Medicine, Faculty of
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Anesthesiology, Pharmacology and Therapeutics, Department of
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Antioxidants 13 (10): 1167 (2024)
Wang, Tutu
Ning, Miaomiao
Mo, Yurou
Tian, Xinyu
Fu, Yu
Laher, Ismail
Li, Shunchang
The increasing prevalence of type 2 diabetes mellitus (T2DM) leads to significant global health challenges, including cardiac structural and functional deficits, which in severe cases can progress to heart failure that can further strain healthcare resources. Aerobic exercise can ameliorate cardiac dysfunction in individuals with diabetes, although a comprehensive understanding of its underlying mechanisms remains elusive. This study utilizes untargeted metabolomics to reveal aerobic-exercise-activated metabolic biomarkers in the cardiac tissues of Sprague Dawley rats with T2DM. Metabolomics analysis revealed that diabetes altered 1029 myocardial metabolites, while aerobic exercise reversed 208 of these metabolites, of which 112 were upregulated and 96 downregulated. Pathway topology analysis suggested that these metabolites predominantly contributed to purine metabolism and arginine biosynthesis. Furthermore, receiver operating characteristic curve analysis identified 10 potential biomarkers, including xanthine, hypoxanthine, inosine, dGMP, l-glutamic acid, l-arginine, l-tryptophan, (R)-3-hydroxybutyric acid, riboflavin, and glucolepidiin. Finally, data from Pearson correlation analysis indicated that some metabolic biomarkers strongly correlated with cardiac function. Our data suggest that certain metabolic biomarkers play an important role in ameliorating diabetes-related cardiac dysfunction by aerobic exercise.
Citation: Wang, T.; Ning, M.; Mo, Y.;Tian, X.; Fu, Y.; Laher, I.; Li, S.Metabolomic Profiling Reveals ThatExercise Lowers Biomarkers ofCardiac Dysfunction in Rats withType 2 Diabetes. Antioxidants 2024, 13,1167. https://doi.org/10.3390/antiox13101167Academic Editor: AlessandraNapolitanoReceived: 2 August 2024Revised: 5 September 2024Accepted: 16 September 2024Published: 26 September 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).antioxidantsArticleMetabolomic Profiling Reveals That Exercise Lowers Biomarkersof Cardiac Dysfunction in Rats with Type 2 DiabetesTutu Wang 1, Miaomiao Ning 1, Yurou Mo 1, Xinyu Tian 1, Yu Fu 1, Ismail Laher 2 and Shunchang Li 1,*1 Institute of Sports Medicine and Health, Chengdu Sport University, Chengdu 610041, China;wtt0306@126.com (T.W.); 15662088661@163.com (M.N.); 18508189746@163.com (Y.M.);t1138790522@163.com (X.T.); flytogether0909@163.com (Y.F.)2 Department of Anesthesiology, Pharmacology and Therapeutics, Faculty of Medicine, University of BritishColumbia, Vancouver, BC V6T 1Z3, Canada; ismail.laher@ubc.ca* Correspondence: lishunchang@cdsu.edu.cnAbstract: The increasing prevalence of type 2 diabetes mellitus (T2DM) leads to significant globalhealth challenges, including cardiac structural and functional deficits, which in severe cases canprogress to heart failure that can further strain healthcare resources. Aerobic exercise can amelioratecardiac dysfunction in individuals with diabetes, although a comprehensive understanding of itsunderlying mechanisms remains elusive. This study utilizes untargeted metabolomics to revealaerobic-exercise-activated metabolic biomarkers in the cardiac tissues of Sprague Dawley rats withT2DM. Metabolomics analysis revealed that diabetes altered 1029 myocardial metabolites, while aero-bic exercise reversed 208 of these metabolites, of which 112 were upregulated and 96 downregulated.Pathway topology analysis suggested that these metabolites predominantly contributed to purinemetabolism and arginine biosynthesis. Furthermore, receiver operating characteristic curve analysisidentified 10 potential biomarkers, including xanthine, hypoxanthine, inosine, dGMP, l-glutamicacid, l-arginine, l-tryptophan, (R)-3-hydroxybutyric acid, riboflavin, and glucolepidiin. Finally, datafrom Pearson correlation analysis indicated that some metabolic biomarkers strongly correlatedwith cardiac function. Our data suggest that certain metabolic biomarkers play an important role inameliorating diabetes-related cardiac dysfunction by aerobic exercise.Keywords: type 2 diabetes; cardiac function; aerobic exercise; metabolomics1. IntroductionThe global incidence of type 2 diabetes mellitus (T2DM) is increasing at an alarmingrate, and is estimated to affect over 700 million patients worldwide by 2045 [1]. Diabetes isa metabolic disorder characterized by elevated blood glucose levels due to either insulindeficiency or insulin resistance [2]. Persistent hyperglycemia leads to insulin resistance,mitochondrial dysfunction, oxidative stress, myocardial fibrosis, β-adrenergic receptordownregulation, and other detrimental consequences that can contribute to cardiac dys-function, which can lead to heart failure [3–5]. The pathophysiology of diabetes-relatedcardiac dysfunction is not well understood, and current treatment options are primarilycentered on traditional strategies including extended caloric restriction, blood glucose andlipid management, and insulin resistance control [6].Exercise is a favored approach for managing the health of individuals with T2DM,given its potential to ameliorate insulin resistance and reduce blood glucose levels [7].Moreover, exercise has long been known to provide cardiovascular benefits while alsobeing an important approach to manage heart disease [8]. Exercise enhances cardiores-piratory fitness, boosts ejection fraction and cardiac output, reduces the mortality rate ofT2DM and the incidence of diabetic cardiac diseases [9]. Prolonged aerobic exercise in arodent model of T2DM ameliorated cardiac structural and functional changes by augment-ing myocardial glucose uptake, mitigating mitochondrial dysfunction, and diminishingAntioxidants 2024, 13, 1167. https://doi.org/10.3390/antiox13101167 https://www.mdpi.com/journal/antioxidantsAntioxidants 2024, 13, 1167 2 of 19myocardial fibrosis [10]. In addition, exercise training can increase the expression of β1-adrenergic receptors in the myocardium, thereby enhancing the myocardial responsivenessto catecholamine [11].Metabolomics is a sensitive method of identifying endogenous small molecules incells, bodily fluids, and tissues. For example, metabolites identified through metabolomicscould be used as disease biomarkers useful for predicting and treating T2DM [12]. Usingmetabolomics techniques, researchers observed disrupted energy metabolism in T2DMpatients. However, after engaging in various forms of exercise, such as running, tai chi, andbrisk walking, improvements were noted in metabolic processes, including branched-chainamino and arachidonic acid metabolism [13,14]. Notably, metabolomic screening employ-ing ultra-high-performance liquid chromatography coupled with quadruple time-of-flightmass spectrometry (UPLC-Q-TOF-MS) allows for improved assessment of metabolic pro-cesses, for example, by providing additional insights into the physiological and pathologicalpathways of T2DM [15,16].We used untargeted metabolomics to assess the metabolic features of cardiac tissuefrom rats with T2DM subjected to aerobic exercise. Our study tested the hypothesis thataerobic exercise improves cardiac structure and function in a rat model of T2DM by alteringmetabolites and cardiac biomarkers. The findings of our study could be used to identifynovel therapeutic targets to improve diabetic cardiac function based on the metabolic effectsof aerobic exercise.2. Materials and Methods2.1. Experimental AnimalsEight-week-old male Sprague Dawley (SD) rats were purchased from ChengduDashuo Experimental Animal Co., Ltd. (Chengdu, China). The rats