Cardiovascular outcome of Glucagon-Like Peptide-1 Receptor Agonists vs Dipeptidyl Peptidase-4 Inhibitor on End-stage Renal Disease patients with Heart Failure: An Emulated Target Trial in Patients with Diabetes

Cardiovascular outcome of Glucagon-Like Peptide-1 Receptor Agonists vs Dipeptidyl Peptidase-4 Inhibitor on End-stage Renal Disease patients with Heart Failure: An Emulated Target Trial in Patients with Diabetes | 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 Article Cardiovascular outcome of Glucagon-Like Peptide-1 Receptor Agonists vs Dipeptidyl Peptidase-4 Inhibitor on End-stage Renal Disease patients with Heart Failure: An Emulated Target Trial in Patients with Diabetes Chih-Hen Yu, Po-Yi Liu, Chao-Kuei Shih, Miyuki Hsing-Chun Hsieh, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7722921/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Globally, an estimated 4.6 million individuals live with end-stage renal disease (ESRD), with cardiovascular disease the leading cause of death. Heart failure (HF) affects over one-third of dialysis patients, yet effective therapies remain scarce as trials largely excluded this population. Using TriNetX, we emulated a target trial comparing glucagon-like peptide-1 receptor agonists (GLP-1RAs) with dipeptidyl peptidase-4 inhibitors (DPP-4is) in ESRD patients with HF. Among 5,087 eligible patients, 1:1 propensity score matching yielded 1,257 pairs. GLP-1RAs use was associated with risk reduction of the primary composite of ischemic cardiovascular events and HF exacerbations (30.1% vs. 40.1%; HR 0.71(0.62–0.81), p <0.001), lowering ischemic events (HR 0.69), HF exacerbations (HR 0.73), and mortality (HR 0.67). Subgroup analyses showed benefit across prevention settings, baseline therapy, and HF subtypes. Findings suggest GLP-1RAs may provide cardiovascular benefit in ESRD patients with HF, a high-risk group underserved in clinical trials, and warrant confirmation in randomized studies. Health sciences/Diseases/Cardiovascular diseases/Heart failure Health sciences/Diseases/Kidney diseases/Chronic kidney disease/End-stage renal disease Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Globally, approximately 4.6 million individuals are living with end-stage renal disease (ESRD) requiring kidney replacement therapy 1 . Among them, heart failure (HF) emerges as a prevalent and devastating complication, defining a particularly high-risk phenotype 2 . More than one-third of patients undergoing dialysis develop HF 3, 4 . The presence of HF in ESRD dramatically worsens outcomes 5, 6 , with cardiovascular events, including HF exacerbations, accounting for nearly half of all deaths in this population 7, 8 . Despite this substantial burden, few therapeutic interventions have convincingly reduced cardiovascular events in this vulnerable group 9, 10, 11 . Evidence remains scarce because randomized clinical trials of foundational guideline-directed medical therapies (GDMT) of HF, including β-blockers, renin–angiotensin system inhibitors, angiotensin receptor–neprilysin inhibitors, mineralocorticoid receptor antagonists, and sodium–glucose cotransporter-2 inhibitors, have systematically excluded patients with ESRD 12 . As a result, current guideline recommendations offer little guidance on disease-modifying pharmacotherapies for HF in this population 12, 13, 14 . Moreover, observational data consistently show that patients with HF in dialysis are less likely to receive GDMT and consequently experience worse outcomes than their non-ESRD counterparts 15 , underscoring the urgent need to identify effective and broadly applicable therapies in this high-risk setting. In this context, glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have emerged as promising agents with cardiovascular benefits beyond glycemic control. While earlier cardiovascular outcome trials (CVOTs) established their efficacy in reducing major adverse cardiovascular events (MACE) in high-risk patients with type 2 diabetes (T2D) 16 , more recent evidence suggests that their clinical relevance may extend to heart failure as well 17 . GLP-1 RAs significantly reduced HF worsening events and the composite of cardiovascular death or HF hospitalization 18 . The FLOW trial, specifically in chronic kidney disease population, demonstrated that semaglutide significantly lowered the risk of heart failure (HF) events and cardiovascular death 19 . Given the shared risk factors and pathophysiological continuum between CKD and ESRD, these findings raise the possibility that GLP-1 RAs could provide meaningful cardiovascular benefit in patients with HF in ESRD—a subgroup at especially high risk yet largely underserved by current pharmacologic evidence. Building on these signals, a critical and urgent question is whether GLP-1 RAs can improve cardiovascular outcomes in patients with HF in ESRD—a population with profound cardiovascular vulnerability and virtually no proven pharmacologic options. Historically, ESRD represents one of the most treatment-refractory settings for cardiovascular disease and no established pharmacotherapy treatment has consistently reduced major adverse cardiovascular events or improved survival among patients with ESRD 20 . Prior observational evidence, such as a nationwide retrospective cohort study in Taiwan, has suggested benefit for reducing mortality of GLP-1 RAs in diabetic advanced CKD or ESRD compared with cardiovascular-neutral agents like DPP-4 inhibitors 21 . Whether these agents can also reduce cardiovascular events—including ischemic events and HF exacerbations—that ultimately drive mortality in ESRD is largely unknown. To address this evidence gap, we emulated a target trial within the global TriNetX research network to evaluate the comparative effectiveness of GLP-1 RAs versus DPP-4 inhibitors in patients with HF in ESRD. This design enabled us, in a real-world setting, to broaden the representativeness of the study population and directly test the hypothesis that GLP-1 RAs may confer cardiovascular benefit in this uniquely high-risk and underserved group. Results Patient selection and Demographic characteristics Between August 1, 2016, and August 22, 2025, 176,327,258 individuals were identified in TriNetX. After applying inclusion and exclusion criteria, 5,087 patients with HF and ESRD enrolled, including 1,571 GLP-1RA initiators and 3,516 DPP-4i initiators. Following 1:1 propensity score matching, 1,257 pairs (n = 2,514) comprised the analytic cohort for the target emulated trial (Fig. 1 , Supplementary Table 1). For the pre-matching enrolled cohort (n = 5,087), we included only new users who initiated the study medication subsequent to receiving both a HF diagnosis within the previous 2 years and an ESRD diagnosis within the previous 2 years. This population was confirmed by total coverage of first incident HF (ICD10CM:I50) and first incident ESRD diagnosis (ICD10CM:N18.6) ( Table 1 , Supplementary Fig. 1 and Supplementary Table 2). Before propensity score matching, baseline characteristics differed substantially between GLP-1RA initiators (n = 1,571) and DPP-4 inhibitor initiators (n = 3,516). Patients receiving GLP-1RA were younger (61.0 ± 11.5 vs 66.4 ± 12.3 years) and more often had overweight/obesity (50.3% vs 25.0%). They more frequently had histories of ischemic heart disease (53.8% vs 48.3%), hypertension (71.0% vs 56.3%) and hypertensive heart disease (20.6% vs 13.1%), and hyperlipidemia (59,8% vs 48.5%), and exhibited higher hemoglobin A1c (HbA1c) values. Left ventricular ejection fraction (LVEF) was comparable between the GLP-1RA and DPP-4i groups (51.1 ± 15.4 vs 52.8 ± 14.5, n = 171 and 345), among the subset of patients with available measurements. HF GDMTs—including SGLT2 inhibitors, β-blockers, renin-angiotensin system inhibitors, and mineralocorticoid receptor antagonists—were more frequently used in the GLP-1RA group (Table 1 , Supplementary Table 3, and Supplementary Table 4). After 1:1 propensity score matching, 1,257 pairs were retained with well-balanced baseline covariates (all standardized mean differences < 0.10; Fig. 2 ). Table 1 Baseline characteristics of GLP-1 RA and DPP4i groups before and after matching. Variables† Before matching After matching GLP-1 RA group‡ (n = 1,571) DPP4i group (n = 3,516) Standardized difference GLP-1 RA group (n = 1,257) DPP4i group (n = 1,257) Standardized difference Age at index, years Mean (SD) 61 (11.5) 66.4 (12.3) 0.453 62.3 (11) 62.1 (12.2) 0.02 Sex, n (%) Female 670 (42.6) 1,496 (42.5) 0.002 540 (43) 530 (42.2) 0.016 Male 869 (55.3) 1,957 (55.7) 0.007 695 (55.3) 706 (56.2) 0.018 Race, n (%) White 778 (49.5) 1,255 (35.7) 0.282 593 (47.2) 584 (46.5) 0.014 Black or African American 409 (26) 824 (23.4) 0.06 329 (26.2) 335 (26.7) 0.011 Asian 74 (4.7) 480 (13.7) 0.313 73 (5.8) 81 (6.4) 0.027 Other Race 141 (9) 192 (5.5) 0.136 112 (8.9) 110 (8.8) 0.006 Unknown Race 116 (7.4) 682 (19.4) 0.358 108 (8.6) 106 (8.4) 0.006 Diagnosis of Heart failure, n (%)§ Heart failure 1,571(100) 3.516(100) 1,257(100) 1,257(100) HFpEF 637 (40.5) 1,110 (31.6) 0.188 474 (37.7) 473 (37.6) 0.002 HFrEF or HFmrEF 451 (28.7) 933 (26.5) 0.049 339 (27) 340 (27) 0.002 Combined HFpEF and HFrEF 243 (15.5) 435 (12.4) 0.09 177 (14.1) 165 (13.1) 0.028 Heart failure, unspecified 887 (56.5) 1,816 (51.7) 0.097 671 (53.4) 669 (53.2) 0.003 Other heart failure 97 (6.2) 112 (3.2) 0.142 54 (4.3) 59 (4.7) 0.019 Left ventricular failure 32 (2) 88 (2.5) 0.031 29 (2.3) 21 (1.7) 0.046 Dialysis profile, n (%) ¶ End-stage renal disease 1,571(100) 3.516(100) 1,257(100) 1,257(100) Peritoneal dialysis 16 (1) 23 (0.7) 0.04 10 (0.8) 12 (1) 0.017 Dialysis procedure other than hemodialysis 66(4.3) 87(2.8) 0.08 50 (4.1) 42 (3.4) 0.034 Types of hemodialysis access, n (%) Arteriovenous fistula 167 (10.6) 240 (6.8) 0.135 116 (9.2) 117 (9.3) 0.003 Arteriovenous graft 42 (2.7) 82 (2.3) 0.022 32 (2.5) 33 (2.6) 0.005 Temporary hemodialysis catheter insertion 10 (0.6) 10 (0.3) 0.052 10 (0.8) 10 (0.8) < 0.001 Cardiovascular disease, n (%) Ischemic heart diseases 846 (53.9) 1,699 (48.3) 0.111 639 (50.8) 624 (49.6) 0.024 Hypertensive heart disease 323 (20.6) 460 (13.1) 0.201 226 (18) 213 (16.9) 0.027 Cardiomyopathy 259 (16.5) 470 (13.4) 0.088 186 (14.8) 179 (14.2) 0.016 Chronic rheumatic heart diseases 187 (11.9) 450 (12.8) 0.027 143 (11.4) 139 (11.1) 0.01 Atrial fibrillation 280 (17.8) 575 (16.4) 0.039 207 (16.5) 199 (15.8) 0.017 Cerebrovascular diseases 261 (16.6) 526 (15) 0.045 199 (15.8) 198 (15.8) 0.002 Peripheral vascular disease 270 (17.2) 517 (14.7) 0.068 201 (16) 188 (15) 0.029 Alcohol related disorders 55 (3.5) 79 (2.2) 0.075 39 (3.1) 41 (3.3) 0.009 Nicotine dependence 108 (6.9) 171 (4.9) 0.086 74 (5.9) 87 (6.9) 0.042 Hypertension 1,116 (71) 1,981 (56.3) 0.309 838 (66.7) 828 (65.9) 0.017 Dyslipidemia 939 (59.8) 1,707 (48.5) 0.227 714 (56.8) 705 (56.1) 0.014 Overweight and obesity 791 (50.4) 879 (25) 0.542 538 (42.8) 542 (43.1) 0.006 Comorbidities, n (%) Chronic obstructive pulmonary diseases 274 (17.4) 525 (14.9) 0.068 209 (16.6) 207 (16.5) 0.004 Neoplasms 451 (28.7) 933 (26.5) 0.049 339 (27) 340 (27) 0.002 Liver cirrhosis 137 (8.7) 261 (7.4) 0.048 99 (7.9) 93 (7.4) 0.018 Systemic lupus erythematosus 10 (0.6) 20 (0.6) 0.009 10 (0.8) 10 (0.8) < 0.001 Glomerular diseases 137 (8.7) 261 (7.4) 0.048 99 (7.9) 93 (7.4) 0.018 Systolic blood pressure, mmHg, Mean (SD) 131.8 (23.4) 131.3 (27.9) 0.016 132.1 (23.5) 131.7 (27.7) 0.017 BMI, kg/m², Mean (SD) 34.8 (8.2) 29.3 (7.2) 33.5 (7.8) 32.5 (7.5) ≤18.5, n (%) 50 (3.2) 175 (5) 0.091 39 (3.1) 37 (2.9) 0.009 18.5–25, n (%) 207 (13.2) 822 (23.4) 0.266 191 (15.2) 184 (14.6) 0.016 25–30, n (%) 425 (27.1) 1,051 (29.9) 0.063 365 (29) 359 (28.6) 0.011 30–35, n (%) 552 (35.1) 859 (24.4) 0.236 421 (33.5) 418 (33.3) 0.005 35–40, n (%) 489 (31.1) 485 (13.8) 0.425 324 (25.8) 326 (25.9) 0.004 ≥ 40, n (%) 393 (25) 324 (9.2) 0.429 228 (18.1) 233 (18.5) 0.01 Laboratory examination Sodium, mmol/L, Mean (SD) 137.4 (3.7) 136.6 (4) 0.222 137.5 (3.7) 136.8 (3.8) 0.012 Potassium, mmol/L, Mean (SD) 4.4 (0.6) 4.4 (0.7) 0.027 4.4 (0.6) 4.4 (0.7) 0.005 Calcium, mmol/L, Mean (SD) 8.9 (0.9) 8.7 (0.9) 0.097 8.9 (0.9) 8.7 (0.8) 0.012 Phosphate, mmol/L, Mean (SD) 4.5 (1.5) 4.6 (1.6) 0.054 4.4 (1.5) 4.6 (1.5) 0.091 Albumin, g/dL, Mean (SD) 3.5 (0.6) 3.3 (0.7) 0.126 3.5 (0.6) 3.4 (0.7) < 0.001 BUN, mg/dL, Mean (SD) 44.9 (23.4) 49.5 (27.6) 0.181 45.3 (23.4) 47.7 (25.9) 0.095 Hemoglobin, g/dL, Mean (SD) 10.8 (2) 10 (1.8) 0.038 10.7 (2) 10.2 (1.9) 0.021 Iron, µg/dL, Mean (SD) 58.4 (34.7) 55.2 (33.5) 0.128 58.3 (35) 54 (32.1) < 0.001 Transferrin, mg/dL, Mean (SD) 194.9 (57.4) 177.7 (55.1) 0.307 188.8 (54.8) 187.5 (59.6) 0.024 Iron binding capacity, µg/dL, Mean (SD) 791.3 (4060.6) 3009.1 (24452) 0.127 787.4 (4036.7) 1358.8 (11170.2) 0.068 Parathyroid hormone, pg/mL, Mean (SD) 273.4 (288.4) 278.8 (276.5) 0.019 269.1 (258.8) 265.3 (295.9) 0.014 Ferritin, µg/dL, Mean (SD) 483.5 (611) 871.1 (6473.2) 499.8 (647) 516.1 (716.7) ≤100, n (%) 224 (14.3) 348 (9.9) 0.134 157 (12.5) 155 (12.3) 0.005 100–200, n (%) 221 (14.1) 376 (10.7) 0.103 162 (12.9) 172 (13.7) 0.023 ≥200, n (%) 434 (27.6) 936 (26.6) 0.023 322 (25.6) 318 (25.3) 0.007 Hemoglobin A1c, %, Mean (SD) 8 (2.2) 7.3 (1.8) 7.9 (2.2) 7.6 (1.9) 5-6.5, n (%) 461 (29.3) 1,020 (29) 0.007 359 (28.6) 340 (27) 0.034 6.5–7.5, n (%) 490 (31.2) 969 (27.6) 0.08 377 (30) 385 (30.6) 0.014 7.5–12, n (%) 643 (40.9) 985 (28) 0.274 453 (36) 456 (36.3) 0.005 ≥12, n (%) 136 (8.7) 151 (4.3) 0.178 81 (6.4) 88 (7) 0.022 Cholesterol in LDL, mg/dL, Mean (SD) 78.8 (43.5) 79 (43.5) 76.6 (40) 76.1 (42.1) ≤55, n (%) 321 (20.4) 543 (15.4) 0.13 228 (18.1) 223 (17.7) 0.01 55–70, n (%) 247 (15.7) 468 (13.3) 0.069 186 (14.8) 178 (14.2) 0.018 70–100, n (%) 369 (23.5) 620 (17.6) 0.145 248 (19.7) 253 (20.1) 0.01 100–130, n (%) 224 (14.3) 388 (11) 0.097 163 (13) 153 (12.2) 0.024 130–160, n (%) 104 (6.6) 186 (5.3) 0.056 77 (6.1) 71 (5.6) 0.02 ≥ 160, n (%) 108 (6.9) 158 (4.5) 0.103 74 (5.9) 73 (5.8) 0.003 Natriuretic peptide B, pg/mL, Mean (SD) 1165.6 (3862.1) 2033.9 (5579.4) 1373.9 (4338.1) 1626.9 (4841.8) ≤100, n (%) 164 (10.4) 175 (5) 0.206 111 (8.8) 118 (9.4) 0.019 100–300, n (%) 214 (13.6) 291 (8.3) 0.172 146 (11.6) 156 (12.4) 0.024 300–600, n (%) 181 (11.5) 296 (8.4) 0.104 130 (10.3) 135 (10.7) 0.013 ≥600, n (%) 254 (16.2) 561 (16) 0.006 197 (15.7) 200 (15.9) 0.007 Natriuretic peptide.B prohormone N-Terminal, pg/mL, Mean (SD) 9160.4 (14210.6) 17390.6 (18498.8) 10841.2 (15225.4) 13104 (14720.8) ≤400, n (%) 65 (4.1) 52 (1.5) 0.161 32 (2.5) 35 (2.8) 0.015 400–800, n (%) 73 (4.6) 51 (1.5) 0.187 31 (2.5) 39 (3.1) 0.039 800–1200, n (%) 49 (3.1) 52 (1.5) 0.11 25 (2) 23 (1.8) 0.012 1200–70000, n (%) 248 (15.8) 551 (15.7) 0.003 186 (14.8) 184 (14.6) 0.004 ≥70000, n (%) 10 (0.6) 21 (0.6) 0.005 10 (0.8) 10 (0.8) < 0.001 LVEF, %, Mean (SD) 51.1 (15.4) 52.8 (14.5) 0.035 50.7 (15.4) 53.6 (13.2) 0.011 Heart failure guideline-directed medical therapy, n (%) Beta blockers 1,135 (72.2) 2,136 (60.8) 0.245 849 (67.5) 850 (67.6) 0.002 ACEi 426 (27.1) 628 (17.9) 0.223 306 (24.3) 312 (24.8) 0.011 ARB/ARNI 497 (31.6) 891 (25.3) 0.14 365 (29) 342 (27.2) 0.041 Spironolactone 206 (13.1) 281 (8) 0.167 134 (10.7) 123 (9.8) 0.029 SGLT2i 176 (11.2) 120 (3.4) 0.303 97 (7.7) 80 (6.4) 0.053 Cardiovascular agents, n (%) Aspirin 837 (53.3) 1,553 (44.2) 0.183 631 (50.2) 621 (49.4) 0.016 Heparin 881 (56.1) 1,686 (48) 0.163 665 (52.9) 646 (51.4) 0.03 Clopidogrel 317 (20.2) 595 (16.9) 0.084 221 (17.6) 220 (17.5) 0.002 Ticagrelor 39 (2.5) 67 (1.9) 0.039 27 (2.1) 27 (2.1) < 0.001 Prasugrel 10 (0.6) 17 (0.5) 0.021 10 (0.8) 10 (0.8) < 0.001 Organic nitrates 589 (37.5) 1,203 (34.2) 0.068 443 (35.2) 426 (33.9) 0.028 Digoxin 40 (2.5) 82 (2.3) 0.014 30 (2.4) 29 (2.3) 0.005 Amiodarone 148 (9.4) 293 (8.3) 0.038 105 (8.4) 101 (8) 0.012 Hypertensive drugs, n (%) Calcium channel blockers 905 (57.6) 1,738 (49.4) 0.164 685 (54.5) 680 (54.1) 0.008 Hydralazine 754 (48) 1,352 (38.5) 0.194 555 (44.2) 559 (44.5) 0.006 Anti-diabetic drugs, n (%) Sulfonylurea 242 (15.4) 451 (12.8) 0.074 193 (15.4) 194 (15.4) 0.002 Insulin and analogues 1,093 (69.6) 2,091 (59.5) 0.212 834 (66.3) 817 (65) 0.028 Lipid-lowering agents, n (%) Antilipemic agents 1,081 (68.8) 1,842 (52.4) 0.341 803 (63.9) 775 (61.7) 0.046 HMG CoA reductase inhibitors 1,051 (66.9) 1,798 (51.1) 0.325 783 (62.3) 754 (60) 0.047 Fibrates 71 (4.5) 83 (2.4) 0.119 48 (3.8) 47 (3.7) 0.004 Ezetimibe 70 (4.5) 104 (3) 0.079 53 (4.2) 52 (4.1) 0.004 Nicotinic acid and derivatives 29 (1.8) 64 (1.8) 0.002 22 (1.8) 18 (1.4) 0.025 NSAID, n (%) 394 (25.1) 593 (16.9) 0.203 277 (22) 268 (21.3) 0.017 Abbreviations : ACEi angiotensin-converting enzyme inhibitor,ARB angiotensin receptor blocker, ARNI angiotensin receptor–neprilysin inhibitor, BMI body mass index, BUN blood urea