluded. Definition of Outcome We identified contrast-induced acute kidney injury (CI-AKI) events using a laboratory based algorithm, which identifies events based on the European Society of Urogenital Radiology (ESUR) serum creatinine criteria (increase in serum creatinine by ≥ 44.2 µmol/L or 0.5 mg/dL within 72 h or increase in serum creatinine by ≥ 1.25 times baseline value; hereafter referred to as CI-AKI ESUR ). 16 As part of a sensitivity analysis, we additionally identified inpatient episodes of acute kidney injury (AKI) using Kidney Disease: Improving Global Outcomes (KDIGO) serum creatinine criteria (increase in serum creatinine by ≥ 26.52 µmol/L (0.3 mg/dL) within 48 h or increase in serum creatinine by ≥ 1.5 times baseline value before CAG; hereafter referred to as AKI KDIGO ) and recorded the corresponding dates. 17 The final laboratory values before patients underwent CAG were used as the baseline data for analysis. This approach aimed to accurately represent each patient’s baseline condition before the procedure. The renal functions (serum creatinine and urea nitrogen) of patients with CCS were collected and measured upon admission and at 24, 48, and 72 h after PCI or CAG or CCTA. Statistical analysis All baseline characteristics data did not show normal distribution, so quantitative data were expressed as median and interquartile range (IQR) and qualitative data were expressed as numbers and percentages. To make baseline characteristics similar between the groups, we conducted a matched cohort-study based on propensity-score matching (PSM) with a matching ratio of 1:1 (caliper value 0.05). Propensity scores were estimated by age, gender, BMI, smoking and drinking status, and renal function. We excluded the patients for whom no matched control patients were found. The Chi-square test and Fisher's exact probability test were used for categorical variables. Spearman correlation test was used to evaluate the correlation between two sets of continuous variables. All statistical analyses were performed using the SPSS statistics software (version 26.0, IBM). P < 0.05 was considered statistically significant and 0.05 divided by the number of times of comparison was termed as the adjusted p value. All reported p -values were two-sided. Ethics and Trial registration The study protocol complied with Good Clinical Practice standards for drugs and the ethical guidelines specified in the revised Declaration of Helsinki (2013). Beijing Hospital Ethics Committee approved this study (Approval Letter Number: 2023BJYYEC-266-01) and the study was registered at Chinese Clinical Trial Registry (Registration number: ChiCTR2300076484). Data extracted from medical records was retrospected, de-identified and anonymized before analysis; therefore, the requirement for informed consent was waived for this study. Meta-analysis We further performed a meta-analysis on renal outcomes (CI-AKI ESUR and CI-AKI KDIGO ) by pooling the results from prior studies and the present study. Two reviewers (Zhao and Zhang) independently searched studies from the PubMed, Embase, and Cochrane Library from the inception of database to August 2nd, 2024 that reported the prevention of contrast-induced AKI. The search strategy and key terms were listed in Additional File: Supplementary File eTable 1 . We previously registered the protocol in PROSPERO. We included observational studies without imposing any language restrictions. Data were presented as mean diference with 95% CIs. The fix and random effect model was used to obtained pooled risk ratios (RR). The statistical heterogeneity was assessed by the statistic I 2 . Data were analyzed by Review Manager version 5.3. We assessed the risk of bias using the Newcastle-Ottawa scale (NOS) 18 for cohort studies. We examined the publication bias using funnel plots. RESULTS Cohort study A total of 18,601 patients with T2DM undergoing PCI/CAG/CCTA were admitted from December 2017 to July 2024 in Beijing Hospital. Out of the admitted patients, a total of 2,350 were taking dapagliflozin and and 16,251 did not take any SGLT2i. After PSM, 2.071 cases and 2,071 controls were finally paired. The selection process is summarized in Fig. 1 . Baseline demographic and clinical characteristics before and after matching are described in Table 1 and Table 2 . Each patient taking dapagliflozin was matched to a patient without taking any SGLT2i using clinical baseline variables and/or factors that may affect renal function. After PSM, the 2,071 matched pair of patients showed no significant difference in baseline characteristics such as age, gender, and BMI. There was no statistically significant difference in eGFR, BUN, AST, ALT, and others between the two groups either. Table 1 Basic characteristics of patients in two groups before propensity matching. Parameter DAPA users (n = 2,350) Control (n = 16,251) P -value Baseline characteristics Age, years (IQR) 66 (59–72) 66 (59–73) 0.077 Female gender, n (%) 601 (25.57) 5,110 (31.44) 0.078 BMI, kg/m 2 25.69 (23.70−27.99) 25.54 (23.44–27.78) 0.230 SBP, mmHg 135 (120–148) 135 (121–148) 0.979 DBP, mmHg 79 (68–86) 79 (68–87) 0.255 HR, bpm 74 (66–82) 74 (65–83) 0.281 Comorbidities Smoking, n (%) 931 (39.62) 7,056 (43.42) 0.049 Drinking, n (%) 790 (33.62) 6,073 (37.37) 0.211 Hypertension, n (%) 1,274 (54.21) 10,194 (62.74) 0.884 Dyslipidemia, n (%) 1,481 (63.02) 12,088 (74.38) 0.119 COPD, n (%) 131 (5.57) 909 (5.59) 0.123 Angina, n (%) 664 (28.26) 4,794 (29.50) 0.004 Prior PCI, n (%) 402 (17.11) 2,851 (17.54) 0.015 Prior CABG, n (%) 29 (1.23) 228 (1.40) 0.939 Prior MI, n (%) 288 (12.26) 2,026 (12.47) 0.032 Prior CI, n (%) 70 (2.98) 492 (3.03) 0.312 AF, n (%) 63 (2.68) 377 (2.32) 0.032 HF, n (%) 116 (4.94) 749 (4.61) 0.029 Laboratory variables eGFR, ml/min/1.73 m 2 86.20 (67.73−106.31) 85.41 (66.95−105.49) 0.109 HbA1c, % 6.80 (6.20–7.80) 7.10 (6.10–8.10) 0.097 hs-TNI, pg/mL 4.50 (0.13–12.70) 0.55 (0.01−7.00) 0.003 CK, U/L 75.00 (56.00−105.00) 73.00 (52.00−107.00) 0.061 CK-MB, U/L 1.30 (0.90–2.10) 1.40 (0.90–2.30) 0.136 Total cholesterol, mg/dL 134.38 (111.76−161.64) 134.96 (113.69−162.03) 0.069 LDL-c, mg/dL 74.63 (57.62–97.84) 75.02 (56.84−99.00) 0.085 HDL-c, mg/dL 40.22 (34.42–47.18) 40.22 (34.42–47.56) 0.328 Triglyceride, mg/dL 45.24 (31.71–63.03) 47.18 (34.03–66.51) 0.182 Hemoglobin, g/L 16.00 (124.00−146.00) 135.08 ± 16.58 0.127 C-reactive protein, mg/L 0.68 (0.30–2.10) 0.60 (0.20–1.90) 0.121 BUN, mg/dL 15.49 (12.60−19.16) 15.23 (12.54–18.68) 0.078 ALT, U/L 18.00 (13.00–26.00) 18.00 (13.00–26.00) 0.545 AST, U/L 19.00 (16.00–24.00) 18.00 (15.00–23.00) 0.207 Medications, n (%) Metformin 862 (36.68) 6,808 (41.89) 0.704 Sulfonylureas 129 (5.49) 1,160 (7.14) 0.183 DPP−4i 278 (11.83) 1,934 (11.90) 0.022 GLP−1RA 87 (3.70) 512 (3.15) 0.007 Glitazone 56 (2.38) 339 (2.09) 0.052 Insulin 460 (19.57) 3206 (19.73) 0.002 Anti platelets 1,876 (79.83) 14,734 (90.67) 0.044 Anti coagulation 47 (2.00) 259 (1.59) 0.018 ACEI 98 (4.71) 953 (5.86) 0.055 ARB 1,096 (46.64) 7,113 (43.77) < 0.000 β-blockers 1,165 (49.57) 8,129 (50.02) CCB 701 (29.83) 5,641 (34.71) 0.802 Diuretics 463 (19.70) 3,021 (18.59) < 0.000 Statins 1,845 (78.51) 14,280 (87.87) 0.001 Ezetimibe 500 (21.28) 3,082 (18.96) < 0.000 DAPA, Dapagliflozin; BMI, Body Mess Index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; HR, Heart Rate; COPD, Chronic Obstructive Pulmonary Disease; PCI, Percutaneous Coronary Intervention; CABG, Coronary Artery Bypass Grafting; MI, Myocardial Infarction; CI, Cerebral Infarction; AF, Atrial Fibrillation; HF, Heart Failure; eGFR, estimated Glomerular Filtration Rate; HbA1c, glycated Hemoglobin A1c; hs-TnI, high-sensitivity Troponin I; CK, Creatine Kinase; CK-MB, Creatine Kinase-MB; LDL-c, Low-Density Lipoprotein Cholesterol; HDL-c, High-Density Lipoprotein Cholesterol; BUN, Blood Urea Nitrogen; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; DPP-4i, Dipeptidyl Peptidase-4 inhibitor; GLP-1RA, Glucagon-like Peptide-1 Receptor Agonist; ACEI, Angiotensin-Converting Enzyme Inhibitor; ARB, Angiotensin Receptor Blocker; CCB, Calcium Channel Blocker. Table 2 Basic characteristics of patients in two groups in propensity-matched dataset. Parameter DAPA users (n = 2,071) Control (n = 2,071) P -value Baseline characteristics Age, years 66 (59–72) 66 (58–72) 0.490 Female gender, n (%) 601 (29.02) 549 (26.51) 0.071 BMI, kg/m 2 26.69 (23.74–27.99) 25.71 (23.59–27.99) 0.620 SBP, mmHg 135 (121−148.5) 134 (120–148) 0.999 DBP, mmHg 79 (69–86) 79 (67–87) 0.108 HR, bpm 74 (66–82) 74 (64–83) 0.863 Comorbidities Smoking, n (%) 931 (44.95) 964 (46.55) 0.303 Drinking, n (%) 790 (38.15) 832 (40.17) 0.181 Hypertension, n (%) 1,274 (61.52) 1,232 (59.49) 0.182 Dyslipidemia, n (%) 1,481 (71.51) 1,521 (73.44) 0.164 COPD, n (%) 131 (6.33) 108 (5.21) 0.125 Angina, n (%) 664 (32.06) 650 (31.39) 0.640 Prior PCI, n (%) 402 (19.41) 374 (18.06) 0.265 Prior CABG, n (%) 29 (1.40) 28 (1.35) 0.894 Prior MI, n (%) 288 (13.91) 238 (11.49) 0.020 Prior CI, n (%) 70 (3.38) 60 (2.90) 0.373 AF, n (%) 63 (3.04) 56 (2.70) 0.515 HF, n (%) 116 (5.60) 92 (4.44) 0.088 Laboratory variables eGFR, ml/min/1.73 m 2 86.17 (67.28−106.43) 87.19 (68.53−107.65) 0.647 HbA1c, % 6.90 (6.20–7.90) 7.10 (6.20–8.10) 0.066 hs-TNI, pg/mL 4.80 (1.04–13.59) 0.94 (0.01–7.59) 0.063 CK, U/L 75.00 (56.00−104.00) 73.00 (53.00−109.00) 0.045 CK-MB, U/L 1.30 (0.90–2.10) 1.40 (0.90–2.30) 0.229 Total cholesterol, mg/dL 133.02 (110.60−160.09) 134.18 (112.92−161.64) 0.700 LDL-c, mg/dL 73.86 (56.46–96.49) 75.02 (56.84−99.00) 0.595 HDL-c, mg/dL 39.83 (34.42–46.79) 39.44 (33.64–47.18) 0.233 Triglyceride, mg/dL 45.63 (32.10−63.23) 47.56 (34.80–66.90) 0.049 Hemoglobin, g/L 137.00 (124.50–147.00) 137.00 (126.00−148.00) 0.547 C-reactive protein, mg/L 0.70 (0.30–2.19) 0.60 (0.20−2.00) 0.408 BUN, mg/dL 15.74 (12.85–19.54) 15.34 (12.67–18.83) 0.076 ALT, U/L 18.00 (13.00–26.00) 18.00 (13.00–26.00) 0.775 AST, U/L 19.00 (16.00–24.00) 18.00 (15.00–23.00) 0.294 Medications, n (%) Metformin 862 (41.62) 856 (41.33) 0.850 Sulfonylureas 129 (6.23) 138 (6.66) 0.569 DPP−4i 278 (13.42) 222 (10.72) 0.008 GLP−1RA 87 (4.20) 68 (3.28) 0.120 Glitazone 56 (2.70) 35 (1.69) 0.026 Insulin 460 (22.21) 419 (20.23) 0.119 Anti platelets 1,876 (90.58) 1,852 (89.43) 0.214 Anti coagulation 47 (2.27) 42 (2.03) 0.592 ACEI 98 (4.73) 110 (5.31) 0.393 ARB 1096 (52.92) 905 (43.70) < 0.000 β-blockers 1,165 (56.25) 1,019 (49.20) < 0.000 CCB 701 (33.85) 694 (33.51) 0.818 Diuretics 463 (22.36) 372 (17.96) < 0.000 Statins 1845 (89.09) 1,793 (86.58) 0.013 Ezetimibe 500 (24.14) 386 (18.64) < 0.000 Before PSM, the unadjusted ORs of patients with CI-AKI ESUR were 59.8% lower in the dapagliflozin user group [OR 0.402, 95%CI 0.322–0.501, P < 0.000 ] compared with the control group. Correlation analysis using KDIGO definition also rendered similar results [OR 0.306, 95% CI 0.235–0.399, P < 0.000 ]. After PSM, the adjusted OR of CI-AKI ESUR remained 57.3% lower in the dapagliflozin group [OR 0.427, 95% CI 0.329–0.554, P < 0.000 ] than the control group. We also conducted subgroup analyses for different operation methods, including PCI, CAG and CCTA. The unadjusted and adjusted point estimates were qualitatively similar to the overall results. Results are shown in Table 3 . Table 3 Renal outcomes in the dapagliflozin group and control group. Parameter Unmatched population Matched population OR 95% CI P -value OR 95% CI P -value CI-AKI ESUR Total 0.402 0.322–0.501 < 0.000 0.427 0.329–0.554 < 0.000 PCI 0.432 0.320–0.582 < 0.000 0.503 0.348–0.726 < 0.000 CAG 0.384 0.275–0.535 < 0.000 0.381 0.261–0.559 < 0.000 CCTA 0.125 0.016–0.947 < 0.000 0.119 0.013–1.067 0.029 CI-AKI KDIGO Total 0.306 0.235–0.399 < 0.000 0.316 0.234–0.427 < 0.000 PCI 0.319 0.222–0.458 < 0.000 0.354 0.232–0.540 0.006 CAG 0.310 0.210–0.458 < 0.000 0.306 0.197–0.473 < 0.000 CCTA 0.833 0.780–0.891 < 0.000 0.408 0.291–0.572 < 0.000 CAG, Coronary Angiography; CCTA, Coronary Computed Tomography Angiography; OR, Odd Ratio; CI, Confidence Interval. Meta-analysis of cohorts Study Selection . Including the present study, we considered 6 studies with up to a total of 1,933 T2DM patients for meta-analysis. Data extraction flow was detailed in Additional File: PRISMA 2020 flow diagram. Initially, we conducted a meta-analysis, including 6 cohort studies. 13 , 14 , 19 – 22 Study Characteristics . The characteristics of the included studies are provided in Additional File: Supplementary File eTable 2 . Of the 6 studies in this meta-analysis, two were prospective cohort studies (PCS) 19 , 13 and four were retrospective cohort studies (RCS). 13 , 14 , 19 , 22 Regarding outcome treatment, one study used both ESUR and KDIGO criteria simultaneously. 13 Two studies used the ESUR criteria. 14 , 19 One article used the KDIGO criteria. 21 Another two articles adopted other standards. 20 , 22 GRADE assessment . We upgraded the level of CoE as all the studies included in the meta-analysis showed a low risk of bias. For details, please refer to the Additional File: Supplementary File eTable 3 . Indirectness (the included studies compared similar interventions, similar populations, and similar outcomes), imprecision (this meta-analysis included 6,075 patients with diabetes undergoing CAG or PCI, 2,735 SGLT2i users, and 3,340 events of CI-AKI), publication bias, and inconsistency ( I 2 = 0 ) did not impact significantly the CoE. We assessed the CoE according to GRADE criteria as moderate. Meta-analysis . Due to the different definition criteria of the outcome indicator CI-AKI in the included studies, some studies selected both ESUR and KDIGO criteria. This meta-analysis conducted two sets of analyses. In one set of analyses, when a study had both standards simultaneously, the results corresponding to the ESUR standard were selected for inclusion in the meta-analysis (Primary Outcome A). In the other set of analyses, when a study had both standards simultaneously, the results corresponding to the KDIGO criteria were selected for inclusion in the meta-analysis (Primary Outcome B). Primary Outcomes . Among 6,075 patients in the 7 cohort studies, the use of SGLT2i were associated with significantly reduced CI-AKI outcomes [Primary Outcome A: RR 0.42, 95%CI 0.35–0.52, P < 0.000 ; Primary Outcome B: RR 0.37, 95%CI 0.30–0.45, P < 0.000 ] in T2DM patients after PCI/CAG/CCTA. The results are shown in detail in Fig. 2A and B. Secondary Outcomes . In this meta-analysis, four included studies used the ESUR criteria for result statistics. A subgroup analysis was conducted based on the outcome treatment with the ESUR criteria. The results showed that SGLT2i could significantly reduce the incidence of CI-AKI ESUR [RR 0.43, 95%CI 0.35–0.53, P < 0.000 ]. Three included studies used the KDIGO criteria for result statistics. A subgroup analysis was conducted based on the outcome treatment with the KDIGO criteria, and the results indicated that SGLT2i could significantly reduce the incidence of CI-AKI KDIGO [RR 0.36, 95%CI 0.28–0.47, P < 0.000 ]. Please refer to Additional File: Supplementary File eFigure 1 (A, B) in the attachment for details. Publication Bias . The funnel plot of the included studies in our final metaanalysis did not suggest a publication bias. The Funnel plot of the included studies in the meta-analysis on the effect of developing CI-AKI after PCI/CAG/CCTA were shown in Additional File: Supplementary File eFigure 2 (A, B). DISCUSSION Key Results This large, real-world comparative effectiveness study of SGLT2i in a Chinese population with T2DM and CCS in a single medical institution comprehensively evaluated the benefits of SGLT2i on CI-AKI. We found that dapagliflozin was associated with a much lower risk of CI-AKI in patients with T2DM after PCI/CAG/CCTA no matter using ESUR or KDIGO criteria. Moreover, across all pre-specified subgroups, whether dapagliflozin is administered prior to PCI, CAG, or CCTA, it can significantly reduce the occurrence risk of CI-AKI. The meta-analysis of the same type of cohort studies further confirmed the main result of this study, that is, SGLT2i can significantly reduce the risk of CI-AKI in patients with T2DM and CCS undergoing angiography. Comparison with Previous Studies Our study demonstrated that SGLT2i significantly reduce the risk of CI-AKI by up to 6.38% in patients with T2DM undergoing PCI/CAG/CCTA, compared to those not using any SGLT2i. This finding is consistent with several previous studies that have shown the potential nephroprotective effects of SGLT2i in this patient population. However, there are some differences in the study populations and the definition of CI-AKI. For example, Hua et al. 13 conducted a study on patients with T2DM undergoing PCI and defined CI-AKI based on the ESUR and KDIGO criteria. They found that SGLT2 inhibitors were associated with a lower risk of CI-AKI through a propensity - matched analysis, which is consistent with our finding. Paolisso et al. 14 And Çabuk et al. 19 focused on diabetic patients with AMI undergoing PCI and used ESUR criteria. They found that SGLT2i users had a lower rate of CI-AKI, especially in patients without chronic kidney disease, supporting the nephroprotective effect of SGLT2i. Özkan et al. 20 specifically studied diabetic patients with non-ST segment elevation myocardial infarction (NSTEMI) and defined CI-AKI as a 0.5 mg/dL (absolute) or 25% (relative) increase in creatinine value within 48 h, an increase in creatinine level of more than 1.5 times the baseline within 7 days, or a urinary output of less than 0.5 mL/kg/h for at least 6 h after using the contrast agent compared to its level before the procedure. They found that the use of SGLT2i significantly reduced the risk of CI-AKI, which is in line with our conclusion. Our study differs from some previous studies in terms of study design. For example, Bernardini et al. 15 included patients undergoing PCI and divided them into three groups: those treated with new-antidiabetic drugs (including SGLT2 inhibitors), those treated with traditional antidiabetic drugs, and non-diabetic patients. They found that patients treated with new-antidiabetic drugs had a similar incidence of CI-AKI compared to non-diabetic patients, suggesting a possible protective role of these drugs. The study by Feitosa et al. 21 was a randomized controlled trial (RCT) that aimed to evaluate the safety of empagliflozin in diabetic patients submitted to elective PCI regarding kidney function. Although they found that the use of empagliflozin was safe regarding kidney function, the incidence of CI-AKI was similar between the empagliflozin group and the control group. Our study, on the other hand, is a meta-analysis that combines the results of multiple studies, providing a more comprehensive assessment of the effect of SGLT2 inhibitors on CI-AKI. Our study is consistent with previous studies that have shown the potential nephroprotective effects of SGLT2i in patients with T2DM undergoing angiography. However, further studies are needed to confirm our findings and to explore the optimal dose, type, and duration of SGLT2 inhibitor therapy to prevent CI-AKI. Study Limitations Several limitations of this study should be acknowledged. First, this was a single-center study and sample selection bias was inevitable. Besides, the construction of the study model was based on a cohort study with the risk of observation and confounding biases, which were minimized by applying the PSM protocol. Furthermore, as an observational study, some information was not available, such as dosage of contrast agent. To reduce the research bias caused by the unavailability of this data, we conducted a subgroup analysis of the types of angiography operations, namely, PCI, CAG, or CCTA. CONCLUSION Our study provided more information on the protective effect of SGLT2i on CI-AKI in patients with T2DM after angiography. In real-world patients, SGLT2i was associated with much lower CI-AKI risk no matter undergoing PCI or CAG or CCTA. Declarations Author Acknowledgements None. