The impact of dapagliflozin for the myocardial metabolomic profiles of mice with chronic heart failure induced by a high fat diet

The impact of dapagliflozin for the myocardial metabolomic profiles of mice with chronic heart failure induced by a high fat diet | 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 The impact of dapagliflozin for the myocardial metabolomic profiles of mice with chronic heart failure induced by a high fat diet Feng Hu, Jinhua Huang, Qiong Jiang, Wenkun Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6881055/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The molecular mechanisms responsible for the clinical benefits of sodium-glucose cotransporter-2 inhibitors (SGLT2i) in patients with heart failure and a reduced or preserved ejection fraction, both diabetic and non-diabetic, are still not fully understood. This study aimed to examine the myocardial metabolomic profiles of mice with chronic heart failure induced by high-fat diet (HDF) and assess the impact of dapagliflozin (DAPA) on these profiles. Methods An experimental model of chronic heart failure in mice was established by long-term HDF for six months, and verified using immunohistochemistry and echocardiography. Myocardial specimens were obtained from three groups: chow, HDF, and DAPA. Subsequently, all samples were subjected to non-targeted metabolomic analyses using untargeted liquid chromatography-mass spectrometry. Principal component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis were used to identify differential metabolites or lipid molecules. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to determine the metabolic pathways associated with these identified metabolites. Results Echocardiography revealed that mice with chronic heart failure established through HDF exhibited systolic dysfunction compared to the control chow group. However, DAPA treatment partially restored these dysfunctions and protected against myocardial fibrosis and hypertrophy. Furthermore, a total of 72 upregulated and 34 downregulated differential metabolites were observed between the Chow and HDF groups, along with 40 upregulated and 25 downregulated differential metabolites between the HDF and DAPA groups. A total of 141 upregulated and 167 downregulated differential lipid metabolites were observed between the Chow group and HDF groups, along with 67 upregulated and 59 downregulated differential lipid metabolites between the HDF and DAPA groups, respectively. Dysregulated metabolites or lipids altered by DAPA treatment were found to significantly enrich several metabolic pathways, as identified by the KEGG database. Conclusions DAPA exhibited protective effects against myocardial fibrosis and hypertrophy, and enhanced systolic function in mice with chronic heart failure induced by HDF. Furthermore, we conducted a comprehensive analysis of myocardial profiles, focusing on various differential metabolites, including lipid molecules, as well as prominent metabolic pathways, in these mice. In addition, we assessed the impact of DAPA treatment on these profiles. Metabolomics high fat diet heart failure dapagliflozin high-fat diet Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have been shown to reduce the risk of exacerbating heart failure or cardiovascular death in individuals with heart failure and a reduced ejection fraction (HFrEF) 1 – 3 or preserved ejection fraction (HFpEF), both diabetic and non-diabetic 4 , 5 . Notably, patients with chronic HFpEF exhibited noteworthy enhancements in symptomatology, physical restrictions, and exercise capacity following a 12-week course of dapagliflozin (DAPA) treatment 6 . Despite the apparent clinical advantages of these cardiovascular benefits, the precise pathophysiological mechanisms remain incompletely elucidated 7 , 8 . Induced glycosuria and natriuresis, beneficial hemodynamic changes, reductions in anemia and glomerular hyperfiltration, decline of sympathetic nervous system activity and renin-angiotensin-aldosterone system, anti-fibrotic and anti-inflammatory action, upregulated adipocytokines, and improved endothelial dysfunction and myocardial metabolism all contribute to the cardiovascular benefits 7 , 8 . Consequently, additional investigations are warranted to gain a more comprehensive understanding of the molecular mechanisms underlying the favorable effects of these medications in patients with HFrEF and HFpEF. Enhanced comprehension of the metabolic signature of SGLT2i could yield valuable insights into the metabolic mechanisms associated with cardiovascular well-being. Investigations conducted on both preclinical and clinical models of diabetes have established a correlation between SGLT2i and a shift away from glucose metabolism towards fatty acid and ketone body metabolism 9 – 15 . Following the administration of empagliflozin, a notable elevation in acetyl- and propionylcarnitine levels was observed, indicating the breakdown of free fatty acids (FAAs), amino acids, and ketone bodies, consequently activating the tricarboxylic acid cycle, as evidenced by increased levels of aconitate and fumarate 14 . Empagliflozin exhibited a notable effect on the intermediate metabolites of the urea cycle, indicating its activation and validating the heightened utilization of amino acids 14 . However, alterations in fuel selection have not been consistently observed 16 , 17 . In diabetic db/db mice, the administration of empagliflozin results in augmented cardiac ATP production rates, primarily attributed to the supplementary involvement of ketone oxidation rather than elevated levels of circulating ketones 16 . Furthermore, in diabetic and obese rats with spontaneously hypertensive heart failure, empagliflozin effectively mitigated blood pressure and hepatic congestion while leaving glucose metabolism and myocardial function unaffected, but did not increase myocardial ketone utilization despite increased circulating levels 17 . Metabolic profiling in individuals diagnosed with heart failure offers valuable insights into the correlation between SGLT2i treatment, distinctively metabolized molecules, and unfavorable outcomes. Notably, patients with HFrEF undergoing DAPA therapy exhibited an increase in ketone-related metabolites and short/medium-chain acylcarnitines 13 . Furthermore, long-chain acylcarnitine, dicarboxylacylcarnitine, and aromatic amino acid metabolite clusters were linked to diminished quality of life and elevated NT-proBNP levels, irrespective of the administration of DAPA treatment 13 . Given the high mortality among diabetic and non-diabetic patients with HFrEF or HFpEF, this study aimed to examine the myocardial metabolomic profiles of mice with chronic heart failure induced by a high-fat diet (HDF) and assess the impact of DAPA on these profiles. Methods ##Ethics Statements Male C57BL/6N mice were procured from Fuzhou Wu's Laboratory Animal Co., Ltd. (Hunan, China) and housed under standard conditions at a temperature of 22 ± 2.0°C and humidity of 50% ± 5%. The mice were subjected to a 12-hour light-dark cycle and had ad libitum access to water and food. Random allocation was used to assign the mice to different treatment groups. All animal experiments were conducted in accordance with the National Institutes of Health (NIH) policies outlined in the Guide for the Care and Use of Laboratory Animals and approved by the Animal Care and Use Committee of Fujian Medical University Union Hospital. We have adhered to ARRIVE guidelines and upload a completed checklist. ## Mouse model In the control group, mice were provided with normal chow, while the other two groups were fed an HDF consisting of 60% calories from fat (MD12033, Meidisen, China, n = 8 per group). Immunohistochemical analysis and echocardiographic assessment corroborated the successful establishment of a chronic heart failure mouse model induced by six months of HFD feeding. One group of mice with hyperlipidemia (DAPA group) received dapagliflozin (DAPA, 1 mg/kg/day) in their drinking water 18 . All mice were maintained on their respective dietary regimens for the duration of the study. At the conclusion of the six-month dietary intervention, mice were sacrificed through overdose of anesthetic using an i.p. injection of sodium pentobarbital (200 mg/kg). Subsequently, cardiac perfusion was performed, and select myocardial tissues were cryogenically preserved for subsequent metabolomic analysis. ## Echocardiography A 30 MHz linear array ultrasound transducer (MS-400, VisualSonics Inc.) was employed in conjunction with a Vevo2100 ultrasound imaging system to evaluate cardiac structure and function in anesthetized animals receiving 1 L/min of oxygen 19 . To determine the maximum length of the left ventricle (LV), B-mode images of the parasternal long-axis were acquired. Subsequently, M-mode imaging was performed in this view by positioning the cursor perpendicular to the maximum dimensions at both end-diastole and end-systole, facilitating the measurement of chamber dimensions, left ventricular ejection fraction (LVEF), and fractional shortening (LVFS). ## Histological studies The experimental procedure involved perfusing the mice with cold saline, followed by the extraction of the hearts. The hearts were subsequently fixed with 4% paraformaldehyde on a room temperature heating block for an duration of 24 hours, and then embedded in paraffin wax. Serial tissue sections were stained with Masson's trichrome to detect collagen matrix deposition and examined using an optical microscope (Olympus, Japan). The semi-quantitative analysis of the tissue staining was conducted using Image-Pro plus 6.0 softwares. ## Immunofluorescent staining Following deparaffinization, a paraffin-embedded cardiac section with a thickness of 4 mm was rehydrated and subjected to antigen retrieval in a buffer containing EDTA. Cardiomyocyte dimensions were evaluated by initially blocking the slide surfaces with 3% bovine serum albumin for 30 minutes, followed by an overnight incubation with FITC-conjugated wheat germ agglutinin (WGA, #L4895, Sigma). The stained sections were subsequently examined using fluorescence microscopy (Olympus). ## Chemicals The chemicals utilized in this study were of analytical or high-performance liquid chromatography (HPLC) grade. Methanol and acetonitrile were procured from Fisher Scientific (Waltham, MA, USA), while formic acid was obtained from TCI (Shanghai, China). Chloroform was supplied by Sinopharm (Shanghai, China), ultrapure water was provided by Millipore (Billerica, MA, USA), and 2-Amino-3-(2-chlorophenyl)-propionic acid was sourced from Aladdin (Shanghai, China). ## Instruments The high-speed freezing centrifuge was supplied by Xiangyi Experiment Equipment Co. Ltd. (Hunan, China), while the vortex mixer was obtained from Haimen Kylin-Bell Lab Instruments Co. Ltd. (China). The ultrasonic cleaner was procured from Kunshan Shumei Experiment Equipment Co. Ltd. (China), the tissue grinders from Zhejiang Meibi Experiment Equipment Co. Ltd. (China), and the microporous membrane filters from Tianjin Jinteng Experiment Equipment Co. Ltd. (China). ##Sample preparation 20 – 22 Myocardial specimens from chow, HFD, and DAPA groups underwent detailed weighing and centrifugation. A 2 mL centrifuge tube containing three steel balls was used to hold a 1000 µL tissue extract composed of 75% methanol:chloroform (9:1) and 25% water. The sample was then ground twice for 60 seconds at a frequency of 50 Hz, followed by ultrasound treatment at room temperature for 30 minutes. Subsequently, the supernatant was centrifuged at 12,000 rpm for 10 minutes at 4°C to facilitate concentration and drying, and then transferred to a new two mL centrifuge tube. Samples were dissolved in 200 litres of 50% acetonitrile, then filtered through a 0.22 m membrane and transferred into an untargeted liquid chromatography-mass spectrometry (LC-MS) detection container using 4-amino-3-(2-chlorophenyl)-propionic acid (4 ppm). Quality control (QC) was conducted on 20 µL of each sample. ## Metabolomic profiling The derivative samples were subjected to analysis utilizing a Vanquish UHPLC system (Thermo Fisher Scientific, USA), which was integrated with an ACQUITY UPLC HSS T3 chromatographic column (150 mm x 2.1 mm x 1.8 µm) (Waters, Milford, USA). The flow rate and injection volume were precisely controlled at 0.25 mL/min and 2 µL, respectively, while the column temperature was consistently maintained at 40°C. For LC-ESI (+)-MS analysis, the mobile phase consisted of 0.1% formic acid in acetonitrile, whereas 0.1% formic acid in water was employed as the stationary phase 21 – 23 . During LC-ESI (-)-MS analysis 21 – 23 , analytes were eluted using a mobile phase composed of acetonitrile and ammonium formate. We detected metabolites using an Orbitrap Exploris 120 (Thermo Fisher Scientific, USA) mass spectrometer. A simultaneous MS/MS acquisition was performed to detect the metabolites using data-dependent MS/MS acquisitions 21 – 23 . ##Lipid extraction 24 The sample was ground with two steel balls for 60 seconds following a 30-second soaking in 750 mL of a chloroform-methanol mixture, with the process repeated twice at 50 Hz. Subsequently, the sample underwent vortexing for 30 seconds before being placed on ice for an additional 10 minutes, following an initial 40-minute cooling period. The sample was then centrifuged at 12,000 rpm for five minutes. A volume of 300 µL from the lower layer was transferred to a new tube, to which 500 µL of the chloroform-methanol mixture was added and shaken for 30 seconds. The lower layer fluids were centrifuged at ambient temperature for 5 minutes at 12,000 rpm before being carefully transferred to a fresh centrifuge tube to be concentrated and subsequently dried. After dissolution in 100 µL of isopropanol, the samples were filtered using a 0.22 µm membrane. ## Lipodomic