nitrogen, DPP-4i dipeptidyl peptidase-4 inhibitor, GLP-1 RA glucagon-like peptide-1 receptor agonist, HFpEF heart failure with preserved ejection fraction, HFrEF heart failure with reduced ejection fraction, HFmrEF heart failure with mildly reduced ejection fraction, LDL low-density lipoprotein, NSAID non-steroidal anti-inflammatory drug, SD standard deviation. Note : † Baseline covariates definitions are provided in Supplementary Table 3 . ‡ Distribution of GLP-1 receptor agonist use among study participants is provided in Supplementary Table 4 . § Heart failure phenotypes were classified based on ICD-10-CM codes. HFpEF was approximated using I50.3 (“diastolic heart failure”), while reduced-EF heart failure (combining HFrEF and HFmrEF) was defined using I50.2 (“systolic heart failure”). Systolic HF codes (I50.2) identify patients with EF ≤ 50% with a positive predictive value (PPV) of ~ 90%, and diastolic HF codes (I50.3) identify patients with EF > 50% with a PPV of ~ 92% 36 . ¶ Population with dialysis related code or kidney transplant code are provided in Supplementary Table 8. Main analysis The prespecified primary outcome was a composite of ischemic cardiovascular events (acute myocardial infarction, ischemic stroke, or cardiac arrest, with diagnosis codes that may also capture cardiovascular deaths) and heart failure exacerbation, approximating a conventional 3-point MACE (Supplementary Table 5). For the primary outcome, event rate of composite ischemic cardiovascular and heart failure exacerbation events was significantly lower in GLP-1 receptor agonist than DPP-4 inhibitor (30.1% vs. 40.1%; hazard ratio [HR], 0.71; 95% CI, 0.62–0.81; p < 0.001; Table 2 ). Kaplan-Meier analysis showed separation of the curves within the follow-up period, with consistently fewer events in the GLP-1 receptor agonist group (log-rank p < 0.001; Fig. 3 ). The calculated E-value was 2.2 (95% upper confidence limit, 1.8), indicating that only a strong unmeasured confounder could explain the observed association. Ischemic cardiovascular events occurred at rates of 16.9% vs 24% (HR, 0.69; 95% CI, 0.58–0.83; p < 0.001; E-value, 2.2 [1.7]), and heart failure exacerbation events at 20.7% vs 27.3% (HR, 0.73; 95% CI, 0.62–0.83; p < 0.001; E-value, 2.1 [1.7]). Rates of acute myocardial infarction, stroke, and cardiac arrest were also significantly lower in the GLP-1 RA group, with hazard ratios of 0.76 ( p = 0.012), 0.71 ( p = 0.026), and 0.47 ( p < 0.001), respectively. Consistent with these results, multivariable Cox regression in the entire cohort—adjusting for the same covariates used in the propensity-score model—yielded a similar effect estimate (HR, 0.76; 95% CI, 0.68–0.85; p < 0.001, Supplementary Table 6). Secondary outcomes, both all-cause mortality (event rates 11.8% vs 17.7%, HR, 0.67; 95%CI, 0.54–0.86; p < 0.001) and hospitalization, were significantly reduced in the GLP-1 receptor agonist group ( p < 0.001). The mean follow-up time was 464 ± 285 days in the GLP-1RA group and 473 ± 289 days in the DPP-4i group, and the median follow-up time was 581 days in the GLP-1RA group and 651 days in the DPP-4i group. An exploratory analysis shows that the mean levels of HbA1c and body mass index (BMI) remained largely stable throughout follow-up (Supplementary Table 7). Table 2 Incidence of outcomes of interest among the glucagon-like peptide 1 receptor agonists users compared to dipeptidyl peptidase-4 inhibitor users after propensity score matching GLP-1 receptor agonist DPP-4 inhibitor Hazard Ratio (95% CI) p value E-value(95% UCL) Outcomes Events, n (%) Events, n (%) Primary outcome †‡§ Composite ischemic cardiovascular and heart failure exacerbation events†‡ 379/1257 (30.1%) 504/1257(40.1%) 0.708(0.62–0.809) < 0.0001 2.2(1.8) Heart failure exacerbation† 260/1257(20.7%) 343/1257(27.3%) 0.73(0.624–0.827) < 0.0001 2.1(1.7) Ischemic cardiovascular event†‡ 213/1257(16.9%) 302/1257(24.0%) 0.694(0.582–0.827) < 0.0001 2.2(1.7) Acute myocardial infarction† 140/1257(11.1%) 185/1257(14.7%) 0.755(0.606–0.94) 0.0121 2.0(1.3) Stroke† 74/1257(5.9%) 104/1257(8.3%) 0.714(0.53–0.962) 0.0267 2.1(1.2) Cardiac arrest† 33/1257(2.6%) 69/1257(5.5%) 0.466 (0.308–0.704) 0.0002 3.7(2.2) Secondary outcome All-cause mortality* 149/1257(11.8%) 223/1257(17.7%) 0.67(0.544–0.861) 0.0006 2.3(1.6) Hospitalization¶ 697/1257(55.4%) 803/1257(63.9%) 0.745(0.673–0.825) < 0.0001 2.0(1.7) Abbreviations: UCL upper confidence limit, CI confidence interval, DPP-4i dipeptidyl peptidase-4 inhibitor, GLP-1 RA glucagon-like peptide-1 receptor agonist Note: † Outcome definitions: Composite of ischemic cardiovascular and heart failure (HF) exacerbation events : acute on chronic systolic (congestive) HF (ICD-10-CM: I50.23), acute on chronic diastolic (congestive) HF (I50.33), pulmonary edema (J81), cerebral infarction (I63), cerebral ischemia (I67.82), cardiac arrest (I46), acute myocardial infarction (I21), subsequent ST elevation (STEMI) and non-ST elevation (NSTEMI) myocardial infarction (I22), and acute ischemic heart disease, unspecified (I24.9), Heart failure exacerbation (HFE) : Defined as acute on chronic systolic (congestive) HF (I50.23), acute on chronic diastolic (congestive) HF (I50.33), or pulmonary edema (J81). Ischemic cardiovascular outcome: Defined as cerebral infarction (I63), cerebral ischemia (I67.82), cardiac arrest (I46), acute myocardial infarction (I21), subsequent STEMI and NSTEMI myocardial infarction (I22), or acute ischemic heart disease, unspecified (I24.9). Acute myocardial infarction (AMI): Defined as acute myocardial infarction (I21), subsequent STEMI and NSTEMI myocardial infarction (I22), or acute ischemic heart disease, unspecified (I24.9). Stroke: Defined as cerebral infarction (I63) or cerebral ischemia (I67.82). Cardiac arrest: Defined as cardiac arrest infarction (I46) (Supplementary Table 5) ‡ These definitions were not restricted to non-fatal myocardial infarction or non-fatal stroke; the diagnosis codes used for myocardial infarction and stroke may also encompass cardiovascular deaths. Accordingly, the composite outcome in this study more closely reflects a 3-point major adverse cardiovascular event (MACE), consistent with the operational definitions adopted in prior database studies 40, 41 § Sensitivity analyses for composite ischemic cardiovascular events, heart failure exacerbations, and all-cause mortality accounting for competing risk of death are provided in Supplementary Table 13. * All-cause Mortality : Defined by demographic records indicating death (Deceased) or diagnosis of ill-defined and unknown cause of mortality (ICD-10-CM: R99). ¶ Hospitalization : Defined by any of the following visit types: inpatient acute (HL7V3.0:Visit ACUTE), inpatient encounter (IMP), inpatient non-acute (NONAC), emergency (EMER), short stay (SS), or observation encounter (OBSENC) (Supplementary Table 5) Subgroup analyses Subgroup analyses showed no mediation effects for primary outcome by sex, age, types, coded-based HF subtype, and prevalent or incident HF in ESRD (all interaction test p > 0.05). Stratification by baseline use of GDMTs for heart failure (β-blockers, renin–angiotensin system inhibitors, mineralocorticoid receptor antagonists, and SGLT2 inhibitors) demonstrated consistent protective associations. When stratified by ischemic cardiovascular history, the hazard ratio was 0.81 (95% CI, 0.63–1.03; p = 0.087) among patients with prior events, and 0.71 (95% CI, 0.6–0.83; p < 0.001) among those without prior events, without mediation effect ( p for interaction 0.418) (Fig. 4 ). HF subtype, classified according to coded-based heart failure with reduced ejection fraction (HFrEF), mildly reduced ejection fraction (HFmrEF), or preserved ejection fraction (HFpEF), showed consistent protective associations (HFpEF vs HFmrEF or HFrEF, p for interaction = 0.335). Subgroup analysis of all-cause mortality was demonstrated in Supplementary Fig. 2. Sensitivity analysis Distribution of dialysis-related codes or kidney transplant codes are described in detail in Supplementary Table 8. Restricting the cohort to patients coded for dialysis dependence preserved the protective association (HR, 0.70; 95% CI, 0.59–0.84; p < 0.001; Supplementary Table 9). Excluding patients who underwent kidney transplantation after index day (HR, 0.74; 95% CI, 0.64–0.85; p < 0.001; Supplementary Table 10) or those who switched to the alternative drug class produced results consistent with the primary analysis (HR, 0.71; 95% CI, 0.61–0.83; p < 0.001; Supplementary Table 11). Findings were also similar in analyses restricted to patients with extended population with prevalent heart failure (Supplementary Table 12). Sensitivity analyses accounting for competing risks showed a consistent protective association for the composite outcome, suggesting minimal impact of competing risk (Supplementary Table 13, Supplementary Fig. 3). Varying the permissible window for treatment initiation at 6, 9, 12, 15, and 18 months yielded hazard ratios consistent with the main analysis (Supplementary Table 14). Evaluation of negative-control outcomes (skin cancer and traumatic brain injury) showed no significant associations, whereas the positive-control outcome (nausea) showed an increased risk among GLP-1 receptor agonist users as expected (Supplementary Table 15). Discussion In this large-scale target trial emulation, we provide the first real-world evidence that GLP-1 RAs significantly reduce cardiovascular events—including ischemic cardiovascular complications and HF exacerbations—in patients with HF in ESRD. This ESRD population was historically excluded from randomized controlled trials, carries extraordinarily high cardiovascular risk. Importantly, the benefits were consistent across clinically relevant subgroups, including those with or without prior MACE, HF subtypes (coded based HFpEF vs HFmrEF or HFrEF), and remained evident on top of contemporary GDMT. These findings suggest that GLP-1 RAs may address a longstanding therapeutic gap in ESRD, by improving outcomes in a population where reducing MACE has traditionally been extremely challenging and where conventional pharmacotherapies have proven ineffective or lack robust supporting evidence, potential translating to survival benefit. Confirmation in future trial study is warranted. Our study leverages the global TriNetX network to address the challenges of studying HF in ESRD, a population difficult to recruit and underrepresented in trials. Our comprehensive baseline characterization of this population—including HF status, cardiovascular comorbidities, dialysis modality, vascular access, anemia, iron status, body weight, lipid profile, use of cardiovascular medications, and compatible coverage of GDMT use—allowed us to recognize disease characters and discrepancies before matching and conduct a comprehensive confounding control. Prior to matching, the GLP-1 RA group had a higher burden of cardiovascular risk factors and advanced disease, including ischemic heart disease, hypertension, and hyperlipidemia, but also received more comprehensive GDMT. The combination of more severe baseline disease and concurrent optimal therapy could theoretically reduce the magnitude of the observed treatment effect, yielding estimates closer to the null. After rigorous matching, the beneficial effects of GLP-1 RAs persisted across multiple outcomes. Although residual confounding cannot be fully excluded, the consistency of these findings in a large, clinically detailed ESRD-HF cohort supports reliability. HF in ESRD remains exceptionally challenging, with therapeutic options limited and GDMT use hampered by scarce supporting evidence. Since early trials such as those by Cice et al. (carvedilol, telmisartan) 9 , 22 , subsequent interventions have rarely demonstrated clear prognostic benefit. Trials of spironolactone (ALCHEMIST) 10 and ramipril (ACRDIA) 11 also failed to demonstrate MACE or survival improvement in this high cardiovascular risk ESRD population. Even statins—cornerstones of lipid management with proven efficacy in earlier stages of CKD—failed to significantly reduce MACE in large dialysis trials such as 4D and AURORA, with benefits largely confined to non-dialysis CKD patients, as seen in SHARP 23 . Other approaches, including PCSK9 inhibitors and anti-inflammatory agents such as IL-6 blockade, remain largely investigational with only preliminary evidence 24 , 25 . Against this backdrop, our finding that GLP-1 RAs may confer cardiovascular protection in dialysis patients is unexpected and potentially paradigm-shifting. Although the result appears striking, we acknowledge that the observational nature of our study limits the strength of causal inference. Such therapeutic benefits require further validation through phase 3 or 4 clinical trials. Our findings should therefore be regarded as a hypothesis-generating signal, particularly valuable in populations where randomized trials are challenging to conduct. In our study enrolling ESRD population, HbA1c and BMI were carefully matched and in our exploratory analysis, shows that the mean levels of HbA1c and BMI remained largely stable throughout with no significant between-group differences observed between the GLP-1 RA and DPP-4i groups (Supplementary Table 7). In previous GLP-1 trials applied in HFpEF or HFmrEF, most enrolled obese populations with average BMI 34.0 ± 5.4 to 37.4 ± 5.9, and improvement of heart failure signs was recognized as largely mediated by weight reduction 18 , 26 . In our study, the enrollment BMI was 33.5 ± 7.8 and 32.5 ± 7.5, while the subgroup of BMI < 30—below the threshold used in those trials—still demonstrated CV protection with GLP-1 RAs. This suggests that observed benefits cannot be explained solely by glycemic control or weight loss. Beyond metabolic effects, GLP-1 RAs have been shown to improve myocardial remodeling, calcium handling, endothelial function, vascular inflammation, and cardiorenal signaling, while also lowering blood pressure, improving lipids, and exerting anti-atherosclerotic actions 27 , 28 . These pleiotropic pathways may underline cardiovascular benefits in non-obese patients as well. However, in ESRD, the relative contribution of these mechanisms remains uncertain, and it is still unknown which targets represent the principal drivers of benefit in this high-risk population. Notably, beneficial effects have also been observed in patients with HFrEF in our cohort, challenging previous concerns regarding the limited potential of liraglutide to improve LVEF and highlighting the pending need for dedicated trials with tirzepatide 17 , 29 . However, due to the lack of comprehensive cohort data on EF distribution, the current indications being predominantly in HFpEF, and the uncertainty surrounding heart failure with improved ejection fraction (HFimpEF) within the baseline assessment, we remain cautious in interpreting the efficacy of GLP-1 receptor agonists in HFrEF. Key strengths of this study include the large sample size and the use of a target trial emulation framework, which helped mitigate major sources of bias inherent to observational studies and reduced the risk of confounding by indication. Of note, consistent benefit was also observed in the full unmatched cohort (HR for the primary composite outcome, 0.76 [95% CI, 0.68–0.85]; p < 0.001; Supplementary Table 16). The persistence of this effect despite baseline imbalances—where the GLP-1 RA group had greater disease burden and GDMT exposure—suggests that the association is unlikely to be an analytic artifact. In addition, our study design minimized immortal time bias through an active comparator approach and sensitivity analyses using varied time windows for medication initiation. Robustness was further supported by analyses across alternative model specifications, including varying definitions of ESRD and prevalent HF. Importantly, we focused on cardiovascular- and HF-specific endpoints rather than all-cause mortality or composites including mortality, thereby providing greater specificity than most prior real-world studies. Finally, the multinational coverage of the TriNetX network enhances the external validity of our findings, suggesting that the observed benefits may generalize across diverse dialysis populations and healthcare systems. This study also has limitations. First, competing risks of death are an inherent limitation when evaluating cardiovascular outcomes in the ESRD population, as mortality may preclude the occurrence of non-fatal events such as MACE. In our study, the median follow-up was modestly shorter in the GLP-1 RA group (581 days) compared with the DPP-4i group (651 days). However, all-cause mortality was lower among GLP-1 RA users, suggesting that the