Untargeted metabolomics analysis of differences in metabolite levels in congenital heart disease of varying severity

Untargeted metabolomics analysis of differences in metabolite levels in congenital heart disease of varying severity | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Untargeted metabolomics analysis of differences in metabolite levels in congenital heart disease of varying severity Yahong Li, Yun Sun, Peiying Yang, Xin Wang, Xiaojuan Zhang, Ping Hu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2464935/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 Congenital heart disease (CHD) is characterized by various phenotypes, however, differences in metabolic profiles associated with CHD of various severity have not been elucidated. In this study, differences in metabolite concentrations among mild, moderate, and severe forms of CHD were explored, providing novel clues for our understanding of the mechanism of CHD. Methods Maternal amniotic fluid samples from fetuses with mild (n = 15), moderate (n = 7), and severe (n = 29) CHD lesions were analyzed by GC-TOF/MS. PCA, PLS-DA, and differential metabolite analysis among these three groups were conducted. Results PCA and PLS-DA models showed that metabolic profiles were comparable among CHD of different severity. Significant differences between mild and moderate CHD lesions were observed in the levels of gluconolactone, ornithine, threonine, sorbose, pentadecanoic acid, and the uric acid/xanthine ratio. Of these six differential metabolites, gluconolactone (r = 0.469, P = 0.028), sorbose (r = 0.577, P = 0.005) and the uric acid/xanthine ratio (r = 0.438, P = 0.041) were positively correlated with moderate CHD lesions, while ornithine (r=-0.531, P = 0.011), threonine (r=-0.546, P = 0.009), and pentadecanoic acid (r=-0.454, P = 0.034) were negatively associated. We found 9 differential metabolites between mild and severe CHD lesions, among which the alpha-ketoisovaleric acid/valine ratio (r=-0.383, P = 0.010), gluconolactone (r = 0.391, P = 0.009), and 4-hydroxycinnamic acid (r = 0.342, P = 0.023) were correlated with severe CHD lesions. Only sorbose showed significant differences between moderate and severe CHD lesions, and was negatively associated with severe CHD lesions (r=-0.341, P = 0.042). Conclusions Compared with mild CHD, specific differences were observed in metabolites or metabolite ratios in moderate and severe CHD lesions of CHD, several of which were significantly correlated with CHD severity. These results can help to understand the metabolic status of the affected fetus and provide new possibilities for exploring the pathological mechanism of CHD. Congenital heart disease Severity Metabolomics Differential metabolites Figures Figure 1 Background Congenital heart disease (CHD) is the most common birth defect, with a prevalence of approximately 10 per 1,000 live births. CHD is characterized by various phenotypes, for example, ventricular septal defect (VSD), atrial septal defect (ASD), tetralogy of Fallot(TOF), persistent truncus arteriosus and double outlet right ventricle༈DORV༉[ 1 , 2 ]. CHD may cause miscarriage, premature delivery, low birth weight, or death [ 3 ]. In addition, CHD can result in an impairment of brain growth and development, such as a delay in cortical development or white matter injury [ 4 , 5 ]. The impact of CHD on the fetus varies with its severity. CHD with mild lesions, such as VSD with a spontaneous closure ratio of 40–60%, has relatively limited consequences on patients’ health [ 6 – 8 ]. However, severe CHD, such as left heart dysplasia, transposition of the great arteries, truncus arteriosus, heterotaxia, double-outlet right ventricle, can result in death either during or after birth [ 9 ]. Currently, the differences in the metabolic status among fetuses in the second trimester of pregnancy with different types of CHD are unknown and are worth further exploration. The composition of the pool of metabolites is the result of the interaction of biological and environmental factors and holds a large amount of information that is capable of reflecting phenotypes. Metabolomics aims to characterize different types of small molecule metabolites from different sample types, and untargeted metabolomics methods focus on detecting a larger number of possible metabolites [ 10 ]. Therefore, metabolomics has become a powerful platform for novel technologies; it is an interdisciplinary discipline that combines biochemistry, analytical chemistry, bioinformatics, and statistics [ 11 ]. The unique advantages of metabolomics helped identify disease biomarkers, and promoted progress in toxicological and pharmacokinetic studies [ 12 , 13 ]. Previous reports were mainly focused on metabolic differences between normal fetuses and those with CHD, but few studies addressed the variation of metabolic profiles with different CHD severity [ 14 – 16 ]. The present investigation utilized untargeted metabolomics methods to explore the metabolic profiles and differential metabolites associated with mild, moderate, and severe CHD lesions. Our findings may advance the understanding of the metabolic status of the fetus affected by CHD with various degrees of lesions and provide novel concepts for studying the pathological mechanism of CHD with different severity. Methods Samples This study was approved by the Medical Ethics Committee of Nanjing Maternity and Child Health Care Hospital. From the 1859 samples collected from January 2012 to December 2018, maternal amniotic fluid from fetuses affected by CHD with mild (15 cases), moderate (7 cases), and severe (29 cases) lesions were selected for untargeted metabolomics analysis. Table 1 shows the specific types of the selected 51 CHD cases. The severity of CHD lesions was classified according to published literature [ 17 – 19 ]. There was no significant difference in gestational age, maternal age, and fetus sex among the three groups with different CHD severity (Table 2 ). All specimens were residual samples left after clinical molecular diagnostic tests and were stored at -80°C before analysis. All cases were single pregnancies from the Jiangsu province diagnosed with CHD by fetal echocardiogram and then referred to the prenatal diagnosis center of the Nanjing Maternity and Child Health Care Hospital. Except for CHD, other structural or chromosomal abnormalities and pregnancy complications, such as hypertension and diabetes, were excluded from all samples. Except for 28 terminated pregnancies, postnatal ultrasound diagnosis was consistent with prenatal results in the remaining 23 cases of CHD. Table 1 Specific CHD types of mild, moderate, and severe lesions groups Group CHD type Number of cases Mild lesions VSD 15 Moderate lesions TOF 5 PVS + VSD + ASD 1 PVS + VSD 1 Severe lesions Heterotaxy 4 IAA,B 2 IAA,B + VSD 3 IAA,B + VSD + ASD 1 IAA,B + MA 1 DORV + VSD 3 DORV + PVS + VSD 1 d-TGA + VSD 4 HLHS 1 HLHS + VSD 1 HLHS + APVR 1 HLHS + VSD + MA 1 HLHS + MA + DORV + mid-aortic dysplasia 1 Persistent truncus arteriosus + VSD 2 TOF + PA 1 TOF + interruption of inferior vena cava 1 Severe PA + mitral insufficiency 1 VSD, ventricular septal defect; ASD, atrial septal defect; TOF, tetralogy of fallot; PVS, pulmonary valve stenosis; IAA, B, interrupted aortic arch type B; MA, mitral atresia; DORV, double outlet right ventricle; d-TGA, d-transposition of the great arteries; HLHS, hypoplastic left heart syndrome; APVR, partial anomalous pulmonary venous return; PA, pulmonary atresia. Table 2 Demographic characteristics