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships. Funding This work was financially supported by National High Level Hospital Clinical Research Funding (Grant number: BJ-2023-199). Author Contribution Deping LIU, Xin HU, Pengfei JIN and Zinan ZHAO designed the research. Tianqi ZHANG, Yatong ZHANG, Ming LAN and Chao TIAN performed experiments and collected data. Chenguang YANG, Chi ZHANG and Zinan ZHAO analyzed the data. Zinan ZHAO wrote the manuscript. Deping LIU, Pengfei JIN, and Zinan ZHAO participated in the discussion of the results. All authors have read and approved the final manuscript. References Johanne Silvain, Lee S, Nguyen V, Spagnoli M, Kerneis P, Guedeney N, Vignolles, et al. Contrast-induced acute kidney injury and mortality in ST elevation myocardial infarction treated with primary percutaneous coronary intervention. Heart. 2018;104(9):767–72. 10.1136/heartjnl-2017-311975 . Dawlat Sany H, Refaat Y, Elshahawy A, Mohab H, Ezzat. Frequency and risk factors of contrast-induced nephropathy after cardiac catheterization in type II diabetic patients: a study among Egyptian patients. Ren Fail. 2014;36(2):191–7. 10.3109/0886022X.2013.843400 . Natalia V, Zaytseva MS, Shamkhalova MV, Shestakova, Simon T, Matskeplishvili, Elvina F, Tugeeva, Ury I, Buziashvili, et al. Contrast-induced nephropathy in patients with type 2 diabetes during coronary angiography: risk-factors and prognostic value. Diabetes Res Clin Pract. 2009;86(Suppl 1):S63–9. 10.1016/S0168-8227(09)70012-9 . Girish N, Nadkarni R, Ferrandino A, Chang A, Surapaneni K, Chauhan P, Poojary, et al. Acute Kidney Injury in Patients on SGLT2 Inhibitors: A Propensity-Matched Analysis. Diabetes Care. 2017;40(11):1479–85. 10.2337/dc17-1011 . Rajiv Agarwal SD, Anker G, Filippatos B, Pitt P, Rossing, Luis M, Ruilope, et al. Effects of canagliflozin versus finerenone on cardiorenal outcomes: exploratory post hoc analyses from FIDELIO-DKD compared to reported CREDENCE results. Nephrol Dial Transpl. 2022;37(7):1261–9. 10.1093/ndt/gfab336 . Hiddo JL, Heerspink M, Kosiborod SE, Inzucchi DZI, Cherney. Renoprotective effects of sodium-glucose cotransporter-2 inhibitors. Kidney Int. 2018;94(1):26–39. 10.1016/j.kint.2017.12.027 . Hiddo JL, Heerspink BA, Perkins DH, Fitchett. Mansoor Husain, David Z I Cherney. Sodium Glucose Cotransporter 2 Inhibitors in the Treatment of Diabetes Mellitus: Cardiovascular and Kidney Effects, Potential Mechanisms, and Clinical Applications. Circulation. 2016;134(10):752–72. 10.1161/CIRCULATIONAHA.116.021887 . Ofri Mosenzon SD, Wiviott A, Cahn A, Rozenberg I, Yanuv, Erica L, Goodrich, et al. Effects of dapagliflozin on development and progression of kidney disease in patients with type 2 diabetes: an analysis from the DECLARE-TIMI 58 randomised trial. Lancet Diabetes Endocrinol. 2019;7(8):606–17. 10.1016/S2213-8587(19)30180-9 . Christoph Wanner. EMPA-REG OUTCOME: The Nephrologist's Point of View. Am J Cardiol. 2017;120(1S):S59–67. 10.1016/j.amjcard.2017.05.012 . Parving H-H. Hiddo Lambers-Heerspink, Dick de Zeeuw. Empagliflozin and Progression of Kidney Disease in Type 2 Diabetes. N Engl J Med. 2016;375(18):1800–1. 10.1056/NEJMc1611290 . Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, et al. Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes. N Engl J Med. 2017;377(7):644–57. 10.1056/NEJMoa1611925 . Stephen D, Wiviott I, Raz, Marc P, Bonaca. Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2019;380(4):347–57. 10.1056/NEJMoa181238 . Ding RHN, Guo H, Wu Y, Yuan Z, Li T. Contrast-Induced Acute Kidney Injury in Patients on SGLT2 Inhibitors Undergoing Percutaneous Coronary Interventions: A Propensity-Matched Analysis. Front Cardiovasc Med. 2022;9:918167. 10.3389/fcvm.2022.918167 . Paolisso P, Bergamaschi L, Cesaro A, Gallinoro E, Gragnano F, Sardu C, et al. Impact of SGLT2-inhibitors on contrast-induced acute kidney injury in diabetic patients with acute myocardial infarction with and without chronic kidney disease: Insight from SGLT2-I AMI PROTECT registry. Multicenter Study Diabetes Res Clin Pract. 2023;202:110766. 10.1016/j.diabres.2023.110766 . Bernardini F, Nusca A, Giannone S, Mangiacapra F, Melfi R, Ricottini E, et al. Role of new antidiabetic drugs in the prevention of contrast-induced nephropathy in diabetic patients undergoing percutaneous coronary intervention. Eur Heart J Suppl. 2022;24:K179. 10.1093/eurheartjsupp/suac121.499 . James MT, Ghali WA, Knudtson ML, Ravani P, Tonelli M, Faris P, et al. Associations between acute kidney injury and cardiovascular and renal outcomes after coronary angiography. Circulation. 2011;123:409–16. 10.1161/CIRCULATIONAHA.110.970160 . Kellum JA, Lameire N, Group KAGW. Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). Crit Care. 2013;17:204. 10.1186/cc11454 . Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute. https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp [Accessed October 30, 2023]. Gizem, Çabuk. Kutluhan Eren Hazır. Do Sodium-Glucose Cotransporter 2 Inhibitors Decrease the Risk of Contrast-Associated Acute Kidney Injury in Patients with Type II Diabetes Mellitus? Anatol J Cardiol, 2024, 28(5): 222–228. doi: 20.14744/AnatolJCardiol.2024.3980. Uğur Özkan M, Gürdoğan. The Effect of SGLT2 Inhibitors on the Development of Contrast-Induced Nephropathy in Diabetic Patients with Non-ST Segment Elevation Myocardial Infarction. Med (Kaunas). 2023;59(3):505. 10.3390/medicina59030505 . Feitosa MPM, Lima EG et al. Alexandre Antônio Cunha Abizaid,. The safety of SGLT-2 inhibitors in diabetic patients submitted to elective percutaneous coronary intervention regarding kidney function: SAFE-PCI pilot study. Diabetol Metab Syndr, 2023, 15(1): 138. 10.1186/s13098-023-01107-9 Carlos G, Santos-Gallego G, Palamara JA, Requena-Ibanez, et al. Pretreatment with SGLT2 inhibitors ameliorates contrast-induced nephropathy. JACC. 2020;74(11):1351–13. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5244417","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":364884586,"identity":"d4aaeb01-b86f-4147-840c-cb042fd045b6","order_by":0,"name":"Zinan ZHAO","email":"","orcid":"","institution":"Department of Pharmacy, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Zinan","middleName":"","lastName":"ZHAO","suffix":""},{"id":364884587,"identity":"2977d36f-861f-4671-a50d-65dfd0eb6828","order_by":1,"name":"Tianqi ZHANG","email":"","orcid":"","institution":"Department of Pharmacy, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Tianqi","middleName":"","lastName":"ZHANG","suffix":""},{"id":364884588,"identity":"56df7358-1d0c-4475-bf5c-1988dd34bf7e","order_by":2,"name":"Yatong ZHANG","email":"","orcid":"","institution":"Department of Pharmacy, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Yatong","middleName":"","lastName":"ZHANG","suffix":""},{"id":364884589,"identity":"1fb00250-ccb1-42a8-a224-2fd5109b197f","order_by":3,"name":"Chao TIAN","email":"","orcid":"","institution":"Department of Pharmacy, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"TIAN","suffix":""},{"id":364884590,"identity":"bc0b862d-dc69-49e8-bf42-2b0bb9be82cb","order_by":4,"name":"Chenguang YANG","email":"","orcid":"","institution":"Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Chenguang","middleName":"","lastName":"YANG","suffix":""},{"id":364884593,"identity":"2e84fb1e-3c54-42fd-b0b8-edb3cc27c92d","order_by":5,"name":"Ming LAN","email":"","orcid":"","institution":"Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"LAN","suffix":""},{"id":364884594,"identity":"81e79327-4dd4-46b9-af71-f6217ceffc0e","order_by":6,"name":"Chi ZHANG","email":"","orcid":"","institution":"The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Chi","middleName":"","lastName":"ZHANG","suffix":""},{"id":364884595,"identity":"794c07cf-9b81-4edf-8980-ed4908ed645e","order_by":7,"name":"Xin HU","email":"","orcid":"","institution":"Department of Pharmacy, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"HU","suffix":""},{"id":364884596,"identity":"4f1de3b4-5631-405e-98d4-0ba9905d5413","order_by":8,"name":"Pengfei JIN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACxvmPDxxIMJCQY2NvP0Ckloa0xAcPKiyM+XjOJBBrT46x4YMzFYnzJBwMiNPA3HDGTCKxTSK9TYIhgeFHxTYiHNbYVgbSktsm3XiAsefMbSK0NDNvg2iROZDAzNhGjJY2BojD2CQSDIjU0sNibJBwRiKBBC0z2BIfJFRIGLYBA/kgUX4xnMF84OAPgzp5+fb2gw9+VBCjpQGJc4CweiCQJ0rVKBgFo2AUjGwAAEXIPVh6yhWrAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Pharmacy, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing","correspondingAuthor":true,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"JIN","suffix":""},{"id":364884597,"identity":"c5046d41-74fd-4ab4-afca-87417fac1cc3","order_by":9,"name":"Deping LIU","email":"","orcid":"","institution":"Department of Cardiology, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Deping","middleName":"","lastName":"LIU","suffix":""}],"badges":[],"createdAt":"2024-10-11 08:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5244417/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5244417/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67277148,"identity":"00fcff10-d816-447a-aed4-d95220690b3b","added_by":"auto","created_at":"2024-10-23 08:36:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101669,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of study population enrollment\u003c/p\u003e","description":"","filename":"Figure1Flowdiagramofstudypopulationenrollment.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5244417/v1/01e460e3a142f836bec47ab0.jpg"},{"id":67277149,"identity":"123e242f-5588-4ac9-a0a9-e677d91cb7d4","added_by":"auto","created_at":"2024-10-23 08:36:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115831,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of SGLT2i on CI-AKI\u003c/p\u003e","description":"","filename":"Figure2ForestplotofSGLT2ionCIAKI.