had body weightsranging from 200 to 210 g and were maintained under the following conditions: an am-bient temperature of 21–23 ◦C, relative humidity of 40–60%, and a 12 h light/dark cycle.All rats had unrestricted access to both food and water. The experimental protocol re-ceived approval from the Academic Committee of Chengdu Sport University (ApprovalNo: 2022-3).A total of 40 rats were divided into two groups: the control group (C, n = 10) and theprediabetic model group (PD, n = 30). Rats with prediabetes were randomly divided intodiabetic (D) and diabetic exercise (DE) subgroups.2.2. STZ-Induced Rat Model of T2DMThirty rats were fed a high-sugar and high-fat diet for seven weeks, followed byintraperitoneal injection of streptozotocin (STZ, 30 mg/kg) that was dissolved in sodiumcitrate buffer (0.1 mol/L, pH = 4.4) [17,18]. Rats in group C were administered an in-traperitoneal injection of sodium citrate buffer (0.25 mL/kg). Rats were considered to bediabetic when blood glucose levels reached ≥16.7 mmol/L 72 h after being injected withSTZ injection [19]. Approximately 70% of the rats were successfully induced with T2DM.Normal rat feed consisted of 5% fat, 50% carbohydrates, and 23% protein. High-fatfeed included 67% regular feed, 10% lard, 20% sucrose, 2% cholesterol, and 1% sodiumcholate [20]. Both regular feed and high-fat feed were provided by Chengdu DashuoBiotechnology Co., Ltd. (Chengdu, China).2.3. Exercise ProtocolAerobic exercise was performed on a rat treadmill using the Bedford exercise model [21],with formal exercise starting after a 3-day adaptation period. Rats in the DE group ran onthe treadmill, beginning at a speed of 8 m/min that was gradually escalated to 15 m/min,and maintained at this speed for 1 h daily, 5 days a week, for 8 weeks.Antioxidants 2024, 13, 1167 3 of 192.4. Assessment of Blood Glucose, Insulin and Insulin Resistance IndexPlasma glucose levels were determined using a blood glucose meter (ACON Biotech,Hangzhou, China). Insulin levels were measured using a rat insulin ELISA kit (ImmunoWayBiotechnology Company, San Jose, CA, USA) and an enzyme immunoassay reader set at450 nm (SpectraMax M5 from Thermo Fish Scientific, Waltham, MA, USA). The insulinresistance index was computed using the following formula: HOMA-IR = Fasting BloodGlucose (FBG) (mmol/L) × Fasting Insulin (FINS) (mU/L)/22.5 [22].2.5. Histological StainingMyocardial tissue was obtained from 4% paraformaldehyde (Biosharp, Hefei, China)fixed tissues that were embedded in paraffin and stained with hematoxylin–eosin (HE).Following sectioning, the tissue slices underwent deparaffinization, hematoxylin staining,differentiation, eosin counterstaining, dehydration, xylene-based air-drying, and mount-ing with neutral resin. Rat myocardial tissue histology was examined using an opticalmicroscope (Olympus Corporation, Tokyo, Japan).2.6. EchocardiographyAfter anesthetizing the rats, cardiac structure and function were assessed using smallanimal echocardiography (Philips, Shanghai, China). The following cardiac parameterswere assessed: left ventricular ejection fraction (EF), early-to-late diastolic filling velocityratio (E/A), left ventricular end-diastolic internal diameter (LVIDd), left ventricular end-systolic internal diameter (LVIDs), left ventricular posterior wall end-diastolic thickness(LVPWd), and left ventricular posterior wall end-systolic thickness (LVPWs).2.7. UPLC-Q-TOF/MS AnalysisMyocardial tissues from the rats under study end point were immediately frozen andstored at −80 ◦C. Upon thawing, 50 mg of myocardial tissue was weighed and 1000 µL ofextraction solution containing an internal standard (methanol:acetonitrile:water = 2:2:1,internal standard concentration 20 mg/L) was added, followed by vortexing for 30 s. Steelbeads were then added, and the sample was subjected to ultrasonic grinding at 45 Hzfor 10 min in an ice-water bath. The tissue was centrifuged at 12,000 rpm for 15 min at4 ◦C. Next, 500 µL of the supernatant was transferred to an EP tube and dried in a vacuumconcentrator. The dried metabolites were reconstituted in 160 µL of extraction solution(acetonitrile:water = 1:1), vortexed for 30 s, and sonicated in an ice-water bath for 10 min.The sample was centrifuged again at 12,000 rpm for 15 min at 4 ◦C. Finally, 120 µL of thesupernatant was transferred to a 2 mL vial, and 10 µL from each sample was preparedfor analysis.Myocardial metabolomics were analyzed via a liquid chromatography–mass spectrom-etry (LC-MS) system that featured a Waters UPLC Acquity I-Class PLUS high-performanceliquid chromatography instrument (Waters Corporation, Milford, MA, USA) linked to aWaters UPLC Xevo G2-XS QTOF high-resolution mass spectrometer (Waters Corporation,Milford, MA, USA). Additionally, it was furnished with a Waters Acquity UPLC HSST3 chromatographic column (1.8 µm, 2.1 mm × 100 mm) (Waters Corporation, Milford,MA, USA). The mobile phase solvents for the liquid chromatography consisted of thefollowing: 0.1% formic acid aqueous solution (A) and 0.1% formic acid acetonitrile solution(B). The mobile phase conditions were as follows: (a) 0–0.25 min, 98% (A) and 2% (B);(b) 0.25–10 min, a gradient from 98% (A) and 2% (B) to 2% (A) and 98% (B); (c) 10–13 min,2% (A) and 98% (B); (d) 13–13.1 min, a gradient from 2% (A) and 98% (B) to 98% (A) and2% (B); (e) 13.1–15 min, 98% (A) and 2% (B). The sample injection volume was 1 µL, andthe flow rate was maintained at 400 µL/min.High-resolution mass spectrometry data acquisition was controlled using MassLynxV4.2 software (Waters Corporation, Milford, MA, USA). The mass-to-charge ratio (m/z)range for data collection was configured from 50 to 1200. The electrospray ionization (ESI)source was characterized by the following parameters: capillary voltage set at 2500 V orAntioxidants 2024, 13, 1167 4 of 19−2000 V, cone voltage at 30 V, ion source temperature at 100 ◦C, desolvation gas temperatureat 500 ◦C, nebulizer gas flow rate of 50 L/h, and desolvation gas flow rate of 800 L/h.2.8. Data Pre-ProcessingRaw data obtained from MassLynx V4.2 were imported into the Progenesis QI V3.0software for tasks such as peak detection and alignment. Identification was conductedusing the Progenesis QI software against the METLIN database, HMDB database, andKEGG database, with theoretical fragment identification performed simultaneously. Amass error tolerance of 100 ppm was applied for precursor ions, and 50 ppm for fragmentions. Subsequent analysis ensued following the normalization of raw peak area data againstthe total peak area.2.9. Statistical AnalysisData were processed using MetaboAnalyst 5.0 software (https://www.metaboanalyst.ca, accessed on 15 December 2023). This involved eliminating missing values and log-arithmic transformation of the metabolite data. Subsequently, differential metabolitesunderwent topological analysis and enrichment analysis via the ‘Pathway Analyst’ and‘Enrichment Analyst’ modules. Receiver operating characteristic (ROC) curves were gener-ated using OmicStudio tools (https://www.omicstudio.cn/tool, accessed on 18 December2023). Prior to performing Pearson correlation analysis and one-way analysis of vari-ance (ANOVA), we assessed the normality and homogeneity of variances for the data.Pearson correlation analysis was then conducted to evaluate the relationships betweenmetabolite concentrations and echocardiographic indices, while group differences wereanalyzed using ANOVA, utilizing SPSS 26.0 or GraphPad prism 8. Data are expressed asmeans ± standard deviation (SD), with statistical significance defined as p < 0.05.3. Results3.1. Aerobic Exercise and Myocardial Weight, Insulin Resistance and Myocardial Structure inDiabetic RatsThe body weights of rats in group D were 21.5% lower than those in group