of samples used for metabolic profile analysis Subject characteristics Mild lesions Moderate lesions Severe lesions P value Number of samples 15 7 29 Gestational age (weeks) 23(21–25) 25(22–26) 24(20–26) 0.169 Maternal age (years) 29(24–39) 30(22–32) 28(23–37) 0.421 Fetal gender(female/male) 6/9 1/6 12/17 0.399 Gestational age and maternal age are represented by median. Reagents and instruments Ultrapure water was prepared using a Milli-Q reference system (Millipore, Billerica, MA, USA). Fatty acid methyl ester (C7–C30, FAMEs) standards, anhydrous sodium sulfate, methoxyamine HCl, and pyridine were purchased from Sigma-Aldrich (St. Louis, MO, USA). Acetonitrile (Optima LC-MS), N-methyl-N(trimethylsilyl)trifluoroacetamide (MSTFA) with 1% (vol/vol) trimethylchlorosilane (TMCS), hexane, methanol (Optima LC-MS), chloroform, dichloromethane, and acetone were obtained from Thermo-Fisher Scientific (Fair Lawn, NJ, USA). A Rxi-5 ms capillary column (30 m × 250 µm i.d., 0.25 µm film thickness, Restek Corporation, Bellefonte, PA, USA) was used for separation. GC-TOF/MS (Pegasus HT, Leco Corp., St. Joseph, MO, USA), equipped with an Agilent 7890B gas chromatograph (Agilent, Santa Clara, CA, USA) and a Gerstel multipurpose sampler MPS2 with dual heads (Gerstel, Muehlheim, Germany) was applied for sample analysis. Sample preparation and analysis conditions for GC/TOF-MS Sample preparation, mass spectrometry conditions, and detection methods of GC/TOF-MS were as we previously reported [ 14 ] . Data analysis XploreMET 3.0 software was applied for peak picking, automated baseline denoising, signal alignment, and deconvolution. Metabolites were annotated by comparing mass spectrometry data and retention indices with the JiaLib metabolite database. After transforming each data into a comparable data vector, MetaboAnalyst 5.0, an open software, was used for metabolomics analysis, which included the principal component analysis (PCA), partial least square discrimination analysis (PLS-DA), and variable importance in the projection (VIP) value. Mann-Whitney U tests, Student’s t -tests, and Spearman correlation analyses were performed using SPSS 22.0. Metabolites shown by univariate analysis to have P 1 on multivariate analysis were considered reliable differential metabolites [ 20 ]. For Spearman correlation analysis, P < 0.05 was considered significant [ 21 ]. Results Absence of significant differences in metabolic profiles among CHD with mild, moderate, and severe lesions A total of 265 small molecule metabolites were detected in amniotic fluid, and 178 metabolites were identified successfully. We performed multivariate statistical analysis of the 178 metabolites and 50 metabolite ratios based on the KEGG database. An unsupervised PCA model showed no significant separation trend among CHD with mild, moderate, and severe lesions (Fig. 1 A). Moreover, using a supervised PLS-DA model, we confirmed the lack of significant differences in metabolic profiles between the three groups (Fig. 1 B). In the PLS-DA model, although a separation trend could be observed among the three groups (R 2 Y = 0.90, Q 2 = -0.36), a negative value of Q 2 indicated that the model was over-fitted. In fact, the results of the permutation test of this model yielded the P- value of 0.673, demonstrating that the separation trend was caused by the over-fitting of the model. These results showed no significant difference in metabolic profiles among CHD with mild, moderate, and severe lesions. Differential metabolite levels among CHD with mild, moderate, and severe lesions Although the overall metabolic profile was comparable between the three groups, univariate and multivariate analyses were conducted to identify the differential metabolites among the three groups. Comparison of differential metabolites between each two groups was performed by Student’s t -test or the Mann-Whitney U test. Normally distributed data were analyzed by t -tests while if one or more of the two datasets were non-normally distributed, the Mann-Whitney U test was used. VIP values were obtained according to the PLS-DA model. The identified differential metabolites between the different groups are shown in Table 3 . Table 3 Differential metabolites of CHD with different lesions and the trend of change Group Metabolites or metabolite ratios P value t value Z value VIP value Trend in the latter mild lesions VS moderate lesions Ornithine 0.002 2.789 1.37 ↓ Threonine 0.005 0.26 1.68 ↓ Gluconolactone 0.007 -2.162 2.58 ↑ Sorbose 0.008 -2.643 1.76 ↑ Uridine/Cytidine ratio 0.016 -2.621 0.69 ↑ Pentadecanoic acid 0.033 2.289 1.69 ↓ Putrescine/Ornithine ratio 0.041 -2.183 0.99 ↑ Uric acid/Xanthine ratio 0.041 -2.186 1.97 ↑ D-Xylose/D-Xylitol ratio 0.045 -2.009 0.66 ↑ mild lesions VS severe lesions Tryptamine 0.014 -2.556 0.55 ↑ Alpha-ketoisovaleric acid/L-Valine ratio 0.012 -2.513 2.99 ↓ Gluconolactone 0.022 -2.382 2.58 ↑ 4-Hydroxycinnamic acid 0.024 -2.338 2.41 ↑ Ornithine 0.026 2.313 1.37 ↓ Linoleic acid 0.035 2.175 2.03 ↓ Xanthine/Xanthosine ratio 0.041 2.113 2.40 ↓ Hippuric acid 0.037 2.149 2.04 ↓ 4-Hydroxyproline 0.042 1.769 0.16 ↓ MG182 0.047 2.045 2.2 ↓ moderate lesions VS severe lesions Uridine/Cytidine ratio 0.002 3.368 0.69 ↓ Sorbose 0.049 2.043 1.76 ↓ VIP, variable importance in the projectio The results of the univariate analysis indicated that the concentrations of 9 metabolites or metabolite ratios, namely, ornithine, threonine, gluconolactone, sorbose, the pentadecanoic acid, uridine/cytidine ratio, the putrescine/ornithine ratio, the uric acid/xanthine ratio, and the D-xylose/D-xylitol ratio differed significantly in mild and moderate lesions. Of these, ornithine, threonine, gluconolactone, sorbose, pentadecanoic acid, and the uric acid/xanthine ratio met the VIP > 1 requirement, indicating that these 6 metabolites or metabolite ratios were more reliable. The levels of ornithine, threonine, and pentadecanoic acid were significantly reduced in the moderate CHD lesions, while the levels of gluconolactone, sorbose, and the uric acid/xanthine ratio levels were significantly elevated. The univariate analysis showed that the levels of 10 metabolites or metabolite ratios differed significantly between mild and severe CHD. These were ornithine, gluconolactone, tryptamine, linoleic acid, hippuric acid, 4-hydroxycinnamic acid, 4-hydroxyproline, MG182, the alpha-ketoisovaleric acid/valine ratio, and the xanthine/xanthosine ratio, with only tryptamine and 4-hydroxyproline not satisfying the VIP > 1 criterion. Of the 8 differential metabolites verified by multivariate statistical analysis, only the levels of gluconolactone and 4-hydroxycinnamic acid were increased in severe CHD, while those of the other metabolites were decreased. There was significant difference in sorbose levels and the uridine/cytidine ratio between moderate and severe CHD; however, only sorbose was verified by the multivariate analysis, showing a decrease. In general, although there were no significant differences in the overall metabolic profile among mild, moderate, and severe CHD, there were still several metabolite levels that differed between groups. However, we did not identify a metabolite that was able to distinguish between mild, moderate, and severe CHD at the same time. Correlation analysis between differential metabolites and CHD severity Spearman correlation analysis was performed to analyze the associations between differential metabolites and CHD severity (Table 4 ). Gluconolactone (r = 0.469, P = 0.028), sorbose (r = 0.577, P = 