profiling 25 Chromatographic separation was conducted utilizing an ACQUITY UPLC® BEH C18 column (100 mm x 2.1 mm x 1.7 µm, Waters), with the autosampler maintained at 8°C. A gradient elution of analytes was achieved using a mobile phase consisting of acetonitrile and water in a 60:40 ratio (0.1% formic acid + 10 mM ammonium formate) (C), and a mixture of isopropanol and acetonitrile in a 90:10 ratio (0.1% formic acid + 10 mM ammonium formate), at a flow rate of 0.25 mL/min. Sample injections (2 µL) were performed following equilibration. The spray voltages were set to 3.5 kV for positive mode and − 2.5 kV for negative mode. The sheath and auxiliary gases were configured at 30 arbitrary units and 10 arbitrary units, respectively. For normalized collision events, a comprehensive scan over the m/z range of 150-2,000 was executed utilizing an Orbitrap analyzer with a mass resolution of 35,000. Dynamic exclusion was employed to eliminate redundant information from the MS/MS spectra. ## Data processing and multivariate analysis ProteoWizard (v3.0.8789) 26 used MSConvert to convert raw data to mzXML for detection, correction of retention time, and alignment of data using XCMS 27 . It was identified metabolites using accurate mass and MS/MS data, matching it to HMDB ( http://www.hmdb.ca ), MassBank ( http://www.massbank.jp/ ), LipidMaps ( http://www.lipidmaps.org ), and MZCloud ( https://www.mzcloud.org ) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) ( http://www.genome.jp/kegg/ ). The dataset underwent normalization using robust LOESS signal correction to mitigate systematic bias. Post-normalization, only peak ions exhibiting RSDs below 30% were retained to ensure accurate metabolite identification. Modeling and analysis of multivariate data were carried out using Ropls 28 software 28 . The data were scaled, and models were constructed using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Metabolic profiling was conducted through the examination of score plots, load plots, and S-plots to identify metabolites influencing clustering. Permutation tests were applied to all evaluated models to assess the potential for overfitting. In the context of OPLS-DA, variables contributing to classification were identified using the variable importance on projection (VIP) scores and fold change (FC) metrics. Metabolites were considered statistically significant if they exhibited a P value less than 0.05 and a VIP value greater than 1 29 . ## Pathway analysis Utilizing MetabolAnalyst, we conducted an analysis of various metabolites through integrated pathway enrichment and pathway topology analyses. Metabolite identification via metabolomics was correlated with KEGG pathways to elucidate broader systemic functions. Methylates and their corresponding pathways were graphically represented using the KEGG Mapper. Metabolites were considered statistically significant if they exhibited a P value less than 0.05 and a VIP value greater than 1. Results ##Cardiac fibrosis and hypertrophy Compared to the control groups, mice with chronic heart failure established through HDF exhibited significant collagen matrix deposition in the myocardium according to Masson’s trichrome staining. However, DAPA treatment attenuated HDF-induced myocardial fibrosis in the heart (Fig. 1A/B). In addition, this study used WGA staining to quantify in vivo cardiomyocyte size and to evaluate cardiac hypertrophy. The results showed that mice with chronic heart failure established through HDF displayed significantly larger cardiomyocyte size compared to the Chow control arms, where DAPA treatment decreased HDF-induced myocardial hypertrophy in the hearts (Fig. 1C/D). ## DAPA treatment improved systolic function Cardiac analysis by echocardiography demonstrated that mice with chronic heart failure established through HDF showed reduced LVEF, and LVFS indicated worse cardiac systolic dysfunction as compared to that in controls, which was ameliorated by DAPA treatment (Fig. 1E/F). ## Quality control and quality assurance QC was assessed (base peak chromatogram) using internal standards and quality control samples, and a data matrix was subsequently derived. There were differential peak heights between the different comparison groups, as shown in Fig. 2A for the negative ion mode and Figure S1A for the positive ion mode. However, similar trends were observed, indicating excellent repeatability. The PCA score plots suggested that QC samples were gathered, indicating good repeatability and reliable results (Fig. 2B for the negative ion mode and Figure S1B for the positive ion mode). Based on QC, quality assurance (QA) was carried out to delete the characteristic peaks with poor repeatability according to internal standards with an RSDs greater than 0.3 in QC samples (Fig. 2C for negative ion mode and Figure S1C for positive ion mode), in order to obtain a higher quality data set, which was more conducive to the detection of biomarkers. The PCA score plots suggested that the QA results were gathered, indicating good repeatability and reliability (Fig. 2D for the negative ion mode and Figure S1D for the positive ion mode). This 3-dimensional matrix included sample information, peak names, retention times, retention indices, mass-to-charge ratios, and signal intensities. After screening, all the peak signal intensities in each sample were segmented and normalized. The data matrix was obtained by removing redundancies and merging peaks after normalization (Table S1 for the negative ion mode and Table S2 for the positive ion mode). ##Detection of metabolites Serum metabolites between the Chow and HDF groups were comparatively and qualitatively characterized, with 12,233 and 16,096 molecular features ultimately acquired and analyzed in the negative ion mode (Table S3) and positive ion mode (Table S7), respectively. We identified 3,377 and 3,913 metabolites in the negative ion mode (Table S4) and positive ion mode (Table S8), respectively. Similarly, metabolites between the HDF and DAPA groups were also comparatively and qualitatively characterized, with 12,233 or 16,096 molecular features ultimately acquired and analyzed in the negative ion mode (Table S5) or positive ion mode (Table S9), respectively. We identified 2,102 and 2,396 metabolites in the negative ion mode (Table S6) and positive ion mode (Table S10), respectively. Ultimately, 6,764 metabolites were comparatively and qualitatively characterized in different comparison groups in the negative or positive ion mode (Table S11). The Venn diagram could intuitively show the similarity and overlap of metabolite composition in different comparison groups (Fig. 3A for the negative ion mode and Figure S2A for the positive ion mode). The statistical histogram indicated that there were 2,244 up-regulated and 1,133 down-regulated metabolites between the Chow group and HDF group, and 995 up-regulated and 1,107 down-regulated metabolites between the HDF and DAPA groups in the negative ion mode (Fig. 3B). Similarly, the statistical histogram indicated that there were 2,661 up-regulated and 1,252 down-regulated metabolites between the Chow and HDF groups, and 1,364 up-regulated and 1,032 down-regulated metabolites between the HDF and DAPA groups in the positive ion mode (Figure S2B). The volcano plot directly shows the distribution of metabolites in different comparison groups (Fig. 3C and 3D for the negative ion mode, Figure S2C and S2D for the positive ion mode). Agglomerate hierarchical clustering visually showed the relative quantitative values of metabolites in different comparison groups (Fig. 3E and 3F for the negative ion mode, Figure S2E and S2F for the positive ion mode). ## Multivariate analysis in different comparison groups The PCA score plots derived from 7-fold cross-validation indicated separation tendencies between the Chow and HDF groups in negative (R2X = 0.56; Fig. 4A) or positive ion mode (R2X = 0.52; Figure S4A). There was a separation between the Chow group and HDF group in the PLS-DA score plots (R2X = 0.30, R2Y = 0.99, Q2 = 0.87; Fig. 4B) and the OPLS-DA score plots (R2X = 0.30, R2Y = 0.99, Q2 = 0.85; Fig. 4C) at negative ion mode. There was a separation between the Chow group and HDF group in the PLS-DA score plots (R2X = 0.25, R2Y = 0.99, Q2 = 0.82; Figure S4B) and the OPLS-DA score plots (R2X = 0.25, R2Y = 0.99, Q2 = 0.78; Figure S4C) at positive ion mode. The S-plot of OPLS-DA between the Chow and HDF groups showed metabolites that were strongly related to major components of biological processes in the negative (Fig. 4D) or positive ion mode (Figure S4D). The scatter plot of mass-to-charge ratio with P-value of metabolites clearly showed the distribution of different metabolites between the Chow and HDF groups in the negative ion mode (Figure S3A) or positive ion mode (Figure S5A). The PCA score plots indicated separation tendencies between the HDF and DAPA groups in negative (R2X = 0.53; Fig. 4E) or positive ion mode (R2X = 0.57; Figure S4E). There was a separation between the HDF group and DAPA group in the PLS-DA score plots (R2X = 0.24, R2Y = 0.99, Q2 = 0.71; Fig. 4F) and the OPLS-DA score plots (R2X = 0.24, R2Y = 0.99, Q2 = 0.61; Fig. 4G) at negative ion mode. There was a separation between the HDF group and DAPA group in the PLS-DA score plots (R2X = 0.20, R2Y = 0.99, Q2 = 0.68; Figure S4F) and the OPLS-DA score plots (R2X = 0.20, R2Y = 0.99, Q2 = 0.60; Figure S4G) at positive ion mode. The S-plot of OPLS-DA between the HDF and DAPA groups showed metabolites that were strongly related to major components of biological processes in the negative (Fig. 4H) or positive ion mode (Figure S4H). The scatter plot of mass-to-charge ratio with P-value of metabolites clearly showed the distribution of different metabolites between the HDF and DAPA groups in the negative ion mode (Figure S3B) or positive ion mode (Figure S5B). ##Detection of differential metabolites Ultimately, 388 differential metabolites were acquired and analyzed in different comparison groups in negative or positive ion mode according to P value 1 29 (Table S12). There were 72 upregulated and 34 downregulated differential metabolites between the Chow and HDF groups (Fig. 4A and Table 1) and 40 upregulated and 25 downregulated differential metabolites between the HDF and DAPA groups (Fig. 5A and Table 2). The Venn diagram intuitively shows the similarity and overlap of differential metabolite compositions in the different comparison groups (Fig. 5B). The scatter plot of mass-to-charge ratio with P-value clearly showed the distribution of differential metabolites between the Chow and HDF groups (Fig. 5C) and between the HDF and DAPA groups (Fig. 5E). In addition, the volcano plot showed the distribution of differential metabolites between the Chow group and HDF groups (Fig. 5D), HDF group, and DAPA group (Fig. 5F). Agglomerate hierarchical clustering visually showed the relative quantitative values of the differential metabolites between the Chow group and HDF groups (Fig. 6A), HDF group, and DAPA group (Fig. 6C). The Z-score was a value based on the relative content of metabolites, which was used to measure the relative content of metabolites at the same level. The Z-score map showed the relative content of differential metabolites between the Chow and HDF groups (Fig. 6B), the HDF group, and the DAPA group (Fig. 6D). The correlation of differential metabolites between the Chow and HDF groups is presented in Figure S6A, which shows the degree of correlation between discrepant metabolites. The correlation of differential metabolites between the HDF and DAPA groups is shown in Figure S6B. ##Metabolic pathways KEGG analysis revealed 171 metabolic pathways enriched by 419 differential metabolites between the Chow group and HDF group (Table S13). The 20 most prominent metabolic pathways between the Chow group and HDF group are shown in the histogram of influencing factors (Fig. 7A). The network of the ten most prominent metabolic pathways associated with differential metabolites between the Chow group and HDF groups is shown in the network diagram (Fig. 7B). KEGG analysis revealed 120 metabolic pathways enriched by 250 differential metabolites between the HDF and DAPA groups (Table S14). The 20 most prominent metabolic pathways in the HDF and DAPA groups are shown in the histogram of influencing factors (Fig. 7C). The network of the ten most prominent metabolic pathways associated with differential metabolites between the HDF and DAPA groups is shown in the network diagram (Fig. 7D). ## Lipid metabolites Annotated obtained lipids according to lipid chains and fat after data preprocessing were divided into BisMePA, Cer, ChE, CL, Co, TG, DG, dMePE, GM2, GM3, Hex1Cer, Hex2Cer, LdMePE, PC, PE, MePC, MGDG, PG, PI, PS, SM, SPH, StE and other categories (Fig. 8A). The PCA score plots suggest that the QC samples were gathered, indicating good repeatability and reliable results (Fig. 8B). Ultimately, 3,702 lipid metabolites were comparatively and qualitatively characterized between the Chow group and HDF group (Table S15). There was a separation between the Chow group and HDF groups in the OPLS-DA score plots at the negative (R2X = 0.55, R2Y = 0.99, Q2 = 0.98; Fig. 8C) or positive ion mode (R2X = 0.53, R2Y = 0.99, Q2 = 0.97; Fig. 8E). 