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5244417/v1/65d9b44d711320c60ed47acb.jpg"},{"id":67279232,"identity":"ef2a756d-fdcd-4480-b5e3-bf82e3506e30","added_by":"auto","created_at":"2024-10-23 08:52:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1083180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5244417/v1/e13eaf88-8c28-4784-a6c5-41a87951cf9d.pdf"},{"id":67277147,"identity":"f50d934c-c2c2-400b-a5cc-543b75b1cbea","added_by":"auto","created_at":"2024-10-23 08:36:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":119754,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-5244417/v1/91c74ce217be3c178eff851e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Protective Effect of SGLT2i on contrast-induced AKI after Angiography in Patients with Type 2 Diabetes Mellitus and Chronic Coronary Syndrome: A 6-year Ambispective Cohort Study and Meta-analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDiabetes mellitus is a common chronic disease, and its complications seriously affect the quality of life and prognosis of patients. Cardiovascular disease is one of the main complications of diabetes, and angiography and interventional therapy are important diagnostic and treatment methods for cardiovascular diseases. However, the contrast agents used in these procedures may lead to contrast-induced acute kidney injury (CI-AKI), especially in diabetic patients, who have a higher risk of developing this condition.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSodium-glucose cotransporter 2 inhibitors (SGLT2i) are a new class of hypoglycemic drugs\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e that have been proven to have various cardiovascular and renal protective effects.\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Numerous studies have shown that SGLT2i can reduce the risk of cardiovascular events, delay the progression of kidney disease, and lower the mortality rate in diabetic patients.\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e However, the evidence regarding the protective effect of SGLT2i on CI-AKI during angiography is still insufficient.\u003c/p\u003e \u003cp\u003eSome studies suggest that SGLT2i may be beneficial in preventing CI-AKI. For example, Hua et al.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e found that SGLT2i could reduce the risk of CI-AKI in diabetic patients undergoing PCI. Paolisso et al.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e also demonstrated the potential renal protective effect of SGLT2i in patients with acute myocardial infarction (AMI). However, there are also studies that have raised different opinions on this matter. A randomized controlled trial by Feitosa et al.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e did not find a significant advantage of SGLT2 inhibitors in preventing CI-AKI.\u003c/p\u003e \u003cp\u003eTherefore, we aim to conduct a cohort study to investigate the potential protective effect of SGLT2i against CI-AKI in patients with T2DM undergoing percutaneous coronary interventions (PCI) or coronary arteriography (CAG) or Coronary Computed Tomographic Angiography (CCTA). Furthermore, we plan to perform a meta-analysis to combine the results of relevant studies and provide a more comprehensive assessment of the efficacy of SGLT2i in preventing CI-AKI in this patient population. By doing so, we hope to provide more robust evidence to guide clinical decision-making and optimize the management of T2DM patients undergoing angiography.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data sources\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional analysis of patients with T2DM and CCS undergoing PCI or CAG or CCTA. Data was extracted from the hospital information system (HIS) of Beijing hospital (a tertiary general hospital) from December 1, 2017 to July 31, 2024. The database encompassed comprehensive details on admission and discharge, age, sex, drinking status, medications, procedures, and laboratory test results of the patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient recruitment criteria\u003c/h3\u003e\n\u003cp\u003eEligibility criteria included the following: inpatients with complete data sets, aged 18 years, diagnosis of T2DM with CCS, documentation of dapagliflozin use\u0026thinsp;\u0026gt;\u0026thinsp;2 days before PCI or CAG or CCTA (study group) or no SGLT2 inhibitors during PCI or CAG or CCTA (control group), normal or mildly impaired liver function, and normal or mildly impaired renal function. For multiple related admissions, each admission data was recorded to avoid any omission.\u003c/p\u003e \u003cp\u003eExclusion criteria were as follows: drug allergies and ketoacidosis that occurred after taking dapagliflozin; serious diseases that affect life span, such as malignant tumors, organ failure caused by various causes, severe immune system diseases, hemodynamic instability, severe anemia, and severe infections; taking drugs with clear nephrotoxicity, incomplete information (illogical data and missing or insufficient data); and lost to follow-up; and any patients with such history was excluded.\u003c/p\u003e\n\u003ch3\u003eDefinition of Outcome\u003c/h3\u003e\n\u003cp\u003eWe identified contrast-induced acute kidney injury (CI-AKI) events using a laboratory based algorithm, which identifies events based on the European Society of Urogenital Radiology (ESUR) serum creatinine criteria (increase in serum creatinine by \u0026ge;\u0026thinsp;44.2 \u0026micro;mol/L or 0.5 mg/dL within 72 h or increase in serum creatinine by \u0026ge;\u0026thinsp;1.25 times baseline value; hereafter referred to as CI-AKI\u003csub\u003eESUR\u003c/sub\u003e).\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e As part of a sensitivity analysis, we additionally identified inpatient episodes of acute kidney injury (AKI) using Kidney Disease: Improving Global Outcomes (KDIGO) serum creatinine criteria (increase in serum creatinine by \u0026ge;\u0026thinsp;26.52 \u0026micro;mol/L (0.3 mg/dL) within 48 h or increase in serum creatinine by \u0026ge;\u0026thinsp;1.5 times baseline value before CAG; hereafter referred to as AKI\u003csub\u003eKDIGO\u003c/sub\u003e) and recorded the corresponding dates.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e The final laboratory values before patients underwent CAG were used as the baseline data for analysis. This approach aimed to accurately represent each patient\u0026rsquo;s baseline condition before the procedure. The renal functions (serum creatinine and urea nitrogen) of patients with CCS were collected and measured upon admission and at 24, 48, and 72 h after PCI or CAG or CCTA.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll baseline characteristics data did not show normal distribution, so quantitative data were expressed as median and interquartile range (IQR) and qualitative data were expressed as numbers and percentages. To make baseline characteristics similar between the groups, we conducted a matched cohort-study based on propensity-score matching (PSM) with a matching ratio of 1:1 (caliper value 0.05). Propensity scores were estimated by age, gender, BMI, smoking and drinking status, and renal function. We excluded the patients for whom no matched control patients were found. The Chi-square test and Fisher's exact probability test were used for categorical variables. Spearman correlation test was used to evaluate the correlation between two sets of continuous variables. All statistical analyses were performed using the SPSS statistics software (version 26.0, IBM). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant and 0.05 divided by the number of times of comparison was termed as the adjusted p value. All reported \u003cem\u003ep\u003c/em\u003e-values were two-sided.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics and Trial registration\u003c/h3\u003e\n\u003cp\u003e The study protocol complied with Good Clinical Practice standards for drugs and the ethical guidelines specified in the revised Declaration of Helsinki (2013). Beijing Hospital Ethics Committee approved this study (Approval Letter Number: 2023BJYYEC-266-01) and the study was registered at Chinese Clinical Trial Registry (Registration number: ChiCTR2300076484). Data extracted from medical records was retrospected, de-identified and anonymized before analysis; therefore, the requirement for informed consent was waived for this study.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeta-analysis\u003c/h2\u003e \u003cp\u003eWe further performed a meta-analysis on renal outcomes (CI-AKI\u003csub\u003eESUR\u003c/sub\u003e and CI-AKI\u003csub\u003eKDIGO\u003c/sub\u003e) by pooling the results from prior studies and the present study. Two reviewers (Zhao and Zhang) independently searched studies from the PubMed, Embase, and Cochrane Library from the inception of database to August 2nd, 2024 that reported the prevention of contrast-induced AKI. The search strategy and key terms were listed in Additional File: Supplementary File \u003cem\u003eeTable 1\u003c/em\u003e. We previously registered the protocol in PROSPERO.\u003c/p\u003e \u003cp\u003eWe included observational studies without imposing any language restrictions. Data were presented as mean diference with 95% CIs. The fix and random effect model was used to obtained pooled risk ratios (RR). The statistical heterogeneity was assessed by the statistic \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. Data were analyzed by Review Manager version 5.3.\u003c/p\u003e \u003cp\u003eWe assessed the risk of bias using the Newcastle-Ottawa scale (NOS)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e for cohort studies. We examined the publication bias using funnel plots.