difference in follow-up duration was unlikely to be driven by earlier deaths. Furthermore, sensitivity analyses incorporating all-cause mortality into a composite endpoint—an approach frequently employed in large EHR-based studies when Fine–Gray sub distribution models are not feasible—yielded consistent results (Supplementary Table 13). Taken together, these findings reduce the likelihood that competing risks or unequal follow-up fully account for the observed cardiovascular benefit of GLP-1 RA therapy. Second, due to the definitions available in TriNetX, we were unable to directly ascertain cardiovascular mortality. Nevertheless, because our primary outcome was assessed using survival analysis, cardiovascular deaths were indirectly captured through diagnosis codes for cardiovascular events. We therefore followed prior literature by defining MACE in a standardized manner, while excluding all-cause mortality to maintain specificity. Third, the lack of detailed HF profiling—such as NYHA functional class, symptom burden, and limited availability of echocardiographic parameters—limits clinical granularity. Nevertheless, coded-based HF subtypes, which have been validated in prior studies, were applied, and subgroup analyses stratified by HF subtype, prevalent versus incident HF, and primary prevention status largely captured variations in disease severity and background, showing consistent associations. In addition, restricting HF to new diagnoses within 2 years reduced heterogeneity related to disease chronicity and approximated a more uniform baseline risk, while sensitivity analyses including all prevalent dialysis patients without this restriction yielded similar results, supporting the robustness of our findings (Supplementary Table 12). Last, our study focused on diabetic ESRD patients, limiting extrapolation to non-diabetic populations. Notably, recent trials such as SELECT and STEP-HFpEF have demonstrated benefits of GLP-1 receptor agonists in HF patients without diabetes 29 . However, our use of DPP-4 inhibitors as an active comparator necessitated the selection of a diabetic cohort. Future studies are needed to clarify whether similar benefits extend to non-diabetic ESRD patients with HF. In conclusion, this large-scale target trial emulation provides the first real-world evidence that, compared with DPP-4 inhibitors, GLP-1 receptor agonists reduce ischemic complications and heart failure exacerbations in patients with ESRD and heart failure—a population with extreme cardiovascular vulnerability and limited therapeutic options. These findings suggest GLP-1 RAs may confer cardiovascular protection and survival benefits and should be considered a treatment option in this high-risk group, awaiting confirmation in randomized trials Methods Data Source We conducted a cohort study using the TriNetX Global Health Research Network, which aggregates deidentified electronic health records from ~ 150 health-care organizations encompassing > 180 million patients worldwide. Available data include diagnoses, medications, procedures, laboratory results, demographics, and health-care utilization. Cohorts and outcomes were prespecified within the platform. This platform has been widely used to conduct real-world studies addressing treatment effectiveness and safety in specific disease cohorts and under-represented populations 40, 41 . (The study was approved by the Institutional Review Board of National Cheng Kung University Hospital, Taiwan, with a waiver of informed consent due to the use of deidentified, aggregated data.) The study complied with the Declaration of Helsinki and followed Strengthening the Reporting of Observational studies in Epidemiology (STROBE) reporting guidelines 30 . Study Design and Population We applied a target trial emulation framework to observational data to emulate the design of a randomized controlled trial, following the TARGET reporting checklist 31 (Supplementary Table 1). From the TriNetX platform, participants aged ≥ 18 years with documented HF and ESRD during August 2016 and August 2025 were assigned to two exposure groups. The GLP-1 RA group included patients who initiated a glucagon-like peptide-1 receptor agonist within 2 years after the first diagnosis of ESRD and, concomitantly, within 2 years following the first diagnosis of HF. The comparator group consisted of patients who initiated a DPP-4i (Supplementary Fig. 1). To ensure a new-user, active-comparator design, we excluded individuals with any prior use of GLP-1 RAs or DPP-4i, those who had undergone kidney transplantation before the index date, and those with a diagnosis of cardiovascular events within 3 months preceding the index date. End-stage renal disease (ESRD, also termed end-stage kidney disease [ESKD]) is operationally defined as chronic kidney failure requiring kidney replacement therapy (KRT), including maintenance dialysis or kidney transplantation, to sustain life, consistent with the definition applied in the US Renal Data System (USRDS). HF and ESRD were identified using ICD-10-CM codes I50 and N18.6, respectively. In the SCREAM cohort, 99.1% of dialysis patients had a registry diagnosis of renal failure, and the coding scheme identified eGFR < 30 with high accuracy (positive predictive value (PPV) 93.5%, negative predictive value (NPV) 99.2%) 32 . Moreover, multiple studies have applied ICD-10-CM N18.6 as the definition of ESRD for enrollment or outcome 33, 34, 35 . ICD-10-CM codes (I50.x) in the first diagnostic position, which have shown a PPV of 98% for acute HF hospitalizations 36 . HF phenotypes were classified using ICD-10-CM codes: systolic HF (I50.2) was used as a proxy for HFrEF or HFmrEF with LVEF ≤ 50%, and diastolic HF (I50.3) as a proxy for HFpEF with LVEF > 50%. These codes have been validated against echocardiography, with positive predictive values of ~ 90% for EF ≤ 50% and ~ 92% for EF > 50%. We therefore refer to these as ‘code-based’ HFrEF/HFmrEF and HFpEF subgroups throughout the study 36 . The index date was the date of the first prescription for the exposures; baseline covariates were assessed using most recent data during the 3 years preceding the index date. Primary analyses used an intention-to-treat framework, preserving baseline comparability and yielding policy-relevant effectiveness estimates despite post-index treatment changes Follow-up began 1 day after the index date and continued until the first occurrence of an outcome, loss to follow-up, or 2 years, whichever occurred first. Follow up time was calculated by last record or death in TriNetX. Detailed cohort definitions and code lists are provided in Supplementary Table 2. Prespecified Outcomes The primary outcome was a composite of ischemic cardiovascular events and heart failure exacerbation (HFE). Ischemic cardiovascular events included acute myocardial infarction (MI), ischemic stroke, and cardiac arrest. These definitions were not restricted to nonfatal events, as diagnosis codes for MI and stroke may also capture cardiovascular deaths; deaths potentially related to HF, such as cardiogenic shock, may likewise be encompassed. Thus, the composite outcome more closely reflects a conventional 3-point MACE 37 . All-cause mortality was analyzed separately. Restriction to ischemic (rather than hemorrhagic) stroke was adopted to minimize potential confounding from trauma-related hemorrhage, consistent with prior real-world data studies. Heart failure exacerbation was identified using ICD-10-CM codes I50.23, I50.33 (acute on chronic systolic or diastolic heart failure), and J81 (pulmonary edema). Risk of individual components within the primary outcome were also analyzed. Secondary outcomes included the all-cause mortality and hospitalization. In addition to propensity score–matched analyses, we applied a multivariable Cox proportional hazards regression model in the overall cohort, adjusting for the same baseline covariates, to estimate hazard ratios for the study outcomes (Supplementary Table 6). Temporal changes in HbA1c levels and BMI were evaluated between the matched groups to determine whether the observed cardiovascular protective effect of GLP-1 receptor agonists could be explained by improvements in glycemic control or weight reduction, or instead may reflect alternative underlying mechanisms. Complete outcome code lists are provided in Supplementary Table 3. Covariates Baseline covariates were assessed using the most recent data available within 3 years before the index date and were selected. Because they provide relevant clinical information, were potential confounders or important risk factors. Covariates included demographics, lifestyle factors, comorbidities (eg, obesity, type 2 diabetes mellitus, heart failure subtype, cardiovascular and cerebrovascular disease, peripheral vascular disease), and medication use (GDMT, hypoglycemic, cardiovascular, and lipid-lowering agents, nonsteroidal anti-inflammatory drugs). Dialysis-related variables comprised vascular access type and peritoneal dialysis. Clinical and laboratory measures included body mass index, blood pressure, hemoglobin A1c, albumin, hemoglobin, renal and lipid profiles, and iron indices. Cardiac parameters included LVEF, B-type natriuretic peptide, and N-terminal pro–B-type natriuretic peptide. Detailed definitions and coding are provided in Supplementary Table 4. Statistical analysis Baseline characteristics were summarized, with categorical variables reported as numbers and percentages and continuous variables as means with standard deviations. We performed 1:1 propensity score (PS) matching before the primary, subgroup, and sensitivity analyses to reduce confounding. Relevant covariates were used to estimate a propensity score for each subject using logistic regression to model the probability of receiving GLP-1 RA therapy. Patients in the treatment group were then matched to comparators using a greedy nearest-neighbor algorithm, with an absolute standardized mean difference (SMD) of less than 0.1 considered indicative of adequate balance. Kaplan–Meier methods with log-rank tests were used to depict and compare survival probabilities, while other time-to-event outcomes were analyzed using Cox proportional hazards models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). The E-value represents the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need with both treatment and outcome to fully explain away the observed effect estimate, analogous to a sensitivity measure derived from the observed hazard ratio. All analyses were conducted within the TriNetX platform on 2025/08/22. Prespecified Subgroup Analysis To evaluate heterogeneity of treatment effect, we prespecified subgroup analyses by sex, age (18–64 vs ≥ 65 years), heart failure phenotype (code-based HFpEF vs HFrEF or HFmrEF), history of cardiovascular events, use of guideline-directed medical therapy, HbA1c (≤ 7% vs > 7%), and BMI. Within each subgroup, propensity score matching was repeated and hazard ratios were estimated using Cox proportional hazards models, thereby addressing subgroup-specific confounding. Sensitivity analyses We restricted patients with an ICD-10-CM code for dependence on renal dialysis (Z99.2) to enhance the specificity of ESRD ascertainment. We excluded patients who underwent kidney transplantation after the index date to minimize bias from altered cardiovascular risk after transplantation 38 , and we excluded individuals who switch to the other treatment during follow-up to approximate a per-protocol effect. We additionally analyzed an extended cohort of patients with a history of prevalent heart failure. We conducted varying the permissible windows for treatment initiation after the first recorded HF and ESRD diagnoses to assess the robustness of our findings to different definitions of time zero. We evaluated a composite outcome including ischemic cardiovascular events, heart failure exacerbations, and all-cause mortality to account for competing risks. Moreover, to assess potential unmeasured confounding, we used traumatic brain injury and selected skin cancers as negative-control outcomes for which no association was expected, and nausea as a positive-control outcome for which an increased risk was expected among GLP-1 receptor agonist users 39 . Declarations Data availability statement The data used in this study were obtained from the TriNetX platform and are available only in de-identified, aggregated form. Individual-level data are not accessible to the investigators. Because the dataset contains protected health information, access is restricted. Researchers interested in using these data must apply through the TriNetX platform, providing appropriate credentials, a clear research purpose, and compliance with relevant privacy regulations. The application and approval process may take several weeks. For further details, please visit the https://trinetx.com or contact TriNetX via their email ( [email protected] ). Source data are provided with this paper. Acknowledgements: This work was supported by grants from the National Science and Technology Council (NSTC 112-2314-B-006-088-MY3, NTSC 114-2314-B-006 -018), the National Cheng Kung University Hospital (NCKUH-11306001 and NCKUH-11304023). The funding sources did not influence the study design, analysis, interpretation or approval of the manuscript. The authors would like to acknowledge the assistance from Yu-Han Lin and Tso-Chi Chang during the preparation of this manuscript. Authors’ contributions: Po-Yi Liu: Data curation; Formal analysis; Investigation; Software; Validation; Visualization; Writing – original draft Chao-Kuei Shih: Validation; Methodology (statistical advice). Miyuki Hsing-Chun Hsieh: Methodology; TriNetX sensitivity analysis advice; Statistical/pharmacy consultation; Writing – review & editing. Ching-Chun Lin: Conceptualization; Domain knowledge discussion. Chieh-Yen Liu: Conceptualization; Domain knowledge discussion. Edward Chia-Cheng Lai: Methodology; TriNetX sensitivity analysis advice; Epidemiology consultation; Supervision; Statistical/pharmacy advice. Chung-Yi Li: Formal analysis; Methodology; Supervision; Validation; Statistical consultation. Chih-Hen Yu: Conceptualization; Funding acquisition; Investigation; Project administration; Writing – original draft; Writing – review & editing. Junne-Ming Sung: Conceptualization; Funding acquisition; Supervision; Writing – review & editing. Conflict of interest disclosures All the authors declare that there’s no relevant conflict of interest. References Bikbov B , et al. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 395 , 709-733 (2020). Gansevoort RT , et al. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet 382 , 339-352 (2013). Khan MS , et al. 