Dapagliflozin Reduces Systemic Inflammation in Patients with Type 2 Diabetes Without Known Heart Failure

Dapagliflozin Reduces Systemic Inflammation in Patients with Type 2 Diabetes Without Known Heart Failure | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dapagliflozin Reduces Systemic Inflammation in Patients with Type 2 Diabetes Without Known Heart Failure Dennis Wang, Anna Naumova, Daniel Isquith, Jaime Sapp, Kim Anh Huynh, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4132581/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jun, 2024 Read the published version in Cardiovascular Diabetology → Version 1 posted 10 You are reading this latest preprint version Abstract Objective: Sodium glucose cotransporter 2 (SGLT2) inhibitors significantly improve cardiovascular outcomes in diabetic patients; however, the mechanism is unclear. We hypothesized that dapagliflozin improves cardiac outcomes via beneficial effects on systemic and cardiac inflammation and cardiac fibrosis. Research and Design Methods: This randomized placebo-controlled clinical trial enrolled 62 adult patients (mean age 62, 17% female) with type 2 diabetes (T2D) without known heart failure. Subjects were randomized to 12 months of daily 10 mg dapagliflozin or placebo. For all patients, blood/plasma samples and cardiac magnetic resonance imaging (CMRI) were obtained at time of randomization and at the end of 12 months. Systemic inflammation was assessed by plasma IL-1B, TNFα, IL-6 and ketone levels and PBMC mitochondrial respiration, an emerging marker of sterile inflammation. Cardiac fibrosis was assessed by T1 mapping to calculate extracellular volume fraction (ECV); cardiac tissue inflammation was assessed by T2 mapping. Results: Between the baseline and 12-month time point, plasma IL-1B was reduced (-1.8 pg/mL, P=0.003) while ketones were increased (0.26 mM, P=0.0001) in patients randomized to dapagliflozin. PBMC maximal oxygen consumption rate (OCR) decreased over the 12-month period in the placebo group but did not change in patients receiving dapagliflozin (-158.9 pmole/min/10 6 cells, P=0.0497 vs -45.2 pmole/min/10 6 cells, P=0.41), a finding consistent with an anti-inflammatory effect of SGLT2i. ECV and T2 relaxation time did not change in both study groups. Conclusion: This study demonstrates that 12 months of dapagliflozin reduces IL-1B mediated systemic inflammation but affect cardiac fibrosis in T2D. Clinical Trial.gov Registration NCT03782259 Type 2 Diabetes inflammation IL-1B PBMC respiration CMRI cardiac fibrosis SGLT2 inhibitor Figures Figure 1 Figure 2 Introduction Sodium glucose cotransporter 2 (SGLT2) inhibitors have unique characteristics to inhibit the reabsorption of glucose in kidneys, causing an increase in urinary glucose excretion and reduction of plasma glucose. In the EMPA-REG OUTCOME and CANVAS trials, SGLT2 inhibitors have been shown to significantly reduce cardiovascular events in T2D patients [ 1 , 2 ]. These benefits are independent of glycemic control. While the mechanism by which SGLT2 inhibition improves cardiovascular outcomes in T2D remains elusive, there is ample evidence in pre-clinical and clinical studies to suggest that SGLT2 inhibition is associated with a reduction in inflammation [ 3 , 4 ]. Recently, subgroup analysis of the CANTOS trial suggests that anti- interleukin (IL)-1B therapy may reduce heart failure (HF) hospitalizations in myocardial infarction (MI) patients with elevated high sensitivity C-reactive protein (CRP) [ 5 ], implicating IL-1B as a key mediator in cardiac inflammation. Experimental T2D animal models suggest that SGLT2 inhibitors increase the rate of glucose and fatty acid oxidation leading to an increase in circulating ketone levels, which was shown to inhibit NLRP3 inflammasome activation, resulting in reduced IL-1B production in macrophages [ 6 ]. Taken together, it is hypothesized that SGLT2 inhibitor’s effect in improving cardiac outcomes is mediated through antagonizing IL-1B. However, to date, there has been no double-blind placebo controlled randomized trials to demonstrate that SGLT2 inhibition reduces systemic IL-1B in T2D patients. Cardiac fibrosis, which is known to adversely affect diastolic function [ 7 , 8 ], plays an important role in the pathogenesis of diabetic cardiomyopathy [ 9 ]. Detailed pathological examinations of diabetic heart reveal myocardial hypertrophy, interstitial fibrosis, capillary endothelial changes, and capillary basal laminae thickening [ 10 ]. Cardiac MRI using T1-mapping is capable of quantifying myocardial extracellular volume (ECV), a surrogate of fibrosis, with excellent inter- and intra-observer variability and could, therefore, be potentially employed for investigations in diabetic cardiomyopathy [ 11 ]. A recent study [ 12 ] showed that ECV by T1-mapping increased as the duration of diabetes increased from 3 to 9 months in diabetic rabbits, consistent with the changes in myocardial fibrosis verified by pathology. In addition, a relatively new feature tracking technique for Cardiac Magnetic Resonance Imaging (CMRI) used for myocardial strain assessment can provide insights into the regional functional abnormalities that cannot be detected in global measurements of cardiac function. Further, T2-mapping can help to assess myocardial edema caused by injury and inflammation [ 13 ]. The aims of this double-blind placebo-controlled study were to investigate whether dapagliflozin treatment for 12 months could reduce systemic and myocardial inflammation and improve myocardial fibrosis in T2D patients. Research Design and Methods Study Setting and Patients This double-blind, randomized trial assigned adults with T2D to placebo or 10 mg dapagliflozin daily for 12 months. The study began on 2/26/2019; initial screening of patients and follow up visits occurred at the Clinical Atherosclerosis Research Laboratory at Harborview Medical Center, University of Washington. The study was completed on 11/16/2022. Inclusion Criteria Age > 18; T2D history > 5 years; Hemoglobin A1c (7–10%), glucose control medication: insulin, metformin, and/or sulfonylurea. Exclusion Criteria Current use of SGLT2 inhibitor; hypersensitivity to SGLT2 inhibitor; contraindications to MRI; eGFR < 45 ml/min/1.73m 2 ; unstable or progressing renal disease; SBP < 100 mmHg; severe hepatic disease (Child-Pugh Class C); active hepatitis B or C, CV disease within 3 months before enrollment (myocardial infarction; CABG, coronary intervention; NYHA Class IV heart failure; TIA; stroke, PAD); Bladder cancer; or high risk of DKA, high risk of fracture (osteoporosis, osteopenia). Enrollment and Randomization T2D patients without known heart failure were enrolled in the study. During the enrollment period, 95 patients were assessed for eligibility, 62 patients were randomized, 56 patients completed the study protocol (Fig. 1 ). Randomization was stratified according to use of glucagon-like peptide (GLP-1) and angiotensin-II receptor blockers (ARBs). Study Intervention Following screening visit and informed consent, patients were randomized 1:1 to placebo or 10 mg dapagliflozin daily for 12 months. Randomization was performed by the Investigational Drug Services at Harborview Medical Center. During the randomization visit, blood samples were collected for peripheral blood mononuclear cell (PBMC) respiration assessment and plasma samples were collected for cytokine and ketone measurements. Patients also received a baseline CMRI and laboratory evaluation. Patients had clinical visits at 3, 6, 9 months, and at the 12-month visit patients underwent a final CMRI along with blood and plasma sample collection. Outcomes Primary outcomes: Changes in global myocardial strain and ECV as assessed by T1 mapping (baseline to 12 months). Secondary outcomes: Changes in plasma IL-1B, TNFα, IL-6, IL-10, plasma ketones, T2 relaxation time (baseline to 12 months). Exploratory outcome: PBMC mitochondrial respiration Plasma Cytokine and Ketone Quantifications Plasma samples were obtained from whole blood collected in EDTA-containing vacutainers post 2000g x 10’ at 4 o C and stored in -80 o C. Plasma concentrations of cytokines were determined by ELISA following manufacturer’s protocol (Biolegend): IL-1B (Cat: 437004), TNFα (Cat: 430204), IL-6 (Cat: 430504), and IL-10 (Cat: 430601). Plasma concentrations of β-hydroxybutyrate and acetoacetate were determined by EnzyChrom™ Ketone Body Assay Kit following manufacturer’s protocol (BioAssay Systems, Cat: EKBD-100). PBMC Oxygen Consumption Rate (OCR) Measurement PBMC was isolated from whole blood collected in acid-citrate-dextrose vacutainers post density gradient (Histopaque-1077, Sigma-Aldrich Cat: 10771) centrifugation. Freshly isolated PBMCs were resuspended in Seahorse XF medium (Cat: 102353-100) and then plated (10 6 cells per well) onto Seahorse XFe24 cell culture plate. PBMC mitochondrial respiratory function was assessed by measuring the oxygen consumption rate at basal and maximal stimulated conditions using Seahorse XFe24 Analyzer as described previously [ 14 ]. Cardiac Magnetic Resonance Imaging CMRI examination was done at a 3T clinical whole-body scanner (Ingenia, Phillips®) located at the BioMolecular Imaging Center (BMIC) at the University of Washington, South Lake Union campus. CMRI protocol included: steady state free precession (SSFP) cine imaging to measure heart LV chamber volumes (assessing dilatation and hypertrophy), contractile function and myocardial strain; naïve and post-contrast T1 mapping and ECV fraction to assess changes in diffused myocardial fibrosis; T2 mapping to assess myocardial inflammation; T2* mapping to assess iron deposition; Late gadolinium enhancement for visualize focal fibrosis. All imaging acquisitions were done with ECG gating and breath hold technique. Imaging parameters are shown in Supplemental Table 1 . Image Processing and Analysis Volumetric LV analysis and analysis of quantitative maps (T1, T2, T2*) were performed using Philips IntelliSpace Portal (ISP) software. Volumetric parameters are reported as indexes, after adjustment for body surface area. Variables are compared to normal age specific ranges reported in the literature. ECV maps were generated offline using MATLAB software. ECV was calculated from native and post-contrast T1 values for blood and myocardial tissue, the partition coefficient lambda (λ), and hematocrit using the following formulas: ECV = λ(1-hematocrit); λ = (1/T1 myocardium post-contrast-1/T1 myocardium-native)/(1/T1 blood post-contrast-1/T1 blood-native). Feature tracking was performed using Circle Cardiovascular Software (cvi-42, Circle Cardiovascular Imaging Inc., Calgary, Alberta, Canada) to measure myocardial strain and strain rate from the bSSFP short-axis and long-axis cine images. Long-axis cine images were further used to compute global myocardial longitudinal strain. Short-axis images were used to compute circumferential and radial strain and strain rate. The global values were obtained through averaging the values according to an American Heart Association 17-segment model [ 15 ]. Statistical Analysis For systemic inflammatory endpoints (plasma cytokines, plasma ketones, and PBMC OCR), we compared baseline and 1-year post-intervention values in the dapagliflozin and placebo groups. P-values were determined by paired two-tailed t-test. Parametric t-test was used if distribution passes normality test, otherwise non-parametric t-test (Wilcoxon) was used. For CMRI outcomes, we compared baseline characteristics in the dapagliflozin and placebo groups and expressed age as mean and standard deviation and categorical variables as numbers and percents. The difference between baseline and 1-year results was calculated for the primary outcomes in the dapagliflozin and placebo groups. Within each group, the difference between baseline and 1 year was assessed with the paired t-test. The differences between drug and placebo groups were compared with the Wilcoxon Rank Sum Test. To adjust for multiple comparisons, the level of statistical significance was set at 0.0125 (.05/4) for primary outcomes, and 0.0083 (.05/6) for secondary outcomes. Ethical Oversight The trial was approved by the Institutional Review Board at the University of Washington. Results Baseline Characteristics As shown in Table 1 , mean age of participants was 62 years and 17% were female. Of the participants, 61% had hypertension, 60% had hyperlipidemia, and 40% had a family history of coronary artery disease. No clinically meaningful differences were noted in the baseline characteristics between the dapagliflozin and placebo groups following randomization. Table 1 Baseline characteristics Characteristic Dapagliflozin (n = 31) Placebo (n = 31) Age, (mean, standard deviation) 62 (9) 62 (11) Women 6 (19%) 5 (16%) Hispanic 1 (3%) 4 (13%) Race White 23 (74%) 20 (64%) Black 3 (10%) 3 (10%) Asian 3 (10%) 6 (19%) Native American 2 (6%) 1 (3%) Not provided 0 (0%) 1 (3%) Hypertension 19 (61%) 19 (61%) Diabetes mellitus 31 (100%) 31 (100%) Hyperlipidemia 21 (68%) 18 (58%) Current smoker 2(6%) 3 (10%) Family history MI 12 (39%) 11 (36%) Family history stroke 14 (45%) 11 (36%) History MI 1 (3%) 2 (6%) Angina 1 (3%) 2 (6%) Coronary artery bypass surgery 0 (0%) 2 (6%) Percutaneous coronary intervention 3 (10%) 1 (3%) P > 0.05 for all treatment group comparisons MI, myocardial infarction. Systemic Inflammatory Endpoints Between baseline and 1-year follow-up, circulating CRP did not change significantly in either dapagliflozin or the placebo group (Table 3 ). Among the plasma proinflammatory cytokines, we observed a significant reduction in IL-1B, but not TNFα or IL-6, in patients randomized to dapagliflozin (Figs. 2 A- 2 F). IL-10, a known pro-fibrotic and anti-inflammatory cytokine, also did not change significantly in either group. (Figs. 2 G and 2 H). Next, plasma ketones have been implicated as an important mediator for the beneficial effects associated with SGLT2 inhibitors, including anti-inflammation [ 16 ]. Here, we observed a significant increase of plasma ketones (β-hydroxybutyrate and acetoacetate) in patients randomized to dapagliflozin but not in the placebo group (Figs. 2 I and 2 J). PBMC mitochondrial function is an emerging marker of sterile inflammation: a decline of PBMC maximal oxygen consumption rate (OCR), measured in the presence of mitochondrial uncoupler to facilitate maximal electron transport chain activity, is associated with increased pro-inflammatory cytokine expressions in chronic heart failure [ 14 , 17 ]. Here, we observed that while the PBMC basal OCR did not change in either dapagliflozin or placebo group ( Supplemental Fig. 1 ), the PBMC maximal OCR of patients receiving placebo declined while that of those receiving dapagliflozin did not change between baseline and 1-year follow-up (Figs. 2 K and 2 L), suggesting that SGLT2 inhibitor had an anti-inflammatory effect. Given PBMC is a major source of circulating cytokines, these results suggest that dapagliflozin may reduce systemic inflammation by acting on the PBMC – IL-1B axis, possibly mediated by ketones. Cardiac MR Endpoints As shown in Table 2 , ECV, a measurement of cardiac fibrosis, and myocardial strain values did not differ between baseline and at 1-year follow-up in either study group. The global radial peak strain in placebo group at 1-year trended higher in comparison to baseline (31.3 ± 10.4% vs. 27.3 ± 7.1%, P = 0.043, respectively). However, after adjusting for multiple comparisons (corrected standard P = 0.0125), the P-value of 0.043 was not statistically significant. Table 2 CMRI outcomes Drug Placebo Difference (1 year – baseline) Outcome Baseline 1 year P* Baseline 1 year P* Drug Placebo P** ECV 27.7 (2.9) 28.4 (2.0) 0.20 28.5 (2.1) 28.7 (2.3) 0.56 0.71 (2.75) (n = 26) 0.24 (2.16) (n = 28) 0.20 Radial peak strain global 31.2 (8.7) 33.9 (9.5) 0.14 27.3 (7.1) 31.3 (10.4) 0.043 2.71 (9.38) (n = 27) 4.01 (10.19) (n = 29) 0.80 Circumferential peak strain global -17.8 (2.9) -17.7 (3.6) 0.93 -15.7 (2.8) -16.7 (2.9) 0.12 0.05 (3.21) (n = 27) -0.97 (3.28) (n = 29) 0.43 Longitudinal peak strain global -12.9 (3.4) -12.0 (3.8) 0.27 -11.0 (3.9) -11.3 (5.1) 0.81 0.88 (4.06) (n = 27) -0.24 (5.32) (n = 29) 0.62 T2 relaxation time 50.5 (4.3) 50.0 (5.7) 0.70 48.6 (3.3) 50.2 (3.8) 0.045 -0.51 (6.59) (n = 25) 1.61 (4.14) (n = 29) 0.30 For ECV, radial, circumferential, and longitudinal peak strain measurements, statistical significance set to P < = 0.0125 with Bonferroni correction. For T2 relaxation time, statistical significance set to P < = 0.0083 with Bonferroni correction Table 3 Additional Outcomes Drug Placebo Difference (1 year – baseline) Outcome Baseline 1 year P* Baseline 1 year P* Drug Placebo P** C-reactive protein 1.7 (1.6) 2.5 (4.1) 0.08 2.4 (0.5) 1.8 (0.3) 0.78 0.81 (2.95) (n = 28) 0.12 (2.20) (n = 29) 0.50 Glucose 159.8 (42.5) 126.8 (36.9) 0.004 164.5 (46.4) 161.0 (55.9) 0.64 -33.04 (55.08) (n = 28) -3.48 (39.56) (n = 29) 0.006 A1c 7.9 (0.8) 7.4 (0.8) 0.007 7.8 (0.9) 8.0 (1.0) 0.63 -0.52 (0.95) (n = 28) 0.11 (1.17) (n = 28) 0.012 Hematocrit 42.5 (4.0) 40.9 (3.1) 0.08 41.4 (1.8) 41.4 (2.8) 0.96 -0.02 (0.05) (n = 26) 0.00 (0.02) (n = 29) 0.33 Body surface area 2.10 (0.21) 2.06 (0.21) < 0.0001 2.05 (0.16) 2.04 (0.15) 0.14 -0.04 (0.04) (n = 26) -0.01 (0.04) (n = 29) 0.005 BNP 37.1 (38.0) 55.1 (72.1) 0.08 31.9 (27.7) 36.4 (24.4) 0.31 17.96 (52.56) (n = 28) 4.58 (23.61) (n = 29) 0.94 All variables expressed as mean (standard deviation) * by paired t-test. ** by Wilcoxon Rank Sum Test BNP Brain natriuretic peptide. T2 relaxation time, a measurement of cardiac inflammation, of patients receiving placebo trended higher, suggesting worsening inflammation (48.6 ± 3.3 ms vs. 50.2 ± 3.8 ms, P = 0.045), between baseline and 1-year follow-up, while that of those receiving dapagliflozin did not change. However, the P-value of 0.045 was not statistically significant when compared to the corrected standard P = 0.0083 for multiple comparison. The representative CMRI of the same patient before intervention and at 1-year follow up are shown in Supplemental Fig. 2 . Additional Endpoints In comparison to the placebo group, patients randomized to dapagliflozin demonstrated significant reductions in random serum glucose (-33 mg/dl) vs (-3.48 mg/dl) (P = 0.006) and hemoglobin A1c (-0.52%) vs (0.11%) (P = 0.012) between baseline and 12-month follow-up. No differences were noted in brain natriuretic peptide (BNP) (Table 3 ) Conclusion In this study, 12-months treatment of dapagliflozin reduced plasma IL-1B level. To our knowledge, this is the first time that SGLT2 inhibitor is shown to lower IL-1B in a placebo-controlled double-blind randomized clinical trial. However, dapagliflozin treatment did not result in a change in plasma IL6, plasma TNFα or serum CRP, consistent with the majority of prior clinical studies [ 18 ]. Furthermore, dapaglifozin significantly increased plasma ketones and attenuated the decline of PBMC maximal OCR, which was previously shown to inversely correlate with IL-1B expression in chronic heart failure [ 14 , 17 ]. Despite the improvements in circulatory inflammatory endpoints, we did not observe significant changes in CMRI measurements of myocardial fibrosis and strain in patients receiving dapagliflozin. Of note, in the placebo group, there was a trend of worsening T2 relaxation time (inflammation), which was not observed in the dapagliflozin group, suggesting dapagliflozin may attenuate the progression of cardiac inflammation. IL-1B is an inducible pro-inflammatory cytokine made primarily by immune cells, such as monocytes and macrophages, to function in cardiac repair as well as injury [ 19 ]. Under the NFkB-mediated transcriptional regulation, Pro-IL-1B is produced and stored intracellularly. Activation of NLRP3 inflammasome results in the cleavage and maturation of IL-1B prior to secretion. IL-1B has