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCohort study\u003c/h2\u003e \u003cp\u003eA total of 18,601 patients with T2DM undergoing PCI/CAG/CCTA were admitted from December 2017 to July 2024 in Beijing Hospital. Out of the admitted patients, a total of 2,350 were taking dapagliflozin and and 16,251 did not take any SGLT2i. After PSM, 2.071 cases and 2,071 controls were finally paired. The selection process is summarized in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eBaseline demographic and clinical characteristics before and after matching are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Each patient taking dapagliflozin was matched to a patient without taking any SGLT2i using clinical baseline variables and/or factors that may affect renal function. After PSM, the 2,071 matched pair of patients showed no significant difference in baseline characteristics such as age, gender, and BMI. There was no statistically significant difference in eGFR, BUN, AST, ALT, and others between the two groups either.\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\u003eBasic characteristics of patients in two groups before propensity matching.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAPA users\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,350)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;16,251)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (59\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (59\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.077\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale gender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e601 (25.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,110 (31.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.078\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.69 (23.70\u0026minus;27.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.54 (23.44\u0026ndash;27.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.230\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (120\u0026ndash;148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (121\u0026ndash;148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.979\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (68\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (68\u0026ndash;87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.255\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR, bpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (66\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (65\u0026ndash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.281\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e931 (39.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,056 (43.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.049\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e790 (33.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,073 (37.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.211\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,274 (54.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,194 (62.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.884\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,481 (63.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,088 (74.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.119\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (5.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e909 (5.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.123\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngina, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e664 (28.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,794 (29.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior PCI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e402 (17.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,851 (17.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.015\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior CABG, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228 (1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.939\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior MI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288 (12.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,026 (12.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.032\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior CI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (2.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e492 (3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.312\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e377 (2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.032\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e749 (4.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.029\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.20 (67.73\u0026minus;106.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.41 (66.95\u0026minus;105.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.109\u003c/em\u003e\u003c/p\u003e \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\u003e6.80 (6.20\u0026ndash;7.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.10 (6.10\u0026ndash;8.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.097\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-TNI, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.50 (0.13\u0026ndash;12.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55 (0.01\u0026minus;7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.00 (56.00\u0026minus;105.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.00 (52.00\u0026minus;107.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.061\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK-MB, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 (0.90\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (0.90\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.136\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134.38 (111.76\u0026minus;161.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.96 (113.69\u0026minus;162.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.069\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.63 (57.62\u0026ndash;97.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.02 (56.84\u0026minus;99.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.085\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.22 (34.42\u0026ndash;47.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.22 (34.42\u0026ndash;47.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.328\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.24 (31.71\u0026ndash;63.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.18 (34.03\u0026ndash;66.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.182\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.00 (124.00\u0026minus;146.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.08\u0026thinsp;\u0026plusmn;\u0026thinsp;16.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.127\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.30\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60 (0.20\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.121\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.49 (12.60\u0026minus;19.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.23 (12.54\u0026ndash;18.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.078\u003c/em\u003e\u003c/p\u003e \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\u003e18.00 (13.00\u0026ndash;26.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.00 (13.00\u0026ndash;26.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.545\u003c/em\u003e\u003c/p\u003e \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\u003e19.00 (16.00\u0026ndash;24.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.00 (15.00\u0026ndash;23.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.207\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e862 (36.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,808 (41.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.704\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonylureas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (5.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,160 (7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.183\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPP\u0026minus;4i\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278 (11.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,934 (11.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.022\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLP\u0026minus;1RA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512 (3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.007\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlitazone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339 (2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.052\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e460 (19.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3206 (19.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti platelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,876 (79.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,734 (90.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.044\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti coagulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.018\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (4.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e953 (5.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.055\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,096 (46.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,113 (43.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,165 (49.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,129 (50.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e701 (29.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,641 (34.