C(p < 0.01). However, after 8 weeks of aerobic exercise, rats in group DE had body weightsthat were 17.9% higher than those in group D, and had body weights similar to those ingroup C (p > 0.05). Additionally, myocardial wet weight/body weight ratios increased by16.6% in diabetic rats, indicating myocardial hypertrophy (p < 0.01) (Figure 1A). Fastingblood glucose levels and the HOMA-IR index were higher in rats from group D than ratsin group C with increases of 327.4% and 99.9%, respectively (both p < 0.01), while bloodglucose levels and the HOMA-IR index in the DE group were lower than in rats in group Dwith reductions of 40.6% and 15.6%, respectively (both p < 0.05), indicating that 8 weeksof aerobic exercise reduced insulin resistance and blood glucose levels in diabetic rats(Figure 1A).We next monitored morphological changes in myocardial structure in HE-stainedtissues using an optical microscope. Myocardial fibers in rats from group C were continu-ous and well organized, devoid of any signs of cell infiltration or vacuolar deformation,while myocardial cells from diabetic rats displayed bending deformations, infiltration ofinflammatory cells, partial cell vacuolar degeneration, and accompanying microvascularcongestion, as indicated by the arrows. After aerobic exercise, myocardial cell damage wasrestored in diabetic rats, with reduced cellular inflammation and myocardial microvas-cular congestion (Figure 1B), suggesting that 8 weeks of aerobic exercise improved themyocardial cell structure in diabetic rats.Antioxidants 2024, 13, 1167 5 of 19Antioxidants 2024, 13, x FOR PEER REVIEW 5 of 20 Figure 1. Changes in body weight, myocardial wet weight-to-body weight ratio, fasting blood glu-cose, fasting insulin, HOMA-IR index, and myocardial structure in T2DM rats after 8 weeks of aer-obic exercise intervention. (A) Rats from group D had increased myocardial wet weight/body weight ratios compared to rats from group C (p < 0.01), whereas exercise reduced this ratio in dia-betic rats (p < 0.01). Blood glucose and insulin resistance levels were higher in diabetic rats (p < 0.01), which were reduced by aerobic exercise (p < 0.05). (B) Representative histological HE staining im-ages of myocardial longitudinal sections from different rat groups (×100), with a scale bar of 100 µm. The area indicated by the arrow shows inflammatory infiltration and vacuolar degeneration in rat cardiomyocytes. Different leers indicate significant differences between groups (p < 0.05), groups with the same leer are not significantly different (p > 0.05). HOMA-IR = homeostatic model assess-ment for insulin resistance; HE = hematoxylin–eosin. Numerical values are presented as means ± standard deviation. C, control group; D, diabetic group; DE, diabetic exercise group. We next monitored morphological changes in myocardial structure in HE-stained tis-sues using an optical microscope. Myocardial fibers in rats from group C were continuous and well organized, devoid of any signs of cell infiltration or vacuolar deformation, while myocardial cells from diabetic rats displayed bending deformations, infiltration of inflam-matory cells, partial cell vacuolar degeneration, and accompanying microvascular conges-tion, as indicated by the arrows. After aerobic exercise, myocardial cell damage was re-stored in diabetic rats, with reduced cellular inflammation and myocardial microvascular congestion (Figure 1B), suggesting that 8 weeks of aerobic exercise improved the myocar-dial cell structure in diabetic rats. 3.2. Aerobic Exercise and Cardiac Function in Diabetic Rats We used M-mode echocardiography to evaluate multiple parameters of cardiac func-tion in diabetic rats, as shown by representative echocardiographic images from all rat groups (Figure 2A). There were alterations in all parameters of cardiac systolic dysfunc-tion, such as EF, LVIDd, LVIDs, LVPWd, and LVPWs, in rats from group D compared to cardiac tissue from group C (both p < 0.05). Exercise reversed these changes (both p < 0.05), except for LVIDd (p > 0.05) (Figure 2B). Diabetic rats had decreases in E/A ratios (a reflec-tion of cardiac diastolic function), indicating impaired diastolic function (p < 0.01). Figure 1. Changes in body weight, myocardial wet weight-to-body weight ratio, fasting bloodglucose, fasting insulin, HOMA-IR index, and myocardial structure in T2DM rats after 8 weeks ofaerobic exercise intervention. (A) Rats from group D had increased myocardial wet weight/bodyweight ratios compared to rats from group C (p < 0.01), whereas exercise reduced this ratio in diabeticrats (p < 0.01). Blood glucose and insulin resistance levels were higher in diabetic rats (p < 0.01),which were reduced by aerobic exercise (p < 0.05). (B) Representative histological HE stainingimages of myocardial longitudinal sections from different rat groups (×100), with a scale bar of100 µm. The area indicated by the arrow shows inflammatory infiltration and vacuolar degenerationin rat cardiomyocytes. Different letters indicate significant differences between groups (p < 0.05),groups with the same letter are not significantly different (p > 0.05). HOMA-IR = homeostatic modelassessment for insulin resistance; HE = hematoxylin–eosin. Numerical values are presented asmeans ± standard deviation. C, control group; D, diabetic group; DE, diabetic exercise group.3.2. Aerobic Exercise and Cardiac Function in Diabetic RatsWe used M-mode echocardiography to evaluate multiple parameters of cardiac func-tion in diabetic rats, as shown by representative echocardiographic images from all ratgroups (Fi ure 2A). There were lteratio in all parameters of cardiac systolic dysfunction,such as EF, LVIDd, LVIDs, LVPWd, and LVPWs, in rats from group D compared to cardiactissue from group C (both p < 0.05). Exercise reversed these changes (both p < 0.05), exceptfor LVIDd (p > 0.05) (Figure 2B). Diabetic rats had decreases in E/A ratios (a reflectionof cardiac diastolic function), indicating impaired diastolic function (p < 0.01). Exerciseimproved the E/A ratios (p < 0.05). These findings collectively imply that aerobic exercisereduced cardiac dysfunction in rats with T2DM (Figure 2B).Antioxidants 2024, 13, 1167 6 of 19Antioxidants 2024, 13, x FOR PEER REVIEW 6 of 20 Exercise improved the E/A ratios (p < 0.05). These findings collectively imply that aerobic exercise reduced cardiac dysfunction in rats with T2DM (Figure 2B). Figure 2. Effects of an 8-week aerobic exercise program on cardiac function in diabetic rats. (A) Representative M-mode echocardiographic images from rats in groups C, D, and DE. (B) Echocar-diographic results indicated that diabetes diminished parameters of cardiac function such as EF, E/A, LVIDd; LVIDs (p < 0.05). Exercise increased all these parameters (p < 0.05), except for LVIDd, which remained unchanged (p > 0.05). LVPWd and LVPW values rats from group D were increased compared to those in group C (p < 0.05); exercise reversed these changes (p < 0.05). Different leers indicate significant differences between groups (p < 0.05), groups with the same leer are not signif-icantly different (p > 0.05). EF = ejection fraction; E/A = early-to-late diastolic filling velocity ratio; LVIDd = left ventricular end-diastolic internal diameter; LVIDs = left ventricular end-systolic inter-nal diameter; LVPWd = left ventricular posterior wall end-diastolic thickness; LVPWs = left ventric-ular posterior wall end-systolic thickness. Numerical values are presented as means ± standard de-viation. C, control group; D, diabetic group; DE, diabetic exercise group. 