been shown to worsen myocardial contractile function and relaxation and induce hypertrophy [ 20 – 23 ]. Furthermore, in multiple animal studies, SGLT2 inhibitor is shown to antagonize the NLRP3-IL-1B axis to improve cardiovascular outcomes [ 4 ], substantiating IL-1B’s role as a potential mediator of cardiac inflammation. The recent CANTOS trial subgroup analysis provides clinical evidence that circulating IL-1B is not just a bystander but actively contributes to heart failure pathogenesis. The conjecture is corroborated by a number of small clinical trials using anakinra, a recombinant IL-1 receptor antagonist. In these studies, anakinra is shown to reduce CRP and HF hospitalization in acute STEMI patients [ 24 , 25 ] and enhance exercise capacity and LVEF in HF patient with elevated CRP [ 26 ]. PBMC mitochondrial respiration is an emerging biomarker of sterile inflammation [ 14 , 27 – 29 ], particularly in the setting of heart failure, and was previously shown to inversely correlate with the expressions of proinflammatory cytokines, such as IL-1B [ 14 , 17 ]. It is postulated that upon stimulation by circulating mitochondrial damage associated molecular patterns (MitoDAMPs), PBMC produces IL-6, leading to mitochondrial dysfunction and mitochondrial ROS production in an autocrine manner [ 14 ]. Mitochondrial ROS subsequently activates the NLRP3 inflammasome, resulting in the maturation and release of IL-1B family of cytokines [ 30 ]. In the current study, we observed that SGLT2 inhibitor sustains mitochondrial oxidative capacity of circulating immune cells, supporting the notion that PBMC may be a source of circulating IL-1B, and a potential therapeutic target in T2D. Together, our results are consistent with the proposed mechanism that SGLT2 inhibitor lowers systemic inflammation by increasing plasma ketones, which acts on peripheral immune cells (PBMCs) to inhibit NLRP3 inflammasome activation and IL-1B production [ 6 ]. Recently, transcriptomic analyses indicate that disruption of mitochondrial pathways (TCA cycle and oxidative phosphorylation) in circulating monocytes is a marker of elevated cardiovascular risk in T2D patients [ 31 ]. Whether and how mitochondrial dysfunction in circulating monocytes plays a role in the pathogenesis of T2D and heart failure is an area of active research. In addition to anti-inflammation, SGLT2 inhibitors have a wide range of effects on hemodynamic, neurohormonal, metabolic and endothelial function. There are several potential direct and indirect pathways leading to improvement of cardiac structure and function and myocardial substrate utilization in T2D. For example, SGLT2 inhibitors provide glycemic control, reduce blood pressure, reduce arterial stiffness, decrease body weight and reduce visceral adiposity, which could indirectly lead to improved cardiac function in T2D patients [ 32 ]. On the cellular level, SGLT2 inhibitors could directly affect cardiac function by reducing oxidative stress, attenuating myocardial fibrosis [ 33 , 34 ]. In this study we did not find a significant effect of dapagliflozin on strain when compared to placebo. We also did not find a significant change in strain measures in the placebo control group over a 12-month period; raising the question whether the enrolled T2D patients truly had underlying structural heart disease. Myocardial T1 mapping methods are used to measure tissue fibrosis and reflects a composite signal from intracellular and extracellular compartments; however, T1 measurements can be confounded by renal clearance, hematocrit, and type and dosage of gadolinium [ 35 ]. To adjust for these factors, the most widely accepted approach to determine fibrosis is to measure ECV which has been shown to be a good measure of interstitial fibrosis. In the current study, we found that 12 months of SGLT2 inhibition compared to placebo did not alter ECV of T2D patients. Our findings are consistent with a prospective study which enrolled 35 T2D subjects: Before and after CMRI was performed following 6 months of Empagliflozin showed no significant effect on ECV [ 36 ]. Furthermore, in our study, SGLT2 inhibition did not change the level of plasma IL-10, a pro-fibrotic cytokine. While these results do not support the notion that the cardioprotective effects of SGLT2 inhibition is mediated via improving cardiac fibrosis, further studies are required to unravel the mechanism of action by which SGLT2 inhibitors enhance cardiac outcomes. There are several strengths of this study. First, there was excellent adherence with treatment as evidenced by significant reduction in blood glucose, hemoglobin A1c, and BMI in subjects randomized to dapagliflozin (Table 3 ). Second, the randomized study design was a strength, since many previous studies on SGLT2 inhibitors utilized a prospective study design. Third, the use of circulating inflammatory endpoints as well as CMRI allowed for the assessment of anti-inflammatory effect of SGLT2 inhibitor at both the systemic and organ levels. There are several limitations and weaknesses. First, this is a relatively small clinical trial which was not powered to detect smaller effect sizes. For example, while there is a trend that dapagliflozin attenuates the progression of cardiac inflammation (T2 relaxation time), the study is underpowered to detect a statistically significant difference. Second, previous animal studies of SGLT2 inhibition utilized direct pathologic examination of inflammation and fibrosis, whereas CMRI are known to be less sensitive in assessing these parameters. Third, the 12-month treatment period may not have been sufficient to result in detectable cardiac structural and functional changes, although in a prospective study, 3 months of SGLT2 inhibition was sufficient to demonstrate improvement in diastolic function [ 37 ]. Fourth, we did not have a matched, non-diabetic control group. Fifth, higher ECV values (greater than 32%) are associated with worse outcomes in patients with known myocarditis [ 38 ]. In contrast, the T2D cohort in our study had baseline ECV of 27–28%. To address the possibility that patients with higher baseline ECV would derive more benefits from SGLT2 inhibition, we performed a subgroup analysis in patients in the upper half of ECV values at baseline (ECV ~ 30%); however, in this cohort (N = 17) we still did not find a significant difference in ECV between the drug and placebo groups. In conclusion we demonstrated that 12 months of daily dapagliflozin in T2D patients reduces circulating IL-1B, increases plasma ketones, and prevents the decline of PBMC mitochondrial maximal respiration, but does not improve cardiac fibrosis or strain by CMRI. Declarations Ethics approval and consent to participate The trial was approved by the Institutional Review Board at the University of Washington. Consent for publication All authors approve the manuscript and give their consent for submission and publication Availability of data and materials All data will be uploaded and made available to the public once the manuscript has been published. Competing interests: All authors declare that they have no conflicts of interest. Funding: AstraZeneca ESR 17-13124 (XQZ) NIH R01HL157261 (XQZ, FK) NIH R01HL13955 (FK) Veteran Affairs BLRD Career Development Award 1IK2BX006111-01A1 (DW). Authors' contributions: DW, AVN, DI, JS, KAH, IT, NB, AV, BC, KO were involved in the conduct of the study. CM, DW, RT, XQZ, FK were involved in conception, design and analysis and interpretation of the result. DW, FK wrote the first draft of the manuscript and all authors approved the final version of the paper. FK and CM are the guarantors of this work, as such had full access to all of the data. Acknowledgements: N/A References Zinman B, et al. 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Abbate A, et al. Interleukin-1 and the Inflammasome as Therapeutic Targets in Cardiovascular Disease. Circ Res. 2020;126(9):1260–80. Shirakawa R, et al. Mitochondrial reactive oxygen species generation in blood cells is associated with disease severity and exercise intolerance in heart failure patients. Sci Rep. 2019;9(1):14709. Li P, et al. Mitochondrial respiratory dysfunctions of blood mononuclear cells link with cardiac disturbance in patients with early-stage heart failure. Sci Rep. 2015;5:10229. Sack MN. Mitochondrial fidelity and metabolic agility control immune cell fate and function. J Clin Invest. 2018;128(9):3651–61. Traba J, et al. Fasting and refeeding differentially regulate NLRP3 inflammasome activation in human subjects. J Clin Invest. 2015;125(12):4592–600. Julla JB, et al. Blood Monocyte Phenotype Is A Marker of Cardiovascular Risk in Type 2 Diabetes. Circ Res; 2023. Cowie MR, Fisher M. SGLT2 inhibitors: mechanisms of cardiovascular benefit beyond glycaemic control. Nat Rev Cardiol. 2020;17(12):761–72. Lin B, et al. Glycemic control with empagliflozin, a novel selective SGLT2 inhibitor, ameliorates cardiovascular injury and cognitive dysfunction in obese and type 2 diabetic mice. Cardiovasc Diabetol. 2014;13:148. Zhang N, et al. Dapagliflozin improves left ventricular remodeling and aorta sympathetic tone in a pig model of heart failure with preserved ejection fraction. Cardiovasc Diabetol. 2019;18(1):107. Puntmann VO, et al. T1 Mapping in Characterizing Myocardial Disease: A Comprehensive Review. Circ Res. 2016;119(2):277–99. Hsu JC, et al. Effect of Empagliflozin on Cardiac Function, Adiposity, and Diffuse Fibrosis in Patients with Type 2 Diabetes Mellitus. Sci Rep. 2019;9(1):15348. Lan NSR, et al. The effects of sodium-glucose cotransporter 2 inhibitors on left ventricular function: current evidence and future directions. ESC Heart Fail. 2019;6(5):927–35. Grani C, et al. Prognostic Value of Cardiac Magnetic Resonance Tissue Characterization in Risk Stratifying Patients With Suspected Myocarditis. J Am Coll Cardiol. 2017;70(16):1964–76. Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 07 Jun, 2024 Read the published version in Cardiovascular Diabetology → Version 1 posted Editorial decision: Revision requested 14 Apr, 2024 Reviews received at journal 14 Apr, 2024 Reviewers agreed at journal 14 Apr, 2024 Reviews received at journal 02 Apr, 2024 Reviewers agreed at journal 25 Mar, 2024 Reviewers agreed at journal 23 Mar, 2024 Reviewers invited by journal 20 Mar, 2024 Editor assigned by journal 20 Mar, 2024 Submission checks completed at journal 20 Mar, 2024 First submitted to journal 19 Mar, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4132581","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281745678,"identity":"10931107-8027-4660-85e1-90a3e2217597","order_by":0,"name":"Dennis Wang","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Dennis","middleName":"","lastName":"Wang","suffix":""},{"id":281745681,"identity":"69975894-9d88-452b-95c7-1668af10366b","order_by":1,"name":"Anna Naumova","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Naumova","suffix":""},{"id":281745686,"identity":"1f1620b6-b700-43a4-9aae-cffd91cc2ca0","order_by":2,"name":"Daniel Isquith","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Isquith","suffix":""},{"id":281745687,"identity":"4a5baf8c-5a10-4c56-a9a1-067d8b706d45","order_by":3,"name":"Jaime Sapp","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Jaime","middleName":"","lastName":"Sapp","suffix":""},{"id":281745688,"identity":"ec94620e-dec9-40f1-bfb1-0d1415076345","order_by":4,"name":"Kim Anh Huynh","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Kim","middleName":"Anh","lastName":"Huynh","suffix":""},{"id":281745689,"identity":"946fcc40-85e8-4066-b3e3-2ca133859a28","order_by":5,"name":"Isabella Tucker","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Isabella","middleName":"","lastName":"Tucker","suffix":""},{"id":281745690,"identity":"ce01ad9e-50ef-46b6-be4c-f843d945638e","order_by":6,"name":"Niranjan Balu","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Niranjan","middleName":"","lastName":"Balu","suffix":""},{"id":281745691,"identity":"2dd8851c-a71a-4fd8-a242-6c3a43730980","order_by":7,"name":"Anna Voronyuk","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Voronyuk","suffix":""},{"id":281745692,"identity":"58c26648-f695-4cff-aab4-d88677201440","order_by":8,"name":"Baocheng Chu","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Baocheng","middleName":"","lastName":"Chu","suffix":""},{"id":281745693,"identity":"acc3791d-1689-496b-9ef6-2c21b2dd6aac","order_by":9,"name":"Karen Ordovas","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Ordovas","suffix":""},{"id":281745694,"identity":"a86cacaf-8992-43d7-92df-a730a5a0c730","order_by":10,"name":"Charles Maynard","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Maynard","suffix":""},{"id":281745695,"identity":"ed87f0e7-4eca-4414-af1a-122ddde9ae34","order_by":11,"name":"Rong Tian","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Tian","suffix":""},{"id":281745696,"identity":"f7dd2189-6ae7-4b5d-843d-a19469f34cb0","order_by":12,"name":"Xue-Qiao Zhao","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Xue-Qiao","middleName":"","lastName":"Zhao","suffix":""},{"id":281745697,"identity":"40466c2a-1e08-4ead-8dcd-d9cf538966b2","order_by":13,"name":"Francis Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBAC9h7GBoYPYCYPwwGYqAQ+LTxnGBsYZyC0GBCjhYGBmQeqhYE4LTyHWzfbtjEk9vefPXi44NcfOXMG5oO3efBp4W1su50L1DLjRl7C4Zl9BsaWDWzJ1vi02PMzgrXkNtzgMTjM22OQuOEAj5k0XltAWiyBWuafPwPTwv8NvxaQwxiBWjYcyDE4zPMDbAsbfi08B9tu9pyTqN94A6iFt8HY2OAwm7HlHLxa0p/d+FFmYyx3/ozxZ54/cnIGx5sf3niDRwsUQCMC6EJgNBFWjgz+kKZ8FIyCUTAKRgYAAIxDTnp3Ov8pAAAAAElFTkSuQmCC","orcid":"","institution":"University of Washington","correspondingAuthor":true,"prefix":"","firstName":"Francis","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2024-03-19 19:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4132581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4132581/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12933-024-02294-z","type":"published","date":"2024-06-07T14:47:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53418397,"identity":"81287087-c4e0-44bd-a386-6443801c2abc","added_by":"auto","created_at":"2024-03-25 18:07:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84094,"visible":true,"origin":"","legend":"\u003cp\u003eTrial CONSORT Flow Diagram\u003c/p\u003e\n\u003cp\u003eMR, magnetic resonance; DM, diabetes mellites, GLP1, Glucagon-like peptide-1\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4132581/v1/12487726672ccce23674110c.png"},{"id":53418398,"identity":"bccbe221-4267-49f9-a8da-ec8ed9a5a989","added_by":"auto","created_at":"2024-03-25 18:07:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140587,"visible":true,"origin":"","legend":"\u003cp\u003eDapagliflozin reduces systemic inflammation\u003c/p\u003e\n\u003cp\u003eLevels of pre- and post- 12-months treatment of placebo or dapagliflozin: A-H, Plasma IL-1B, TNFα, IL-6, and IL-10; I-J, Plasma ketones (acetoacetate + βhydroxybutyrate); K-L, PBMC maximal oxygen consumption rate (OCR). P-value determined by paired two-tailed T-test. Parametric t-test is used if distribution passes normality tests, otherwise non-parametric t-test is used. For plasma cytokines (IL-1B, TNFα, IL-6, and IL-10) and ketone, statistical significance set to P \u0026lt;= 0.0083 (0.05/6) with Bonferroni correction.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4132581/v1/11e3c24b979df2228ab68f59.png"},{"id":58821734,"identity":"501a7b5a-2a69-4af0-8531-6c236d1ece63","added_by":"auto","created_at":"2024-06-21 16:12:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":951431,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4132581/v1/9da69c40-5fc3-4ca8-a8bf-96cad0adb242.pdf"},{"id":53418399,"identity":"7bdf8e46-8aaf-49a0-bd3f-45d3cd8092e1","added_by":"auto","created_at":"2024-03-25 18:07:01","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":601384,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4132581/v1/c100362bb8decaa45d41dbe9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dapagliflozin Reduces Systemic Inflammation in Patients with Type 2 Diabetes Without Known Heart Failure","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSodium glucose cotransporter 2 (SGLT2) inhibitors have unique characteristics to inhibit the reabsorption of glucose in kidneys, causing an increase in urinary glucose excretion and reduction of plasma glucose. In the EMPA-REG OUTCOME and CANVAS trials, SGLT2 inhibitors have been shown to significantly reduce cardiovascular events in T2D patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These benefits are independent of glycemic control. While the mechanism by which SGLT2 inhibition improves cardiovascular outcomes in T2D remains elusive, there is ample evidence in pre-clinical and clinical studies to suggest that SGLT2 inhibition is associated with a reduction in inflammation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recently, subgroup analysis of the CANTOS trial suggests that anti- interleukin (IL)-1B therapy may reduce heart failure (HF) hospitalizations in myocardial infarction (MI) patients with elevated high sensitivity C-reactive protein (CRP) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], implicating IL-1B as a key mediator in cardiac inflammation. Experimental T2D animal models suggest that SGLT2 inhibitors increase the rate of glucose and fatty acid oxidation leading to an increase in circulating ketone levels, which was shown to inhibit NLRP3 inflammasome activation, resulting in reduced IL-1B production in macrophages [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Taken together, it is hypothesized that SGLT2 inhibitor\u0026rsquo;s effect in improving cardiac outcomes is mediated through antagonizing IL-1B. However, to date, there has been no double-blind placebo controlled randomized trials to demonstrate that SGLT2 inhibition reduces systemic IL-1B in T2D patients.\u003c/p\u003e \u003cp\u003eCardiac fibrosis, which is known to adversely affect diastolic function [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], plays an important role in the pathogenesis of diabetic cardiomyopathy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Detailed pathological examinations of diabetic heart reveal myocardial hypertrophy, interstitial fibrosis, capillary endothelial changes, and capillary basal laminae thickening [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Cardiac MRI using T1-mapping is capable of quantifying myocardial extracellular volume (ECV), a surrogate of fibrosis, with excellent inter- and intra-observer variability and could, therefore, be potentially employed for investigations in diabetic cardiomyopathy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A recent study [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] showed that ECV by T1-mapping increased as the duration of diabetes increased from 3 to 9 months in diabetic rabbits, consistent with the changes in myocardial fibrosis verified by pathology. In addition, a relatively new feature tracking technique for Cardiac Magnetic Resonance Imaging (CMRI) used for myocardial strain assessment can provide insights into the regional functional abnormalities that cannot be detected in global measurements of cardiac function. Further, T2-mapping can help to assess myocardial edema caused by injury and inflammation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aims of this double-blind placebo-controlled study were to investigate whether dapagliflozin treatment for 12 months could reduce systemic and myocardial inflammation and improve myocardial fibrosis in T2D patients.