0.005), and the uric acid/xanthine ratio (r = 0.438, P = 0.041) were positively correlated with moderate CHD lesions, while ornithine (r=-0.531, P = 0.011), threonine (r=-0.546, P = 0.009), and pentadecanoic acid (r=-0.454, P = 0.034) were negatively associated with moderate CHD lesions in comparison with mild lesions. Among the 6 metabolites, ornithine, threonine, and sorbose showed moderate correlations, while gluconolactone, pentadecanoic acid, and the uric acid/xanthine ratio showed lower associations. When mild and severe CHD lesions were compared, we found several differential metabolites that were significantly correlated with severe CHD, including the alpha-ketoisovaleric acid/valine ratio (r=-0.383, P = 0.010), gluconolactone (r = 0.391, P = 0.009), and 4-hydroxycinnamic acid (r = 0.342, P = 0.023); however, the degree of correlation was low. Compared with moderate CHD lesions, we found only one differential metabolite, sorbose (r=-0.341, P = 0.042), to be significantly associated with severe CHD; however, the correlation coefficient was low. Table 4 Spearman correlation analysis of differential metabolites and the severity of CHD Group Metabolites or metabolite ratios P value Correlation Coefficient mild lesions VS moderate lesions Ornithine 0.011 -0.531 Threonine 0.009 -0.546 Gluconolactone 0.028 0.469 Sorbose 0.005 0.577 Pentadecanoic acid 0.034 -0.454 Uric acid/Xanthine ratio 0.041 0.438 mild lesions VS severe lesions Alpha-ketoisovaleric acid/Valine ratio 0.010 -0.383 Gluconolactone 0.009 0.391 4-Hydroxycinnamic acid 0.023 0.342 Ornithine 0.051 -0.296 Linoleic acid 0.061 -0.285 Xanthine/Xanthosine ratio 0.061 -0.285 Hippuric acid 0.061 -0.285 MG182 0.117 -0.240 moderate lesions VS severe lesions Sorbose 0.042 -0.341 Overall, ornithine, threonine, sorbose, gluconolactone, pentadecanoic acid, and the uric acid/xanthine ratio were correlated with moderate CHD lesions, while the alpha-ketoisovaleric acid/valine ratio, gluconolactone, 4-hydroxycinnamic acid, and sorbose were correlated with severe CHD lesions. Discussion CHD is the most common birth defect, and its phenotype varies widely. However, metabolic profiles in CHD of different severity were rarely investigated. In this study, GC-TOF/MS-based untargeted metabolomics was used to explore the metabolic profile of CHD with mild, moderate, and severe lesions, and the differences in metabolite concentrations among the three groups were determined. Multivariate analysis models, such as PCA and PLS-DA, did not identify any significant differences in metabolic profiles among mild, moderate, and severe types of CHD. However, univariate and multivariate statistical analysis showed that the levels of gluconolactone, threonine, ornithine, sorbose, pentadecanoic acid and the uric acid/xanthine ratio were statistically different between the mild and moderate lesions of CHD, while the alpha-ketoisovaleric acid/valine ratio, ornithine, gluconolactone, linoleic acid, hippuric acid, 4-hydroxycinnamic acid, MG182, and the xanthine/xanthosine ratio levels were significantly different between mild and severe CHD lesions. Only one differential metabolite, sorbose, was identified between moderate and severe CHD lesions. Of the identified differential metabolites, ornithine, threonine, sorbose, gluconolactone, pentadecanoic acid, and the uric acid/xanthine ratio were correlated with moderate CHD lesions, while the alpha-ketoisovaleric acid/valine ratio, gluconolactone, 4-hydroxycinnamic acid, and sorbose were correlated with severe CHD lesions. At present, there are few studies on the relationship between small-molecule metabolites and CHD severity. Next, we will discuss several differential metabolites that are associated with the CHD severity. Threonine is an essential amino acid for humans. It must be obtained from food and plays an important role in macromolecule biosynthesis, the modulation of nutritional metabolism, and gut homeostasis [ 22 , 23 ]. The multiple pathways of threonine catabolism provide carbon for biosynthesis and epigenetic regulation of biological macromolecules affecting the self-renewal of embryonic stem cells (ESC) [ 24 ]. Dong et al. [ 25 ] found that the concentration of threonine in cardiac tissue is higher in cyanotic than acyanotic congenital heart disease. Conversely, in the present study, we observed a decrease in threonine level in amniotic fluid in CHD with moderate lesions compared to CHD with mild lesions. We hypothesized that these contrasting results might reflect the differences in sample types included in our analysis and that of Dong et al. [ 25 ]; however, the underlying mechanism remains to be identified. Ornithine is a non-essential amino acid generated from arginine. It is mostly involved in the urea cycle, and the abnormalities in its metabolism are implicated in gyrate atrophy, cancer, hyperornithinemia, and hyperammonemia [ 26 ]. Schlüter and collaborators demonstrated that hypoxia could induce an increase in arginase, as an early response to hypoxia, which catalyzes the formation of ornithine and urea [ 27 ]. According to their findings, hypoxia may lead to increased ornithine levels. Conversely, in the present study, we observed a significant decrease in ornithine levels in CHD with moderate and severe lesions than in CHD with mild lesions. Thus, we suspect that the response to hypoxia may depend on CHD severity: mild hypoxia may increase ornithine concentration, but severe hypoxia may have the opposite effect. However, other possible explanations cannot be excluded. Gluconolactone, a naturally occurring glucose derivative and antioxidant, has cardioprotective effects in humans [ 28 ]. We found significantly higher gluconolactone levels in moderate and severe types of CHD than in mild CHD types. This increase may represent a self-protective mechanism against cardiac disease. Pentadecanoic acid is an odd-chain saturated fatty acid, mainly obtained through dairy products, and there is a negative correlation between circulating pentadecanoic acid and the risk of metabolic disease [ 29 ]. Research from Khaw et al. [ 30 ] indicated that pentadecanoic acid was negatively associated with the risk of coronary heart disease. In this study, we found that the level of pentadecanoic acid in moderate CHD lesions was significantly lower than that in mild lesions, with a Spearman's correlation coefficient of -0.454, which was consistent with the findings of Khaw et al. [ 30 ]. Sorbose is a monosaccharide that can be detected in human feces, blood, and urine, while 4-hydroxycinnamic acid, a derivate of cinnamic acid, is abundant in plant seeds and leaves [ 31 – 34 ]. At present, there are no reports on the relationship between sorbose, 4-hydroxycinnamic acid, and CHD severity. In summary, untargeted metabolomics documented the absence of significant differences in the overall metabolic profile among CHD with mild, moderate, and severe lesions. However, there were some differences in the levels of metabolites among the different groups. The main shortcomings of this study are the relatively small sample sizes of each group and the lack of validation samples. If conditions permit, we will collect verification cohort samples for verification in the subsequent work. In addition, due to the lack of normal control samples matched for gestational age, the use of differential metabolites for distinguishing non-CHD requires further investigation. Despite this, this performed analysis study can enable us to clarify the overall metabolic status of CHD with different