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.802\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e463 (19.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,021 (18.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,845 (78.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,280 (87.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEzetimibe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 (21.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,082 (18.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eDAPA, Dapagliflozin; BMI, Body Mess Index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; HR, Heart Rate; COPD, Chronic Obstructive Pulmonary Disease; PCI, Percutaneous Coronary Intervention; CABG, Coronary Artery Bypass Grafting; MI, Myocardial Infarction; CI, Cerebral Infarction; AF, Atrial Fibrillation; HF, Heart Failure; eGFR, estimated Glomerular Filtration Rate; HbA1c, glycated Hemoglobin A1c; hs-TnI, high-sensitivity Troponin I; CK, Creatine Kinase; CK-MB, Creatine Kinase-MB; LDL-c, Low-Density Lipoprotein Cholesterol; HDL-c, High-Density Lipoprotein Cholesterol; BUN, Blood Urea Nitrogen; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; DPP-4i, Dipeptidyl Peptidase-4 inhibitor; GLP-1RA, Glucagon-like Peptide-1 Receptor Agonist; ACEI, Angiotensin-Converting Enzyme Inhibitor; ARB, Angiotensin Receptor Blocker; CCB, Calcium Channel Blocker.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eBasic characteristics of patients in two groups in propensity-matched dataset.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAPA users\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,071)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,071)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (59\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (58\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.490\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale gender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e601 (29.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e549 (26.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.071\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.69 (23.74\u0026ndash;27.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.71 (23.59\u0026ndash;27.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.620\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (121\u0026minus;148.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (120\u0026ndash;148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.999\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (69\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (67\u0026ndash;87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.108\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR, bpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (66\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (64\u0026ndash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.863\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e931 (44.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e964 (46.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.303\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e790 (38.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e832 (40.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.181\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,274 (61.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,232 (59.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.182\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,481 (71.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,521 (73.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.164\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (6.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.125\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngina, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e664 (32.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e650 (31.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.640\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior PCI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e402 (19.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e374 (18.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.265\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior CABG, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.894\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior MI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288 (13.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (11.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.020\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior CI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.373\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.515\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (5.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.088\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.17 (67.28\u0026minus;106.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.19 (68.53\u0026minus;107.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.647\u003c/em\u003e\u003c/p\u003e \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\u003e6.90 (6.20\u0026ndash;7.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.10 (6.20\u0026ndash;8.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.066\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-TNI, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.80 (1.04\u0026ndash;13.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.01\u0026ndash;7.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.063\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.00 (56.00\u0026minus;104.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.00 (53.00\u0026minus;109.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.045\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK-MB, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 (0.90\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (0.90\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.229\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.02 (110.60\u0026minus;160.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.18 (112.92\u0026minus;161.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.700\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.86 (56.46\u0026ndash;96.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.02 (56.84\u0026minus;99.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.595\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.83 (34.42\u0026ndash;46.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.44 (33.64\u0026ndash;47.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.233\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.63 (32.10\u0026minus;63.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.56 (34.80\u0026ndash;66.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.049\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.00 (124.50\u0026ndash;147.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.00 (126.00\u0026minus;148.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.547\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.30\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60 (0.20\u0026minus;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.408\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.74 (12.85\u0026ndash;19.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.34 (12.67\u0026ndash;18.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.076\u003c/em\u003e\u003c/p\u003e \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\u003e18.00 (13.00\u0026ndash;26.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.00 (13.00\u0026ndash;26.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.775\u003c/em\u003e\u003c/p\u003e \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\u003e19.00 (16.00\u0026ndash;24.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.00 (15.00\u0026ndash;23.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.294\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e862 (41.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e856 (41.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.850\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonylureas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (6.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.569\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPP\u0026minus;4i\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278 (13.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (10.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLP\u0026minus;1RA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (4.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.120\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlitazone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.026\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e460 (22.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e419 (20.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.119\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti platelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,876 (90.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,852 (89.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.214\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti coagulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.592\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (5.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.393\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1096 (52.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e905 (43.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,165 (56.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,019 (49.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e701 (33.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e694 (33.