\u003c/p\u003e"},{"header":"Research Design and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting and Patients\u003c/h2\u003e \u003cp\u003eThis double-blind, randomized trial assigned adults with T2D to placebo or 10 mg dapagliflozin daily for 12 months. The study began on 2/26/2019; initial screening of patients and follow up visits occurred at the Clinical Atherosclerosis Research Laboratory at Harborview Medical Center, University of Washington. The study was completed on 11/16/2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInclusion Criteria\u003c/h2\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;18; T2D history\u0026thinsp;\u0026gt;\u0026thinsp;5 years; Hemoglobin A1c (7\u0026ndash;10%), glucose control medication: insulin, metformin, and/or sulfonylurea.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExclusion Criteria\u003c/h2\u003e \u003cp\u003eCurrent use of SGLT2 inhibitor; hypersensitivity to SGLT2 inhibitor; contraindications to MRI; eGFR\u0026thinsp;\u0026lt;\u0026thinsp;45 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e; unstable or progressing renal disease; SBP\u0026thinsp;\u0026lt;\u0026thinsp;100 mmHg; severe hepatic disease (Child-Pugh Class C); active hepatitis B or C, CV disease within 3 months before enrollment (myocardial infarction; CABG, coronary intervention; NYHA Class IV heart failure; TIA; stroke, PAD); Bladder cancer; or high risk of DKA, high risk of fracture (osteoporosis, osteopenia).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEnrollment and Randomization\u003c/h2\u003e \u003cp\u003eT2D patients without known heart failure were enrolled in the study. During the enrollment period, 95 patients were assessed for eligibility, 62 patients were randomized, 56 patients completed the study protocol (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Randomization was stratified according to use of glucagon-like peptide (GLP-1) and angiotensin-II receptor blockers (ARBs).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy Intervention\u003c/h2\u003e \u003cp\u003eFollowing screening visit and informed consent, patients were randomized 1:1 to placebo or 10 mg dapagliflozin daily for 12 months. Randomization was performed by the Investigational Drug Services at Harborview Medical Center. During the randomization visit, blood samples were collected for peripheral blood mononuclear cell (PBMC) respiration assessment and plasma samples were collected for cytokine and ketone measurements. Patients also received a baseline CMRI and laboratory evaluation. Patients had clinical visits at 3, 6, 9 months, and at the 12-month visit patients underwent a final CMRI along with blood and plasma sample collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003ePrimary outcomes: Changes in global myocardial strain and ECV as assessed by T1 mapping (baseline to 12 months).\u003c/p\u003e \u003cp\u003eSecondary outcomes: Changes in plasma IL-1B, TNFα, IL-6, IL-10, plasma ketones, T2 relaxation time (baseline to 12 months).\u003c/p\u003e \u003cp\u003eExploratory outcome: PBMC mitochondrial respiration\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePlasma Cytokine and Ketone Quantifications\u003c/h2\u003e \u003cp\u003ePlasma samples were obtained from whole blood collected in EDTA-containing vacutainers post 2000g x 10\u0026rsquo; at 4\u003csup\u003eo\u003c/sup\u003eC and stored in -80\u003csup\u003eo\u003c/sup\u003eC. Plasma concentrations of cytokines were determined by ELISA following manufacturer\u0026rsquo;s protocol (Biolegend): IL-1B (Cat: 437004), TNFα (Cat: 430204), IL-6 (Cat: 430504), and IL-10 (Cat: 430601). Plasma concentrations of β-hydroxybutyrate and acetoacetate were determined by EnzyChrom\u0026trade; Ketone Body Assay Kit following manufacturer\u0026rsquo;s protocol (BioAssay Systems, Cat: EKBD-100).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePBMC Oxygen Consumption Rate (OCR) Measurement\u003c/h2\u003e \u003cp\u003ePBMC was isolated from whole blood collected in acid-citrate-dextrose vacutainers post density gradient (Histopaque-1077, Sigma-Aldrich Cat: 10771) centrifugation. Freshly isolated PBMCs were resuspended in Seahorse XF medium (Cat: 102353-100) and then plated (10\u003csup\u003e6\u003c/sup\u003e cells per well) onto Seahorse XFe24 cell culture plate. PBMC mitochondrial respiratory function was assessed by measuring the oxygen consumption rate at basal and maximal stimulated conditions using Seahorse XFe24 Analyzer as described previously [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCardiac Magnetic Resonance Imaging\u003c/h2\u003e \u003cp\u003eCMRI examination was done at a 3T clinical whole-body scanner (Ingenia, Phillips\u0026reg;) located at the BioMolecular Imaging Center (BMIC) at the University of Washington, South Lake Union campus. CMRI protocol included: steady state free precession (SSFP) cine imaging to measure heart LV chamber volumes (assessing dilatation and hypertrophy), contractile function and myocardial strain; na\u0026iuml;ve and post-contrast T1 mapping and ECV fraction to assess changes in diffused myocardial fibrosis; T2 mapping to assess myocardial inflammation; T2* mapping to assess iron deposition; Late gadolinium enhancement for visualize focal fibrosis.\u003c/p\u003e \u003cp\u003eAll imaging acquisitions were done with ECG gating and breath hold technique. Imaging parameters are shown in \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImage Processing and Analysis\u003c/h2\u003e \u003cp\u003eVolumetric LV analysis and analysis of quantitative maps (T1, T2, T2*) were performed using Philips IntelliSpace Portal (ISP) software. Volumetric parameters are reported as indexes, after adjustment for body surface area. Variables are compared to normal age specific ranges reported in the literature.\u003c/p\u003e \u003cp\u003eECV maps were generated offline using MATLAB software. ECV was calculated from native and post-contrast T1 values for blood and myocardial tissue, the partition coefficient lambda (λ), and hematocrit using the following formulas: ECV\u0026thinsp;=\u0026thinsp;λ(1-hematocrit); λ = (1/T1 myocardium post-contrast-1/T1 myocardium-native)/(1/T1 blood post-contrast-1/T1 blood-native).\u003c/p\u003e \u003cp\u003eFeature tracking was performed using Circle Cardiovascular Software (cvi-42, Circle Cardiovascular Imaging Inc., Calgary, Alberta, Canada) to measure myocardial strain and strain rate from the bSSFP short-axis and long-axis cine images. Long-axis cine images were further used to compute global myocardial longitudinal strain. Short-axis images were used to compute circumferential and radial strain and strain rate. The global values were obtained through averaging the values according to an American Heart Association 17-segment model [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eFor systemic inflammatory endpoints (plasma cytokines, plasma ketones, and PBMC OCR), we compared baseline and 1-year post-intervention values in the dapagliflozin and placebo groups. P-values were determined by paired two-tailed t-test. Parametric t-test was used if distribution passes normality test, otherwise non-parametric t-test (Wilcoxon) was used.\u003c/p\u003e \u003cp\u003eFor CMRI outcomes, we compared baseline characteristics in the dapagliflozin and placebo groups and expressed age as mean and standard deviation and categorical variables as numbers and percents. The difference between baseline and 1-year results was calculated for the primary outcomes in the dapagliflozin and placebo groups. Within each group, the difference between baseline and 1 year was assessed with the paired t-test. The differences between drug and placebo groups were compared with the Wilcoxon Rank Sum Test.\u003c/p\u003e \u003cp\u003eTo adjust for multiple comparisons, the level of statistical significance was set at 0.0125 (.05/4) for primary outcomes, and 0.0083 (.05/6) for secondary outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEthical Oversight\u003c/h2\u003e \u003cp\u003eThe trial was approved by the Institutional Review Board at the University of Washington.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, mean age of participants was 62 years and 17% were female. Of the participants, 61% had hypertension, 60% had hyperlipidemia, and 40% had a family history of coronary artery disease. No clinically meaningful differences were noted in the baseline characteristics between the dapagliflozin and placebo groups following randomization.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDapagliflozin (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlacebo (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, (mean, standard deviation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNative American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot provided\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history MI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistory MI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAngina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary artery bypass surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercutaneous coronary intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all treatment group comparisons\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eMI, myocardial infarction.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eSystemic Inflammatory Endpoints\u003c/h2\u003e\n \u003cp\u003eBetween baseline and 1-year follow-up, circulating CRP did not change significantly in either dapagliflozin or the placebo group (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the plasma proinflammatory cytokines, we observed a significant reduction in IL-1B, but not TNF\u0026alpha; or IL-6, in patients randomized to dapagliflozin (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF). IL-10, a known pro-fibrotic and anti-inflammatory cytokine, also did not change significantly in either group. (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e\n \u003cp\u003eNext, plasma ketones have been implicated as an important mediator for the beneficial effects associated with SGLT2 inhibitors, including anti-inflammation [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Here, we observed a significant increase of plasma ketones (\u0026beta;-hydroxybutyrate and acetoacetate) in patients randomized to dapagliflozin but not in the placebo group (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eI and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eJ).