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Additional Declarations There is NO Competing Interest. Supplementary Files SupplementESRDHFglpvsdpp4i09181x2.docx Supplement ESRD HF glp vs dpp4i Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7722921","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":523939168,"identity":"e780fa28-82cb-4066-92eb-85fada56cea3","order_by":0,"name":"Chih-Hen Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACxgYQWSEhxyZ/IPExWIiZuYEILWdsjPklGB4bMzAYALUw4tcC0deSljhzBuMzabAWBgJamNtPJz782XA4ccPt5rTqgoo/0fztQC0/KrbhtqAnd7Mx747DxhvuHEu7PeOMQe6Mw4wNjD1nbuPxS+42acYzh2U3HMhJu83bZpDbANTCzNiGR0v/2+0/f7YdZtxwIP9bMUjLfIJaZuRuY+BtS1OcOSMhjRmkZQNhLW83S/OAApnnQDKQYZy7EajlID6/GPbnbvz4AxSV7A2Jn3kq5HLnnT988MGPCjxaGrCJHsCpHgjk8UmOglEwCkbBKAADAOFFYmtE0NDeAAAAAElFTkSuQmCC","orcid":"","institution":"National Cheng Kung University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chih-Hen","middleName":"","lastName":"Yu","suffix":""},{"id":523939169,"identity":"72ecb254-58b7-41cd-8ce7-ff788032fa4d","order_by":1,"name":"Po-Yi Liu","email":"","orcid":"","institution":"National Cheng Kung University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Po-Yi","middleName":"","lastName":"Liu","suffix":""},{"id":523939170,"identity":"eb5c8200-0382-4d4b-9a52-89af08b4b31e","order_by":2,"name":"Chao-Kuei Shih","email":"","orcid":"","institution":"Department of Internal Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chao-Kuei","middleName":"","lastName":"Shih","suffix":""},{"id":523939171,"identity":"6ea2fcb5-1f24-43f9-801b-a4db0b6b3652","order_by":3,"name":"Miyuki Hsing-Chun Hsieh","email":"","orcid":"","institution":"National Cheng Kung University","correspondingAuthor":false,"prefix":"","firstName":"Miyuki","middleName":"Hsing-Chun","lastName":"Hsieh","suffix":""},{"id":523939172,"identity":"71b71379-e350-47b9-bbbc-477e681d0a87","order_by":4,"name":"Ching-Chun Lin","email":"","orcid":"","institution":"Department of Internal Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ching-Chun","middleName":"","lastName":"Lin","suffix":""},{"id":523939173,"identity":"cc883832-99fa-4e5e-8573-b8746244325f","order_by":5,"name":"Chieh-Yen Liu","email":"","orcid":"","institution":"Department of Internal Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chieh-Yen","middleName":"","lastName":"Liu","suffix":""},{"id":523939174,"identity":"b16c6b9e-dce0-4e08-8ea0-1a186bbcf684","order_by":6,"name":"Edward Chia-Cheng Lai","email":"","orcid":"https://orcid.org/0000-0002-5852-7652","institution":"National Cheng Kung University","correspondingAuthor":false,"prefix":"","firstName":"Edward","middleName":"Chia-Cheng","lastName":"Lai","suffix":""},{"id":523939175,"identity":"a04b4bbe-f039-4d26-87a8-80e30bccbf4d","order_by":7,"name":"Chung-Yi Li","email":"","orcid":"https://orcid.org/0000-0002-0321-8908","institution":"Department of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Chung-Yi","middleName":"","lastName":"Li","suffix":""},{"id":523939176,"identity":"4dfebb0d-24e3-4744-84b9-b35a9c2cd59b","order_by":8,"name":"Junne-Ming Sung","email":"","orcid":"","institution":"National Cheng Kung University","correspondingAuthor":false,"prefix":"","firstName":"Junne-Ming","middleName":"","lastName":"Sung","suffix":""}],"badges":[],"createdAt":"2025-09-26 14:51:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7722921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7722921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93571703,"identity":"07acb4a0-04a3-442e-94b2-6a7f07b47ade","added_by":"auto","created_at":"2025-10-15 09:05:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157913,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of cohort selection. After identifying patients with both type 2 diabetes mellitus (T2DM) and end-stage renal disease (ESRD), we enrolled those who also had a diagnosis of heart failure (HF), including prevalent HF (HF diagnosed before ESRD) and incident HF (HF diagnosed after ESRD) (Supplementary Fig.1). Only new users who had newly diagnosed HF and newly established ESRD—defined as patients who first initiated the study medication after receiving both a HF diagnosis within 2 years and an ESRD diagnosis within 2 years—were included in the final enrollment cohort. Abbreviations: HCOs healthcare organizations, T2D type 2 diabetes, HF heart failure, ESRD end-stage renal disease\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722921/v1/9cf5f8f43be45243a07c8beb.png"},{"id":93571706,"identity":"58216979-bd35-4376-ba1e-922a62571ef5","added_by":"auto","created_at":"2025-10-15 09:05:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95018,"visible":true,"origin":"","legend":"\u003cp\u003ePropensity score density plots before and after matching for GLP-1 RA and DPP-4i Cohorts. The figure shows the distribution of propensity scores for the GLP-1 RA and DPP-4i cohorts before (left panel) and after (right panel) 1:1 propensity score matching, demonstrating well-balanced distributions between the two groups post-matching.\u003c/p\u003e\n\u003cp\u003eAbbreviations: DPP-4i dipeptidyl peptidase-4 inhibitor, GLP-1 RA glucagon-like peptide-1 receptor agonist\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7722921/v1/51a14e58e51324ba4221e3c0.png"},{"id":93573846,"identity":"c706e86e-7038-47c2-a0f6-5c9c8e9c2e50","added_by":"auto","created_at":"2025-10-15 09:13:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":211801,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier time-to-event curves comparing GLP-1 RA and DPP-4i groups. Kaplan–Meier curves show the cumulative survival probability over time in the GLP-1 RA (purple) and DPP-4i (teal) cohorts after matching at (a) Composite outcome of ischemic cardiovascular events and heart failure exacerbations. (b) Heart failure exacerbation events. (c) Ischemic cardiovascular events. (d) All-cause mortality. Shaded areas represent 95% confidence intervals. A significant difference was observed between the two groups (log-rank test, \u003cem\u003ep\u003c/em\u003e = 0.0001). Abbreviations: DPP-4i, dipeptidyl peptidase-4 inhibitor; GLP-1 RA, glucagon-like peptide-1 receptor agonist.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7722921/v1/757661c9f0286885b9eb116f.png"},{"id":93571704,"identity":"e1d09cf4-3b82-4dc5-a40f-282738d737fa","added_by":"auto","created_at":"2025-10-15 09:05:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":197311,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of primary outcome, composite ischemic cardiovascular and heart failure exacerbation events\u003c/p\u003e\n\u003cp\u003eNote: Heart failure phenotypes were classified based on ICD-10-CM codes. HFpEF was approximated using I50.3 (“diastolic heart failure”), while reduced-EF heart failure (combining HFrEF and HFmrEF) was defined using I50.2 (“systolic heart failure”). Systolic HF codes (I50.2) identify patients with EF ≤ 50% with a positive predictive value (PPV) of ~90%, and diastolic HF codes (I50.3) identify patients with EF \u0026gt; 50% with a PPV of ~92%\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI body mass index, DPP-4i dipeptidyl peptidase-4 inhibitor, GLP-1 RA glucagon-like peptide-1 receptor agonist, HbA1c glycated hemoglobin, HFmrEF heart failure with mildly reduced ejection fraction, HFpEF heart failure with preserved ejection fraction, HFrEF heart failure with reduced ejection fraction, MRA mineralocorticoid receptor antagonist, RASi renin–angiotensin system inhibitor, SGLT2i sodium–glucose co-transporter 2 inhibitor, y/o years old.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7722921/v1/360754e7c7b63347909afe11.png"},{"id":93575008,"identity":"df6f7e5c-f4aa-4adc-8405-7ea1252e9865","added_by":"auto","created_at":"2025-10-15 09:21:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2503905,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7722921/v1/3c1ac63b-5a1d-4805-9007-104c3c8c24e1.pdf"},{"id":93571707,"identity":"ef9fa85d-aea3-4913-88cf-f16d31cfc8e0","added_by":"auto","created_at":"2025-10-15 09:05:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3583366,"visible":true,"origin":"","legend":"Supplement ESRD HF glp vs dpp4i","description":"","filename":"SupplementESRDHFglpvsdpp4i09181x2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7722921/v1/cca386495ea11a4f4cb78117.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Cardiovascular outcome of Glucagon-Like Peptide-1 Receptor Agonists vs Dipeptidyl Peptidase-4 Inhibitor on End-stage Renal Disease patients with Heart Failure: An Emulated Target Trial in Patients with Diabetes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, approximately 4.6 million individuals are living with end-stage renal disease (ESRD) requiring kidney replacement therapy\u003csup\u003e1\u003c/sup\u003e. Among them, heart failure (HF) emerges as a prevalent and devastating complication, defining a particularly high-risk phenotype \u003csup\u003e2\u003c/sup\u003e. More than one-third of patients undergoing dialysis develop HF \u003csup\u003e3, 4\u003c/sup\u003e. The presence of HF in ESRD dramatically worsens outcomes\u003csup\u003e5, 6\u003c/sup\u003e, with cardiovascular events, including HF exacerbations, accounting for nearly half of all deaths in this population\u003csup\u003e7, 8\u003c/sup\u003e. Despite this substantial burden, few therapeutic interventions have convincingly reduced cardiovascular events in this vulnerable group\u003csup\u003e9, 10, 11\u003c/sup\u003e. Evidence remains scarce because randomized clinical trials of foundational guideline-directed medical therapies (GDMT) of HF, including β-blockers, renin–angiotensin system inhibitors, angiotensin receptor–neprilysin inhibitors, mineralocorticoid receptor antagonists, and sodium–glucose cotransporter-2 inhibitors, have systematically excluded patients with ESRD \u003csup\u003e12\u003c/sup\u003e. As a result, current guideline recommendations offer little guidance on disease-modifying pharmacotherapies for HF in this population \u003csup\u003e12, 13, 14\u003c/sup\u003e. Moreover, observational data consistently show that patients with HF in dialysis are less likely to receive GDMT and consequently experience worse outcomes than their non-ESRD counterparts \u003csup\u003e15\u003c/sup\u003e, underscoring the urgent need to identify effective and broadly applicable therapies in this high-risk setting.\u003c/p\u003e\n\u003cp\u003eIn this context, glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have emerged as promising agents with cardiovascular benefits beyond glycemic control. While earlier cardiovascular outcome trials (CVOTs) established their efficacy in reducing major adverse cardiovascular events (MACE) in high-risk patients with type 2 diabetes (T2D) \u003csup\u003e16\u003c/sup\u003e, more recent evidence suggests that their clinical relevance may extend to heart failure as well\u003csup\u003e17\u003c/sup\u003e. GLP-1 RAs significantly reduced HF worsening events and the composite of cardiovascular death or HF hospitalization\u003csup\u003e18\u003c/sup\u003e. The FLOW trial, specifically in chronic kidney disease population, demonstrated that semaglutide significantly lowered the risk of heart failure (HF) events and cardiovascular death \u003csup\u003e19\u003c/sup\u003e. Given the shared risk factors and pathophysiological continuum between CKD and ESRD, these findings raise the possibility that GLP-1 RAs could provide meaningful cardiovascular benefit in patients with HF in ESRD—a subgroup at especially high risk yet largely underserved by current pharmacologic evidence.\u003c/p\u003e\n\u003cp\u003eBuilding on these signals, a critical and urgent question is whether GLP-1 RAs can improve cardiovascular outcomes in patients with HF in ESRD—a population with profound cardiovascular vulnerability and virtually no proven pharmacologic options.\u0026nbsp;Historically, ESRD represents one of the most treatment-refractory settings for cardiovascular disease and no established pharmacotherapy treatment has consistently reduced major adverse cardiovascular events or improved survival among patients with ESRD \u003csup\u003e20\u003c/sup\u003e. Prior observational evidence, such as a nationwide retrospective cohort study in Taiwan, has suggested benefit for reducing mortality of GLP-1 RAs in diabetic advanced CKD or ESRD compared with cardiovascular-neutral agents like DPP-4 inhibitors\u003csup\u003e21\u003c/sup\u003e. Whether these agents can also reduce cardiovascular events—including ischemic events and HF exacerbations—that ultimately drive mortality in ESRD is largely unknown. To address this evidence gap, we emulated a target trial within the global TriNetX research network to evaluate the comparative effectiveness of GLP-1 RAs versus DPP-4 inhibitors in patients with HF in ESRD. This design enabled us, in a real-world setting, to broaden the representativeness of the study population and directly test the hypothesis that GLP-1 RAs may confer cardiovascular benefit in this uniquely high-risk and underserved group.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003ePatient selection and Demographic characteristics\u003c/h2\u003e\u003cp\u003eBetween August 1, 2016, and August 22, 2025, 176,327,258 individuals were identified in TriNetX. After applying inclusion and exclusion criteria, 5,087 patients with HF and ESRD enrolled, including 1,571 GLP-1RA initiators and 3,516 DPP-4i initiators. Following 1:1 propensity score matching, 1,257 pairs (n\u0026thinsp;=\u0026thinsp;2,514) comprised the analytic cohort for the target emulated trial (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the pre-matching enrolled cohort (n\u0026thinsp;=\u0026thinsp;5,087), we included only new users who initiated the study medication subsequent to receiving both a HF diagnosis within the previous 2 years and an ESRD diagnosis within the previous 2 years. This population was confirmed by total coverage of first incident HF (ICD10CM:I50) and first incident ESRD diagnosis (ICD10CM:N18.6) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Fig.\u0026nbsp;1 and Supplementary Table\u0026nbsp;2). Before propensity score matching, baseline characteristics differed substantially between GLP-1RA initiators (n\u0026thinsp;=\u0026thinsp;1,571) and DPP-4 inhibitor initiators (n\u0026thinsp;=\u0026thinsp;3,516). Patients receiving GLP-1RA were younger (61.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5 vs 66.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3 years) and more often had overweight/obesity (50.3% vs 25.0%). They more frequently had histories of ischemic heart disease (53.8% vs 48.3%), hypertension (71.0% vs 56.3%) and hypertensive heart disease (20.6% vs 13.1%), and hyperlipidemia (59,8% vs 48.5%), and exhibited higher hemoglobin A1c (HbA1c) values. Left ventricular ejection fraction (LVEF) was comparable between the GLP-1RA and DPP-4i groups (51.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4 vs 52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5, n\u0026thinsp;=\u0026thinsp;171 and 345), among the subset of patients with available measurements. HF GDMTs\u0026mdash;including SGLT2 inhibitors, β-blockers, renin-angiotensin system inhibitors, and mineralocorticoid receptor antagonists\u0026mdash;were more frequently used in the GLP-1RA group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table\u0026nbsp;3, and Supplementary Table\u0026nbsp;4). After 1:1 propensity score matching, 1,257 pairs were retained with well-balanced baseline covariates (all standardized mean differences\u0026thinsp;\u0026lt;\u0026thinsp;0.10; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eBaseline characteristics of GLP-1 RA and DPP4i groups before and after matching.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u0026dagger;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBefore matching\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eAfter matching\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGLP-1 RA group\u0026Dagger;\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,571)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDPP4i group\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,516)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandardized\u003c/p\u003e\u003cp\u003edifference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGLP-1 RA group\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,257)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDPP4i group\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,257)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStandardized\u003c/p\u003e\u003cp\u003edifference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eAge at index, years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.4 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62.3 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.1 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eSex, n (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e670 (42.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,496 (42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e540 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e530 (42.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e869 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,957 (55.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e695 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e706 (56.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eRace, n (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e778 (49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,255 (35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e593 (47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e584 (46.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack or African American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e409 (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e824 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e329 (26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e335 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e480 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e112 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e110 (8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e682 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e108 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e106 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis of Heart failure, n (%)\u0026sect;\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\u003eHeart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,571(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.516(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,257(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,257(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHFpEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e637 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,110 (31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e474 (37.