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.818\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e463 (22.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372 (17.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1845 (89.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,793 (86.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.013\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEzetimibe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 (24.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e386 (18.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBefore PSM, the unadjusted ORs of patients with CI-AKI\u003csub\u003eESUR\u003c/sub\u003e were 59.8% lower in the dapagliflozin user group [OR 0.402, 95%CI 0.322\u0026ndash;0.501, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.000\u003c/em\u003e] compared with the control group. Correlation analysis using KDIGO definition also rendered similar results [OR 0.306, 95% CI 0.235\u0026ndash;0.399, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.000\u003c/em\u003e]. After PSM, the adjusted OR of CI-AKI\u003csub\u003eESUR\u003c/sub\u003e remained 57.3% lower in the dapagliflozin group [OR 0.427, 95% CI 0.329\u0026ndash;0.554, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.000\u003c/em\u003e] than the control group. We also conducted subgroup analyses for different operation methods, including PCI, CAG and CCTA. The unadjusted and adjusted point estimates were qualitatively similar to the overall results. Results are shown in 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\u003eRenal outcomes in the dapagliflozin group and control group.\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnmatched population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMatched population\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\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003eCI-AKI\u003csub\u003eESUR\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.322\u0026ndash;0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.329\u0026ndash;0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.320\u0026ndash;0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.348\u0026ndash;0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.275\u0026ndash;0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.261\u0026ndash;0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u0026ndash;0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u0026ndash;1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.029\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI-AKI\u003csub\u003eKDIGO\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.235\u0026ndash;0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.234\u0026ndash;0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.222\u0026ndash;0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.232\u0026ndash;0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.006\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.210\u0026ndash;0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.197\u0026ndash;0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.780\u0026ndash;0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.291\u0026ndash;0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.000\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCAG, Coronary Angiography; CCTA, Coronary Computed Tomography Angiography; OR, Odd Ratio; CI, Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeta-analysis of cohorts\u003c/h2\u003e \u003cp\u003e \u003cem\u003eStudy Selection\u003c/em\u003e. Including the present study, we considered 6 studies with up to a total of 1,933 T2DM patients for meta-analysis. Data extraction flow was detailed in Additional File: PRISMA 2020 flow diagram. Initially, we conducted a meta-analysis, including 6 cohort studies.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eStudy Characteristics\u003c/em\u003e. The characteristics of the included studies are provided in Additional File: Supplementary File \u003cem\u003eeTable 2\u003c/em\u003e. Of the 6 studies in this meta-analysis, two were prospective cohort studies (PCS)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and four were retrospective cohort studies (RCS).\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Regarding outcome treatment, one study used both ESUR and KDIGO criteria simultaneously.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Two studies used the ESUR criteria.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e One article used the KDIGO criteria.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Another two articles adopted other standards.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eGRADE assessment\u003c/em\u003e. We upgraded the level of CoE as all the studies included in the meta-analysis showed a low risk of bias. For details, please refer to the Additional File: Supplementary File \u003cem\u003eeTable 3\u003c/em\u003e. Indirectness (the included studies compared similar interventions, similar populations, and similar outcomes), imprecision (this meta-analysis included 6,075 patients with diabetes undergoing CAG or PCI, 2,735 SGLT2i users, and 3,340 events of CI-AKI), publication bias, and inconsistency (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0\u003c/em\u003e) did not impact significantly the CoE. We assessed the CoE according to GRADE criteria as moderate.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMeta-analysis\u003c/em\u003e. Due to the different definition criteria of the outcome indicator CI-AKI in the included studies, some studies selected both ESUR and KDIGO criteria. This meta-analysis conducted two sets of analyses. In one set of analyses, when a study had both standards simultaneously, the results corresponding to the ESUR standard were selected for inclusion in the meta-analysis (Primary Outcome A). In the other set of analyses, when a study had both standards simultaneously, the results corresponding to the KDIGO criteria were selected for inclusion in the meta-analysis (Primary Outcome B).\u003c/p\u003e \u003cp\u003e \u003cem\u003ePrimary Outcomes\u003c/em\u003e. Among 6,075 patients in the 7 cohort studies, the use of SGLT2i were associated with significantly reduced CI-AKI outcomes [Primary Outcome A: RR 0.42, 95%CI 0.35\u0026ndash;0.52, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.000\u003c/em\u003e; Primary Outcome B: RR 0.37, 95%CI 0.30\u0026ndash;0.45, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.000\u003c/em\u003e] in T2DM patients after PCI/CAG/CCTA. The results are shown in detail in Fig.\u0026nbsp;2A and B.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSecondary Outcomes\u003c/em\u003e. In this meta-analysis, four included studies used the ESUR criteria for result statistics. A subgroup analysis was conducted based on the outcome treatment with the ESUR criteria. The results showed that SGLT2i could significantly reduce the incidence of CI-AKI\u003csub\u003eESUR\u003c/sub\u003e [RR 0.43, 95%CI 0.35\u0026ndash;0.53, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.000\u003c/em\u003e]. Three included studies used the KDIGO criteria for result statistics. A subgroup analysis was conducted based on the outcome treatment with the KDIGO criteria, and the results indicated that SGLT2i could significantly reduce the incidence of CI-AKI\u003csub\u003eKDIGO\u003c/sub\u003e [RR 0.36, 95%CI 0.28\u0026ndash;0.47, \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.000\u003c/em\u003e]. Please refer to Additional File: Supplementary File \u003cem\u003eeFigure 1 (A, B)\u003c/em\u003e in the attachment for details.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePublication Bias\u003c/em\u003e. The funnel plot of the included studies in our final metaanalysis did not suggest a publication bias. The Funnel plot of the included studies in the meta-analysis on the effect of developing CI-AKI after PCI/CAG/CCTA were shown in Additional File: Supplementary File \u003cem\u003eeFigure 2 (A, B).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eKey Results\u003c/h2\u003e \u003cp\u003eThis large, real-world comparative effectiveness study of SGLT2i in a Chinese population with T2DM and CCS in a single medical institution comprehensively evaluated the benefits of SGLT2i on CI-AKI. We found that dapagliflozin was associated with a much lower risk of CI-AKI in patients with T2DM after PCI/CAG/CCTA no matter using ESUR or KDIGO criteria. Moreover, across all pre-specified subgroups, whether dapagliflozin is administered prior to PCI, CAG, or CCTA, it can significantly reduce the occurrence risk of CI-AKI. The meta-analysis of the same type of cohort studies further confirmed the main result of this study, that is, SGLT2i can significantly reduce the risk of CI-AKI in patients with T2DM and CCS undergoing angiography.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Previous Studies\u003c/h2\u003e \u003cp\u003eOur study demonstrated that SGLT2i significantly reduce the risk of CI-AKI by up to 6.38% in patients with T2DM undergoing PCI/CAG/CCTA, compared to those not using any SGLT2i. This finding is consistent with several previous studies that have shown the potential nephroprotective effects of SGLT2i in this patient population.\u003c/p\u003e \u003cp\u003eHowever, there are some differences in the study populations and the definition of CI-AKI. For example, Hua et al.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e conducted a study on patients with T2DM undergoing PCI and defined CI-AKI based on the ESUR and KDIGO criteria. They found that SGLT2 inhibitors were associated with a lower risk of CI-AKI through a propensity - matched analysis, which is consistent with our finding.\u003c/p\u003e \u003cp\u003ePaolisso et al.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e And \u0026Ccedil;abuk et al.