severity and provide novel concepts to be used in investigating the mechanism of CHD. Declarations Ethics approval and consent to participate This study was approved by Medical Ethics Committee of Nanjing Maternity and Child Health Care Hospital (Code number: [2018] NO.91). All participants signed informed consent before undergoing amniocentesis, and agreed for the use of the remaining samples from clinical tests for scientific research. The need for written informed consent was waived by the Nanjing Maternity and Child Health Care Hospital ethics committee due to retrospective nature of the study. All methods were carried out in accordance with relevant guidelines and regulations under Ethics approval. Consent for publication Not applicable because there is no details, images, or videos related to an individual person. Availability of data and materials Data is available from the corresponding author Tao Jiang, and the email is [email protected] . Competing interests The authors declare that they have no competing interests. Funding This work was supported by National Natural Science Foundation of China (81770236), Jiangsu Natural Science Foundation (BK20181121), and the National Key Research and Development Program of China (2018YFC1002402). Authors' contributions YL and YS were responsible for comprehensive sample collection, data collection, experimental operation, data analysis, figure preparation, table preparation and paper writing. PY, XW and XZ were responsible for sample and information collection. ZX, TJ and PH were responsible for the research design, funding, and paper writing. All authors contributed to the article and approved the submitted version. Acknowledgements We thank all the participants for their contributions. References Namuyonga J, Lubega S, Aliku T, Omagino J, Sable C, Lwabi P. Pattern of congenital heart disease among children presenting to the Uganda Heart Institute, Mulago Hospital: a 7-year review. Afr Health Sci. 2020;20(2):745-52. Mazhani T, Steenhoff AP, Tefera E, David T, Patel Z, Sethomo W, et al. Clinical spectrum and prevalence of congenital heart disease in children in Botswana. Cardiovasc J Afr. 2020; 31(5):257-61. Zhang Z, Hu T, Wang J, Hu R, Li Q, Xiao L, et al. 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Influence of carbohydrates on secondary metabolism in Fusarium avenaceum. Toxins (Basel). 2013; 5(9):1655-63. WEST CD, RAPOPORT S. Method for determination of sucrose and sorbose in blood and urine. Proc Soc Exp Biol Med. 1949; 70(1):140. Würsch P, Welsch C, Arnaud MJ. Metabolism of L-sorbose in the rat and the effect of the intestinal microflora on its utilization both in the rat and in the human. Nutr Metab. 1979; 23(3):145-55. Yu Q, Fan L. Understanding the combined effect and inhibition mechanism of 4-hydroxycinnamic acid and ferulic acid as tyrosinase inhibitors. Food Chem. 2021; 352:129369. Additional Declarations No competing interests reported. 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 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-2464935","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":173996352,"identity":"0b408ff3-5695-41f2-994b-a6b68225ece9","order_by":0,"name":"Yahong Li","email":"","orcid":"","institution":"Women’s Hospital of Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yahong","middleName":"","lastName":"Li","suffix":""},{"id":173996353,"identity":"d3e7f57e-e553-476e-85d0-effcb850933f","order_by":1,"name":"Yun Sun","email":"","orcid":"","institution":"Women’s Hospital of Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Sun","suffix":""},{"id":173996354,"identity":"b4ec0620-aa97-4c59-b5cc-53d287262e31","order_by":2,"name":"Peiying Yang","email":"","orcid":"","institution":"Women’s Hospital of Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peiying","middleName":"","lastName":"Yang","suffix":""},{"id":173996355,"identity":"97962de8-98cb-426a-be6f-c18f466fed10","order_by":3,"name":"Xin Wang","email":"","orcid":"","institution":"Women’s Hospital of Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":173996356,"identity":"106b1f67-dcde-4f7d-b44f-2cc8f4987c88","order_by":4,"name":"Xiaojuan Zhang","email":"","orcid":"","institution":"Women’s Hospital of Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Zhang","suffix":""},{"id":173996357,"identity":"ebcf7388-1dd1-4093-a7b4-f40cbb3ad902","order_by":5,"name":"Ping Hu","email":"","orcid":"","institution":"Women’s Hospital of Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Hu","suffix":""},{"id":173996358,"identity":"6cd47bde-d06e-4607-afa4-2e6b94e83213","order_by":6,"name":"Tao Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACxmYog5+/seHABwMbOSK1JDAwSM443PhwRkGaMZF2AbUYNKQ3G/N8OJxIUDFzO/Ozh19/2MkZMBxsk7YxYE5gYD98dAN+h7GZG8skJBubMze2SecYsOUx8KSl3SDgFzNpiQTmxJ0NB0FaeIoZJHjMCGhh/wbUUp+44UBim7SFgURiA2EtPGaSHxIOg7Q0GzMYGBClpUyaIe24seSMg40PewwSjNkI+cWw//g2yR821XL8/O0PDvz481+On/3wMfxaGoABzYMswoZPOQjIgxz3g5CqUTAKRsEoGNkAAKPGSewzcdLUAAAAAElFTkSuQmCC","orcid":"","institution":"Women’s Hospital of Nanjing Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Jiang","suffix":""},{"id":173996359,"identity":"efa4feb7-b719-4dc7-ad92-96513d925922","order_by":7,"name":"Zhengfeng Xu","email":"","orcid":"","institution":"Women’s Hospital of Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhengfeng","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2023-01-11 00:59:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2464935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2464935/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":32654778,"identity":"5767b0fa-1e0a-4ee1-9557-5bca8e8c0d2c","added_by":"auto","created_at":"2023-02-08 15:29:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":719848,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic profiles among CHD with mild, moderate, and severe lesions. (A) Unsupervised PCA model; (B) PLS-DA model, R\u003csup\u003e2\u003c/sup\u003eY=0.90, Q\u003csup\u003e2\u003c/sup\u003e= -0.36.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2464935/v1/86e5e08c5ad5b3200db85459.png"},{"id":38152681,"identity":"b16ce4fb-24fd-41b0-b7e7-d58f6df3f53b","added_by":"auto","created_at":"2023-06-07 10:29:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":557897,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2464935/v1/b9bfb685-1833-4794-963e-c61334fb97c6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Untargeted metabolomics analysis of differences in metabolite levels in congenital heart disease of varying severity","fulltext":[{"header":"Background","content":"\u003cp\u003eCongenital heart disease (CHD) is the most common birth defect, with a prevalence of approximately 10 per 1,000 live births. CHD is characterized by various phenotypes, for example, ventricular septal defect (VSD), atrial septal defect (ASD), tetralogy of Fallot(TOF), persistent truncus arteriosus and double outlet right ventricle༈DORV༉[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. CHD may cause miscarriage, premature delivery, low birth weight, or death [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, CHD can result in an impairment of brain growth and development, such as a delay in cortical development or white matter injury [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The impact of CHD on the fetus varies with its severity. CHD with mild lesions, such as VSD with a spontaneous closure ratio of 40\u0026ndash;60%, has relatively limited consequences on patients\u0026rsquo; health [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, severe CHD, such as left heart dysplasia, transposition of the great arteries, truncus arteriosus, heterotaxia, double-outlet right ventricle, can result in death either during or after birth [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Currently, the differences in the metabolic status among fetuses in the second trimester of pregnancy with different types of CHD are unknown and are worth further exploration.