3.3. Aerobic Exercise and Myocardial Metabolites in Diabetic Rats 3.3.1. Multivariate Statistical Analysis A non-targeted metabolomics approach was used to analyze the metabolic profiles of myocardial samples from rats in the study groups using both positive and negative ion Figure 2. Effects of an 8-week aerobic exercise program on cardiac function in diabetic rats. (A) Repre-sentative M-mode echocardiographic images fro rats in groups C, D, and DE. (B) Echocardiographicresults indicat d that diabet s diminish d para eters of c diac function such as EF, E/A, LVIDd;LVIDs (p < 0.05). Exercise increased all these parameters (p < 0.05), except for LVIDd, which remainedunchanged (p > 0.05). LVPWd and LVPW values rats from group D were increased compared to thosein group C (p < 0.05); exercise reversed these changes (p < 0.05). Different letters indicate significantdifferences between groups (p < 0.05), groups with the same letter are not significantly different(p > 0.05). EF = ejection fraction; E/A = early-to-late diastolic filling velocity ratio; LVIDd = leftventricular end-diastolic internal diameter; LVIDs = left ventricular end-systolic internal diameter;LVPWd = left ventricular posterior wall end-diastolic thickness; LVPWs = left ventricular posteriorwall end-systolic thickness. Numerical values are presented as means ± standard deviation. C,control group; D, diabetic group; DE, diabetic exercise group.3.3. Aerobic Exercise and Myocardial Metabolites in Diabetic Rats3.3.1. Multivariate Statistical AnalysisA non-targeted metabolomics approach was used to analyze the metabolic profilesof myocardial samples from rats in the study groups using both positive and negativeion modes. Multivariate statistical analysis was conducted using the principal componentanalysis (PCA) (Figure S1A) and partial least squares discriminant analysis (PLS-DA)Antioxidants 2024, 13, 1167 7 of 19(Figure 1A) supervised mode, disclosing distinct clustering in rats from groups C, D, and DE.Additionally, after 200 permutation tests, no over-fitting was detected in the PLS-DA model(Figure S1B). The variable importance in the projection (VIP) score plot revealed numeroussubstances in this model, greatly augmenting the metabolic distinctions among the groups.Considering the group separation, we ranked both components. Notably, substanceswith VIP values > 4 included urocortisol, phosphatidylinositols (22:3(10Z,13Z,16Z)/5-isoPGF2VI), and N-oleoyl glutamic acid (Figure 3B).Antioxidants 2024, 13, x FOR PEER REVIEW 7 of 20 modes. Multivariate statistical analysis was conducted using the principal component analysis (PCA) (Figure S1A) and partial least squares discriminant analysis (PLS-DA) (Fig-ure 1A) supervised mode, disclosing distinct clustering in rats from groups C, D, and DE. Additionally, after 200 permutation tests, no over-fiing was detected in the PLS-DA model (Figure S1B). The variable importance in the projection (VIP) score plot revealed numerous substances in this model, greatly augmenting the metabolic distinctions among the groups. Considering the group separation, we ranked both components. Notably, sub-stances with VIP values > 4 included urocortisol, phosphatidylinositols (22:3(10Z,13Z,16Z)/5-iso PGF2VI), and N-oleoyl glutamic acid (Figure 3B). Figure 3. Multivariate statistical analysis of myocardial metabolomics in rats from the C, D, and DE groups. (A,C,E) PLS-DA score plots. There were clear separation trends among all groups, indicat-ing significant differences in metabolites between different groups. (B,D,F) VIP score plots. VIP val-ues indicate that phospholipid metabolites have a significant contribution in the comparisons Figure 3. Multivariate statistical analysis of myocardial metabolomics in rats from the C, D, and DEgroups. (A,C,E) PLS-DA score plots. There were clear separation trends among all groups, indicatingsignificant differences in metabolites between different groups. (B,D,F) VIP score plots. VIP valuesindicate that phospholipid metabolit s have a significant contribution in the comparisons betweendifferent groups. PLS-DA = partial least squares discriminant analysis; VIP = the variable importancein the projection. C, control group; D, diabetic group; DE, diabetic exercise group.Antioxidants 2024, 13, 1167 8 of 19To gain a deeper understanding of alteration in myocardial metabolism in rats withdiabetes and those exposed to aerobic exercise, we carried out separate PLS-DA analysesfor the rats in groups C, D and D, DE. A distinct separation is visible when comparinggroups C and D (Figure 3C), with no indication of over-fitting as confirmed by the per-mutation test (Figure S1C). Several substances associated with phospholipid metabolism,such as CDP-DG(i-12:0/PGF2-α), CDP-DG(i-12:0/6 keto-PGF1-α), and PA(13:0/LTE4), con-tributed substantially (VIP > 5) to this separation (Figure 3D). Similarly, under the PLS-DAsupervised mode, the D and DE groups also showed a distinct separation (Figure 3E),again with no evidence of over-fitting according to the permutation test (Figure S1D).Metabolites with high contributions were also closely related to phospholipid metabolism,including CDP-DG(20:2(11Z,14Z)/PGF1-α), CDP-DG(6 keto-PGF1-α/18:3(6Z,9Z,12Z)),and CDP-DG(20:2(11Z,14Z)/TXB2) (VIP > 6) (Figure 3F).3.3.2. Volcano Diagram and Differential MetabolitesWe further assessed the impact of aerobic exercise on myocardial metabolic character-istics of diabetic rats using volcano plots. There were 12 metabolites that were upregulatedand 49 metabolites that were downregulated in rats from group C compared to rats ingroup D (Figure 4A). Rats in group DE had 22 metabolites that were changed comparedto rats in group D, with 10 metabolites upregulated and 12 metabolites downregulated(Figure 4B). We subsequently analyzed these intergroup differential metabolites, most ofwhich changed independently, except for glucolepidiin (Figure 4C).Antioxidants 2024, 13, x FOR PEER REVIEW 8 of 20 between different groups. PLS-DA = partial least squares discriminant analysis; VIP = the variable importance in the projection. C, control group; D, diabetic group; DE, diabetic exercise group. To gain a deeper understanding of alteration in myocardial metabolism in rats with diabetes and those exposed to aerobic exercise, we carried out separate PLS-DA analyses for the rats in groups C, D and D, DE. A distinct separation is visible when comparing groups C and D (Figure 3C), with no indication of over-fiing as confirmed by the permu-tation test (Figure S1C). Several substances associated with phospholipid metabolism, such as CDP-DG(i-12:0/PGF2-α), CDP-DG(i-12:0/6 keto-PGF1-α), and PA(13:0/LTE4), con-tributed ubstantially (VIP > 5) to this separation (Figure 3D). Similarly, under he PLS-DA supervised mode, the D and DE groups also showed a distinct separation (Figure 3E), again with no evidence of ver-fiing according t the p rmutation test (Figure S1D). Me-tabolites with high contributions were also closely related to phospholipid metabolism, including CDP-DG(20:2(11Z,14Z)/PGF1-α), CDP-DG(6 keto-PGF1-α/18:3(6Z,9Z,12Z)), and CDP-DG(20:2(11Z,14Z)/TXB2) (VIP > 6) (Figure 3F). 3.3.2. Volcano Diagram and Differential Metabolites We further assessed the impact of aerobic exercise on myocardial metabolic charac-teristics of diabetic rats using volcano plots. There were 12 metabolites that were upregu-lated and 49 metabolites that were downregulated in rats from group C compared to rats in group D (Figure 4A). Rats in group DE had 22 metabolites that were changed compared to rats in group D, with 10 metabolites upregulated and 12 metabolites downregulated ( i ure 4B). We subsequently analyzed these intergroup differential metabolites, m st of ich changed independently, exc pt for gluco epidiin (F gure 4C). Figure 4. Differential metabolic in rats from groups D, C and D, DE. (A,B) Volcano plots displayingdifferential metabolites between D, C and D, DE groups, with 61 shared differential metabolitesbetween the C and D groups and 22 shared differential metabolites between the D and DE groups.