3,698 lipid metabolites were comparatively and qualitatively characterized between the Chow group and HDF groups (Table S16). There was a separation between the HDF group and DAPA group in the OPLS-DA score plots at negative (R2X = 0.37, R2Y = 0.96, Q2 = 0.82; Fig. 8D) or positive ion mode (R2X = 0.39, R2Y = 0.99, Q2 = 0.67; Fig. 8F). ## Discrepancies in lipid metabolites The Venn diagram intuitively shows the similarity and overlap of differential lipid metabolite composition in different comparison groups (Fig. 9A). There were 141 upregulated and 167 downregulated differential lipid metabolites between the Chow group and HDF group (Fig. 9B and Table 3). There were 67 upregulated and 59 downregulated differential lipid metabolites between the HDF and DAPA groups, respectively (Fig. 9B and Table 4). The volcano plot shows the distribution of differential lipid metabolites between the two groups (Fig. 9C), HDF group, and DAPA group (Fig. 9D). Lipid correlations often reveal synergy between lipid metabolites. The correlation of differential lipid metabolites between the Chow and HDF groups is presented in Fig. 10A, which shows the degree of correlation between discrepant lipid metabolites. The correlation of differential metabolites between the HDF and DAPA groups is shown in Fig. 10B. Agglomerate hierarchical clustering visually showed the relative quantitative values of differential lipid metabolites between the Chow group and HDF groups (Fig. 10C) and the HDF and DAPA groups (Fig. 10D). The correlation of differential lipid metabolites between the Chow group and HDF groups is presented in Figure S7A, which shows the degree of correlation between discrepant lipid metabolites. The correlation between differential lipid metabolites in the HDF and DAPA groups is presented in Figure S7B. ## Lipid metabolic pathways KEGG analyzed fourteen metabolic pathways enriched by differential lipid metabolites between the Chow and HDF groups (Table S17). The 14 most prominent lipid metabolic pathways between the Chow and HDF groups are shown in the bubble map of influencing factors (Figure S8A). The eight most prominent lipid metabolic pathways between the HDF and DAPA groups are shown in the bubble map of influencing factors (Figure S8B). Discussion The molecular mechanisms underlying SGLT2i's clinical benefits in patients with HFrEF or HFpEF, both diabetic and non-diabetic, are still not fully understood. It is possible that empagliflozin might have contributed to a reduction in left ventricular volume among patients diagnosed with type II diabetes or prediabetes and HFrEF, which could explain the observed decrease in heart failure hospitalizations and mortality associated with SGLT2i 30 . Our study demonstrated that DAPA exhibited protective effects against myocardial fibrosis and hypertrophy and improved systolic function in mice with chronic heart failure induced by a high-fat diet. Furthermore, DAPA has proven to be an effective medication for the prevention and treatment of diabetic cardiomyopathy (DCM), in addition to its positive impact on blood lipids and body weight. Research has demonstrated that DAPA has the potential to protect against cardiac fibrosis in type II diabetic rats by suppressing fibroblast activation and endothelial-to-mesenchymal transition (EndMT) through AMPKα-mediated inhibition of TGF-β/Smad signaling 18 . Additionally, DAPA has been found to suppress myocardial inflammation and fibrosis, improve systolic function, and reduce oxygen radicals and calcium transport channel activity in angiotensin II-stressed diabetic mice 31 . Furthermore, in streptozotocin-induced diabetic rats, DAPA has been shown to inhibit myocardial apoptosis by upregulating the AKT/JAK/MAPK pathways induced by erythropoietin 32 . In a study involving pigs with HFpEF, the administration of DAPA for nine weeks resulted in a decrease in hypertension and a reversal of left ventricle concentric remodeling. This effect was attributed, at least in part, to the inhibition of sympathetic tone in the aorta, which subsequently led to suppression of the inflammatory response and activation of the NO-cGMP-PKG pathway 33 . Furthermore, acute administration of DAPA following cardiac ischemia/reperfusion injury in rats demonstrated cardioprotective effects by reducing cardiac infarct size, improving left ventricular function, and reducing arrhythmias 34 . Interestingly, the literature presents conflicting reports regarding the impact of SGLT2i. DAPA significantly reduced total cholesterol, low-density lipoprotein-cholesterol (LDL-C), and triglyceride levels in diabetic patients with overweight and hyperlipidemia 35 . In conjunction with a low dosage of insulin, the administration of DAPA resulted in a notable decrease in hyperglycemia, hypercholesterolemia, hypertriglyceridemia, and antioxidant status in diabetic rats 36 . Conversely, individuals diagnosed with type II diabetes who underwent DAPA treatment exhibited an elevation in LDL-C and overall cholesterol levels, albeit with an insignificant reduction in triglyceride levels 37 . Cha et al. 38 discovered that patients with type 2 diabetes who were prescribed SGLT2i experienced a substantial increase in HDL-C and LDL-C levels in comparison to those receiving DPP-4 inhibitors over a span of six months. Researchers remain interested in comprehending the impact of treatment on substrate flux and molecular pathways. Through the utilization of non-targeted metabolomics assays, we successfully characterized the myocardial profiles of various differential metabolites, including lipids, in mice with chronic heart failure induced by HDF. We also evaluated the influence of DAPA on these profiles. Our findings indicate that DAPA administration leads to an increase in short/medium-chain acylcarnitines and ketone-related metabolites when compared to the placebo group (Table 1 – 4 ). This observation further supports the growing recognition of altered ketone and fatty acid biology in HFrEF treated with SGLT2i 9 – 15 . The KEGG database revealed several prominent metabolic pathways enriched by dysregulated metabolites (purine metabolism, lysosome, linoleic acid metabolism, alanine metabolism; Fig. 6 C/D and Table S14 ) or lipids (choline metabolism, linoleic acid metabolism, glycerophospholipid metabolism; Figure S8 B and Table S17 ) in mice with chronic heart failure that were altered by SGLT2i. In individuals with diabetes and heart failure characterized by dysregulated cardiac FAAs and impaired glucose uptake, circulating ketones or branched-chain amino acids (BCAAs) could serve as an alternative source of energy. In failing human hearts, the increased utilization of ketones by the myocardium has been identified as a significant metabolic adaptation 15 . Furthermore, the induction of glycosuria by empagliflozin has been shown to enhance β-cell function and improves insulin sensitivity in patients with type II diabetes, resulting in reduced blood glucose levels and a shift in substrate utilization from carbohydrates to lipids 10 , 11 . Empagliflozin has demonstrated positive effects on cardiac function and remodeling in non-diabetic rats with left ventricular dysfunction following myocardial infarction. These effects were observed alongside the normalization of glucose and fatty acid uptake and oxidation, as well as improved utilization of ketone bodies and ATP production in the heart 12 . Despite the decrease in BCAAs catabolism in heart failure, empagliflozin has the potential to restore these deficiencies. Furthermore, empagliflozin, in conjunction with the enhanced production of ketone bodies from BCAAs, offers an optimal source of energy for the heart 39 . An additional advantage could potentially be ascribed to the impact of ketones and BCAAs on the signaling mechanisms of the heart 39 . Instead of relying on glucose as an energy source, empagliflozin utilizes ketone bodies, free fatty acids, and BCAAs to enhance the energetic state of the myocardium and mitigate unfavorable left ventricular remodeling in a non-diabetic porcine model 9 . Our study had some limitations. There has been no comprehensive examination of the effects of DAPA on substrate flux and molecular pathways throughout the entire organism. The identified differential metabolites require further investigation, and comprehensive analysis of myocardial samples from both human and animal subjects is warranted. Furthermore, there remains a dearth of comprehensive analyses regarding alterations in circulatory metabolomics in patients with hyperlipidemia and HFrEF or HFpEF, as well as the impact of SGLT2i on these profiles. The implementation of a serum metabolomic test can facilitate the identification of differentially metabolized molecules and enable the categorization of such patients. In addition, the heterogeneity of cardiac samples and the molecular mechanisms responsible for SGLT2i should be considered and explored. Conclusions In our study, we observed that DAPA exhibited protective effects against myocardial fibrosis and hypertrophy and enhanced systolic function in mice with chronic heart failure induced by HDF. Furthermore, we conducted a comprehensive analysis of myocardial profiles, focusing on various differential metabolites, including lipid molecules, as well as prominent metabolic pathways, in these mice. In addition, we assessed the impact of DAPA treatment on these profiles. These findings contribute to the expanding body of literature suggesting that changes in metabolites might serve as potential mechanisms underlying the clinical benefits of SGLT2i in patients with HFrEF or HFpEF. Nevertheless, further clinical data are required to ascertain the safety and efficacy of DAPA administration in this patient population, and its potential mechanism requires further exploration. Abbreviations BCAAs, branched-chain amino acids; DAPA, dapagliflozin; DCM, diabetic cardiomyopathy; DDA, data dependent acquisition; EndMT, endothelial-to-mesenchymal transition; FAAs, free fatty acids; FC, fold change; FDR, false discovery rate; HDF, high fat diet feed; HFrEF, heart failure and a reduced ejection fraction; HFpEF, heart failure and a preserved ejection fraction; HPLC, high performance liquid chromatography; KEGG, Kyoto Encyclopedia of Genes and Genomes; LC-MS, liquid chromatography-Mass spectrum; LDL-C, low density lipoprotein-cholesterol; LV, left ventricle; LVEF, left ventricle ejection fraction; LVFS, left ventricle fractional shortening; NIH, national Institutes of Health; OPLS-DA, orthogonal partial least square discriminant analysis; PCA, principal component analysis; PLS-DA, partial least square discriminant analysis; QC, quality control; RSD, relative standard deviation; SD, standard deviation; SGLT2i, sodium-glucose cotransporter-2 inhibitors;VIP, variable influence on projectionWGA, wheat germ agglutinin. Declarations Funding This study was supported by grants from the talent start-up capital program of Fujian Medical University Union Hospital (2023XH027), the Science and Technology Innovation Joint Fund Project of Fujian Provincial Science and Technology Department (2019Y9082, 2023Y9183), the National Natural Science Foundation of China (No. 8230020004), Startup Fund for Scientific Research, Fujian Medical University (2023QH1035). Authors' contributions (I) Conception and design: Jinhua Huang; (II) Administrative support: Jinhua Huang; (III) Provision of study materials: Qiong Jiang; (IV) Collection and assembly of data: Feng Hu, Wenkun Liu; (V) Data analysis and interpretation: Feng Hu, Jinhua Huang; (VI) Manuscript writing: Feng Hu; (VII) Final approval of the manuscript: All authors. Ethics approval and consent to participate All animal experiments were conducted in accordance with the National Institutes of Health (NIH) policies outlined in the Guide for the Care and Use of Laboratory Animals and approved by the Animal Care and Use Committee of Fujian Medical University Union Hospital. We have adhered to ARRIVE guidelines and upload a completed checklist. Consent for publication Not applicable. Clinical trial number Not applicable. Conflict of interest The authors declare that they have no competing interests. Availability of data and material The datasets used and/or analyzed during the present study are available from the corresponding author upon reasonable request. Acknowledgements None. References Nassif ME, Windsor SL, Tang F, et al. Dapagliflozin Effects on Biomarkers, Symptoms, and Functional Status in Patients With Heart Failure With Reduced Ejection Fraction: The DEFINE-HF Trial. Circulation. 2019;140(18):1463-1476. Packer M, Anker SD, Butler J, et al. 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Sodium-glucose co-transporter 2 inhibition with empagliflozin improves cardiac function in non-diabetic rats with left ventricular dysfunction after myocardial infarction. Eur J Heart Fail. 2019;21(7):862-873. Selvaraj S, Fu Z, Jones P, et al. Metabolomic Profiling of the Effects of Dapagliflozin in Heart Failure With Reduced Ejection Fraction: DEFINE-HF. Circulation. 2022;146(11):808-818. Kappel BA, Lehrke M, Schütt K, et al. Effect of Empagliflozin on the Metabolic Signature of Patients With Type 2 Diabetes Mellitus and Cardiovascular Disease. Circulation. 2017;136(10):969-972. Bedi KC, Jr., Snyder NW, Brandimarto J, et al. Evidence for Intramyocardial Disruption of Lipid Metabolism and Increased Myocardial Ketone Utilization in Advanced Human Heart Failure. Circulation. 2016;133(8):706-716. Verma S, Rawat S, Ho KL, et al. Empagliflozin Increases Cardiac Energy Production in Diabetes: Novel Translational Insights Into the Heart Failure Benefits of SGLT2 Inhibitors. 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XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006;78(3):779-787. Navarro-Reig M, Jaumot J, García-Reiriz A, Tauler R. Evaluation of changes induced in rice metabolome by Cd and Cu exposure using LC-MS with XCMS and MCR-ALS data analysis strategies. Anal Bioanal Chem. 2015;407(29):8835-8847. Thévenot EA, Roux A, Xu Y, Ezan E, Junot C. Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. J Proteome Res. 2015;14(8):3322-3335. Hu F, Yu H, Zong J, et al. The impact of hypertension for metabolites in patients with acute coronary syndrome. Ann Transl Med. 2023;11(2):50. Lee MMY, Brooksbank KJM, Wetherall K, et al. Effect of Empagliflozin on Left Ventricular Volumes in Patients With Type 2 Diabetes, or Prediabetes, and Heart Failure With Reduced Ejection Fraction (SUGAR-DM-HF). Circulation. 