\u003c/p\u003e \u003cp\u003eThe composition of the pool of metabolites is the result of the interaction of biological and environmental factors and holds a large amount of information that is capable of reflecting phenotypes. Metabolomics aims to characterize different types of small molecule metabolites from different sample types, and untargeted metabolomics methods focus on detecting a larger number of possible metabolites [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, metabolomics has become a powerful platform for novel technologies; it is an interdisciplinary discipline that combines biochemistry, analytical chemistry, bioinformatics, and statistics [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The unique advantages of metabolomics helped identify disease biomarkers, and promoted progress in toxicological and pharmacokinetic studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious reports were mainly focused on metabolic differences between normal fetuses and those with CHD, but few studies addressed the variation of metabolic profiles with different CHD severity [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The present investigation utilized untargeted metabolomics methods to explore the metabolic profiles and differential metabolites associated with mild, moderate, and severe CHD lesions. Our findings may advance the understanding of the metabolic status of the fetus affected by CHD with various degrees of lesions and provide novel concepts for studying the pathological mechanism of CHD with different severity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSamples\u003c/h2\u003e \u003cp\u003eThis study was approved by the Medical Ethics Committee of Nanjing Maternity and Child Health Care Hospital. From the 1859 samples collected from January 2012 to December 2018, maternal amniotic fluid from fetuses affected by CHD with mild (15 cases), moderate (7 cases), and severe (29 cases) lesions were selected for untargeted metabolomics analysis. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the specific types of the selected 51 CHD cases. The severity of CHD lesions was classified according to published literature [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. There was no significant difference in gestational age, maternal age, and fetus sex among the three groups with different CHD severity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All specimens were residual samples left after clinical molecular diagnostic tests and were stored at -80\u0026deg;C before analysis. All cases were single pregnancies from the Jiangsu province diagnosed with CHD by fetal echocardiogram and then referred to the prenatal diagnosis center of the Nanjing Maternity and Child Health Care Hospital. Except for CHD, other structural or chromosomal abnormalities and pregnancy complications, such as hypertension and diabetes, were excluded from all samples. Except for 28 terminated pregnancies, postnatal ultrasound diagnosis was consistent with prenatal results in the remaining 23 cases of CHD.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecific CHD types of mild, moderate, and severe lesions groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHD type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePVS\u0026thinsp;+\u0026thinsp;VSD\u0026thinsp;+\u0026thinsp;ASD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePVS\u0026thinsp;+\u0026thinsp;VSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeterotaxy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAA,B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAA,B\u0026thinsp;+\u0026thinsp;VSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAA,B\u0026thinsp;+\u0026thinsp;VSD\u0026thinsp;+\u0026thinsp;ASD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAA,B\u0026thinsp;+\u0026thinsp;MA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDORV\u0026thinsp;+\u0026thinsp;VSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDORV\u0026thinsp;+\u0026thinsp;PVS\u0026thinsp;+\u0026thinsp;VSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ed-TGA\u0026thinsp;+\u0026thinsp;VSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHLHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHLHS\u0026thinsp;+\u0026thinsp;VSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHLHS\u0026thinsp;+\u0026thinsp;APVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHLHS\u0026thinsp;+\u0026thinsp;VSD\u0026thinsp;+\u0026thinsp;MA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHLHS\u0026thinsp;+\u0026thinsp;MA\u0026thinsp;+\u0026thinsp;DORV\u0026thinsp;+\u0026thinsp;mid-aortic dysplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePersistent truncus arteriosus\u0026thinsp;+\u0026thinsp;VSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOF\u0026thinsp;+\u0026thinsp;PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOF\u0026thinsp;+\u0026thinsp;interruption of inferior vena cava\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere PA\u0026thinsp;+\u0026thinsp;mitral insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVSD, ventricular septal defect; ASD, atrial septal defect; TOF, tetralogy of fallot;\u003c/p\u003e \u003cp\u003ePVS, pulmonary valve stenosis; IAA, B, interrupted aortic arch type B; MA, mitral atresia;\u003c/p\u003e \u003cp\u003eDORV, double outlet right ventricle; d-TGA, d-transposition of the great arteries;\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHLHS, hypoplastic left heart syndrome; APVR, partial anomalous pulmonary venous return;\u003c/p\u003e\u003cp\u003ePA, pulmonary atresia.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of samples used for metabolic profile analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild lesions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate lesions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere lesions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(21\u0026ndash;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(22\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(20\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(24\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(22\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(23\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFetal gender(female/male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGestational age and maternal age are represented by median.