(C) Heatmap representing the average levels of metabolites, with only one differential metabolite,glucolepidiin, that changed in all three groups. C, control group; D, diabetic group; DE, diabeticexercise group.Antioxidants 2024, 13, 1167 9 of 193.3.3. Venn Diagram and Differential MetabolitesThe Venn diagram illustrates the intersection of metabolites across the three groups(C_vs._D, D_vs._DE), revealing a total of 208 differential metabolites, of which 112 wereupregulated and 96 were downregulated, indicating that 8 weeks of aerobic exercise altered208 endogenous metabolites in diabetic rats (Figure 5A). The detailed information ofthe metabolites is provided in the Excel file (Table S1). Hierarchical clustering analysisindicated the changing patterns of differential metabolites by visually displaying alterationsin metabolites related to exercise induced reductions in cardiac dysfunction. Hierarchicalclustering analysis was performed on the 208 differential metabolites, where items markedin blue represented significantly increased metabolites, and those marked in red representedmetabolites that were significantly decreased (Figure 5B). The heatmap of metabolitesrevealed distinct clustering, indicating the reliability of the selected metabolites (Figure 5B).We subsequently performed metabolite enrichment analysis and ranked them based onthe magnitudes of p-values. Differential metabolites were enriched in pathways related topurine metabolism and arginine biosynthesis (Figure 5C).Antioxidants 2024, 13, x FOR PEER REVIEW 10 of 20 Figure 5. Effects of aerobic exercise on myocardial metabolism in diabetic rats. (A) Venn diagram showing the number of differential metabolites in groups C and D vs. D and DE. A total of 208 differential metabolites were identified, including 112 upregulated and 96 downregulated. (B) Clus-ter heatmap analysis of differential metabolites between groups. Metabolites in each group exhib-ited distinct clustering that can be separated. Blue indicates increased metabolite levels, and red signifies decreased levels. (C) KEGG enrichment analysis of differential metabolites. On the left, a Sankey diagram illustrates the connection between metabolites and metabolic pathways, and on the right, metabolites are arranged based on p-values from the enrichment analysis results. The primary enrichment is observed in metabolic pathways like purine metabolism and arginine biosynthesis. (D) Topological analysis of metabolites. A total of 23 metabolic pathways were identified, with 5 pathways having p < 0.05, including purine metabolism, arginine biosynthesis, butanoate metabo-lism, aminoacyl-tRNA biosynthesis, and riboflavin metabolism. (E) Selection of 10 potential meta-bolic markers through topological analysis and volcano plot analysis, along with their concentration changes listed. C, control group; D, diabetic group; DE, diabetic exercise group. Finally, we imported the 208 differential metabolites into MetaboAnalyst 5.0 for top-ological analysis; this showed that these metabolites were involved in 23 metabolic path-ways. Using p < 0.05 as the threshold for filtering metabolic pathways, a total of five path-ways affecting cardiac dysfunction in diabetic rats were identified: purine metabolism, arginine biosynthesis, butanoate metabolism, aminoacyl-tRNA biosynthesis, and ribofla-vin metabolism (Figure 5D). These five pathways collectively comprised nine metabolites Figure 5. Effects of aerobic exercise on myocardial metabolism in diabetic rats. (A) Venn diagramshowing the number of differential metabolites in groups C and D vs. D and DE. A total of 208 differentialAntioxidants 2024, 13, 1167 10 of 19metabolites were identified, including 112 upregulated and 96 downregulated. (B) Cluster heatmapanalysis of differential metabolites between groups. Metabolites in each group exhibited distinctclustering that can be separated. Blue indicates increased metabolite levels, and red signifies decreasedlevels. (C) KEGG enrichment analysis of differential metabolites. On the left, a Sankey diagramillustrates the connection between metabolites and metabolic pathways, and on the right, metabolitesare arranged based on p-values from the enrichment analysis results. The primary enrichment isobserved in metabolic pathways like purine metabolism and arginine biosynthesis. (D) Topologicalanalysis of metabolites. A total of 23 metabolic pathways were identified, with 5 pathways having p< 0.05, including purine metabolism, arginine biosynthesis, butanoate metabolism, aminoacyl-tRNAbiosynthesis, and riboflavin metabolism. (E) Selection of 10 potential metabolic markers throughtopological analysis and volcano plot analysis, along with their concentration changes listed. C,control group; D, diabetic group; DE, diabetic exercise group.Finally, we imported the 208 differential metabolites into MetaboAnalyst 5.0 for topo-logical analysis; this showed that these metabolites were involved in 23 metabolic pathways.Using p < 0.05 as the threshold for filtering metabolic pathways, a total of five pathwaysaffecting cardiac dysfunction in diabetic rats were identified: purine metabolism, argi-nine biosynthesis, butanoate metabolism, aminoacyl-tRNA biosynthesis, and riboflavinmetabolism (Figure 5D). These five pathways collectively comprised nine metabolites thatcould have important roles in exercise-induced improvements of diabetic cardiac function(Figure 5E).3.3.4. Screening of Metabolic MarkersROC curves were used to evaluate the diagnostic value of metabolites for specificdiseases, where ROC area under the curve (AUC) values evaluated the diagnostic value ofmyocardial metabolites in diabetic rats following aerobic exercise. ROC analysis was per-formed on 10 selected metabolites with AUC values greater than 0.8 [23]. The specific AUCvalues for these metabolites were xanthine (AUC = 0.96), hypoxanthine (AUC = 0.92), ino-sine (AUC = 0.88), dGMP (AUC = 1), L-glutamic acid (AUC = 0.84), L-arginine (AUC = 0.96),L-tryptophan (AUC = 0.92), (R)-3-hydroxybutyric acid (AUC = 0.92), riboflavin (AUC = 0.96),and glucolepidiin (AUC = 0.96). These findings indicate that these metabolites could havereliable robust diagnostic functions (Figure 6A).3.3.5. Correlation Analysis between Metabolic Markers and Cardiac FunctionWe further investigated the relationship between differential metabolites and theimprovement of cardiac function in diabetic hearts by performing a Pearson analysis ofthe relationship between the ten metabolic markers we identified and changes in cardiacfunctions. These results are shown in Figure 6, where larger circles indicate strongercorrelations, red circles signify positive correlations and blue circles represent negativecorrelations. When examining correlations surpassing 0.8 [24], we identified a strongassociation between xanthine, inosine, glucolepidiin, and the cardiac E/A, indicating asubstantial correlation between these three metabolites and cardiac diastolic function. Ad-ditionally, dGMP, L-arginine, and L-tryptophan were related to LVIDs, while glucolepidiinwas related to LVPWd, implying a significant association between these four substancesand cardiac systolic function (Figure 6B). Finally, we used metabolites to draw a metabolicpathway map (Figure 7).Antioxidants 2024, 13, 1167 11 of 19Antioxidants 2024, 13, x FOR PEER REVIEW 11 of 20 that could have important roles in exercise-induced improvements of diabetic cardiac function (Figure 5E). 