2021;143(6):516-525. Arow M, Waldman M, Yadin D, et al. Sodium-glucose cotransporter 2 inhibitor Dapagliflozin attenuates diabetic cardiomyopathy. Cardiovasc Diabetol. 2020;19(1):7. El-Sayed N, Mostafa YM, AboGresha NM, Ahmed AAM, Mahmoud IZ, El-Sayed NM. Dapagliflozin attenuates diabetic cardiomyopathy through erythropoietin up-regulation of AKT/JAK/MAPK pathways in streptozotocin-induced diabetic rats. Chem Biol Interact. 2021;347:109617. Zhang N, Feng B, Ma X, Sun K, Xu G, Zhou Y. 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. Lahnwong S, Palee S, Apaijai N, et al. Acute dapagliflozin administration exerts cardioprotective effects in rats with cardiac ischemia/reperfusion injury. Cardiovasc Diabetol. 2020;19(1):91. Calapkulu M, Cander S, Gul OO, Ersoy C. Lipid profile in type 2 diabetic patients with new dapagliflozin treatment; actual clinical experience data of six months retrospective lipid profile from single center. Diabetes Metab Syndr. 2019;13(2):1031-1034. Sayed N, Abdalla O, Kilany O, et al. Effect of dapagliflozin alone and in combination with insulin in a rat model of type 1 diabetes. J Vet Med Sci. 2020;82(4):467-474. Matthaei S, Bowering K, Rohwedder K, Grohl A, Parikh S. Dapagliflozin improves glycemic control and reduces body weight as add-on therapy to metformin plus sulfonylurea: a 24-week randomized, double-blind clinical trial. Diabetes Care. 2015;38(3):365-372. Cha SA, Park YM, Yun JS, et al. A comparison of effects of DPP-4 inhibitor and SGLT2 inhibitor on lipid profile in patients with type 2 diabetes. Lipids Health Dis. 2017;16(1):58. Lopaschuk GD, Ussher JR. Evolving Concepts of Myocardial Energy Metabolism: More Than Just Fats and Carbohydrates. Circ Res. 2016;119(11):1173-1176. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.tif Supplementaryfiguretablelegends.docx FigureS1.tif FigureS2.tif FigureS3.tif FigureS4.tif FigureS5.tif FigureS6.tif FigureS7.tif FigureS8.tif TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx TableS8.xlsx TableS9.xlsx TableS10.xlsx TableS11.xlsx TableS12.xlsx TableS13.xlsx TableS14.xlsx TableS15.xlsx TableS17.xlsx Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6881055","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473702481,"identity":"ececc8d4-f5b4-4d60-b3bb-25c1a071c3f0","order_by":0,"name":"Feng Hu","email":"","orcid":"","institution":"Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Hu","suffix":""},{"id":473702482,"identity":"c592ad25-6322-444e-a58a-5702d605c553","order_by":1,"name":"Jinhua Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACAzDJIwFmHPhgYCPHxt5+gGgtjA9nFKQZ8/GcSSBCC4TBbMzz4XDiPAkHA3w6GMwlkp89/CJjkQdiSM4wYE5vk2BIYPhRsQ2nFssZaebGMjwSxUCGmcQHA7bcNunGA4w9Z27jdtiNBDNpCR6JxA1ABtAWntw2mQMJzIxt+LSkf4NqATJ4DCTS2SQSDAhoyTGT/ADWkmNszGNgkEBYy5k3ZdIMIC1n3hQ+nGGQYNgGDOSDeP1yPH2b5M+eusQNx9M3HPjw57+8fHv7wQc/KnBrAQFm3h40kQN41QMB448fhJSMglEwCkbBiAYASjRaVzL32YkAAAAASUVORK5CYII=","orcid":"","institution":"Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center","correspondingAuthor":true,"prefix":"","firstName":"Jinhua","middleName":"","lastName":"Huang","suffix":""},{"id":473702483,"identity":"c290b70b-21b9-4116-9aea-970c04453173","order_by":2,"name":"Qiong Jiang","email":"","orcid":"","institution":"Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center","correspondingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Jiang","suffix":""},{"id":473702484,"identity":"85a54927-4f2c-4020-8b03-d1c2e035ee79","order_by":3,"name":"Wenkun Liu","email":"","orcid":"","institution":"Fujian Medical University Union Hospital, Fujian Cardiovascular Medical Center, Fujian Institute of Coronary Artery Disease, Fujian Cardiovascular Research Center","correspondingAuthor":false,"prefix":"","firstName":"Wenkun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-12 13:54:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6881055/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6881055/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85391299,"identity":"09d2ccc6-3887-4b0d-a1e6-6a65f289edf3","added_by":"auto","created_at":"2025-06-25 10:27:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12641832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistological structure and echocardiography.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Representative images of collagen matrix deposition in the myocardium according to Masson’s trichrome staining from the different groups (magnification =50x). (B) Corresponding statistic analysis of cardiac fibrosis in A (n=7 per group). (C) Representative images of WGA staining in mice hearts from the different groups. (D) Corresponding statistic analysis of cardiomyocyte size used WGA staining (n=8 per group). (E) Representative images by M-mode echocardiography in mice hearts from the different groups. (F) Echocardiography analysis showing cardiac systolic dysfunction assessed by LVEF and LVFS (n=8 per group). The data are represented as the means ±standard deviation; *P \u0026lt; 0.05, **P \u0026lt; 0.01 versus the Chow group, \u003csup\u003e#\u003c/sup\u003eP \u0026lt; 0.05 versus HDF group. Abbreviation: HDF, high fat diet feed; DAPA, dapagliflozin; LVEF, left ventricle ejection fraction; FS, fractional shortening; WGA, wheat germ agglutinin.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/5ff416fbe3bc12acbe3c0105.png"},{"id":85391298,"identity":"83e7020b-8546-4594-bb4e-0e5584eede75","added_by":"auto","created_at":"2025-06-25 10:27:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2047993,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuality control and quality assurance at negative ion mode.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The base peak chromatograms measured by mass spectrograms between chows, HDF and DAPA groups. There were differential peak heights between different comparison groups. But their trends were similar, indicating the excellent repeatability. (B) The score chart of principle component analysis within quality control samples. QC samples were gathered, indicating good repeatability and reliable results. (C) Relative standard deviation distribution map of quality assurance results. The proportion of characteristic peak with RSDless than 30% could reach about 65%, indicating that the data stability was good. (D) The score chart of principle component analysis within quality assuranceresults. Quality assurance results were gathered, indicating well repeatability and reliable results. Abbreviation: HDF, high fat diet feed; DAPA, dapagliflozin; QC, quality control; RSD, relative standard deviation.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/7a9209ff15b093d5f793562d.png"},{"id":85391854,"identity":"c6487982-6829-42b9-b896-70fddc4174ec","added_by":"auto","created_at":"2025-06-25 10:35:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4541616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of differential metabolites at negative ion mode.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Venn diagram could intuitively show the similarity and overlap of metabolites composition in different comparison groups. Different color regions represented different metabolites of different groups, and overlapping regions were common to different metabolites of different groups. (B) Statistical histogram indicated that there were 2,244 up-regulated and 1,133 down-regulated metabolites between the Chow group and HDF group, 995 up-regulated and 1,107 down-regulated metabolites between the HDF group and DAPA group. (C) Volcano plot could directly show the distribution of metabolites between the Chow group and HDF group. (D) Volcano plot could directly show the distribution of metabolites between the HDF group and DAPA group. (E) Agglomerate hierarchical clustering visually showed the relative quantitative values of metabolites between the Chow group and HDF group. (F) Agglomerate hierarchical clustering visually showed the relative quantitative values of metabolites between the HDF group and DAPA group. The relative content in the Figure (E) and (F) were shown by the difference in color. Columns represented samples, rows represented metabolite names, and the cluster tree on the left of the Figure was the differential metabolite cluster tree. Abbreviation: HDF, high fat diet feed; DAPA, dapagliflozin; FC, fold change; VIP, variable influence on projection.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/79beb6b927ef1b66a9ed1e8a.png"},{"id":85389361,"identity":"e873b561-27d4-4e3a-be85-2127a42d9ffe","added_by":"auto","created_at":"2025-06-25 10:19:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2618372,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariate analysis in different comparison groups at negative ion mode.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The PCA analysis compared for HDF to Chow group. The PCA score plots derived from 7-fold cross-validation indicated separation tendencies between the Chow group and HDF group at negative (R2X=0.56). (B) The PLS-DA analysis compared for HDF to Chow group. There was a separation between the Chow group and HDF group in the PLS-DA score plots (R2X=0.30, R2Y=0.99, Q2=0.87).\u003cstrong\u003e \u003c/strong\u003e(C) The OPLS-DA analysis compared for HDF to Chow group. There was a separation between the Chow group and HDF group in the OPLS-DA score plots (R2X=0.30, R2Y=0.99, Q2=0.85). (D) The S-plot of OPLS-DA was generally used to select metabolites between the HDF group and DAPA group that were strongly related to major components of biological processes. The closer the two corners were, the more important the metabolites were. The first ten molecule names in the upper right corner and the lower left corner were displayed. (E) The PCA analysis compared for DAPA to HDF group. The PCA score plots derived from 7-fold cross-validation indicated separation tendencies between the Chow group and HDF group at negative (R2X=0.53). (F) The PLS-DA analysis compared for DAPA to HDF group. There was a separation between the Chow group and HDF group in the PLS-DA score plots (R2X=0.24, R2Y=0.99, Q2=0.71).\u003cstrong\u003e \u003c/strong\u003e(G) The OPLS-DA analysis compared for DAPA to HDF group. There was a separation between the HDF group and DAPA group in the OPLS-DA score plots (R2X=0.24, R2Y=0.99, Q2=0.61). (H) The S-plot of OPLS-DA was generally used to select metabolite between the HDF group and DAPA group that were strongly related to major components of biological processes. The closer the two corners were, the more important the metabolites were. The first ten molecule names in the upper right corner and the lower left corner were displayed. Abbreviation: HDF, high fat diet feed; DAPA, dapagliflozin; PCA, principal component analysis; PLS-DA, partial least square discriminant analysis; OPLS-DA, orthogonal partial least square discriminant analysis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/884c5548a0a610c08f992123.png"},{"id":85389360,"identity":"71f4a8aa-bf20-4629-a925-7720d987e7e3","added_by":"auto","created_at":"2025-06-25 10:19:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3141177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of differential metabolites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Statistical histogram indicated that there were 72 up-regulated and 34 down-regulated differential metabolitesbetween the Chow group and HDF group, 40 up-regulated and 25 down-regulated differential metabolites between the HDF group and DAPA group. (B) Venn diagram could intuitively show the similarity and overlap of differential metabolites composition in different comparison groups. (C) Scatter plot of mass-to-charge ratio with P-value showed the distribution of differentialmetabolites between the Chow group and HDF group. (D) Volcano plot could directly show the distribution of metabolites between the Chow group and HDF group. (E) Scatter plot of mass-to-charge ratio with P-value showed the distribution of differentialmetabolites between the HDF group and DAPA group. (F) Volcano plot could directly show the distribution of metabolites between the HDF group and DAPA group. Note: Each point in the Figure (C) and (E) represented a metabolite, and the horizontal coordinate represented the mass-charge ratio. The ordinate represented the logarithmic value (-log10) of p-value. The larger the ordinate value, the more significant the differentially expressed metabolites, and the more reliable the differentially expressed metabolites. In the Figure (C) and (E), red dots represented up-regulated differentially expressed metabolites, blue dots represented down-regulated differentially expressed metabolites, and the size of dots represented VIP value. The top five most significant up-regulated and down-regulated differential metabolites were shown. Abbreviation: HDF, high fat diet feed; DAPA, dapagliflozin; VIP, variable influence on projection.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/51f60763b710ce710c4feb75.png"},{"id":85389405,"identity":"7bdf5c6a-c9bf-4c1c-bd33-835eda30cdfe","added_by":"auto","created_at":"2025-06-25 10:19:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":13503840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHierarchical clustering and Z-score map of differential metabolites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Agglomerate hierarchical clustering visually showed the relative quantitative values of differential metabolites between the Chow group and HDF group. (B) Z-score map showed the relative content of differential metabolites between the Chow group and HDF group. (C) Agglomerate hierarchical clustering visually showed the relative quantitative values of differentialmetabolites between the HDF group and DAPA group. (D) Z-score map showed the relative content of differential metabolites between the HDF group and DAPA group. Note: the relative content in the Figure (A) and (C) were shown by the difference in color. Columns represented samples, rows represented metabolite names, and the cluster tree on the left of the Figure was the differential metabolite cluster tree. In the Figure (B) and (D), the vertical coordinate was the name of the metabolite, the color of the points represented different groups, and the horizontal coordinate was the relative content of the metabolite in the group obtained by Z-score conversion. Abbreviation: HDF, high fat diet feed; DAPA, dapagliflozin.