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e473 (37.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHFrEF or HFmrEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e451 (28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e933 (26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e339 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e340 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined HFpEF and HFrEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e243 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e435 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e177 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e165 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure, unspecified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e887 (56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,816 (51.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e671 (53.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e669 (53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther heart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft ventricular failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDialysis profile, n (%)\u003cb\u003e\u0026para;\u003c/b\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\u003eEnd-stage renal disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,571(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.516(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,257(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,257(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeritoneal dialysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDialysis procedure other than hemodialysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66(4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87(2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTypes of hemodialysis access, n (%)\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\u003eArteriovenous fistula\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e116 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e117 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArteriovenous graft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporary hemodialysis catheter insertion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular disease, n (%)\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\u003eIschemic heart diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e846 (53.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,699 (48.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e639 (50.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e624 (49.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertensive heart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e323 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e460 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e226 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e213 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiomyopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e259 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e470 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e186 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e179 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic rheumatic heart diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e450 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e143 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e139 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial fibrillation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e280 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e575 (16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e207 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e199 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e261 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e526 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e199 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e198 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeripheral vascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e270 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e517 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e201 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e188 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol related disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNicotine dependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e171 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,116 (71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,981 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e838 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e828 (65.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e939 (59.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,707 (48.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e714 (56.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e705 (56.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight and obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e791 (50.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e879 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e538 (42.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e542 (43.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidities, n (%)\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\u003eChronic obstructive pulmonary diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e274 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e525 (14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e209 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e207 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeoplasms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e451 (28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e933 (26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e339 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e340 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver cirrhosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e261 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e99 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystemic lupus erythematosus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlomerular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e261 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e99 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93 (7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure, mmHg, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131.8 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.3 (27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e132.1 (23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e131.7 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m\u0026sup2;, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.8 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.3 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.5 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.5 (7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;18.5, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.5\u0026ndash;25, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e207 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e822 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e191 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e184 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;30, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e425 (27.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,051 (29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e365 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e359 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;35, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e552 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e859 (24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e421 (33.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e418 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;40, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e489 (31.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e485 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e324 (25.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e326 (25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge; 40, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e393 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e324 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e228 (18.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e233 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaboratory examination\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\u003eSodium, mmol/L, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137.4 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136.6 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e137.5 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e136.8 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium, mmol/L, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.4 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.4 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.4 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.4 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium, mmol/L, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.9 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.7 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.9 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.7 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphate, mmol/L, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.5 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.6 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.4 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.6 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin, g/dL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.5 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.4 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN, mg/dL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.9 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.5 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.3 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.7 (25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, g/dL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.8 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.7 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.2 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron, \u0026micro;g/dL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.4 (34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.2 (33.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.3 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54 (32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransferrin, mg/dL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194.9 (57.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177.7 (55.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e188.8 (54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e187.5 (59.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron binding capacity, \u0026micro;g/dL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e791.3 (4060.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3009.1 (24452)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e787.4 (4036.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1358.8 (11170.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParathyroid hormone, pg/mL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e273.4 (288.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e278.8 (276.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e269.1 (258.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e265.3 (295.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFerritin, \u0026micro;g/dL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e483.5 (611)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e871.1 (6473.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e499.8 (647)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e516.1 (716.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;100, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e224 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e348 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e157 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e155 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100\u0026ndash;200, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e376 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e162 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e172 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;200, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e434 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e936 (26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e322 (25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e318 (25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin A1c, %, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.3 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.9 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.6 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5-6.5, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e461 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,020 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e359 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e340 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6.5\u0026ndash;7.5, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e490 (31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e969 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e377 (30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e385 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.5\u0026ndash;12, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e643 (40.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e985 (28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e453 (36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e456 (36.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;12, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e151 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCholesterol in LDL, mg/dL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.8 (43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76.6 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76.1 (42.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;55, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e321 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e543 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e228 (18.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e223 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;70, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e468 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e186 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e178 (14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e70\u0026ndash;100, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e369 (23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e620 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e248 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e253 (20.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100\u0026ndash;130, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e224 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e388 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e163 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e153 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e130\u0026ndash;160, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e186 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge; 160, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNatriuretic peptide B, pg/mL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1165.6 (3862.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2033.9 (5579.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1373.9 (4338.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1626.9 (4841.