3.3.4. Screening of Metabolic Markers ROC curves were used to evaluate the diagnostic value of metabolites for specific diseases, where ROC area under the curve (AUC) values evaluated the diagnostic value of myocardial metabolites in diabetic rats following aerobic exercise. ROC analysis was performed on 10 selected metabolites with AUC values greater than 0.8 [23]. The specific AUC values for these metabolites were xanthine (AUC = 0.96), hypoxanthine (AUC = 0.92), inosine (AUC = 0.88), dGMP (AUC = 1), L-glutamic acid (AUC = 0.84), L-arginine (AUC = 0.96), L-tryptophan (AUC = 0.92), (R)-3-hydroxybutyric acid (AUC = 0.92), riboflavin (AUC = 0.96), and glucolepidiin (AUC = 0.96). These findings indicate that these metabo-lites could have reliable robust diagnostic functions (Figure 6A). Figure 6. Validation of metabolic markers and their correlation with cardiac function. (A) ROC val-idation of metabolic markers, indicating that all AUC values are greater than 0.8. (B) Pearson corre-lation analysis was employed to ascertain the relationship between myocardial metabolic markers and heart function. Numerous metabolic markers demonstrated robust correlations with specific heart function indicators. Xanthine, inosine, and glucolepidiin exhibited strong correlations with the E/A. dGMP, arginine, and tryptophan demonstrated strong correlations with LVIDs, and Figure 6. Validation of metabolic markers and their correlation with cardiac function. (A) ROCvalidation of metabolic markers, indicating that all AUC values are greater than 0.8. (B) Pearsoncorrelation analysis was employed to ascertain the relationship between myocardial metabolicmarkers and heart function. Numerous metabolic markers demonstrated robust correlations withspecific heart function indicators. Xanthine, inosine, and glucolepidiin exhibited strong correlationswith the E/A. dGMP, arginine, and tryptophan demonstrated strong correlations with LVIDs, andglucolepidiin showed a strong correlation with LVPWd. The size of the circles in the figure denotesthe strength of the correlation, where red signifies a positive correlation between metabolic markersand heart function indicators, while blue denotes a negative correlation. The intensity of color reflectsthe significance of the correlation. ROC = the receiver operating characteristic curve; AUC = ROC areaunder the curve; EF = ejection fraction; E/A = early-to-late diastolic filling velocity ratio; LVIDd = leftventricular end-diastolic internal diameter; LVIDs = left ventricular end-systolic internal diameter;LVPWd = left ventricular posterior wall end-diastolic thickness; LVPWs = left ventricular posteriorwall end-systolic thickness.Antioxidants 2024, 13, 1167 12 of 19Antioxidants 2024, 13, x FOR PEER REVIEW 13 of 20 Figure 7. Metabolic pathway map. The selected metabolic markers (shown in bold in text boxes) were integrated based on the KEGG database to visualize their interconnections. The arrows in the diagram indicate the conversion direction from one metabolite to another, involving multiple met-abolic pathways such as the citrate cycle, purine metabolism, and riboflavin metabolism. Different colors in the chart are used to distinguish each metabolic pathway. Overall, these metabolites are mostly involved in different parts of amino acid metabolism, energy metabolism, and purine me-tabolism, and they may be interconnected through these metabolic networks. This relationship in the conversion and function of metabolites is likely closely related to the improvement of cardiac function in diabetes by exercise. Figure 7. Metabolic pathway map. The selected metabolic markers (shown in bold in text boxes)were integrated based on the KEGG database to visualize their interconnections. The arrows inthe diagram indicate the conversion direction from on m tabolite to another, involving multiplemet bol c pathways such as the cit ate cycle, puri t bolism, and riboflavin metabolism. Differentc lors in the chart are used to distinguish each metabolic pathway. Overall, the e metabolitesare mostly involved in different parts of amino acid metabolism, energy metabolism, and purinemetabolism, and they may be interconnected through these metabolic networks. This relationship inthe conversion and function of metabolites is likely closely related to the improvement of cardiacfunction in diabetes by exercise.4. DiscussionThis study used a metabolomics approach to identify changes in diabetic cardiacdysfunction following long-term aerobic exercise, with a focus on metabolic pathways andbiomarkers. We subsequently conducted a ROC validation correlation analysis of thesebiomarkers. The findings indicate that aerobic exercise modified five metabolic pathwayswith ten potential metabolic biomarkers in myocardial tissues of diabetic rats. A Pearsoncorrelation analysis indicated a correlation between some biomarkers and cardiac function,providing a theoretical foundation for enhancing cardiac function in diabetic patientsthrough aerobic exercise (Figure 8).Antioxidants 2024, 13, 1167 13 of 19Antioxidants 2024, 13, x FOR PEER REVIEW 14 of 20 4. Discussion This study used a metabolomics approach to identify changes in diabetic cardiac dys-function following long-term aerobic exercise, with a focus on metabolic pathways and biomarkers. We subsequently conducted a ROC validation correlation analysis of these biomarkers. The findings indicate that aerobic exercise modified five metabolic pathways with ten potential metabolic biomarkers in myocardial tissues of diabetic rats. A Pearson correlation analysis indicated a correlation between some biomarkers and cardiac func-tion, providing a theoretical foundation for enhancing cardiac function in diabetic patients through aerobic exercise (Figure 8). Figure 8. Metabolomics reveals that aerobic exercise alleviates cardiac dysfunction by altering my-ocardial metabolic markers in diabetic rats. Type 2 diabetic rats, successfully modeled using a high-fat, high-sugar diet, underwent echocardiographic assessment of cardiac function following 8 weeks of aerobic exercise. After the experience, myocardial tissue was extracted, and non-targeted metab-olomics was used to analyze myocardial metabolites. A total of 208 differential metabolites were identified by comparing the myocardial metabolites across groups. Through topological analysis and receiver operating characteristic curve testing, 10 metabolites were selected. Finally, a Person correlation analysis revealed a relationship between the metabolites and cardiac function. Purine metabolism describes the synthesis and degradation of purine nucleotides, as well as the regulation of guanosine and adenosine nucleotide pools, which are responsible for maintaining intracellular ATP and GTP concentrations [25]. Furthermore, GTP, acting as an intermediate in purine biosynthesis, participates in the initial stages of riboflavin (vitamin B2) synthesis [26]. Riboflavin, as a precursor of flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), is an essential nutrient with roles in preventing myocardial injury and enhancing protein oxidative folding [26]. Previous studies Figure 8. Metabolomics reveals that aerobic exercise alleviates cardiac dysfunction by alteringmyocardial metabolic markers in diabetic rats. Type 2 diabetic rats, successfully modeled using ahigh-fat, high-sugar diet, underwent echocardiographic assessment of cardiac function following8 weeks of aerobic exercise. After the experience, myocardial tissue was extracted, and non-targetedmetabolomics was used to analyze myocardial metabolites. A total of 208 differential metaboliteswere id ntified by comparing the myocardial metabolites across groups. Through topological analysisand receiver operating characteris c curve t sting, 10 metabolites were selected. Finally, a Personcorrelation analysis revealed a relationship between the metabolites and cardiac function.Purine metab lism describes the synthesis and degra ation of purine