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/12babac705eba4507bcc7da3.png"},{"id":85389366,"identity":"5e195e19-7df7-4db9-b01b-34fa4c8eae09","added_by":"auto","created_at":"2025-06-25 10:19:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2952282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe main discrepant pathways.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Histogram of influencing factors between the Chow group and HDF group. The twenty most prominent metabolic pathways were shown at histogram of influencing factors. (B) The network of the ten most prominent metabolic pathways associated with differential metabolites between the Chow group and HDF group. (C) Histogram of influencing factors between the HDF group and DAPA group. The twenty most prominent metabolic pathways were shown at histogram of influencing factors. (D) The network of the ten most prominent metabolic pathways associated with differential metabolites between the HDF group and DAPA group. Note: in the Figure (A) and (C), the vertical coordinate represented the metabolic pathway, and the horizontal coordinate represented the pathway impact values from the pathway topology analysis. The colors of the histogram represented the metabolites in the data with different levels of significance, with blue being the least and red being the most significant. In the Figure (B) and (D), the blue dots represented pathways, and the other dots represented metabolites. The size of a pathway point indicated the number of metabolites associated with it. The metabolite point indicated the magnitude of the log2 (FC) value by gradients. Abbreviation: HDF, high fat diet feed; DAPA, dapagliflozin; FC, fold change.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/e867729a76442b39581fc8a4.png"},{"id":85389376,"identity":"1d1f5568-a94b-417e-b070-43bd5c30116b","added_by":"auto","created_at":"2025-06-25 10:19:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4488362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of lipid metabolites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Annotated obtained lipids according to lipid chains and fat after data preprocessing were divided into BisMePA, Cer, ChE, CL, Co, TG, DG, dMePE, GM2, GM3, Hex1Cer, Hex2Cer, LdMePE, PC, PE, MePC, MGDG, PG, PI, PS, SM, SPH , StE and other categories. (B) The score chart of PCA within quality control samples. QC samples were gathered, indicating good repeatability and reliable results. (C) The OPLS-DA analysis compared for HDF to Chow group at negative ion mode. There was a separation between the Chow group and HDF group in the OPLS-DA score plots (R2X=0.55, R2Y=0.99, Q2=0.98). (D) The OPLS-DA analysis compared for DAPA to HDF group at negative ion mode. There was a separation between the HDF group and DAPA group in the OPLS-DA score plots (R2X=0.37, R2Y=0.96, Q2=0.82). (E) The OPLS-DA analysis compared for HDF to Chow group at positive ion mode. There was a separation between the Chow group and HDF group in the OPLS-DA score plots (R2X=0.53, R2Y=0.99, Q2=0.97). (F) The OPLS-DA analysis compared for DAPA to HDF group at positive ion mode. There was a separation between the HDF group and DAPA group in the OPLS-DA score plots (R2X=0.39, R2Y=0.99, Q2=0.67). Abbreviation: HDF, high fat diet feed; DAPA, dapagliflozin; QC, quality control; PCA, principal component analysis; OPLS-DA, orthogonal partial least square discriminant analysis.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/1fa79779daea656c3ffc488c.png"},{"id":85389369,"identity":"1106465c-7b86-45fb-9ddb-359ef9c098ed","added_by":"auto","created_at":"2025-06-25 10:19:47","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1160741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiscrepancies in lipid metabolites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Venn diagram could intuitively show the similarity and overlap of differential lipid metabolites composition in different comparison groups. (B) Statistical histogram indicated that there were 141 up-regulated and 167 down-regulated differential lipid metabolites between the Chow group and HDF group, 67 up-regulated and 59 down-regulated differential lipid metabolites between the HDF group and DAPA group. (C) Volcano plot could directly show the distribution of lipid metabolites between the Chow group and HDF group. (D) Volcano plot could directly show the distribution of lipid metabolites between the HDF group and DAPA group. Abbreviation: HDF, high fat diet feed; DAPA, dapagliflozin; VIP, variable influence on projection.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/1a2242da35b503ffc7295653.png"},{"id":85391841,"identity":"75b15f5a-f765-49fa-a205-b54f73316463","added_by":"auto","created_at":"2025-06-25 10:35:48","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":16949406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe associated chord diagram and hierarchical clustering of differential lipid metabolites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The associated chord diagram of differential lipid metabolites between the Chow group and HDF group. (B) The associated chord diagram of differential lipid metabolites between the HDF group and DAPA group. (C) Agglomerate hierarchical clustering visually showed the relative quantitative values of differential lipid metabolites between the Chow group and HDF group. (D) Agglomerate hierarchical clustering visually showed the relative quantitative values of differential lipid metabolites between the HDF group and DAPA group. Abbreviation: HDF, high fat diet feed; DAPA, dapagliflozin.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/b97f73956a0f42e024b22e06.png"},{"id":86064585,"identity":"ea6b8367-7b25-4c09-a7b5-0a558e496681","added_by":"auto","created_at":"2025-07-05 10:32:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":61883967,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/b44eae31-090c-40b1-9597-5076bc007157.pdf"},{"id":85389362,"identity":"75bf2317-f3e6-46cc-a766-91c68ef5da46","added_by":"auto","created_at":"2025-06-25 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10:19:49","extension":"xlsx","order_by":23,"title":"","display":"","copyAsset":false,"role":"supplement","size":37466,"visible":true,"origin":"","legend":"","description":"","filename":"TableS13.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/6efb5bd8e8d7debae79217c7.xlsx"},{"id":85389442,"identity":"35748bb7-6bfd-49ad-aa09-986408202fc9","added_by":"auto","created_at":"2025-06-25 10:19:50","extension":"xlsx","order_by":24,"title":"","display":"","copyAsset":false,"role":"supplement","size":26363,"visible":true,"origin":"","legend":"","description":"","filename":"TableS14.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/e9ef65c66188ebae1a1b2abd.xlsx"},{"id":85391295,"identity":"89273400-c568-4ac2-8c92-330634155216","added_by":"auto","created_at":"2025-06-25 10:27:47","extension":"xlsx","order_by":25,"title":"","display":"","copyAsset":false,"role":"supplement","size":1603130,"visible":true,"origin":"","legend":"","description":"","filename":"TableS15.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/37603935edf6619b4943e4c5.xlsx"},{"id":85389394,"identity":"57f4c117-ebdb-4cdf-8a9a-2cdfe22771e7","added_by":"auto","created_at":"2025-06-25 10:19:48","extension":"xlsx","order_by":27,"title":"","display":"","copyAsset":false,"role":"supplement","size":14354,"visible":true,"origin":"","legend":"","description":"","filename":"TableS17.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/d013a5ef5285640cfa42dd90.xlsx"},{"id":85389383,"identity":"71d7951a-05dc-4134-bcc6-930ff75773da","added_by":"auto","created_at":"2025-06-25 10:19:48","extension":"docx","order_by":28,"title":"","display":"","copyAsset":false,"role":"supplement","size":190066,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6881055/v1/61ee84d8842ad834538394a1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of dapagliflozin for the myocardial metabolomic profiles of mice with chronic heart failure induced by a high fat diet","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSodium-glucose cotransporter-2 inhibitors (SGLT2i) have been shown to reduce the risk of exacerbating heart failure or cardiovascular death in individuals with heart failure and a reduced ejection fraction (HFrEF) \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e or preserved ejection fraction (HFpEF), both diabetic and non-diabetic \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Notably, patients with chronic HFpEF exhibited noteworthy enhancements in symptomatology, physical restrictions, and exercise capacity following a 12-week course of dapagliflozin (DAPA) treatment \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Despite the apparent clinical advantages of these cardiovascular benefits, the precise pathophysiological mechanisms remain incompletely elucidated \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Induced glycosuria and natriuresis, beneficial hemodynamic changes, reductions in anemia and glomerular hyperfiltration, decline of sympathetic nervous system activity and renin-angiotensin-aldosterone system, anti-fibrotic and anti-inflammatory action, upregulated adipocytokines, and improved endothelial dysfunction and myocardial metabolism all contribute to the cardiovascular benefits \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Consequently, additional investigations are warranted to gain a more comprehensive understanding of the molecular mechanisms underlying the favorable effects of these medications in patients with HFrEF and HFpEF.\u003c/p\u003e \u003cp\u003eEnhanced comprehension of the metabolic signature of SGLT2i could yield valuable insights into the metabolic mechanisms associated with cardiovascular well-being. Investigations conducted on both preclinical and clinical models of diabetes have established a correlation between SGLT2i and a shift away from glucose metabolism towards fatty acid and ketone body metabolism \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Following the administration of empagliflozin, a notable elevation in acetyl- and propionylcarnitine levels was observed, indicating the breakdown of free fatty acids (FAAs), amino acids, and ketone bodies, consequently activating the tricarboxylic acid cycle, as evidenced by increased levels of aconitate and fumarate \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEmpagliflozin exhibited a notable effect on the intermediate metabolites of the urea cycle, indicating its activation and validating the heightened utilization of amino acids \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, alterations in fuel selection have not been consistently observed \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In diabetic \u003cem\u003edb/db\u003c/em\u003e mice, the administration of empagliflozin results in augmented cardiac ATP production rates, primarily attributed to the supplementary involvement of ketone oxidation rather than elevated levels of circulating ketones \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Furthermore, in diabetic and obese rats with spontaneously hypertensive heart failure, empagliflozin effectively mitigated blood pressure and hepatic congestion while leaving glucose metabolism and myocardial function unaffected, but did not increase myocardial ketone utilization despite increased circulating levels \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMetabolic profiling in individuals diagnosed with heart failure offers valuable insights into the correlation between SGLT2i treatment, distinctively metabolized molecules, and unfavorable outcomes. Notably, patients with HFrEF undergoing DAPA therapy exhibited an increase in ketone-related metabolites and short/medium-chain acylcarnitines \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, long-chain acylcarnitine, dicarboxylacylcarnitine, and aromatic amino acid metabolite clusters were linked to diminished quality of life and elevated NT-proBNP levels, irrespective of the administration of DAPA treatment \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Given the high mortality among diabetic and non-diabetic patients with HFrEF or HFpEF, this study aimed to examine the myocardial metabolomic profiles of mice with chronic heart failure induced by a high-fat diet (HDF) and assess the impact of DAPA on these profiles.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e##Ethics Statements\u003c/h2\u003e\n \u003cp\u003eMale C57BL/6N mice were procured from Fuzhou Wu\u0026apos;s Laboratory Animal Co., Ltd. (Hunan, China) and housed under standard conditions at a temperature of 22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u0026deg;C and humidity of 50% \u0026plusmn; 5%. The mice were subjected to a 12-hour light-dark cycle and had ad libitum access to water and food. Random allocation was used to assign the mice to different treatment groups. All animal experiments were conducted in accordance with the National Institutes of Health (NIH) policies outlined in the Guide for the Care and Use of Laboratory Animals and approved by the Animal Care and Use Committee of Fujian Medical University Union Hospital. We have adhered to ARRIVE guidelines and upload a completed checklist.