\u003c/p\u003e\n \u003cp\u003ePBMC mitochondrial function is an emerging marker of sterile inflammation: a decline of PBMC maximal oxygen consumption rate (OCR), measured in the presence of mitochondrial uncoupler to facilitate maximal electron transport chain activity, is associated with increased pro-inflammatory cytokine expressions in chronic heart failure [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Here, we observed that while the PBMC basal OCR did not change in either dapagliflozin or placebo group (\u003cstrong\u003eSupplemental Fig.\u0026nbsp;1\u003c/strong\u003e), the PBMC maximal OCR of patients receiving placebo declined while that of those receiving dapagliflozin did not change between baseline and 1-year follow-up (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eK and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eL), suggesting that SGLT2 inhibitor had an anti-inflammatory effect. Given PBMC is a major source of circulating cytokines, these results suggest that dapagliflozin may reduce systemic inflammation by acting on the PBMC \u0026ndash; IL-1B axis, possibly mediated by ketones.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eCardiac MR Endpoints\u003c/h2\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, ECV, a measurement of cardiac fibrosis, and myocardial strain values did not differ between baseline and at 1-year follow-up in either study group. The global radial peak strain in placebo group at 1-year trended higher in comparison to baseline (31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4% vs. 27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1%, P\u0026thinsp;=\u0026thinsp;0.043, respectively). However, after adjusting for multiple comparisons (corrected standard P\u0026thinsp;=\u0026thinsp;0.0125), the P-value of 0.043 was not statistically significant.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCMRI outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDifference (1 year \u0026ndash; baseline)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003cp\u003e(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003cp\u003e(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003cp\u003e(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003cp\u003e(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003cp\u003e(2.75)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003e(2.16)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadial peak strain global\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.2\u003c/p\u003e\n \u003cp\u003e(8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003cp\u003e(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003cp\u003e(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.3\u003c/p\u003e\n \u003cp\u003e(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003cp\u003e(9.38)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003cp\u003e(10.19)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCircumferential peak strain global\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.8\u003c/p\u003e\n \u003cp\u003e(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.7\u003c/p\u003e\n \u003cp\u003e(3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.7\u003c/p\u003e\n \u003cp\u003e(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.7\u003c/p\u003e\n \u003cp\u003e(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e(3.21)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.97\u003c/p\u003e\n \u003cp\u003e(3.28)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLongitudinal peak strain global\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.9\u003c/p\u003e\n \u003cp\u003e(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.0\u003c/p\u003e\n \u003cp\u003e(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.0\u003c/p\u003e\n \u003cp\u003e(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.3\u003c/p\u003e\n \u003cp\u003e(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003cp\u003e(4.06)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003cp\u003e(5.32)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2 relaxation time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.5\u003c/p\u003e\n \u003cp\u003e(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003cp\u003e(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.6\u003c/p\u003e\n \u003cp\u003e(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.2\u003c/p\u003e\n \u003cp\u003e(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003cp\u003e(6.59)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003cp\u003e(4.14)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eFor ECV, radial, circumferential, and longitudinal peak strain measurements, statistical significance set to P\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.0125 with Bonferroni correction. For T2 relaxation time, statistical significance set to P\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.0083 with Bonferroni correction\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAdditional Outcomes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDifference (1 year \u0026ndash; baseline)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlacebo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003cp\u003e(4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003cp\u003e(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003cp\u003e(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003cp\u003e(2.95)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e(2.20)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159.8\u003c/p\u003e\n \u003cp\u003e(42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.8\u003c/p\u003e\n \u003cp\u003e(36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164.5\u003c/p\u003e\n \u003cp\u003e(46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.0\u003c/p\u003e\n \u003cp\u003e(55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-33.04\u003c/p\u003e\n \u003cp\u003e(55.08)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.48\u003c/p\u003e\n \u003cp\u003e(39.56)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003cp\u003e(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003cp\u003e(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003cp\u003e(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003cp\u003e(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003cp\u003e(0.95)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e(1.17)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematocrit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.5\u003c/p\u003e\n \u003cp\u003e(4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003cp\u003e(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003cp\u003e(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003cp\u003e(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003cp\u003e(0.05)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody surface area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003cp\u003e(0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003cp\u003e(0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003cp\u003e(0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003cp\u003e(0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003cp\u003e(0.04)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(0.04)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.1\u003c/p\u003e\n \u003cp\u003e(38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.1\u003c/p\u003e\n \u003cp\u003e(72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003cp\u003e(27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003cp\u003e(24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.96\u003c/p\u003e\n \u003cp\u003e(52.56)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003cp\u003e(23.61)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eAll variables expressed as mean (standard deviation)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e* by paired t-test. ** by Wilcoxon Rank Sum Test\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eBNP Brain natriuretic peptide.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eT2 relaxation time, a measurement of cardiac inflammation, of patients receiving placebo trended higher, suggesting worsening inflammation (48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 ms vs. 50.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 ms, P\u0026thinsp;=\u0026thinsp;0.045), between baseline and 1-year follow-up, while that of those receiving dapagliflozin did not change. However, the P-value of 0.045 was not statistically significant when compared to the corrected standard P\u0026thinsp;=\u0026thinsp;0.0083 for multiple comparison. The representative CMRI of the same patient before intervention and at 1-year follow up are shown in \u003cstrong\u003eSupplemental Fig.