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eReagents and instruments\u003c/h2\u003e \u003cp\u003eUltrapure water was prepared using a Milli-Q reference system (Millipore, Billerica, MA, USA). Fatty acid methyl ester (C7\u0026ndash;C30, FAMEs) standards, anhydrous sodium sulfate, methoxyamine HCl, and pyridine were purchased from Sigma-Aldrich (St. Louis, MO, USA). Acetonitrile (Optima LC-MS), N-methyl-N(trimethylsilyl)trifluoroacetamide (MSTFA) with 1% (vol/vol) trimethylchlorosilane (TMCS), hexane, methanol (Optima LC-MS), chloroform, dichloromethane, and acetone were obtained from Thermo-Fisher Scientific (Fair Lawn, NJ, USA). A Rxi-5 ms capillary column (30 m \u0026times; 250 \u0026micro;m i.d., 0.25 \u0026micro;m film thickness, Restek Corporation, Bellefonte, PA, USA) was used for separation. GC-TOF/MS (Pegasus HT, Leco Corp., St. Joseph, MO, USA), equipped with an Agilent 7890B gas chromatograph (Agilent, Santa Clara, CA, USA) and a Gerstel multipurpose sampler MPS2 with dual heads (Gerstel, Muehlheim, Germany) was applied for sample analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample preparation and analysis conditions for GC/TOF-MS\u003c/h2\u003e \u003cp\u003eSample preparation, mass spectrometry conditions, and detection methods of GC/TOF-MS were as we previously reported [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eXploreMET 3.0 software was applied for peak picking, automated baseline denoising, signal alignment, and deconvolution. Metabolites were annotated by comparing mass spectrometry data and retention indices with the JiaLib metabolite database. After transforming each data into a comparable data vector, MetaboAnalyst 5.0, an open software, was used for metabolomics analysis, which included the principal component analysis (PCA), partial least square discrimination analysis (PLS-DA), and variable importance in the projection (VIP) value. Mann-Whitney \u003cem\u003eU\u003c/em\u003e tests, Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests, and Spearman correlation analyses were performed using SPSS 22.0. Metabolites shown by univariate analysis to have \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 on multivariate analysis were considered reliable differential metabolites [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For Spearman correlation analysis, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAbsence of significant differences in metabolic profiles among CHD with mild, moderate, and severe lesions\u003c/h2\u003e \u003cp\u003eA total of 265 small molecule metabolites were detected in amniotic fluid, and 178 metabolites were identified successfully. We performed multivariate statistical analysis of the 178 metabolites and 50 metabolite ratios based on the KEGG database.\u003c/p\u003e \u003cp\u003eAn unsupervised PCA model showed no significant separation trend among CHD with mild, moderate, and severe lesions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Moreover, using a supervised PLS-DA model, we confirmed the lack of significant differences in metabolic profiles between the three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In the PLS-DA model, although a separation trend could be observed among the three groups (R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;=\u0026thinsp;0.90, Q\u003csup\u003e2\u003c/sup\u003e= -0.36), a negative value of Q\u003csup\u003e2\u003c/sup\u003e indicated that the model was over-fitted. In fact, the results of the permutation test of this model yielded the \u003cem\u003eP-\u003c/em\u003evalue of 0.673, demonstrating that the separation trend was caused by the over-fitting of the model. These results showed no significant difference in metabolic profiles among CHD with mild, moderate, and severe lesions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDifferential metabolite levels among CHD with mild, moderate, and severe lesions\u003c/h2\u003e \u003cp\u003eAlthough the overall metabolic profile was comparable between the three groups, univariate and multivariate analyses were conducted to identify the differential metabolites among the three groups. Comparison of differential metabolites between each two groups was performed by Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or the Mann-Whitney \u003cem\u003eU\u003c/em\u003e test. Normally distributed data were analyzed by \u003cem\u003et\u003c/em\u003e-tests while if one or more of the two datasets were non-normally distributed, the Mann-Whitney \u003cem\u003eU\u003c/em\u003e test was used. VIP values were obtained according to the PLS-DA model. The identified differential metabolites between the different groups are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential metabolites of CHD with different lesions and the trend of change\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolites or metabolite ratios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrend in the latter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003emild lesions VS moderate lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrnithine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThreonine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGluconolactone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSorbose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUridine/Cytidine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePentadecanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePutrescine/Ornithine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUric acid/Xanthine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD-Xylose/D-Xylitol ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003emild lesions VS severe lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTryptamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlpha-ketoisovaleric acid/L-Valine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGluconolactone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-Hydroxycinnamic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrnithine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinoleic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXanthine/Xanthosine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHippuric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-Hydroxyproline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMG182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emoderate lesions VS severe lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUridine/Cytidine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSorbose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eVIP, variable importance in the projectio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the univariate analysis indicated that the concentrations of 9 metabolites or metabolite ratios, namely, ornithine, threonine, gluconolactone, sorbose, the pentadecanoic acid, uridine/cytidine ratio, the putrescine/ornithine ratio, the uric acid/xanthine ratio, and the D-xylose/D-xylitol ratio differed significantly in mild and moderate lesions. Of these, ornithine, threonine, gluconolactone, sorbose, pentadecanoic acid, and the uric acid/xanthine ratio met the VIP \u003e 1 requirement, indicating that these 6 metabolites or metabolite ratios were more reliable. The levels of ornithine, threonine, and pentadecanoic acid were significantly reduced in the moderate CHD lesions, while the levels of gluconolactone, sorbose, and the uric acid/xanthine ratio levels were significantly elevated.\u003c/p\u003e \u003cp\u003eThe univariate analysis showed that the levels of 10 metabolites or metabolite ratios differed significantly between mild and severe CHD. These were ornithine, gluconolactone, tryptamine, linoleic acid, hippuric acid, 4-hydroxycinnamic acid, 4-hydroxyproline, MG182, the alpha-ketoisovaleric acid/valine ratio, and the xanthine/xanthosine ratio, with only tryptamine and 4-hydroxyproline not satisfying the VIP \u003e 1 criterion. Of the 8 differential metabolites verified by multivariate statistical analysis, only the levels of gluconolactone and 4-hydroxycinnamic acid were increased in severe CHD, while those of the other metabolites were decreased.