nucleotides, aswell as the regulation of guanosine and adenosine nucleotide pools, which are responsiblefor maintaining intracellular ATP and GTP concentrations [25]. Furthermore, GTP, actingas an intermediate in purine biosynthesis, participates in the initial stages of riboflavin(vitamin B2) synthesis [26]. Riboflavin, as a precursor of flavin mononucleotide (FMN) andflavin adenine dinucleotide (FAD), is an essential nutrient with roles in preventing myocar-dial injury and enhancing protein oxidative folding [26]. Previous studies indicated a linkbetween riboflavin and the future risk of T2DM and its complications such as hypertensionand coronary artery disease [27,28]. Heightened oxidative stress is associated with thedevelopment of diabetes, as shown by increased lipid peroxidation and protein carbonyla-tion in STZ-induced diabetes rats [29]. Nevertheless, riboflavin treatment decreases lipidperoxidation and protein carbonylation, in addition to elevating levels of cardiac hemeoxygenase-1 (HO-1) and superoxide dismutase (SOD) levels [29]. Furthermore, riboflavinsupplementation also reduces the development of cardiac injury markers (CK and LDH) indiabetic rats, potentially preventing left ventricular hypertrophy and improving diastolicfunction [29]. Our study indicates that aerobic exercise restores decreased riboflavin levelsAntioxidants 2024, 13, 1167 14 of 19in the myocardium of diabetic rats, with a positive relationship between riboflavin levelsand diastolic function.The monophosphate forms of purines transform into inosine during the catabolism ofpurines; inosine is then converted to hypoxanthine by purine nucleoside phosphorylase.Xanthine oxidase then converts hypoxanthine into xanthine, ultimately resulting in theproduction of uric acid [30]. It is widely recognized that purine metabolism is closelyassociated with the onset and progression of diabetes. Purine metabolite levels in humanplasma are related to T2DM, with increased concentrations of inosine, hypoxanthine, andxanthine in the plasma and liver of diabetic patients [30]. Additionally, purine metabolitescan act as markers for evaluating the severity of myocardial ischemia and myocardialinfarction, as diabetic patients with acute myocardial infarction have increased plasmalevels of inosine and hypoxanthine, but with no changed in xanthine levels [31]. Ourstudy indicates that diabetic rats have increased levels of inosine and hypoxanthine in themyocardium, with a significant reduction in xanthine levels, likely due to decreased levelsof xanthine oxidase in the early stages of diabetes. Nevertheless, 8 weeks of aerobic exerciseenhanced the levels of myocardial purine metabolites. Correlation analysis demonstrated astrong association between the levels of xanthine and inosine, and the E/A ratio in the heart,suggesting that exercise ameliorates the pathological processes contributing to diastolicdysfunction in the diabetic myocardium.Metabolomic studies indicate that some amino acids, such as glutamate, alanine, andbranched-chain amino acids, could serve as early biomarkers for the onset of diabetes [32].Glutamate, a crucial excitatory neurotransmitter in the central nervous system, also has akey role in controlling the function and activity of pancreatic islet endocrine cells, where itserves as a messenger for stimulating insulin release by pancreatic β-cells [33]. Abnormalglutamate levels cause harm to pancreatic islet cells and increase insulin resistance [34].Persistent hyperglycemia results in vascular damage, leading to myocardial ischemia andcardiac dysfunction [35]. Nevertheless, during myocardial ischemia, glutamate assumesparticular significance as it enhances the mechanical performance of the ischemic my-ocardium [36]. Additionally, glutamate is one of the key components in the synthesis ofglutathione (GSH), which plays a crucial role in antioxidation. A deficiency in GSH canlead to oxidative stress, which is a fundamental mechanism in aging and many metabolicdiseases, including T2DM [37]. Oxidative stress is also one of the primary causes of car-diac dysfunction in diabetic patients, resulting from both excessive production of reactiveoxygen species (ROS) and impaired endogenous antioxidant mechanisms [38,39]. Nu-merous studies have shown that exercise training exerts antioxidant effects [40]. In ourprevious research, although aerobic exercise did not increase the activity of GSH in themyocardium of diabetic rats, it did reduce the production of myocardial ROS and enhancecatalase activity, indicating that aerobic exercise plays an important role in combatingoxidative stress [41].Our analysis indicates that aerobic exercise increases arginine synthesis. An impor-tant role for arginine is as a substrate in the generation of nitric oxide (NO), a vasoactivesubstance released by the vascular endothelium. Tryptophan plays a role in regulatingbody weight and higher levels are linked to the risk of obesity and T2DM [42]. Tryptophanparticipates in three metabolic pathways within the body: the indole pathway, the kynure-nine (Kyn) pathway, and the serotonin (5-HT) pathway [43]. Among these pathways, 5-HTcan stimulate the growth of pancreatic β- cells and enhance insulin production [44]. In theTrp-Kyn pathway, the rate-limiting enzyme, indoleamine 2,3-dioxygenase 1 (IDO1), dimin-ishes insulin resistance by reducing inflammation, suggesting that tryptophan metabolismreduces the complications of T2DM [45]. Furthermore, reduced tryptophan levels areinversely associated with cardiovascular diseases, including atherosclerosis and heart fail-ure [46,47]. The 5-HT metabolic pathway is important in diabetes-related angiogenesis, as5-HT influences blood flow velocity, and its receptors (5-HTR2A and 5-HTR1B) governvascular dilation and constriction [48]. Our study indicates that aerobic exercise elevatesthe myocardial amino acids (glutamate, arginine, and tryptophan) of diabetic rats, andAntioxidants 2024, 13, 1167 15 of 19that these alterations are strongly associated with cardiac function. As a result, exercise en-hances diabetic cardiac function by modifying the amino acid metabolic profile in diabeticcardiac tissue.Hydroxybutyric acid (3HB) is a ketone body generated by the liver and is primarilyused by energy-demanding tissues such as the central nervous system and the heart,where it is converted to acetyl-CoA for energy production [49]. In addition, 3HB alsoserves as a signaling molecule of cellular functions [50]. Initial data suggested that 3HBreduces blood glucose levels in patients with T2DM, and further investigations indicated atherapeutic effect of 3HB in T2DM through phosphorylation of peroxisome proliferator-activated receptor γ (PPARγ) at Ser273 in the adipose tissue of mice with T2DM, alteringthe expression of PPARγ regulatory factors to decrease insulin resistance [51]. Additionally,3HB enhances cardiovascular function. Intravenous administration of 3HB improvescardiac output and ejection fraction in patients with chronic heart failure [52]. These reportsagree with our findings that prolonged aerobic exercise mimics a fasting state (in terms ofmetabolism) by elevating ketone body concentrations and their utilization in the body [49].Therefore, exercise-induced increases in 3HB levels can enhance cardiac function in T2DM.Under normal conditions, the heart primarily relies on the oxidation of fatty acidsand glucose for energy. In patients with diabetes, the levels of free fatty acids and triglyc-erides are significantly elevated, accompanied by an increase in fatty acid utilization bythe myocardium. However, the myocardial fatty acid oxidation capacity is reduced, result-ing in the accumulation of excessive fatty acids and their derivatives in the myocardium,which ultimately