\u0026nbsp;2\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eAdditional Endpoints\u003c/h2\u003e\n \u003cp\u003eIn comparison to the placebo group, patients randomized to dapagliflozin demonstrated significant reductions in random serum glucose (-33 mg/dl) vs (-3.48 mg/dl) (P\u0026thinsp;=\u0026thinsp;0.006) and hemoglobin A1c (-0.52%) vs (0.11%) (P\u0026thinsp;=\u0026thinsp;0.012) between baseline and 12-month follow-up. No differences were noted in brain natriuretic peptide (BNP) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, 12-months treatment of dapagliflozin reduced plasma IL-1B level. To our knowledge, this is the first time that SGLT2 inhibitor is shown to lower IL-1B in a placebo-controlled double-blind randomized clinical trial. However, dapagliflozin treatment did not result in a change in plasma IL6, plasma TNFα or serum CRP, consistent with the majority of prior clinical studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, dapaglifozin significantly increased plasma ketones and attenuated the decline of PBMC maximal OCR, which was previously shown to inversely correlate with IL-1B expression in chronic heart failure [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite the improvements in circulatory inflammatory endpoints, we did not observe significant changes in CMRI measurements of myocardial fibrosis and strain in patients receiving dapagliflozin. Of note, in the placebo group, there was a trend of worsening T2 relaxation time (inflammation), which was not observed in the dapagliflozin group, suggesting dapagliflozin may attenuate the progression of cardiac inflammation.\u003c/p\u003e \u003cp\u003eIL-1B is an inducible pro-inflammatory cytokine made primarily by immune cells, such as monocytes and macrophages, to function in cardiac repair as well as injury [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Under the NFkB-mediated transcriptional regulation, Pro-IL-1B is produced and stored intracellularly. Activation of NLRP3 inflammasome results in the cleavage and maturation of IL-1B prior to secretion. IL-1B has been shown to worsen myocardial contractile function and relaxation and induce hypertrophy [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, in multiple animal studies, SGLT2 inhibitor is shown to antagonize the NLRP3-IL-1B axis to improve cardiovascular outcomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], substantiating IL-1B\u0026rsquo;s role as a potential mediator of cardiac inflammation. The recent CANTOS trial subgroup analysis provides clinical evidence that circulating IL-1B is not just a bystander but actively contributes to heart failure pathogenesis. The conjecture is corroborated by a number of small clinical trials using anakinra, a recombinant IL-1 receptor antagonist. In these studies, anakinra is shown to reduce CRP and HF hospitalization in acute STEMI patients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and enhance exercise capacity and LVEF in HF patient with elevated CRP [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePBMC mitochondrial respiration is an emerging biomarker of sterile inflammation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], particularly in the setting of heart failure, and was previously shown to inversely correlate with the expressions of proinflammatory cytokines, such as IL-1B [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It is postulated that upon stimulation by circulating mitochondrial damage associated molecular patterns (MitoDAMPs), PBMC produces IL-6, leading to mitochondrial dysfunction and mitochondrial ROS production in an autocrine manner [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Mitochondrial ROS subsequently activates the NLRP3 inflammasome, resulting in the maturation and release of IL-1B family of cytokines [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the current study, we observed that SGLT2 inhibitor sustains mitochondrial oxidative capacity of circulating immune cells, supporting the notion that PBMC may be a source of circulating IL-1B, and a potential therapeutic target in T2D. Together, our results are consistent with the proposed mechanism that SGLT2 inhibitor lowers systemic inflammation by increasing plasma ketones, which acts on peripheral immune cells (PBMCs) to inhibit NLRP3 inflammasome activation and IL-1B production [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recently, transcriptomic analyses indicate that disruption of mitochondrial pathways (TCA cycle and oxidative phosphorylation) in \u003cem\u003ecirculating monocytes\u003c/em\u003e is a marker of elevated cardiovascular risk in T2D patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Whether and how mitochondrial dysfunction in circulating monocytes plays a role in the pathogenesis of T2D and heart failure is an area of active research.\u003c/p\u003e \u003cp\u003eIn addition to anti-inflammation, SGLT2 inhibitors have a wide range of effects on hemodynamic, neurohormonal, metabolic and endothelial function. There are several potential direct and indirect pathways leading to improvement of cardiac structure and function and myocardial substrate utilization in T2D. For example, SGLT2 inhibitors provide glycemic control, reduce blood pressure, reduce arterial stiffness, decrease body weight and reduce visceral adiposity, which could indirectly lead to improved cardiac function in T2D patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. On the cellular level, SGLT2 inhibitors could directly affect cardiac function by reducing oxidative stress, attenuating myocardial fibrosis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study we did not find a significant effect of dapagliflozin on strain when compared to placebo. We also did not find a significant change in strain measures in the placebo control group over a 12-month period; raising the question whether the enrolled T2D patients truly had underlying structural heart disease.\u003c/p\u003e \u003cp\u003eMyocardial T1 mapping methods are used to measure tissue fibrosis and reflects a composite signal from intracellular and extracellular compartments; however, T1 measurements can be confounded by renal clearance, hematocrit, and type and dosage of gadolinium [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. To adjust for these factors, the most widely accepted approach to determine fibrosis is to measure ECV which has been shown to be a good measure of interstitial fibrosis. In the current study, we found that 12 months of SGLT2 inhibition compared to placebo did not alter ECV of T2D patients. Our findings are consistent with a prospective study which enrolled 35 T2D subjects: Before and after CMRI was performed following 6 months of Empagliflozin showed no significant effect on ECV [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, in our study, SGLT2 inhibition did not change the level of plasma IL-10, a pro-fibrotic cytokine. While these results do not support the notion that the cardioprotective effects of SGLT2 inhibition is mediated via improving cardiac fibrosis, further studies are required to unravel the mechanism of action by which SGLT2 inhibitors enhance cardiac outcomes.\u003c/p\u003e \u003cp\u003eThere are several strengths of this study. First, there was excellent adherence with treatment as evidenced by significant reduction in blood glucose, hemoglobin A1c, and BMI in subjects randomized to dapagliflozin (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Second, the randomized study design was a strength, since many previous studies on SGLT2 inhibitors utilized a prospective study design. Third, the use of circulating inflammatory endpoints as well as CMRI allowed for the assessment of anti-inflammatory effect of SGLT2 inhibitor at both the systemic and organ levels.\u003c/p\u003e \u003cp\u003eThere are several limitations and weaknesses. First, this is a relatively small clinical trial which was not powered to detect smaller effect sizes. For example, while there is a trend that dapagliflozin attenuates the progression of cardiac inflammation (T2 relaxation time), the study is underpowered to detect a statistically significant difference. Second, previous animal studies of SGLT2 inhibition utilized direct pathologic examination of inflammation and fibrosis, whereas CMRI are known to be less sensitive in assessing these parameters. Third, the 12-month treatment period may not have been sufficient to result in detectable cardiac structural and functional changes, although in a prospective study, 3 months of SGLT2 inhibition was sufficient to demonstrate improvement in diastolic function [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Fourth, we did not have a matched, non-diabetic control group. Fifth, higher ECV values (greater than 32%) are associated with worse outcomes in patients with known myocarditis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In contrast, the T2D cohort in our study had baseline ECV of 27\u0026ndash;28%. To address the possibility that patients with higher baseline ECV would derive more benefits from SGLT2 inhibition, we performed a subgroup analysis in patients in the upper half of ECV values at baseline (ECV\u0026thinsp;~\u0026thinsp;30%); however, in this cohort (N\u0026thinsp;=\u0026thinsp;17) we still did not find a significant difference in ECV between the drug and placebo groups.\u003c/p\u003e \u003cp\u003eIn conclusion we demonstrated that 12 months of daily dapagliflozin in T2D patients reduces circulating IL-1B, increases plasma ketones, and prevents the decline of PBMC mitochondrial maximal respiration, but does not improve cardiac fibrosis or strain by CMRI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe trial was approved by the Institutional Review Board at the University of Washington. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll authors approve the manuscript and give their consent for submission and\u003c/em\u003e publication\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll data will be uploaded and made available to the public once the manuscript has been published.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests:\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eAstraZeneca ESR 17-13124 (XQZ)\u003c/p\u003e\n\u003cp\u003eNIH R01HL157261 (XQZ, FK)\u003c/p\u003e\n\u003cp\u003eNIH R01HL13955 (FK)\u003c/p\u003e\n\u003cp\u003eVeteran Affairs BLRD Career Development Award 1IK2BX006111-01A1 (DW).