\u003c/p\u003e \u003cp\u003eThere was significant difference in sorbose levels and the uridine/cytidine ratio between moderate and severe CHD; however, only sorbose was verified by the multivariate analysis, showing a decrease.\u003c/p\u003e \u003cp\u003eIn general, although there were no significant differences in the overall metabolic profile among mild, moderate, and severe CHD, there were still several metabolite levels that differed between groups. However, we did not identify a metabolite that was able to distinguish between mild, moderate, and severe CHD at the same time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis between differential metabolites and CHD severity\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was performed to analyze the associations between differential metabolites and CHD severity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Gluconolactone (r\u0026thinsp;=\u0026thinsp;0.469, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), sorbose (r\u0026thinsp;=\u0026thinsp;0.577, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), and the uric acid/xanthine ratio (r\u0026thinsp;=\u0026thinsp;0.438, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) were positively correlated with moderate CHD lesions, while ornithine (r=-0.531, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), threonine (r=-0.546, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), and pentadecanoic acid (r=-0.454, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) were negatively associated with moderate CHD lesions in comparison with mild lesions. Among the 6 metabolites, ornithine, threonine, and sorbose showed moderate correlations, while gluconolactone, pentadecanoic acid, and the uric acid/xanthine ratio showed lower associations. When mild and severe CHD lesions were compared, we found several differential metabolites that were significantly correlated with severe CHD, including the alpha-ketoisovaleric acid/valine ratio (r=-0.383, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), gluconolactone (r\u0026thinsp;=\u0026thinsp;0.391, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), and 4-hydroxycinnamic acid (r\u0026thinsp;=\u0026thinsp;0.342, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023); however, the degree of correlation was low. Compared with moderate CHD lesions, we found only one differential metabolite, sorbose (r=-0.341, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), to be significantly associated with severe CHD; however, the correlation coefficient was low.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman correlation analysis of differential metabolites and the severity of CHD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolites or metabolite ratios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrelation Coefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003emild lesions VS moderate lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrnithine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThreonine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGluconolactone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSorbose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePentadecanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUric acid/Xanthine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003emild lesions VS severe lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlpha-ketoisovaleric acid/Valine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGluconolactone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-Hydroxycinnamic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrnithine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinoleic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXanthine/Xanthosine ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHippuric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMG182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate lesions VS severe lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSorbose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, ornithine, threonine, sorbose, gluconolactone, pentadecanoic acid, and the uric acid/xanthine ratio were correlated with moderate CHD lesions, while the alpha-ketoisovaleric acid/valine ratio, gluconolactone, 4-hydroxycinnamic acid, and sorbose were correlated with severe CHD lesions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCHD is the most common birth defect, and its phenotype varies widely. However, metabolic profiles in CHD of different severity were rarely investigated. In this study, GC-TOF/MS-based untargeted metabolomics was used to explore the metabolic profile of CHD with mild, moderate, and severe lesions, and the differences in metabolite concentrations among the three groups were determined. Multivariate analysis models, such as PCA and PLS-DA, did not identify any significant differences in metabolic profiles among mild, moderate, and severe types of CHD. However, univariate and multivariate statistical analysis showed that the levels of gluconolactone, threonine, ornithine, sorbose, pentadecanoic acid and the uric acid/xanthine ratio were statistically different between the mild and moderate lesions of CHD, while the alpha-ketoisovaleric acid/valine ratio, ornithine, gluconolactone, linoleic acid, hippuric acid, 4-hydroxycinnamic acid, MG182, and the xanthine/xanthosine ratio levels were significantly different between mild and severe CHD lesions. Only one differential metabolite, sorbose, was identified between moderate and severe CHD lesions. Of the identified differential metabolites, ornithine, threonine, sorbose, gluconolactone, pentadecanoic acid, and the uric acid/xanthine ratio were correlated with moderate CHD lesions, while the alpha-ketoisovaleric acid/valine ratio, gluconolactone, 4-hydroxycinnamic acid, and sorbose were correlated with severe CHD lesions. At present, there are few studies on the relationship between small-molecule metabolites and CHD severity. Next, we will discuss several differential metabolites that are associated with the CHD severity.\u003c/p\u003e \u003cp\u003eThreonine is an essential amino acid for humans. It must be obtained from food and plays an important role in macromolecule biosynthesis, the modulation of nutritional metabolism, and gut homeostasis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The multiple pathways of threonine catabolism provide carbon for biosynthesis and epigenetic regulation of biological macromolecules affecting the self-renewal of embryonic stem cells (ESC) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Dong et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] found that the concentration of threonine in cardiac tissue is higher in cyanotic than acyanotic congenital heart disease. Conversely, in the present study, we observed a decrease in threonine level in amniotic fluid in CHD with moderate lesions compared to CHD with mild lesions. We hypothesized that these contrasting results might reflect the differences in sample types included in our analysis and that of Dong et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; however, the underlying mechanism remains to be identified.