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e focused on diabetic patients with AMI undergoing PCI and used ESUR criteria. They found that SGLT2i users had a lower rate of CI-AKI, especially in patients without chronic kidney disease, supporting the nephroprotective effect of SGLT2i.\u003c/p\u003e \u003cp\u003e\u0026Ouml;zkan et al.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e specifically studied diabetic patients with non-ST segment elevation myocardial infarction (NSTEMI) and defined CI-AKI as a 0.5 mg/dL (absolute) or 25% (relative) increase in creatinine value within 48 h, an increase in creatinine level of more than 1.5 times the baseline within 7 days, or a urinary output of less than 0.5 mL/kg/h for at least 6 h after using the contrast agent compared to its level before the procedure. They found that the use of SGLT2i significantly reduced the risk of CI-AKI, which is in line with our conclusion.\u003c/p\u003e \u003cp\u003eOur study differs from some previous studies in terms of study design. For example, Bernardini et al.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e included patients undergoing PCI and divided them into three groups: those treated with new-antidiabetic drugs (including SGLT2 inhibitors), those treated with traditional antidiabetic drugs, and non-diabetic patients. They found that patients treated with new-antidiabetic drugs had a similar incidence of CI-AKI compared to non-diabetic patients, suggesting a possible protective role of these drugs.\u003c/p\u003e \u003cp\u003eThe study by Feitosa et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e was a randomized controlled trial (RCT) that aimed to evaluate the safety of empagliflozin in diabetic patients submitted to elective PCI regarding kidney function. Although they found that the use of empagliflozin was safe regarding kidney function, the incidence of CI-AKI was similar between the empagliflozin group and the control group. Our study, on the other hand, is a meta-analysis that combines the results of multiple studies, providing a more comprehensive assessment of the effect of SGLT2 inhibitors on CI-AKI.\u003c/p\u003e \u003cp\u003eOur study is consistent with previous studies that have shown the potential nephroprotective effects of SGLT2i in patients with T2DM undergoing angiography. However, further studies are needed to confirm our findings and to explore the optimal dose, type, and duration of SGLT2 inhibitor therapy to prevent CI-AKI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. First, this was a single-center study and sample selection bias was inevitable. Besides, the construction of the study model was based on a cohort study with the risk of observation and confounding biases, which were minimized by applying the PSM protocol. Furthermore, as an observational study, some information was not available, such as dosage of contrast agent. To reduce the research bias caused by the unavailability of this data, we conducted a subgroup analysis of the types of angiography operations, namely, PCI, CAG, or CCTA.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study provided more information on the protective effect of SGLT2i on CI-AKI in patients with T2DM after angiography. In real-world patients, SGLT2i was associated with much lower CI-AKI risk no matter undergoing PCI or CAG or CCTA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAuthor Acknowledgements\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was financially supported by National High Level Hospital Clinical Research Funding (Grant number: BJ-2023-199).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDeping LIU, Xin HU, Pengfei JIN and Zinan ZHAO designed the research. Tianqi ZHANG, Yatong ZHANG, Ming LAN and Chao TIAN performed experiments and collected data. Chenguang YANG, Chi ZHANG and Zinan ZHAO analyzed the data. Zinan ZHAO wrote the manuscript. Deping LIU, Pengfei JIN, and Zinan ZHAO participated in the discussion of the results. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohanne Silvain, Lee S, Nguyen V, Spagnoli M, Kerneis P, Guedeney N, Vignolles, et al. Contrast-induced acute kidney injury and mortality in ST elevation myocardial infarction treated with primary percutaneous coronary intervention. Heart. 2018;104(9):767\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/heartjnl-2017-311975\u003c/span\u003e\u003cspan address=\"10.1136/heartjnl-2017-311975\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawlat Sany H, Refaat Y, Elshahawy A, Mohab H, Ezzat. 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Ottawa Hospital Research Institute. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ohri.ca/programs/clinical_epidemiology/oxford.asp\u003c/span\u003e\u003cspan address=\"https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Accessed October 30, 2023].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGizem, \u0026Ccedil;abuk. Kutluhan Eren Hazır. Do Sodium-Glucose Cotransporter 2 Inhibitors Decrease the Risk of Contrast-Associated Acute Kidney Injury in Patients with Type II Diabetes Mellitus? Anatol J Cardiol, 2024, 28(5): 222\u0026ndash;228. doi: 20.14744/AnatolJCardiol.2024.3980.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUğur \u0026Ouml;zkan M, G\u0026uuml;rdoğan. The Effect of SGLT2 Inhibitors on the Development of Contrast-Induced Nephropathy in Diabetic Patients with Non-ST Segment Elevation Myocardial Infarction. Med (Kaunas). 2023;59(3):505. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/medicina59030505\u003c/span\u003e\u003cspan address=\"10.3390/medicina59030505\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeitosa MPM, Lima EG et al. Alexandre Ant\u0026ocirc;nio Cunha Abizaid,. The safety of SGLT-2 inhibitors in diabetic patients submitted to elective percutaneous coronary intervention regarding kidney function: SAFE-PCI pilot study. Diabetol Metab Syndr, 2023, 15(1): 138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13098-023-01107-9\u003c/span\u003e\u003cspan address=\"10.1186/s13098-023-01107-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlos G, Santos-Gallego G, Palamara JA, Requena-Ibanez, et al. Pretreatment with SGLT2 inhibitors ameliorates contrast-induced nephropathy. JACC. 2020;74(11):1351\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dapagliflozin, SGLT2 inhibitors, contrast-induced AKI, Type 2 diabetes mellitus, Chronic coronary syndrome, Angiography","lastPublishedDoi":"10.21203/rs.3.rs-5244417/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5244417/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e SGLT2 inhibitor (SGLT2i) may reduce the risk of contrast-induced acute kidney injury (CI-AKI) in patients with type 2 diabetes mellitus (T2DM) with chronic coronary syndrome (CCS) undergoing angiography. However, the evidence is still inconclusive. We aimed to conduct a real world study and systematically review to provide updated and larger-scale evidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e: Ambispective Cohort Study and Meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting \u0026amp; population\u003c/strong\u003e: Patients with T2DM and CCS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e The data was obtained from December 2017 to July 2024. Propensity score techniques were applied to enhance between-group comparability. We analyzed CI-AKI\u003csub\u003eESUR\u003c/sub\u003e and CI-AKI\u003csub\u003eKDIGO\u003c/sub\u003e and conducted subgroup analyses based on the types of angiographic procedures, including percutaneous coronary interventions (PCI), coronary arteriography (CAG), and Coronary Computed Tomographic Angiography (CCTA). We retrieved similar cohort studies from the literature to perform a meta-analysis. Results from trials reporting CI-AKI\u003csub\u003eESUR\u003c/sub\u003e and/or CI-AKI\u003csub\u003eKDIGO\u003c/sub\u003e rates among patients randomized to SGLT2i versus placebo were also meta-analysed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e A total of 2,350 patients receiving dapagliflozin and 16,251 patients did not receiving any SGLT2i were included before PSM. 2,071 SGLT2i users were matched with 2,071 control patients. The incidence of primary outcome 1 and 2 were both significant lower in SGLT2i group than in the control group, which were both confirmed before and after PSM analysis. Subgroup analysis showed that the incidence of CI-AKI in the SGLT2i group was significantly lower after either PCI, CAG or CCTA. The meta-analysis of cohort studies further confirmed this result, that is, the rate of CI-AKI occurrence after angiography in the SGLT2i group was significantly lower than in the control group regardless of which criterion for CI-AKI was used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e Results may be limited by single-center nature, inevitable sample selection bias, etc. and subgroup analysis of angiography operation types was conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e In real-world T2DM patients, SGLT2i was associated with lower CI-AKI risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e: Chinese Clinical Trial Registry, identifier: ChiCTR2300076484\u003c/p\u003e","manuscriptTitle":"Protective Effect of SGLT2i on contrast-induced AKI after Angiography in Patients with Type 2 Diabetes Mellitus and Chronic Coronary Syndrome: A 6-year Ambispective Cohort Study and Meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 08:36:32","doi":"10.21203/rs.3.rs-5244417/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f88093f-39d9-420d-83f0-c03f9a1422d4","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-23T08:36:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-23 08:36:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5244417","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5244417","identity":"rs-5244417","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}