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;100, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111 (8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e118 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100\u0026ndash;300, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214 (13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e291 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e146 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e156 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e300\u0026ndash;600, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e181 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e296 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e130 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;600, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e254 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e561 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e197 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e200 (15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNatriuretic peptide.B prohormone N-Terminal, pg/mL, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9160.4 (14210.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17390.6 (18498.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10841.2 (15225.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13104 (14720.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;400, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e400\u0026ndash;800, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e800\u0026ndash;1200, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1200\u0026ndash;70000, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e248 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e551 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e186 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e184 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;70000, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVEF, %, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.1 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.8 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.7 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.6 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure guideline-directed medical therapy, n (%)\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\u003eBeta blockers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,135 (72.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,136 (60.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e849 (67.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e850 (67.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\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\u003e426 (27.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e628 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e306 (24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e312 (24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARB/ARNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e497 (31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e891 (25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e365 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e342 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpironolactone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e206 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e281 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e134 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e123 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGLT2i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular agents, n (%)\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\u003eAspirin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e837 (53.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,553 (44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e631 (50.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e621 (49.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeparin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e881 (56.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,686 (48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e665 (52.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e646 (51.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClopidogrel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e317 (20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e595 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e221 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e220 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTicagrelor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrasugrel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganic nitrates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e589 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,203 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e443 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e426 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigoxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmiodarone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e293 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e105 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e101 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertensive drugs, n (%)\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\u003eCalcium channel blockers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e905 (57.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,738 (49.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e685 (54.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e680 (54.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHydralazine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e754 (48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,352 (38.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e555 (44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e559 (44.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-diabetic drugs, n (%)\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\u003eSulfonylurea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e242 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e451 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e193 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e194 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin and analogues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,093 (69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,091 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e834 (66.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e817 (65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipid-lowering agents, n (%)\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\u003eAntilipemic agents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,081 (68.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,842 (52.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e803 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e775 (61.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHMG CoA reductase inhibitors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,051 (66.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,798 (51.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e783 (62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e754 (60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFibrates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\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\u003e70 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNicotinic acid and derivatives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSAID, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e394 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e593 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e277 (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e268 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eACEi angiotensin-converting enzyme inhibitor,ARB angiotensin receptor blocker, ARNI angiotensin receptor\u0026ndash;neprilysin inhibitor, BMI body mass index, BUN blood urea nitrogen, DPP-4i dipeptidyl peptidase-4 inhibitor, GLP-1 RA glucagon-like peptide-1 receptor agonist, HFpEF heart failure with preserved ejection fraction, HFrEF heart failure with reduced ejection fraction, HFmrEF heart failure with mildly reduced ejection fraction, LDL low-density lipoprotein, NSAID non-steroidal anti-inflammatory drug, SD standard deviation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u0026dagger; Baseline covariates definitions are provided in \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u0026Dagger; Distribution of GLP-1 receptor agonist use among study participants is provided in \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u0026sect; Heart failure phenotypes were classified based on ICD-10-CM codes. HFpEF was approximated using I50.3 (\u0026ldquo;diastolic heart failure\u0026rdquo;), while reduced-EF heart failure (combining HFrEF and HFmrEF) was defined using I50.2 (\u0026ldquo;systolic heart failure\u0026rdquo;). Systolic HF codes (I50.2) identify patients with EF\u0026thinsp;\u0026le;\u0026thinsp;50% with a positive predictive value (PPV) of ~\u0026thinsp;90%, and diastolic HF codes (I50.3) identify patients with EF\u0026thinsp;\u0026gt;\u0026thinsp;50% with a PPV of ~\u0026thinsp;92%\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026para;\u003c/b\u003e Population with dialysis related code or kidney transplant code are provided in \u003cb\u003eSupplementary Table\u0026nbsp;8.\u003c/b\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\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMain analysis\u003c/h2\u003e\u003cp\u003eThe prespecified primary outcome was a composite of ischemic cardiovascular events (acute myocardial infarction, ischemic stroke, or cardiac arrest, with diagnosis codes that may also capture cardiovascular deaths) and heart failure exacerbation, approximating a conventional 3-point MACE (Supplementary Table\u0026nbsp;5). For the primary outcome, event rate of composite ischemic cardiovascular and heart failure exacerbation events was significantly lower in GLP-1 receptor agonist than DPP-4 inhibitor (30.1% vs. 40.1%; hazard ratio [HR], 0.71; 95% CI, 0.62\u0026ndash;0.81; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Kaplan-Meier analysis showed separation of the curves within the follow-up period, with consistently fewer events in the GLP-1 receptor agonist group (log-rank \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The calculated E-value was 2.2 (95% upper confidence limit, 1.8), indicating that only a strong unmeasured confounder could explain the observed association. Ischemic cardiovascular events occurred at rates of 16.9% vs 24% (HR, 0.69; 95% CI, 0.58\u0026ndash;0.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; E-value, 2.2 [1.7]), and heart failure exacerbation events at 20.7% vs 27.3% (HR, 0.73; 95% CI, 0.62\u0026ndash;0.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; E-value, 2.1 [1.7]). Rates of acute myocardial infarction, stroke, and cardiac arrest were also significantly lower in the GLP-1 RA group, with hazard ratios of 0.76 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), 0.71 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), and 0.47 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. Consistent with these results, multivariable Cox regression in the entire cohort\u0026mdash;adjusting for the same covariates used in the propensity-score model\u0026mdash;yielded a similar effect estimate (HR, 0.76; 95% CI, 0.68\u0026ndash;0.85; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Table\u0026nbsp;6). Secondary outcomes, both all-cause mortality (event rates 11.8% vs 17.7%, HR, 0.67; 95%CI, 0.54\u0026ndash;0.86; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and hospitalization, were significantly reduced in the GLP-1 receptor agonist group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean follow-up time was 464\u0026thinsp;\u0026plusmn;\u0026thinsp;285 days in the GLP-1RA group and 473\u0026thinsp;\u0026plusmn;\u0026thinsp;289 days in the DPP-4i group, and the median follow-up time was 581 days in the GLP-1RA group and 651 days in the DPP-4i group. An exploratory analysis shows that the mean levels of HbA1c and body mass index (BMI) remained largely stable throughout follow-up (Supplementary Table\u0026nbsp;7).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIncidence of outcomes of interest among the glucagon-like peptide 1 receptor agonists users compared to dipeptidyl peptidase-4 inhibitor users after propensity score matching\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGLP-1 receptor agonist\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDPP-4 inhibitor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHazard Ratio (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eE-value(95% UCL)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvents, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEvents, n (%)\u003c/p\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrimary outcome\u003c/b\u003e\u0026dagger;\u0026Dagger;\u0026sect;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComposite ischemic cardiovascular and heart failure exacerbation events\u0026dagger;\u0026Dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e379/1257 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e504/1257(40.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.708(0.62\u0026ndash;0.809)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2(1.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure exacerbation\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e260/1257(20.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e343/1257(27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73(0.624\u0026ndash;0.827)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.1(1.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschemic cardiovascular event\u0026dagger;\u0026Dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213/1257(16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e302/1257(24.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.694(0.582\u0026ndash;0.827)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2(1.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute myocardial infarction\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140/1257(11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185/1257(14.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.755(0.606\u0026ndash;0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.0(1.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74/1257(5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104/1257(8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.714(0.53\u0026ndash;0.962)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.1(1.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiac arrest\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33/1257(2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69/1257(5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.466 (0.308\u0026ndash;0.704)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7(2.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSecondary outcome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause mortality*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149/1257(11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223/1257(17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67(0.544\u0026ndash;0.861)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.3(1.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospitalization\u0026para;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e697/1257(55.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e803/1257(63.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.745(0.673\u0026ndash;0.825)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.0(1.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: UCL upper confidence limit, CI confidence interval, DPP-4i dipeptidyl peptidase-4 inhibitor, GLP-1 RA glucagon-like peptide-1 receptor agonist\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003cp\u003eNote:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e† Outcome definitions: \u003cstrong\u003eComposite of ischemic cardiovascular and heart failure (HF) exacerbation events\u003c/strong\u003e: acute on chronic systolic (congestive) HF (ICD-10-CM: I50.23), acute on chronic diastolic (congestive) HF (I50.33), pulmonary edema (J81), cerebral infarction (I63), cerebral ischemia (I67.82), cardiac arrest (I46), acute myocardial infarction (I21), subsequent ST elevation (STEMI) and non-ST elevation (NSTEMI) myocardial infarction (I22), and acute ischemic heart disease, unspecified (I24.9), \u003cstrong\u003eHeart failure exacerbation (HFE)\u003c/strong\u003e : Defined as acute on chronic systolic (congestive) HF (I50.23), acute on chronic diastolic (congestive) HF (I50.33), or pulmonary edema (J81).