leads to structural and functional abnormalities in the heart [53,54].Additionally, metabolomics data indicate that in the myocardium of STZ-induced rats,a significant increase in the types of long-chain acylcarnitines was observed, which isassociated with impaired fatty acid β-oxidation. This suggests that diabetes leads to areduced rate of myocardial fatty acid oxidation and the development of myocardial lipo-toxicity [55]. Although numerous studies over the past 20 years have shown that exercisecan promote lipid oxidation by enhancing the activity of lipases, it remains challengingto determine the exact changes in myocardial energy substrate utilization resulting fromlong-term exercise [56]. Long-term exercise training has been shown to increase the activityof hydroxyacyl-coenzyme A dehydrogenase (HADH) and carnitine palmitoyl transferase 1(CPT1) in mouse myocardium, along with enhanced expression of peroxisome proliferator-activated receptor α (PPARα) and clusters of differentiation (CD36) genes [57]. However,other studies have found that exercise does not alter factors associated with myocardialfatty acid oxidation, such as CPT1b, PPARα, and CD36 [58,59]. Similarly, our research didnot identify any significant effects of long-term aerobic exercise on pathways related tomyocardial lipid metabolism. Future studies might consider employing different exercisemodalities or intensities to further evaluate the impact of exercise therapy on myocardialfatty acid oxidation in diabetic conditions.It is important to recognize that non-targeted metabolomics, which has many advan-tages, also has some drawbacks. As an unbiased detection method, it can broadly identifyvarious metabolites and their pathways, and is an effective and reliable method to discoverdisease biomarkers. However, as a tool, non-targeted metabolomics can only performsemi-quantitative analysis, which results in the detection of metabolites at relative concen-trations rather than absolute concentrations. Such results can only reflect the relative levelsof metabolites between different groups. Furthermore, although differential metabolitesprovide useful information, the lack of quantitative data may limit the understanding ofthe biological processes involved in metabolic pathways. In mass spectrometry analysis,in-source fragmentation is a common phenomenon that can lead to the formation of ionfragments during the ionization of target metabolites, potentially complicating their identi-fication and resulting in the overinterpretation of metabolic data. There, future researchshould involve targeted metabolomic analysis of the biomarkers we have identified toobtain more accurate data. Additionally, one of the limitations of this study is that thePLS-DA model explains only a small percentage of the overall data variance. This limitedAntioxidants 2024, 13, 1167 16 of 19explanatory power might affect the accuracy and generalizability of the model’s predictions.Future studies should consider using more complex or appropriate models to enhancethe explanatory power of the results. Secondly, the relatively small sample size in ourexperiments may limit detailed exploration of cardiac function in diabetes, and futurestudies with larger sample sizes will validate our findings. Finally, correlation analysis is acommonly used and highly efficient tool in metabolomics, enabling data mining and therapid identification of meaningful metabolites. By correlating metabolites with specifictraits, researchers can gain insights into the biological processes associated with particularmetabolites, thus providing direction for subsequent studies and further exploration. How-ever, it is important to acknowledge the potential limitations of using correlation analysisin the context of metabolomics. Firstly, while correlation analysis can reveal associationsbetween metabolites and traits, it does not exclude the involvement of other metabolites inthese processes. Secondly, due to the limited sample size in this study, the analysis maynot capture associations between low-abundance but biologically significant metabolitesand specific traits. Additionally, in metabolomics, a particular metabolite may not only beassociated with a single trait but could also be involved in other biological processes. There-fore, over-reliance on correlation analysis might obscure the broader biological functionsof metabolites, potentially overlooking their roles in other processes. These limitationscould impact the interpretation of the results, and as such, it is crucial to recognize theseconstraints and approach conclusions with caution.5. ConclusionsOur study demonstrates that continuous aerobic exercise for 8 weeks changes myocar-dial metabolites in diabetic rats and improves cardiac dysfunction by altering metabolicpathways such as purine metabolism and arginine biosynthesis. Furthermore, metabo-lites including xanthine, hypoxanthine, inosine, dGMP, L-glutamic acid, L-arginine, L-tryptophan, (R)-3-hydroxybutyric acid, riboflavin, and glucolepidiin could serve as keyrepresentative biomarkers for the improvement of T2DM-related cardiac dysfunction byaerobic exercise. Our findings provide some potential mechanisms underlying cardiacdamage and indicate that aerobic exercise can prevent cardiac injury in T2DM.Supplementary Materials: The following supporting information can be downloaded athttps://www.mdpi.com/article/10.3390/antiox13101167/s1, Table S1: Raw metabolomics data;Figure S1: PCA plot and PLS-DA permutation test plot.Author Contributions: Conceptualization, T.W. and S.L.; Data curation, X.T.; Funding acquisition,S.L.; Methodology, T.W.; Project administration, S.L.; Software, M.N. and Y.M.; Supervision, S.L.;Visualization, Y.F.; Writing—original draft, T.W.; Writing—review and editing, I.L. and S.L. Allauthors have read and agreed to the published version of the manuscript.Funding: This work was funded by the National Natural Science Foundation of China (grant no.31971104).Institutional Review Board Statement: The animal study protocol was approved by the Ethics Com-mittee of Chengdu Sport University (protocol code: 2022-3 and data of approval: 13 January 2022).Informed Consent Statement: Not applicable.Data Availability Statement: Data are contained within the article and Supplementary Materials.Acknowledgments: Figure 8 was modified taken from Servier Medical Art (http://smart.servier.com/, accessed on 30 December 2023), licensed under a Creative Common Attribution 4.0 GenericLicense. (https://creativecommons.org/licenses/by/4.0/, accessed on 30 December 2023).Conflicts of Interest: None of the other authors reported any conflicts of interest in this study.The results of the study are presented clearly and honestly without fabrication, falsification, orinappropriate manipulation of data.Antioxidants 2024, 13, 1167 17 of 19References1. Donath, M.Y.; Shoelson, S.E. Type 2 diabetes as an inflammatory disease. Nat. Rev. Immunol. 2011, 11, 98–107. [CrossRef][PubMed]2. Sampath Kumar, A.; Maiya, A.G.; Shastry, B.A.; Vaishali, K.; Ravishankar, N.; Hazari, A.; Gundmi, S.; Jadhav, R. Exercise andinsulin resistance in type 2 diabetes mellitus: A systematic review and meta-analysis. Ann. Phys. Rehabil. Med. 2019, 62, 98–103.[CrossRef] [PubMed]3. Karwi, Q.G.; Ho, K.L.; Pherwani, S.; Ketema, E.B.; Sun, Q.; Lopaschuk, G.D. Concurrent diabetes and heart failure: Interplay andnovel therapeutic approaches. Cardiovasc. Res. 2022, 118, 686–715. [CrossRef] [PubMed]4. 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type 2 diabetes
cardiac function
aerobic exercise
metabolomics
Metabolomic Profiling Reveals That Exercise Lowers Biomarkers of Cardiac Dysfunction in Rats with Type 2 Diabetes
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