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions:\u003c/p\u003e\n\u003cp\u003eDW, AVN, DI, JS, KAH, IT, NB, AV, BC, KO were involved in the conduct of the study. CM, DW, RT, XQZ, FK were involved in conception, design and analysis and interpretation of the result. \u0026nbsp;DW, FK wrote the first draft of the manuscript and all authors approved the final version of the paper. \u0026nbsp;FK and CM are the guarantors of this work, as such had full access to all of the data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements: N/A\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZinman B, et al. Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes. N Engl J Med. 2015;373(22):2117\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeal B, et al. Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes. N Engl J Med. 2017;377(7):644\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopaschuk GD, Verma S. Mechanisms of Cardiovascular Benefits of Sodium Glucose Co-Transporter 2 (SGLT2) Inhibitors: A State-of-the-Art Review. JACC Basic Transl Sci. 2020;5(6):632\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScisciola L, et al. Anti-inflammatory role of SGLT2 inhibitors as part of their anti-atherosclerotic activity: Data from basic science and clinical trials. Front Cardiovasc Med. 2022;9:1008922.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRidker PM, et al. Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease. N Engl J Med. 2017;377(12):1119\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SR, et al. SGLT2 inhibition modulates NLRP3 inflammasome activity via ketones and insulin in diabetes with cardiovascular disease. Nat Commun. 2020;11(1):2127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Souza RR. Aging of myocardial collagen. 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The Association between Diffuse Myocardial Fibrosis on Cardiac Magnetic Resonance T1 Mapping and Myocardial Dysfunction in Diabetic Rabbits. Sci Rep. 2017;7:44937.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBohnen S et al. Performance of t1 and t2 mapping cardiovascular magnetic resonance to detect active myocarditis in patients with recent-onset heart failure. Circ Cardiovasc Imaging, 2015. 8(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou B, et al. Boosting NAD level suppresses inflammatory activation of PBMCs in heart failure. J Clin Invest. 2020;130(11):6054\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung J, et al. Patient-specific 17-segment myocardial modeling on a bull's eye map. J Appl Clin Med Phys. 2016;17(5):453\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLupsa BC, Kibbey RG, Inzucchi SE. Ketones: the double-edged sword of SGLT2 inhibitors? Diabetologia. 2023;66(1):23\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang DD, et al. Safety and Tolerability of Nicotinamide Riboside in Heart Failure With Reduced Ejection Fraction. JACC Basic Transl Sci. 2022;7(12):1183\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D, et al. The effect of sodium-glucose cotransporter 2 inhibitors on biomarkers of inflammation: A systematic review and meta-analysis of randomized controlled trials. Front Pharmacol. 2022;13:1045235.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBujak M, Frangogiannis NG. The role of IL-1 in the pathogenesis of heart disease. Arch Immunol Ther Exp (Warsz). 2009;57(3):165\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar A, et al. Tumor necrosis factor alpha and interleukin 1beta are responsible for in vitro myocardial cell depression induced by human septic shock serum. 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Front Pharmacol. 2017;8:363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbate A et al. \u003cem\u003eEffects of interleukin-1 blockade with anakinra on adverse cardiac remodeling and heart failure after acute myocardial infarction [from the Virginia Commonwealth University-Anakinra Remodeling Trial (2) (VCU-ART2) pilot study].\u003c/em\u003e Am J Cardiol, 2013. 111(10): p. 1394\u0026thinsp;\u0026ndash;\u0026thinsp;400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRommens PM, Goffin J. \u003cem\u003e[Osteosynthesis of the dens axis fracture].\u003c/em\u003e Acta Chir Belg, 1991. 91(4): p. 169\u0026thinsp;\u0026ndash;\u0026thinsp;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbate A, et al. Interleukin-1 and the Inflammasome as Therapeutic Targets in Cardiovascular Disease. Circ Res. 2020;126(9):1260\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirakawa R, et al. 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Circ Res; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCowie MR, Fisher M. SGLT2 inhibitors: mechanisms of cardiovascular benefit beyond glycaemic control. Nat Rev Cardiol. 2020;17(12):761\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin B, et al. Glycemic control with empagliflozin, a novel selective SGLT2 inhibitor, ameliorates cardiovascular injury and cognitive dysfunction in obese and type 2 diabetic mice. Cardiovasc Diabetol. 2014;13:148.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang N, et al. Dapagliflozin improves left ventricular remodeling and aorta sympathetic tone in a pig model of heart failure with preserved ejection fraction. Cardiovasc Diabetol. 2019;18(1):107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuntmann VO, et al. T1 Mapping in Characterizing Myocardial Disease: A Comprehensive Review. Circ Res. 2016;119(2):277\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu JC, et al. Effect of Empagliflozin on Cardiac Function, Adiposity, and Diffuse Fibrosis in Patients with Type 2 Diabetes Mellitus. Sci Rep. 2019;9(1):15348.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLan NSR, et al. The effects of sodium-glucose cotransporter 2 inhibitors on left ventricular function: current evidence and future directions. ESC Heart Fail. 2019;6(5):927\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrani C, et al. Prognostic Value of Cardiac Magnetic Resonance Tissue Characterization in Risk Stratifying Patients With Suspected Myocarditis. J Am Coll Cardiol. 2017;70(16):1964\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes, inflammation, IL-1B, PBMC respiration, CMRI, cardiac fibrosis, SGLT2 inhibitor","lastPublishedDoi":"10.21203/rs.3.rs-4132581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4132581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: Sodium glucose cotransporter 2 (SGLT2) inhibitors significantly improve cardiovascular outcomes in diabetic patients; however, the mechanism is unclear. We hypothesized that dapagliflozin improves cardiac outcomes via beneficial effects on systemic and cardiac inflammation and cardiac fibrosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch and Design Methods:\u0026nbsp; This randomized placebo-controlled clinical trial enrolled 62 adult patients (mean age 62, 17% female) with type 2 diabetes (T2D) without known heart failure.\u0026nbsp; Subjects were randomized to 12 months of daily 10 mg dapagliflozin or placebo.\u0026nbsp; For all patients, blood/plasma samples and cardiac magnetic resonance imaging (CMRI) were obtained at time of randomization and at the end of 12 months. Systemic inflammation was assessed by plasma IL-1B, TNFα, IL-6 and ketone levels and PBMC mitochondrial respiration, an emerging marker of sterile inflammation. Cardiac fibrosis was assessed by T1 mapping to calculate extracellular volume fraction (ECV); cardiac tissue inflammation was assessed by T2 mapping.\u003c/p\u003e\n\u003cp\u003eResults: Between the baseline and 12-month time point, plasma IL-1B was reduced (-1.8 pg/mL, P=0.003) while ketones were increased (0.26 mM, P=0.0001) in patients randomized to dapagliflozin. PBMC maximal oxygen consumption rate (OCR) decreased over the 12-month period in the placebo group but did not change in patients receiving dapagliflozin (-158.9 pmole/min/10\u003csup\u003e6\u003c/sup\u003ecells, P=0.0497 vs -45.2 pmole/min/10\u003csup\u003e6\u003c/sup\u003ecells, P=0.41), a finding consistent with an anti-inflammatory effect of SGLT2i. ECV and T2 relaxation time did not change in both study groups.\u003c/p\u003e\n\u003cp\u003eConclusion:\u0026nbsp; This study demonstrates that 12 months of dapagliflozin reduces IL-1B mediated systemic inflammation but affect cardiac fibrosis in T2D.\u003c/p\u003e\n\u003cp\u003eClinical Trial.gov Registration NCT03782259\u003c/p\u003e","manuscriptTitle":"Dapagliflozin Reduces Systemic Inflammation in Patients with Type 2 Diabetes Without Known Heart Failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 18:06:56","doi":"10.21203/rs.3.rs-4132581/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-14T22:12:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-14T22:09:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330d67cf-dcb2-45fc-bb60-89e13e9d8d20","date":"2024-04-14T20:51:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-02T18:00:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63065e1f-e9d1-413f-b46b-76f16db25728","date":"2024-03-25T13:06:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ae3fbdc0-403b-4718-81eb-ade1a34c0b7e","date":"2024-03-23T09:04:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-20T09:37:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-20T08:06:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-20T06:43:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2024-03-19T19:24:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7ac5894-092b-4503-b9ae-180233e1feff","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T14:47:03+00:00","versionOfRecord":{"articleIdentity":"rs-4132581","link":"https://doi.org/10.1186/s12933-024-02294-z","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2024-06-07 14:47:03","publishedOnDateReadable":"June 7th, 2024"},"versionCreatedAt":"2024-03-25 18:06:56","video":"","vorDoi":"10.1186/s12933-024-02294-z","vorDoiUrl":"https://doi.org/10.1186/s12933-024-02294-z","workflowStages":[]},"version":"v1","identity":"rs-4132581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4132581","identity":"rs-4132581","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}