\u003cstrong\u003e\u0026nbsp;Ischemic cardiovascular outcome:\u0026nbsp;\u003c/strong\u003eDefined as cerebral infarction (I63), cerebral ischemia (I67.82), cardiac arrest (I46), acute myocardial infarction (I21), subsequent STEMI and NSTEMI myocardial infarction (I22), or acute ischemic heart disease, unspecified (I24.9).\u003cstrong\u003e\u0026nbsp;Acute myocardial infarction (AMI):\u0026nbsp;\u003c/strong\u003eDefined as acute myocardial infarction (I21), subsequent STEMI and NSTEMI myocardial infarction (I22), or acute ischemic heart disease, unspecified (I24.9).\u003cstrong\u003e\u0026nbsp;Stroke:\u0026nbsp;\u003c/strong\u003eDefined as cerebral infarction (I63) or cerebral ischemia (I67.82).\u003cstrong\u003e\u0026nbsp;Cardiac arrest:\u0026nbsp;\u003c/strong\u003eDefined as cardiac arrest infarction (I46) (Supplementary Table 5)\u003c/p\u003e\n\u003cp\u003e‡ These definitions were not restricted to non-fatal myocardial infarction or non-fatal stroke; the diagnosis codes used for myocardial infarction and stroke may also encompass cardiovascular deaths. Accordingly, the composite outcome in this study more closely reflects a 3-point major adverse cardiovascular event (MACE), consistent with the operational definitions adopted in prior database studies \u003csup\u003e40, 41\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e§ Sensitivity analyses for composite ischemic cardiovascular events, heart failure exacerbations, and all-cause mortality accounting for competing risk of death are provided in Supplementary Table 13.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e* All-cause Mortality\u003c/strong\u003e: Defined by demographic records indicating death (Deceased) or diagnosis of ill-defined and unknown cause of mortality (ICD-10-CM: R99). \u003cstrong\u003e¶\u0026nbsp;Hospitalization\u003c/strong\u003e: Defined by any of the following visit types: inpatient acute (HL7V3.0:Visit ACUTE), inpatient encounter (IMP), inpatient non-acute (NONAC), emergency (EMER), short stay (SS), or observation encounter (OBSENC) (Supplementary Table 5)\u003c/p\u003e\n\u003ch3\u003eSubgroup analyses\u003c/h3\u003e\n\u003cp\u003eSubgroup analyses showed no mediation effects for primary outcome by sex, age, types, coded-based HF subtype, and prevalent or incident HF in ESRD (all interaction test \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Stratification by baseline use of GDMTs for heart failure (β-blockers, renin\u0026ndash;angiotensin system inhibitors, mineralocorticoid receptor antagonists, and SGLT2 inhibitors) demonstrated consistent protective associations. When stratified by ischemic cardiovascular history, the hazard ratio was 0.81 (95% CI, 0.63\u0026ndash;1.03; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.087) among patients with prior events, and 0.71 (95% CI, 0.6\u0026ndash;0.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among those without prior events, without mediation effect (\u003cem\u003ep\u003c/em\u003e for interaction 0.418) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). HF subtype, classified according to coded-based heart failure with reduced ejection fraction (HFrEF), mildly reduced ejection fraction (HFmrEF), or preserved ejection fraction (HFpEF), showed consistent protective associations (HFpEF vs HFmrEF or HFrEF, \u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.335). Subgroup analysis of all-cause mortality was demonstrated in Supplementary Fig.\u0026nbsp;2.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSensitivity analysis\u003c/h3\u003e\n\u003cp\u003eDistribution of dialysis-related codes or kidney transplant codes are described in detail in Supplementary Table\u0026nbsp;8. Restricting the cohort to patients coded for dialysis dependence preserved the protective association (HR, 0.70; 95% CI, 0.59\u0026ndash;0.84; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Table\u0026nbsp;9). Excluding patients who underwent kidney transplantation after index day (HR, 0.74; 95% CI, 0.64\u0026ndash;0.85; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Table\u0026nbsp;10) or those who switched to the alternative drug class produced results consistent with the primary analysis (HR, 0.71; 95% CI, 0.61\u0026ndash;0.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Table\u0026nbsp;11). Findings were also similar in analyses restricted to patients with extended population with prevalent heart failure (Supplementary Table\u0026nbsp;12). Sensitivity analyses accounting for competing risks showed a consistent protective association for the composite outcome, suggesting minimal impact of competing risk (Supplementary Table\u0026nbsp;13, Supplementary Fig.\u0026nbsp;3). Varying the permissible window for treatment initiation at 6, 9, 12, 15, and 18 months yielded hazard ratios consistent with the main analysis (Supplementary Table\u0026nbsp;14). Evaluation of negative-control outcomes (skin cancer and traumatic brain injury) showed no significant associations, whereas the positive-control outcome (nausea) showed an increased risk among GLP-1 receptor agonist users as expected (Supplementary Table\u0026nbsp;15).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large-scale target trial emulation, we provide the first real-world evidence that GLP-1 RAs significantly reduce cardiovascular events\u0026mdash;including ischemic cardiovascular complications and HF exacerbations\u0026mdash;in patients with HF in ESRD. This ESRD population was historically excluded from randomized controlled trials, carries extraordinarily high cardiovascular risk. Importantly, the benefits were consistent across clinically relevant subgroups, including those with or without prior MACE, HF subtypes (coded based HFpEF vs HFmrEF or HFrEF), and remained evident on top of contemporary GDMT. These findings suggest that GLP-1 RAs may address a longstanding therapeutic gap in ESRD, by improving outcomes in a population where reducing MACE has traditionally been extremely challenging and where conventional pharmacotherapies have proven ineffective or lack robust supporting evidence, potential translating to survival benefit. Confirmation in future trial study is warranted.\u003c/p\u003e\u003cp\u003eOur study leverages the global TriNetX network to address the challenges of studying HF in ESRD, a population difficult to recruit and underrepresented in trials. Our comprehensive baseline characterization of this population\u0026mdash;including HF status, cardiovascular comorbidities, dialysis modality, vascular access, anemia, iron status, body weight, lipid profile, use of cardiovascular medications, and compatible coverage of GDMT use\u0026mdash;allowed us to recognize disease characters and discrepancies before matching and conduct a comprehensive confounding control. Prior to matching, the GLP-1 RA group had a higher burden of cardiovascular risk factors and advanced disease, including ischemic heart disease, hypertension, and hyperlipidemia, but also received more comprehensive GDMT. The combination of more severe baseline disease and concurrent optimal therapy could theoretically reduce the magnitude of the observed treatment effect, yielding estimates closer to the null. After rigorous matching, the beneficial effects of GLP-1 RAs persisted across multiple outcomes. Although residual confounding cannot be fully excluded, the consistency of these findings in a large, clinically detailed ESRD-HF cohort supports reliability.\u003c/p\u003e\u003cp\u003eHF in ESRD remains exceptionally challenging, with therapeutic options limited and GDMT use hampered by scarce supporting evidence. Since early trials such as those by Cice et al. (carvedilol, telmisartan) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, subsequent interventions have rarely demonstrated clear prognostic benefit. Trials of spironolactone (ALCHEMIST) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and ramipril (ACRDIA) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e also failed to demonstrate MACE or survival improvement in this high cardiovascular risk ESRD population. Even statins\u0026mdash;cornerstones of lipid management with proven efficacy in earlier stages of CKD\u0026mdash;failed to significantly reduce MACE in large dialysis trials such as 4D and AURORA, with benefits largely confined to non-dialysis CKD patients, as seen in SHARP\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Other approaches, including PCSK9 inhibitors and anti-inflammatory agents such as IL-6 blockade, remain largely investigational with only preliminary evidence\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Against this backdrop, our finding that GLP-1 RAs may confer cardiovascular protection in dialysis patients is unexpected and potentially paradigm-shifting. Although the result appears striking, we acknowledge that the observational nature of our study limits the strength of causal inference. Such therapeutic benefits require further validation through phase 3 or 4 clinical trials. Our findings should therefore be regarded as a hypothesis-generating signal, particularly valuable in populations where randomized trials are challenging to conduct.\u003c/p\u003e\u003cp\u003eIn our study enrolling ESRD population, HbA1c and BMI were carefully matched and in our exploratory analysis, shows that the mean levels of HbA1c and BMI remained largely stable throughout with no significant between-group differences observed between the GLP-1 RA and DPP-4i groups (Supplementary Table\u0026nbsp;7). In previous GLP-1 trials applied in HFpEF or HFmrEF, most enrolled obese populations with average BMI 34.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 to 37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9, and improvement of heart failure signs was recognized as largely mediated by weight reduction\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In our study, the enrollment BMI was 33.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8 and 32.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5, while the subgroup of BMI\u0026thinsp;\u0026lt;\u0026thinsp;30\u0026mdash;below the threshold used in those trials\u0026mdash;still demonstrated CV protection with GLP-1 RAs. This suggests that observed benefits cannot be explained solely by glycemic control or weight loss. Beyond metabolic effects, GLP-1 RAs have been shown to improve myocardial remodeling, calcium handling, endothelial function, vascular inflammation, and cardiorenal signaling, while also lowering blood pressure, improving lipids, and exerting anti-atherosclerotic actions\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These pleiotropic pathways may underline cardiovascular benefits in non-obese patients as well. However, in ESRD, the relative contribution of these mechanisms remains uncertain, and it is still unknown which targets represent the principal drivers of benefit in this high-risk population. Notably, beneficial effects have also been observed in patients with HFrEF in our cohort, challenging previous concerns regarding the limited potential of liraglutide to improve LVEF and highlighting the pending need for dedicated trials with tirzepatide\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, due to the lack of comprehensive cohort data on EF distribution, the current indications being predominantly in HFpEF, and the uncertainty surrounding heart failure with improved ejection fraction (HFimpEF) within the baseline assessment, we remain cautious in interpreting the efficacy of GLP-1 receptor agonists in HFrEF.\u003c/p\u003e\u003cp\u003eKey strengths of this study include the large sample size and the use of a target trial emulation framework, which helped mitigate major sources of bias inherent to observational studies and reduced the risk of confounding by indication. Of note, consistent benefit was also observed in the full unmatched cohort (HR for the primary composite outcome, 0.76 [95% CI, 0.68\u0026ndash;0.85]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Table\u0026nbsp;16). The persistence of this effect despite baseline imbalances\u0026mdash;where the GLP-1 RA group had greater disease burden and GDMT exposure\u0026mdash;suggests that the association is unlikely to be an analytic artifact. In addition, our study design minimized immortal time bias through an active comparator approach and sensitivity analyses using varied time windows for medication initiation. Robustness was further supported by analyses across alternative model specifications, including varying definitions of ESRD and prevalent HF. Importantly, we focused on cardiovascular- and HF-specific endpoints rather than all-cause mortality or composites including mortality, thereby providing greater specificity than most prior real-world studies. Finally, the multinational coverage of the TriNetX network enhances the external validity of our findings, suggesting that the observed benefits may generalize across diverse dialysis populations and healthcare systems.\u003c/p\u003e\u003cp\u003eThis study also has limitations. First, competing risks of death are an inherent limitation when evaluating cardiovascular outcomes in the ESRD population, as mortality may preclude the occurrence of non-fatal events such as MACE. In our study, the median follow-up was modestly shorter in the GLP-1 RA group (581 days) compared with the DPP-4i group (651 days). However, all-cause mortality was lower among GLP-1 RA users, suggesting that the difference in follow-up duration was unlikely to be driven by earlier deaths. Furthermore, sensitivity analyses incorporating all-cause mortality into a composite endpoint\u0026mdash;an approach frequently employed in large EHR-based studies when Fine\u0026ndash;Gray sub distribution models are not feasible\u0026mdash;yielded consistent results (Supplementary Table\u0026nbsp;13). Taken together, these findings reduce the likelihood that competing risks or unequal follow-up fully account for the observed cardiovascular benefit of GLP-1 RA therapy. Second, due to the definitions available in TriNetX, we were unable to directly ascertain cardiovascular mortality. Nevertheless, because our primary outcome was assessed using survival analysis, cardiovascular deaths were indirectly captured through diagnosis codes for cardiovascular events. We therefore followed prior literature by defining MACE in a standardized manner, while excluding all-cause mortality to maintain specificity.\u003c/p\u003e\u003cp\u003eThird, the lack of detailed HF profiling\u0026mdash;such as NYHA functional class, symptom burden, and limited availability of echocardiographic parameters\u0026mdash;limits clinical granularity. Nevertheless, coded-based HF subtypes, which have been validated in prior studies, were applied, and subgroup analyses stratified by HF subtype, prevalent versus incident HF, and primary prevention status largely captured variations in disease severity and background, showing consistent associations. In addition, restricting HF to new diagnoses within 2 years reduced heterogeneity related to disease chronicity and approximated a more uniform baseline risk, while sensitivity analyses including all prevalent dialysis patients without this restriction yielded similar results, supporting the robustness of our findings (Supplementary Table\u0026nbsp;12). Last, our study focused on diabetic ESRD patients, limiting extrapolation to non-diabetic populations. Notably, recent trials such as SELECT and STEP-HFpEF have demonstrated benefits of GLP-1 receptor agonists in HF patients without diabetes\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, our use of DPP-4 inhibitors as an active comparator necessitated the selection of a diabetic cohort. Future studies are needed to clarify whether similar benefits extend to non-diabetic ESRD patients with HF.