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e##\u003c/em\u003eMouse model\u003c/p\u003e\n \u003cp\u003eIn the control group, mice were provided with normal chow, while the other two groups were fed an HDF consisting of 60% calories from fat (MD12033, Meidisen, China, n\u0026thinsp;=\u0026thinsp;8 per group). Immunohistochemical analysis and echocardiographic assessment corroborated the successful establishment of a chronic heart failure mouse model induced by six months of HFD feeding. One group of mice with hyperlipidemia (DAPA group) received dapagliflozin (DAPA, 1 mg/kg/day) in their drinking water \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. All mice were maintained on their respective dietary regimens for the duration of the study. At the conclusion of the six-month dietary intervention, mice were sacrificed through overdose of anesthetic using an i.p. injection of sodium pentobarbital (200 mg/kg). Subsequently, cardiac perfusion was performed, and select myocardial tissues were cryogenically preserved for subsequent metabolomic analysis.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e##\u003c/em\u003eEchocardiography\u003c/p\u003e\n \u003cp\u003eA 30 MHz linear array ultrasound transducer (MS-400, VisualSonics Inc.) was employed in conjunction with a Vevo2100 ultrasound imaging system to evaluate cardiac structure and function in anesthetized animals receiving 1 L/min of oxygen \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. To determine the maximum length of the left ventricle (LV), B-mode images of the parasternal long-axis were acquired. Subsequently, M-mode imaging was performed in this view by positioning the cursor perpendicular to the maximum dimensions at both end-diastole and end-systole, facilitating the measurement of chamber dimensions, left ventricular ejection fraction (LVEF), and fractional shortening (LVFS).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e##\u003c/em\u003eHistological studies\u003c/p\u003e\n \u003cp\u003eThe experimental procedure involved perfusing the mice with cold saline, followed by the extraction of the hearts. The hearts were subsequently fixed with 4% paraformaldehyde on a room temperature heating block for an duration of 24 hours, and then embedded in paraffin wax. Serial tissue sections were stained with Masson\u0026apos;s trichrome to detect collagen matrix deposition and examined using an optical microscope (Olympus, Japan). The semi-quantitative analysis of the tissue staining was conducted using Image-Pro plus 6.0 softwares.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cem\u003e##\u003c/em\u003eImmunofluorescent staining\u003c/h3\u003e\n\u003cp\u003eFollowing deparaffinization, a paraffin-embedded cardiac section with a thickness of 4 mm was rehydrated and subjected to antigen retrieval in a buffer containing EDTA. Cardiomyocyte dimensions were evaluated by initially blocking the slide surfaces with 3% bovine serum albumin for 30 minutes, followed by an overnight incubation with FITC-conjugated wheat germ agglutinin (WGA, #L4895, Sigma). The stained sections were subsequently examined using fluorescence microscopy (Olympus).\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e##\u003c/em\u003eChemicals\u003c/h3\u003e\n\u003cp\u003eThe chemicals utilized in this study were of analytical or high-performance liquid chromatography (HPLC) grade. Methanol and acetonitrile were procured from Fisher Scientific (Waltham, MA, USA), while formic acid was obtained from TCI (Shanghai, China). Chloroform was supplied by Sinopharm (Shanghai, China), ultrapure water was provided by Millipore (Billerica, MA, USA), and 2-Amino-3-(2-chlorophenyl)-propionic acid was sourced from Aladdin (Shanghai, China).\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e##\u003c/em\u003eInstruments\u003c/h3\u003e\n\u003cp\u003eThe high-speed freezing centrifuge was supplied by Xiangyi Experiment Equipment Co. Ltd. (Hunan, China), while the vortex mixer was obtained from Haimen Kylin-Bell Lab Instruments Co. Ltd. (China). The ultrasonic cleaner was procured from Kunshan Shumei Experiment Equipment Co. Ltd. (China), the tissue grinders from Zhejiang Meibi Experiment Equipment Co. Ltd. (China), and the microporous membrane filters from Tianjin Jinteng Experiment Equipment Co. Ltd. (China).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e##Sample preparation\u003c/em\u003e \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eMyocardial specimens from chow, HFD, and DAPA groups underwent detailed weighing and centrifugation. A 2 mL centrifuge tube containing three steel balls was used to hold a 1000 \u0026micro;L tissue extract composed of 75% methanol:chloroform (9:1) and 25% water. The sample was then ground twice for 60 seconds at a frequency of 50 Hz, followed by ultrasound treatment at room temperature for 30 minutes. Subsequently, the supernatant was centrifuged at 12,000 rpm for 10 minutes at 4\u0026deg;C to facilitate concentration and drying, and then transferred to a new two mL centrifuge tube. Samples were dissolved in 200 litres of 50% acetonitrile, then filtered through a 0.22 m membrane and transferred into an untargeted liquid chromatography-mass spectrometry (LC-MS) detection container using 4-amino-3-(2-chlorophenyl)-propionic acid (4 ppm). Quality control (QC) was conducted on 20 \u0026micro;L of each sample.\u003c/p\u003e\u003cdiv class=\"Heading\"\u003e\u003cem\u003e## Metabolomic profiling\u003c/em\u003e\u003c/div\u003e\n\u003cp\u003eThe derivative samples were subjected to analysis utilizing a Vanquish UHPLC system (Thermo Fisher Scientific, USA), which was integrated with an ACQUITY UPLC HSS T3 chromatographic column (150 mm x 2.1 mm x 1.8 \u0026micro;m) (Waters, Milford, USA). The flow rate and injection volume were precisely controlled at 0.25 mL/min and 2 \u0026micro;L, respectively, while the column temperature was consistently maintained at 40\u0026deg;C. For LC-ESI (+)-MS analysis, the mobile phase consisted of 0.1% formic acid in acetonitrile, whereas 0.1% formic acid in water was employed as the stationary phase \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. During LC-ESI (-)-MS analysis \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, analytes were eluted using a mobile phase composed of acetonitrile and ammonium formate. We detected metabolites using an Orbitrap Exploris 120 (Thermo Fisher Scientific, USA) mass spectrometer. A simultaneous MS/MS acquisition was performed to detect the metabolites using data-dependent MS/MS acquisitions \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e##Lipid extraction \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/h2\u003e\n \u003cp\u003eThe sample was ground with two steel balls for 60 seconds following a 30-second soaking in 750 mL of a chloroform-methanol mixture, with the process repeated twice at 50 Hz. Subsequently, the sample underwent vortexing for 30 seconds before being placed on ice for an additional 10 minutes, following an initial 40-minute cooling period. The sample was then centrifuged at 12,000 rpm for five minutes. A volume of 300 \u0026micro;L from the lower layer was transferred to a new tube, to which 500 \u0026micro;L of the chloroform-methanol mixture was added and shaken for 30 seconds. The lower layer fluids were centrifuged at ambient temperature for 5 minutes at 12,000 rpm before being carefully transferred to a fresh centrifuge tube to be concentrated and subsequently dried. After dissolution in 100 \u0026micro;L of isopropanol, the samples were filtered using a 0.22 \u0026micro;m membrane.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e## Lipodomic profiling\u003c/em\u003e \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eChromatographic separation was conducted utilizing an ACQUITY UPLC\u0026reg; BEH C18 column (100 mm x 2.1 mm x 1.7 \u0026micro;m, Waters), with the autosampler maintained at 8\u0026deg;C. A gradient elution of analytes was achieved using a mobile phase consisting of acetonitrile and water in a 60:40 ratio (0.1% formic acid\u0026thinsp;+\u0026thinsp;10 mM ammonium formate) (C), and a mixture of isopropanol and acetonitrile in a 90:10 ratio (0.1% formic acid\u0026thinsp;+\u0026thinsp;10 mM ammonium formate), at a flow rate of 0.25 mL/min. Sample injections (2 \u0026micro;L) were performed following equilibration. The spray voltages were set to 3.5 kV for positive mode and \u0026minus;\u0026thinsp;2.5 kV for negative mode. The sheath and auxiliary gases were configured at 30 arbitrary units and 10 arbitrary units, respectively. For normalized collision events, a comprehensive scan over the m/z range of 150-2,000 was executed utilizing an Orbitrap analyzer with a mass resolution of 35,000. Dynamic exclusion was employed to eliminate redundant information from the MS/MS spectra.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cem\u003e##\u0026nbsp;\u003c/em\u003eData processing and multivariate analysis\u003c/h3\u003e\n\u003cp\u003eProteoWizard (v3.0.8789) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e used MSConvert to convert raw data to mzXML for detection, correction of retention time, and alignment of data using XCMS \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. It was identified metabolites using accurate mass and MS/MS data, matching it to HMDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hmdb.ca\u003c/span\u003e\u003c/span\u003e), MassBank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.massbank.jp/\u003c/span\u003e\u003c/span\u003e), LipidMaps (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lipidmaps.org\u003c/span\u003e\u003c/span\u003e), and MZCloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mzcloud.org\u003c/span\u003e\u003c/span\u003e) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003c/span\u003e). The dataset underwent normalization using robust LOESS signal correction to mitigate systematic bias. Post-normalization, only peak ions exhibiting RSDs below 30% were retained to ensure accurate metabolite identification.\u003c/p\u003e\n\u003cp\u003eModeling and analysis of multivariate data were carried out using Ropls 28 software \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The data were scaled, and models were constructed using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Metabolic profiling was conducted through the examination of score plots, load plots, and S-plots to identify metabolites influencing clustering. Permutation tests were applied to all evaluated models to assess the potential for overfitting. In the context of OPLS-DA, variables contributing to classification were identified using the variable importance on projection (VIP) scores and fold change (FC) metrics. Metabolites were considered statistically significant if they exhibited a P value less than 0.05 and a VIP value greater than 1\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e##\u0026nbsp;\u003cem\u003ePathway analysis\u003c/em\u003e\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eUtilizing MetabolAnalyst, we conducted an analysis of various metabolites through integrated pathway enrichment and pathway topology analyses. Metabolite identification via metabolomics was correlated with KEGG pathways to elucidate broader systemic functions. Methylates and their corresponding pathways were graphically represented using the KEGG Mapper. Metabolites were considered statistically significant if they exhibited a P value less than 0.05 and a VIP value greater than 1.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e##Cardiac fibrosis and hypertrophy\u003c/h2\u003e\n \u003cp\u003eCompared to the control groups, mice with chronic heart failure established through HDF exhibited significant collagen matrix deposition in the myocardium according to Masson\u0026rsquo;s trichrome staining. However, DAPA treatment attenuated HDF-induced myocardial fibrosis in the heart (Fig. 1A/B). In addition, this study used WGA staining to quantify \u003cem\u003ein vivo\u003c/em\u003e cardiomyocyte size and to evaluate cardiac hypertrophy. The results showed that mice with chronic heart failure established through HDF displayed significantly larger cardiomyocyte size compared to the Chow control arms, where DAPA treatment decreased HDF-induced myocardial hypertrophy in the hearts (Fig. 1C/D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e## DAPA treatment improved systolic function\u003c/h2\u003e\n \u003cp\u003eCardiac analysis by echocardiography demonstrated that mice with chronic heart failure established through HDF showed reduced LVEF, and LVFS indicated worse cardiac systolic dysfunction as compared to that in controls, which was ameliorated by DAPA treatment (Fig.\u0026nbsp;1E/F).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e## Quality control and quality assurance\u003c/h2\u003e\n \u003cp\u003eQC was assessed (base peak chromatogram) using internal standards and quality control samples, and a data matrix was subsequently derived. There were differential peak heights between the different comparison groups, as shown in Fig. 2A for the negative ion mode and Figure S1A for the positive ion mode. However, similar trends were observed, indicating excellent repeatability. The PCA score plots suggested that QC samples were gathered, indicating good repeatability and reliable results (Fig. 2B for the negative ion mode and Figure S1B for the positive ion mode).\u003c/p\u003e\n \u003cp\u003eBased on QC, quality assurance (QA) was carried out to delete the characteristic peaks with poor repeatability according to internal standards with an RSDs greater than 0.3 in QC samples (Fig.\u0026nbsp;2C for negative ion mode and Figure S1C for positive ion mode), in order to obtain a higher quality data set, which was more conducive to the detection of biomarkers. The PCA score plots suggested that the QA results were gathered, indicating good repeatability and reliability (Fig.