\u003c/p\u003e\u003cp\u003eIn conclusion, this large-scale target trial emulation provides the first real-world evidence that, compared with DPP-4 inhibitors, GLP-1 receptor agonists reduce ischemic complications and heart failure exacerbations in patients with ESRD and heart failure\u0026mdash;a population with extreme cardiovascular vulnerability and limited therapeutic options. These findings suggest GLP-1 RAs may confer cardiovascular protection and survival benefits and should be considered a treatment option in this high-risk group, awaiting confirmation in randomized trials\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eData Source\u003c/h2\u003e\n \u003cp\u003eWe conducted a cohort study using the TriNetX Global Health Research Network, which aggregates deidentified electronic health records from ~ 150 health-care organizations encompassing \u0026gt; 180\u0026nbsp;million patients worldwide. Available data include diagnoses, medications, procedures, laboratory results, demographics, and health-care utilization. Cohorts and outcomes were prespecified within the platform. This platform has been widely used to conduct real-world studies addressing treatment effectiveness and safety in specific disease cohorts and under-represented populations\u003csup\u003e40, 41\u003c/sup\u003e. (The study was approved by the Institutional Review Board of National Cheng Kung University Hospital, Taiwan, with a waiver of informed consent due to the use of deidentified, aggregated data.) The study complied with the Declaration of Helsinki and followed Strengthening the Reporting of Observational studies in Epidemiology (STROBE) reporting guidelines\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy Design and Population\u003c/h3\u003e\n\u003cp\u003eWe applied a target trial emulation framework to observational data to emulate the design of a randomized controlled trial, following the TARGET reporting checklist \u003csup\u003e31\u003c/sup\u003e(Supplementary Table 1). From the TriNetX platform, participants aged ≥ 18 years with documented HF and ESRD during August 2016 and August 2025 were assigned to two exposure groups. The GLP-1 RA group included patients who initiated a glucagon-like peptide-1 receptor agonist within 2 years after the first diagnosis of ESRD and, concomitantly, within 2 years following the first diagnosis of HF. The comparator group consisted of patients who initiated a DPP-4i (Supplementary Fig. 1). To ensure a new-user, active-comparator design, we excluded individuals with any prior use of GLP-1 RAs or DPP-4i, those who had undergone kidney transplantation before the index date, and those with a diagnosis of cardiovascular events within 3 months preceding the index date. End-stage renal disease (ESRD, also termed end-stage kidney disease [ESKD]) is operationally defined as chronic kidney failure requiring kidney replacement therapy (KRT), including maintenance dialysis or kidney transplantation, to sustain life, consistent with the definition applied in the US Renal Data System (USRDS). HF and ESRD were identified using ICD-10-CM codes I50 and N18.6, respectively. In the SCREAM cohort, 99.1% of dialysis patients had a registry diagnosis of renal failure, and the coding scheme identified eGFR \u0026lt; 30 with high accuracy (positive predictive value (PPV) 93.5%, negative predictive value (NPV) 99.2%) \u003csup\u003e32\u003c/sup\u003e. Moreover, multiple studies have applied ICD-10-CM N18.6 as the definition of ESRD for enrollment or outcome \u003csup\u003e33, 34, 35\u003c/sup\u003e. ICD-10-CM codes (I50.x) in the first diagnostic position, which have shown a PPV of 98% for acute HF hospitalizations\u003csup\u003e36\u003c/sup\u003e. HF phenotypes were classified using ICD-10-CM codes: systolic HF (I50.2) was used as a proxy for HFrEF or HFmrEF with LVEF ≤ 50%, and diastolic HF (I50.3) as a proxy for HFpEF with LVEF \u0026gt; 50%. These codes have been validated against echocardiography, with positive predictive values of ~ 90% for EF ≤ 50% and ~ 92% for EF \u0026gt; 50%. We therefore refer to these as ‘code-based’ HFrEF/HFmrEF and HFpEF subgroups throughout the study \u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe index date was the date of the first prescription for the exposures; baseline covariates were assessed using most recent data during the 3 years preceding the index date. Primary analyses used an intention-to-treat framework, preserving baseline comparability and yielding policy-relevant effectiveness estimates despite post-index treatment changes\u003c/p\u003e\n\u003cp\u003eFollow-up began 1 day after the index date and continued until the first occurrence of an outcome, loss to follow-up, or 2 years, whichever occurred first. Follow up time was calculated by last record or death in TriNetX. Detailed cohort definitions and code lists are provided in Supplementary Table\u0026nbsp;2.\u003c/p\u003e\n\u003ch3\u003ePrespecified Outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was a composite of ischemic cardiovascular events and heart failure exacerbation (HFE). Ischemic cardiovascular events included acute myocardial infarction (MI), ischemic stroke, and cardiac arrest. These definitions were not restricted to nonfatal events, as diagnosis codes for MI and stroke may also capture cardiovascular deaths; deaths potentially related to HF, such as cardiogenic shock, may likewise be encompassed. Thus, the composite outcome more closely reflects a conventional 3-point MACE\u003csup\u003e37\u003c/sup\u003e. All-cause mortality was analyzed separately. Restriction to ischemic (rather than hemorrhagic) stroke was adopted to minimize potential confounding from trauma-related hemorrhage, consistent with prior real-world data studies. Heart failure exacerbation was identified using ICD-10-CM codes I50.23, I50.33 (acute on chronic systolic or diastolic heart failure), and J81 (pulmonary edema). Risk of individual components within the primary outcome were also analyzed. Secondary outcomes included the all-cause mortality and hospitalization. In addition to propensity score–matched analyses, we applied a multivariable Cox proportional hazards regression model in the overall cohort, adjusting for the same baseline covariates, to estimate hazard ratios for the study outcomes (Supplementary Table\u0026nbsp;6). Temporal changes in HbA1c levels and BMI were evaluated between the matched groups to determine whether the observed cardiovascular protective effect of GLP-1 receptor agonists could be explained by improvements in glycemic control or weight reduction, or instead may reflect alternative underlying mechanisms. Complete outcome code lists are provided in Supplementary Table\u0026nbsp;3.\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eCovariates\u003c/h2\u003e\n \u003cp\u003eBaseline covariates were assessed using the most recent data available within 3 years before the index date and were selected. Because they provide relevant clinical\u003c/p\u003e\n \u003cp\u003einformation, were potential confounders or important risk factors. Covariates included demographics, lifestyle factors, comorbidities (eg, obesity, type 2 diabetes mellitus, heart failure subtype, cardiovascular and cerebrovascular disease, peripheral vascular disease), and medication use (GDMT, hypoglycemic, cardiovascular, and lipid-lowering agents, nonsteroidal anti-inflammatory drugs). Dialysis-related variables comprised vascular access type and peritoneal dialysis. Clinical and laboratory measures included body mass index, blood pressure, hemoglobin A1c, albumin, hemoglobin, renal and lipid profiles, and iron indices. Cardiac parameters included LVEF, B-type natriuretic peptide, and N-terminal pro–B-type natriuretic peptide. Detailed definitions and coding are provided in Supplementary Table\u0026nbsp;4.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eBaseline characteristics were summarized, with categorical variables reported as numbers and percentages and continuous variables as means with standard deviations. We performed 1:1 propensity score (PS) matching before the primary, subgroup, and sensitivity analyses to reduce confounding. Relevant covariates were used to estimate a propensity score for each subject using logistic regression to model the probability of receiving GLP-1 RA therapy. Patients in the treatment group were then matched to comparators using a greedy nearest-neighbor algorithm, with an absolute standardized mean difference (SMD) of less than 0.1 considered indicative of adequate balance. Kaplan–Meier methods with log-rank tests were used to depict and compare survival probabilities, while other time-to-event outcomes were analyzed using Cox proportional hazards models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). The E-value represents the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need with both treatment and outcome to fully explain away the observed effect estimate, analogous to a sensitivity measure derived from the observed hazard ratio. All analyses were conducted within the TriNetX platform on 2025/08/22.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003ePrespecified Subgroup Analysis\u003c/h2\u003e\n \u003cp\u003eTo evaluate heterogeneity of treatment effect, we prespecified subgroup analyses by sex, age (18–64 vs ≥ 65 years), heart failure phenotype (code-based HFpEF vs HFrEF or HFmrEF), history of cardiovascular events, use of guideline-directed medical therapy, HbA1c (≤ 7% vs \u0026gt; 7%), and BMI. Within each subgroup, propensity score matching was repeated and hazard ratios were estimated using Cox proportional hazards models, thereby addressing subgroup-specific confounding.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eSensitivity analyses\u003c/h2\u003e\n \u003cp\u003eWe restricted patients with an ICD-10-CM code for dependence on renal dialysis (Z99.2) to enhance the specificity of ESRD ascertainment. We excluded patients who underwent kidney transplantation after the index date to minimize bias from altered cardiovascular risk after transplantation\u003csup\u003e38\u003c/sup\u003e, and we excluded individuals who switch to the other treatment during follow-up to approximate a per-protocol effect. We additionally analyzed an extended cohort of patients with a history of prevalent heart failure. We conducted varying the permissible windows for treatment initiation after the first recorded HF and ESRD diagnoses to assess the robustness of our findings to different definitions of time zero. We evaluated a composite outcome including ischemic cardiovascular events, heart failure exacerbations, and all-cause mortality to account for competing risks. Moreover, to assess potential unmeasured\u003c/p\u003e\n \u003cp\u003econfounding, we used traumatic brain injury and selected skin cancers as negative-control outcomes for which no association was expected, and nausea as a positive-control outcome for which an increased risk was expected among GLP-1 receptor agonist users\u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were obtained from the TriNetX platform and are available only in de-identified, aggregated form. Individual-level data are not accessible to the investigators. Because the dataset contains protected health information, access is restricted. Researchers interested in using these data must apply through the TriNetX platform, providing appropriate credentials, a clear research purpose, and compliance with relevant privacy regulations. The application and approval process may take several weeks. For further details, please visit the https://trinetx.com or contact TriNetX via their email ([email protected]). Source data are provided with this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Science and Technology Council (NSTC 112-2314-B-006-088-MY3, NTSC 114-2314-B-006 -018), the National Cheng Kung University Hospital (NCKUH-11306001 and NCKUH-11304023). The funding sources did not influence the study design, analysis, interpretation or approval of the manuscript. The authors would like to acknowledge the assistance from Yu-Han Lin and Tso-Chi Chang during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePo-Yi Liu: Data curation; Formal analysis; Investigation; Software; Validation; Visualization; Writing \u0026ndash; original draft\u003cbr\u003eChao-Kuei Shih: Validation; Methodology (statistical advice).\u003cbr\u003eMiyuki Hsing-Chun Hsieh: Methodology; TriNetX sensitivity analysis advice; Statistical/pharmacy consultation; Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003eChing-Chun Lin: Conceptualization; Domain knowledge discussion.\u003cbr\u003eChieh-Yen Liu: Conceptualization; Domain knowledge discussion.\u003cbr\u003eEdward Chia-Cheng Lai: Methodology; TriNetX sensitivity analysis advice; Epidemiology consultation; Supervision; Statistical/pharmacy advice.\u003cbr\u003eChung-Yi Li: Formal analysis; Methodology; Supervision; Validation; Statistical consultation.\u003cbr\u003eChih-Hen Yu: Conceptualization; Funding acquisition; Investigation; Project administration; Writing \u0026ndash; original draft; Writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003eJunne-Ming Sung: Conceptualization; Funding acquisition; Supervision; Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare that there\u0026rsquo;s no relevant conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBikbov B\u003cem\u003e, et al.\u003c/em\u003e Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. \u003cem\u003eLancet\u003c/em\u003e\u003cstrong\u003e395\u003c/strong\u003e, 709-733 (2020).\u003c/li\u003e\n\u003cli\u003eGansevoort RT\u003cem\u003e, et al.\u003c/em\u003e Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. \u003cem\u003eLancet\u003c/em\u003e\u003cstrong\u003e382\u003c/strong\u003e, 339-352 (2013).\u003c/li\u003e\n\u003cli\u003eKhan MS\u003cem\u003e, et al.\u003c/em\u003e Managing Heart Failure in Patients on Dialysis: State-of-the-Art Review. \u003cem\u003eJ Card Fail\u003c/em\u003e\u003cstrong\u003e29\u003c/strong\u003e, 87-107 (2023).\u003c/li\u003e\n\u003cli\u003eHouse AA\u003cem\u003e, et al.\u003c/em\u003e Heart failure in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. \u003cem\u003eKidney Int\u003c/em\u003e\u003cstrong\u003e95\u003c/strong\u003e, 1304-1317 (2019).\u003c/li\u003e\n\u003cli\u003eRangaswami J\u003cem\u003e, et al.\u003c/em\u003e Cardiorenal Syndrome: Classification, Pathophysiology, Diagnosis, and Treatment Strategies: A Scientific Statement From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e\u003cstrong\u003e139\u003c/strong\u003e, e840-e878 (2019).\u003c/li\u003e\n\u003cli\u003eShiba N, Shimokawa H. 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Effectiveness of tirzepatide in patients with HFpEF using a target trial emulation retrospective cohort study. \u003cem\u003eNat Commun\u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 4471 (2025).\u003c/li\u003e\n\u003cli\u003ePan H-C\u003cem\u003e, et al.\u003c/em\u003e Sodium-Glucose Cotransport Protein 2 Inhibitors in Patients With Type 2 Diabetes and Acute Kidney Disease. \u003cem\u003eJAMA Netw Open\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, e2350050-e2350050 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7722921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7722921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobally, an estimated 4.6 million individuals live with end-stage renal disease (ESRD), with cardiovascular disease the leading cause of death. Heart failure (HF) affects over one-third of dialysis patients, yet effective therapies remain scarce as trials largely excluded this population. Using TriNetX, we emulated a target trial comparing glucagon-like peptide-1 receptor agonists (GLP-1RAs) with dipeptidyl peptidase-4 inhibitors (DPP-4is) in ESRD patients with HF. Among 5,087 eligible patients, 1:1 propensity score matching yielded 1,257 pairs. GLP-1RAs use was associated with risk reduction of the primary composite of ischemic cardiovascular events and HF exacerbations (30.1% vs. 40.1%; HR 0.71(0.62–0.81),\u003cem\u003e p\u003c/em\u003e\u0026lt;0.001), lowering ischemic events (HR 0.69), HF exacerbations (HR 0.73), and mortality (HR 0.67). Subgroup analyses showed benefit across prevention settings, baseline therapy, and HF subtypes. Findings suggest GLP-1RAs may provide cardiovascular benefit in ESRD patients with HF, a high-risk group underserved in clinical trials, and warrant confirmation in randomized studies.\u003c/p\u003e","manuscriptTitle":"Cardiovascular outcome of Glucagon-Like Peptide-1 Receptor Agonists vs Dipeptidyl Peptidase-4 Inhibitor on End-stage Renal Disease patients with Heart Failure: An Emulated Target Trial in Patients with Diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 09:05:11","doi":"10.21203/rs.3.rs-7722921/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c9adb1fc-14e4-441b-89e5-669c291324b9","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55687105,"name":"Health sciences/Diseases/Cardiovascular diseases/Heart failure"},{"id":55687106,"name":"Health sciences/Diseases/Kidney diseases/Chronic kidney disease/End-stage renal disease"},{"id":55687107,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes"}],"tags":[],"updatedAt":"2025-10-15T09:05:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 09:05:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7722921","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7722921","identity":"rs-7722921","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}