\u0026nbsp;2D for the negative ion mode and Figure S1D for the positive ion mode).\u003c/p\u003e\n \u003cp\u003eThis 3-dimensional matrix included sample information, peak names, retention times, retention indices, mass-to-charge ratios, and signal intensities. After screening, all the peak signal intensities in each sample were segmented and normalized. The data matrix was obtained by removing redundancies and merging peaks after normalization (Table S1 for the negative ion mode and Table S2 for the positive ion mode).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e##Detection of metabolites\u003c/h2\u003e\n \u003cp\u003eSerum metabolites between the Chow and HDF groups were comparatively and qualitatively characterized, with 12,233 and 16,096 molecular features ultimately acquired and analyzed in the negative ion mode (Table S3) and positive ion mode (Table S7), respectively. We identified 3,377 and 3,913 metabolites in the negative ion mode (Table S4) and positive ion mode (Table S8), respectively. Similarly, metabolites between the HDF and DAPA groups were also comparatively and qualitatively characterized, with 12,233 or 16,096 molecular features ultimately acquired and analyzed in the negative ion mode (Table S5) or positive ion mode (Table S9), respectively. We identified 2,102 and 2,396 metabolites in the negative ion mode (Table S6) and positive ion mode (Table S10), respectively.\u003c/p\u003e\n \u003cp\u003eUltimately, 6,764 metabolites were comparatively and qualitatively characterized in different comparison groups in the negative or positive ion mode (Table S11). The Venn diagram could intuitively show the similarity and overlap of metabolite composition in different comparison groups (Fig. 3A for the negative ion mode and Figure S2A for the positive ion mode). The statistical histogram indicated that there were 2,244 up-regulated and 1,133 down-regulated metabolites between the Chow group and HDF group, and 995 up-regulated and 1,107 down-regulated metabolites between the HDF and DAPA groups in the negative ion mode (Fig. 3B). Similarly, the statistical histogram indicated that there were 2,661 up-regulated and 1,252 down-regulated metabolites between the Chow and HDF groups, and 1,364 up-regulated and 1,032 down-regulated metabolites between the HDF and DAPA groups in the positive ion mode (Figure S2B). The volcano plot directly shows the distribution of metabolites in different comparison groups (Fig. 3C and 3D for the negative ion mode, Figure S2C and S2D for the positive ion mode). Agglomerate hierarchical clustering visually showed the relative quantitative values of metabolites in different comparison groups (Fig. 3E and 3F for the negative ion mode, Figure S2E and S2F for the positive ion mode).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e\u003cem\u003e## Multivariate analysis in different comparison groups\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eThe PCA score plots derived from 7-fold cross-validation indicated separation tendencies between the Chow and HDF groups in negative (R2X\u0026thinsp;=\u0026thinsp;0.56; Fig.\u0026nbsp;4A) or positive ion mode (R2X\u0026thinsp;=\u0026thinsp;0.52; Figure S4A). There was a separation between the Chow group and HDF group in the PLS-DA score plots (R2X\u0026thinsp;=\u0026thinsp;0.30, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.87; Fig.\u0026nbsp;4B) and the OPLS-DA score plots (R2X\u0026thinsp;=\u0026thinsp;0.30, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.85; Fig.\u0026nbsp;4C) at negative ion mode. There was a separation between the Chow group and HDF group in the PLS-DA score plots (R2X\u0026thinsp;=\u0026thinsp;0.25, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.82; Figure S4B) and the OPLS-DA score plots (R2X\u0026thinsp;=\u0026thinsp;0.25, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.78; Figure S4C) at positive ion mode. The S-plot of OPLS-DA between the Chow and HDF groups showed metabolites that were strongly related to major components of biological processes in the negative (Fig.\u0026nbsp;4D) or positive ion mode (Figure S4D). The scatter plot of mass-to-charge ratio with P-value of metabolites clearly showed the distribution of different metabolites between the Chow and HDF groups in the negative ion mode (Figure S3A) or positive ion mode (Figure S5A).\u003c/p\u003e\n \u003cp\u003eThe PCA score plots indicated separation tendencies between the HDF and DAPA groups in negative (R2X\u0026thinsp;=\u0026thinsp;0.53; Fig. 4E) or positive ion mode (R2X\u0026thinsp;=\u0026thinsp;0.57; Figure S4E). There was a separation between the HDF group and DAPA group in the PLS-DA score plots (R2X\u0026thinsp;=\u0026thinsp;0.24, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.71; Fig. 4F) and the OPLS-DA score plots (R2X\u0026thinsp;=\u0026thinsp;0.24, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.61; Fig. 4G) at negative ion mode. There was a separation between the HDF group and DAPA group in the PLS-DA score plots (R2X\u0026thinsp;=\u0026thinsp;0.20, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.68; Figure S4F) and the OPLS-DA score plots (R2X\u0026thinsp;=\u0026thinsp;0.20, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.60; Figure S4G) at positive ion mode. The S-plot of OPLS-DA between the HDF and DAPA groups showed metabolites that were strongly related to major components of biological processes in the negative (Fig. 4H) or positive ion mode (Figure S4H). The scatter plot of mass-to-charge ratio with P-value of metabolites clearly showed the distribution of different metabolites between the HDF and DAPA groups in the negative ion mode (Figure S3B) or positive ion mode (Figure S5B).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e##Detection of differential metabolites\u003c/h2\u003e\n \u003cp\u003eUltimately, 388 differential metabolites were acquired and analyzed in different comparison groups in negative or positive ion mode according to P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and VIP values\u0026thinsp;\u0026gt;\u0026thinsp;1 \u003csup\u003e29\u003c/sup\u003e (Table S12). There were 72 upregulated and 34 downregulated differential metabolites between the Chow and HDF groups (Fig. 4A and Table 1) and 40 upregulated and 25 downregulated differential metabolites between the HDF and DAPA groups (Fig. 5A and Table 2). The Venn diagram intuitively shows the similarity and overlap of differential metabolite compositions in the different comparison groups (Fig. 5B). The scatter plot of mass-to-charge ratio with P-value clearly showed the distribution of differential metabolites between the Chow and HDF groups (Fig. 5C) and between the HDF and DAPA groups (Fig. 5E). In addition, the volcano plot showed the distribution of differential metabolites between the Chow group and HDF groups (Fig. 5D), HDF group, and DAPA group (Fig. 5F). \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAgglomerate hierarchical clustering visually showed the relative quantitative values of the differential metabolites between the Chow group and HDF groups (Fig. 6A), HDF group, and DAPA group (Fig. 6C). The Z-score was a value based on the relative content of metabolites, which was used to measure the relative content of metabolites at the same level. The Z-score map showed the relative content of differential metabolites between the Chow and HDF groups (Fig. 6B), the HDF group, and the DAPA group (Fig. 6D). The correlation of differential metabolites between the Chow and HDF groups is presented in Figure S6A, which shows the degree of correlation between discrepant metabolites. The correlation of differential metabolites between the HDF and DAPA groups is shown in Figure S6B.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e##Metabolic pathways\u003c/h2\u003e\n \u003cp\u003eKEGG analysis revealed 171 metabolic pathways enriched by 419 differential metabolites between the Chow group and HDF group (Table S13). The 20 most prominent metabolic pathways between the Chow group and HDF group are shown in the histogram of influencing factors (Fig. 7A). The network of the ten most prominent metabolic pathways associated with differential metabolites between the Chow group and HDF groups is shown in the network diagram (Fig. 7B).\u003c/p\u003e\n \u003cp\u003eKEGG analysis revealed 120 metabolic pathways enriched by 250 differential metabolites between the HDF and DAPA groups (Table S14). The 20 most prominent metabolic pathways in the HDF and DAPA groups are shown in the histogram of influencing factors (Fig.\u0026nbsp;7C). The network of the ten most prominent metabolic pathways associated with differential metabolites between the HDF and DAPA groups is shown in the network diagram (Fig.\u0026nbsp;7D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e\u003cem\u003e## Lipid metabolites\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eAnnotated obtained lipids according to lipid chains and fat after data preprocessing were divided into BisMePA, Cer, ChE, CL, Co, TG, DG, dMePE, GM2, GM3, Hex1Cer, Hex2Cer, LdMePE, PC, PE, MePC, MGDG, PG, PI, PS, SM, SPH, StE and other categories (Fig.\u0026nbsp;8A). The PCA score plots suggest that the QC samples were gathered, indicating good repeatability and reliable results (Fig.\u0026nbsp;8B). Ultimately, 3,702 lipid metabolites were comparatively and qualitatively characterized between the Chow group and HDF group (Table S15). There was a separation between the Chow group and HDF groups in the OPLS-DA score plots at the negative (R2X\u0026thinsp;=\u0026thinsp;0.55, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.98; Fig.\u0026nbsp;8C) or positive ion mode (R2X\u0026thinsp;=\u0026thinsp;0.53, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.97; Fig.\u0026nbsp;8E). 3,698 lipid metabolites were comparatively and qualitatively characterized between the Chow group and HDF groups (Table S16). There was a separation between the HDF group and DAPA group in the OPLS-DA score plots at negative (R2X\u0026thinsp;=\u0026thinsp;0.37, R2Y\u0026thinsp;=\u0026thinsp;0.96, Q2\u0026thinsp;=\u0026thinsp;0.82; Fig.\u0026nbsp;8D) or positive ion mode (R2X\u0026thinsp;=\u0026thinsp;0.39, R2Y\u0026thinsp;=\u0026thinsp;0.99, Q2\u0026thinsp;=\u0026thinsp;0.67; Fig.\u0026nbsp;8F).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003e## Discrepancies in lipid metabolites\u003c/h2\u003e\n \u003cp\u003eThe Venn diagram intuitively shows the similarity and overlap of differential lipid metabolite composition in different comparison groups (Fig. 9A). There were 141 upregulated and 167 downregulated differential lipid metabolites between the Chow group and HDF group (Fig. 9B and Table 3). There were 67 upregulated and 59 downregulated differential lipid metabolites between the HDF and DAPA groups, respectively (Fig. 9B and Table 4). The volcano plot shows the distribution of differential lipid metabolites between the two groups (Fig. 9C), HDF group, and DAPA group (Fig. 9D). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLipid correlations often reveal synergy between lipid metabolites. The correlation of differential lipid metabolites between the Chow and HDF groups is presented in Fig. 10A, which shows the degree of correlation between discrepant lipid metabolites. The correlation of differential metabolites between the HDF and DAPA groups is shown in Fig. 10B. Agglomerate hierarchical clustering visually showed the relative quantitative values of differential lipid metabolites between the Chow group and HDF groups (Fig. 10C) and the HDF and DAPA groups (Fig. 10D). The correlation of differential lipid metabolites between the Chow group and HDF groups is presented in Figure S7A, which shows the degree of correlation between discrepant lipid metabolites. The correlation between differential lipid metabolites in the HDF and DAPA groups is presented in Figure S7B.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003e## Lipid metabolic pathways\u003c/h2\u003e\n \u003cp\u003eKEGG analyzed fourteen metabolic pathways enriched by differential lipid metabolites between the Chow and HDF groups (Table S17). The 14 most prominent lipid metabolic pathways between the Chow and HDF groups are shown in the bubble map of influencing factors (Figure S8A). The eight most prominent lipid metabolic pathways between the HDF and DAPA groups are shown in the bubble map of influencing factors (Figure S8B).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe molecular mechanisms underlying SGLT2i's clinical benefits in patients with HFrEF or HFpEF, both diabetic and non-diabetic, are still not fully understood. It is possible that empagliflozin might have contributed to a reduction in left ventricular volume among patients diagnosed with type II diabetes or prediabetes and HFrEF, which could explain the observed decrease in heart failure hospitalizations and mortality associated with SGLT2i \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Our study demonstrated that DAPA exhibited protective effects against myocardial fibrosis and hypertrophy and improved systolic function in mice with chronic heart failure induced by a high-fat diet.\u003c/p\u003e \u003cp\u003eFurthermore, DAPA has proven to be an effective medication for the prevention and treatment of diabetic cardiomyopathy (DCM), in addition to its positive impact on blood lipids and body weight. Research has demonstrated that DAPA has the potential to protect against cardiac fibrosis in type II diabetic rats by suppressing fibroblast activation and endothelial-to-mesenchymal transition (EndMT) through AMPKα-mediated inhibition of TGF-β/Smad signaling \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Additionally, DAPA has been found to suppress myocardial inflammation and fibrosis, improve systolic function, and reduce oxygen radicals and calcium transport channel activity in angiotensin II-stressed diabetic mice \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Furthermore, in streptozotocin-induced diabetic rats, DAPA has been shown to inhibit myocardial apoptosis by upregulating the AKT/JAK/MAPK pathways induced by erythropoietin \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In a study involving pigs with HFpEF, the administration of DAPA for nine weeks resulted in a decrease in hypertension and a reversal of left ventricle concentric remodeling. This effect was attributed, at least in part, to the inhibition of sympathetic tone in the aorta, which subsequently led to suppression of the inflammatory response and activation of the NO-cGMP-PKG pathway \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Furthermore, acute administration of DAPA following cardiac ischemia/reperfusion injury in rats demonstrated cardioprotective effects by reducing cardiac infarct size, improving left ventricular function, and reducing arrhythmias \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, the literature presents conflicting reports regarding the impact of SGLT2i. DAPA significantly reduced total cholesterol, low-density lipoprotein-cholesterol (LDL-C), and triglyceride levels in diabetic patients with overweight and hyperlipidemia \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In conjunction with a low dosage of insulin, the administration of DAPA resulted in a notable decrease in hyperglycemia, hypercholesterolemia, hypertriglyceridemia, and antioxidant status in diabetic rats \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Conversely, individuals diagnosed with type II diabetes who underwent DAPA treatment exhibited an elevation in LDL-C and overall cholesterol levels, albeit with an insignificant reduction in triglyceride levels \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Cha \u003cem\u003eet al.\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e discovered that patients with type 2 diabetes who were prescribed SGLT2i experienced a substantial increase in HDL-C and LDL-C levels in comparison to those receiving DPP-4 inhibitors over a span of six months.\u003c/p\u003e \u003cp\u003eResearchers remain interested in comprehending the impact of treatment on substrate flux and molecular pathways. Through the utilization of non-targeted metabolomics assays, we successfully characterized the myocardial profiles of various differential metabolites, including lipids, in mice with chronic heart failure induced by HDF. We also evaluated the influence of DAPA on these profiles. Our findings indicate that DAPA administration leads to an increase in short/medium-chain acylcarnitines and ketone-related metabolites when compared to the placebo group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This observation further supports the growing recognition of altered ketone and fatty acid biology in HFrEF treated with SGLT2i \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The KEGG database revealed several prominent metabolic pathways enriched by dysregulated metabolites (purine metabolism, lysosome, linoleic acid metabolism, alanine metabolism; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC/D and Table \u003cspan refid=\"MOESM14\" class=\"InternalRef\"\u003eS14\u003c/span\u003e) or lipids (choline metabolism, linoleic acid metabolism, glycerophospholipid metabolism; Figure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eB and Table \u003cspan refid=\"MOESM17\" class=\"InternalRef\"\u003eS17\u003c/span\u003e) in mice with chronic heart failure that were altered by SGLT2i.\u003c/p\u003e \u003cp\u003eIn individuals with diabetes and heart failure characterized by dysregulated cardiac FAAs and impaired glucose uptake, circulating ketones or branched-chain amino acids (BCAAs) could serve as an alternative source of energy. In failing human hearts, the increased utilization of ketones by the myocardium has been identified as a significant metabolic adaptation \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Furthermore, the induction of glycosuria by empagliflozin has been shown to enhance β-cell function and improves insulin sensitivity in patients with type II diabetes, resulting in reduced blood glucose levels and a shift in substrate utilization from carbohydrates to lipids \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Empagliflozin has demonstrated positive effects on cardiac function and remodeling in non-diabetic rats with left ventricular dysfunction following myocardial infarction. These effects were observed alongside the normalization of glucose and fatty acid uptake and oxidation, as well as improved utilization of ketone bodies and ATP production in the heart \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Despite the decrease in BCAAs catabolism in heart failure, empagliflozin has the potential to restore these deficiencies. Furthermore, empagliflozin, in conjunction with the enhanced production of ketone bodies from BCAAs, offers an optimal source of energy for the heart \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. An additional advantage could potentially be ascribed to the impact of ketones and BCAAs on the signaling mechanisms of the heart \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Instead of relying on glucose as an energy source, empagliflozin utilizes ketone bodies, free fatty acids, and BCAAs to enhance the energetic state of the myocardium and mitigate unfavorable left ventricular remodeling in a non-diabetic porcine model \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study had some limitations. There has been no comprehensive examination of the effects of DAPA on substrate flux and molecular pathways throughout the entire organism. The identified differential metabolites require further investigation, and comprehensive analysis of myocardial samples from both human and animal subjects is warranted. Furthermore, there remains a dearth of comprehensive analyses regarding alterations in circulatory metabolomics in patients with hyperlipidemia and HFrEF or HFpEF, as well as the impact of SGLT2i on these profiles. The implementation of a serum metabolomic test can facilitate the identification of differentially metabolized molecules and enable the categorization of such patients. In addition, the heterogeneity of cardiac samples and the molecular mechanisms responsible for SGLT2i should be considered and explored.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn our study, we observed that DAPA exhibited protective effects against myocardial fibrosis and hypertrophy and enhanced systolic function in mice with chronic heart failure induced by HDF. Furthermore, we conducted a comprehensive analysis of myocardial profiles, focusing on various differential metabolites, including lipid molecules, as well as prominent metabolic pathways, in these mice. In addition, we assessed the impact of DAPA treatment on these profiles. These findings contribute to the expanding body of literature suggesting that changes in metabolites might serve as potential mechanisms underlying the clinical benefits of SGLT2i in patients with HFrEF or HFpEF. Nevertheless, further clinical data are required to ascertain the safety and efficacy of DAPA administration in this patient population, and its potential mechanism requires further exploration.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBCAAs, branched-chain amino acids;\u0026nbsp;DAPA, dapagliflozin; DCM, diabetic cardiomyopathy;\u0026nbsp;DDA, data dependent acquisition; EndMT, endothelial-to-mesenchymal transition; FAAs, free fatty acids;\u0026nbsp;FC, fold change; FDR, false discovery rate; HDF, high fat diet feed; HFrEF, heart failure and a reduced ejection fraction; HFpEF, heart failure and a preserved ejection fraction; HPLC, high performance liquid chromatography; KEGG, Kyoto Encyclopedia of Genes and Genomes; LC-MS, liquid chromatography-Mass spectrum; LDL-C, low density lipoprotein-cholesterol; LV, left ventricle; \u0026nbsp;LVEF, left ventricle ejection fraction; LVFS, left ventricle fractional shortening; NIH, national Institutes of Health; OPLS-DA, orthogonal partial least square discriminant analysis;\u0026nbsp;PCA, principal component analysis; PLS-DA, partial least square discriminant analysis; QC, quality control; RSD, relative standard deviation;\u0026nbsp;SD, standard deviation; SGLT2i, sodium-glucose cotransporter-2 inhibitors;VIP, variable influence on projectionWGA, wheat germ agglutinin.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the talent start-up capital program\u0026nbsp;of\u0026nbsp;Fujian Medical University Union Hospital (2023XH027), the Science and Technology Innovation Joint Fund Project of Fujian Provincial Science and Technology Department (2019Y9082, 2023Y9183), the National Natural Science Foundation of China (No. 8230020004), Startup Fund for Scientific Research, Fujian Medical University (2023QH1035).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(I) Conception and design: Jinhua Huang;\u0026nbsp;(II) Administrative support: Jinhua Huang; (III) Provision of study materials: Qiong Jiang; (IV) Collection and assembly of data: Feng Hu, Wenkun Liu; (V) Data analysis and interpretation: Feng Hu, Jinhua Huang; (VI) Manuscript writing: Feng Hu; (VII) Final approval of the manuscript: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal experiments were conducted in accordance with the National Institutes of Health (NIH) policies outlined in the Guide for the Care and Use of Laboratory Animals and approved by the Animal Care and Use Committee of Fujian Medical University Union Hospital. We have adhered to ARRIVE guidelines and upload a completed checklist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the present study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNassif ME, Windsor SL, Tang F, et al. 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Evolving Concepts of Myocardial Energy Metabolism: More Than Just Fats and Carbohydrates. \u003cem\u003eCirc Res. \u003c/em\u003e2016;119(11):1173-1176.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metabolomics, high fat diet, heart failure, dapagliflozin, high-fat diet","lastPublishedDoi":"10.21203/rs.3.rs-6881055/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6881055/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe molecular mechanisms responsible for the clinical benefits of sodium-glucose cotransporter-2 inhibitors (SGLT2i) in patients with heart failure and a reduced or preserved ejection fraction, both diabetic and non-diabetic, are still not fully understood. This study aimed to examine the myocardial metabolomic profiles of mice with chronic heart failure induced by high-fat diet (HDF) and assess the impact of dapagliflozin (DAPA) on these profiles.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn experimental model of chronic heart failure in mice was established by long-term HDF for six months, and verified using immunohistochemistry and echocardiography. Myocardial specimens were obtained from three groups: chow, HDF, and DAPA. Subsequently, all samples were subjected to non-targeted metabolomic analyses using untargeted liquid chromatography-mass spectrometry. Principal component analysis, partial least squares discriminant analysis, and orthogonal partial least squares discriminant analysis were used to identify differential metabolites or lipid molecules. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to determine the metabolic pathways associated with these identified metabolites.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEchocardiography revealed that mice with chronic heart failure established through HDF exhibited systolic dysfunction compared to the control chow group. However, DAPA treatment partially restored these dysfunctions and protected against myocardial fibrosis and hypertrophy. Furthermore, a total of 72 upregulated and 34 downregulated differential metabolites were observed between the Chow and HDF groups, along with 40 upregulated and 25 downregulated differential metabolites between the HDF and DAPA groups. A total of 141 upregulated and 167 downregulated differential lipid metabolites were observed between the Chow group and HDF groups, along with 67 upregulated and 59 downregulated differential lipid metabolites between the HDF and DAPA groups, respectively. Dysregulated metabolites or lipids altered by DAPA treatment were found to significantly enrich several metabolic pathways, as identified by the KEGG database.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDAPA exhibited protective effects against myocardial fibrosis and hypertrophy, and enhanced systolic function in mice with chronic heart failure induced by HDF. Furthermore, we conducted a comprehensive analysis of myocardial profiles, focusing on various differential metabolites, including lipid molecules, as well as prominent metabolic pathways, in these mice. In addition, we assessed the impact of DAPA treatment on these profiles.\u003c/p\u003e","manuscriptTitle":"The impact of dapagliflozin for the myocardial metabolomic profiles of mice with chronic heart failure induced by a high fat diet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 10:19:40","doi":"10.21203/rs.3.rs-6881055/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b253dd90-31e1-4658-abdb-644b7ebb7c8a","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-05T10:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 10:19:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6881055","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6881055","identity":"rs-6881055","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}