\u003c/p\u003e \u003cp\u003eOrnithine is a non-essential amino acid generated from arginine. It is mostly involved in the urea cycle, and the abnormalities in its metabolism are implicated in gyrate atrophy, cancer, hyperornithinemia, and hyperammonemia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Schl\u0026uuml;ter and collaborators demonstrated that hypoxia could induce an increase in arginase, as an early response to hypoxia, which catalyzes the formation of ornithine and urea [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. According to their findings, hypoxia may lead to increased ornithine levels. Conversely, in the present study, we observed a significant decrease in ornithine levels in CHD with moderate and severe lesions than in CHD with mild lesions. Thus, we suspect that the response to hypoxia may depend on CHD severity: mild hypoxia may increase ornithine concentration, but severe hypoxia may have the opposite effect. However, other possible explanations cannot be excluded.\u003c/p\u003e \u003cp\u003eGluconolactone, a naturally occurring glucose derivative and antioxidant, has cardioprotective effects in humans [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We found significantly higher gluconolactone levels in moderate and severe types of CHD than in mild CHD types. This increase may represent a self-protective mechanism against cardiac disease. Pentadecanoic acid is an odd-chain saturated fatty acid, mainly obtained through dairy products, and there is a negative correlation between circulating pentadecanoic acid and the risk of metabolic disease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Research from Khaw \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] indicated that pentadecanoic acid was negatively associated with the risk of coronary heart disease. In this study, we found that the level of pentadecanoic acid in moderate CHD lesions was significantly lower than that in mild lesions, with a Spearman's correlation coefficient of -0.454, which was consistent with the findings of Khaw et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSorbose is a monosaccharide that can be detected in human feces, blood, and urine, while 4-hydroxycinnamic acid, a derivate of cinnamic acid, is abundant in plant seeds and leaves [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. At present, there are no reports on the relationship between sorbose, 4-hydroxycinnamic acid, and CHD severity.\u003c/p\u003e \u003cp\u003eIn summary, untargeted metabolomics documented the absence of significant differences in the overall metabolic profile among CHD with mild, moderate, and severe lesions. However, there were some differences in the levels of metabolites among the different groups. The main shortcomings of this study are the relatively small sample sizes of each group and the lack of validation samples. If conditions permit, we will collect verification cohort samples for verification in the subsequent work. In addition, due to the lack of normal control samples matched for gestational age, the use of differential metabolites for distinguishing non-CHD requires further investigation. Despite this, this performed analysis study can enable us to clarify the overall metabolic status of CHD with different severity and provide novel concepts to be used in investigating the mechanism of CHD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by Medical Ethics Committee of Nanjing Maternity and Child Health Care Hospital (Code number: [2018] NO.91). All participants signed informed consent before undergoing amniocentesis, and agreed for the use of the remaining samples from clinical tests for scientific research. The need for written informed consent was waived by the Nanjing Maternity and Child Health Care Hospital ethics committee due to retrospective nature of the study. All methods were carried out in accordance with relevant guidelines and regulations under Ethics approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable because there is no details, images, or videos related to an individual person.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available from the corresponding author Tao Jiang, and the email is [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (81770236), Jiangsu Natural Science Foundation (BK20181121), and the National Key Research and Development Program of China (2018YFC1002402).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL and YS were responsible for comprehensive sample collection, data collection, experimental operation, data analysis, figure preparation, table preparation and paper writing. \u0026nbsp;PY, XW and XZ were responsible for sample and information collection. \u0026nbsp;ZX, TJ and PH were responsible for the research design, funding, and paper writing. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the participants for their contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNamuyonga J, Lubega S, Aliku T, Omagino J, Sable C, Lwabi P. Pattern of congenital heart disease among children presenting to the Uganda Heart Institute, Mulago Hospital: a 7-year review. Afr Health Sci. 2020;20(2):745-52.\u003c/li\u003e\n\u003cli\u003eMazhani T, Steenhoff AP, Tefera E, David T, Patel Z, Sethomo W, et al. Clinical spectrum and prevalence of congenital heart disease in children in Botswana. Cardiovasc J Afr. 2020; 31(5):257-61.\u003c/li\u003e\n\u003cli\u003eZhang Z, Hu T, Wang J, Hu R, Li Q, Xiao L, et al. Pregnancy outcomes of fetuses with congenital heart disease after a prenatal diagnosis with chromosome microarray. 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Am J Transl Res. 2021; 13(6):6724-30. \u003c/li\u003e\n\u003cli\u003eTang Q, Tan P, Ma N, Ma X. Physiological Functions of Threonine in Animals: Beyond Nutrition Metabolism. Nutrients. 2021; 13(8):2592.\u003c/li\u003e\n\u003cli\u003eChapman KP, Courtney-Martin G, Moore AM, Ball RO, Pencharz PB. Threonine requirement of parenterally fed postsurgical human neonates. Am J Clin Nutr. 2009;89(1):134-41. \u003c/li\u003e\n\u003cli\u003eChen G, Wang J. Threonine metabolism and embryonic stem cell self-renewal. Curr Opin Clin Nutr Metab Care. 2014;17(1):80-5.\u003c/li\u003e\n\u003cli\u003eDong S, Wu L, Duan Y, Cui H, Chen K, Chen X, et al. Metabolic profile of heart tissue in cyanotic congenital heart disease. Am J Transl Res. 2021; 13(5):4224-32. \u003c/li\u003e\n\u003cli\u003eSivashanmugam M, J J, V U, K N S. Ornithine and its role in metabolic diseases: An appraisal. Biomed Pharmacother. 2017; 86:185-94. \u003c/li\u003e\n\u003cli\u003eSchl\u0026uuml;ter KD, Schulz R, Schreckenberg R. Arginase induction and activation during ischemia and reperfusion and functional consequences for the heart. Front Physiol. 2015; 6:65. \u003c/li\u003e\n\u003cli\u003eQin X, Liu B, Gao F, Hu Y, Chen Z, Xu J, et al. Gluconolactone Alleviates Myocardial Ischemia/Reperfusion Injury and Arrhythmias via Activating PKC\u0026epsilon;/Extracellular Signal-Regulated Kinase Signaling. Front Physiol. 2022; 13:856699.\u003c/li\u003e\n\u003cli\u003eFu WC, Li HY, Li TT, Yang K, Chen JX, Wang SJ, et al. Pentadecanoic acid promotes basal and insulin-stimulated glucose uptake in C2C12 myotubes. Food Nutr Res. 2021; 65. \u003c/li\u003e\n\u003cli\u003eKhaw KT, Friesen MD, Riboli E, Luben R, Wareham N. Plasma phospholipid fatty acid concentration and incident coronary heart disease in men and women: the EPIC-Norfolk prospective study. PLoS Med. 2012; 9(7):e1001255. \u003c/li\u003e\n\u003cli\u003eS\u0026oslash;rensen JL, Giese H. Influence of carbohydrates on secondary metabolism in Fusarium avenaceum. Toxins (Basel). 2013; 5(9):1655-63. \u003c/li\u003e\n\u003cli\u003eWEST CD, RAPOPORT S. Method for determination of sucrose and sorbose in blood and urine. Proc Soc Exp Biol Med. 1949; 70(1):140.\u003c/li\u003e\n\u003cli\u003eW\u0026uuml;rsch P, Welsch C, Arnaud MJ. Metabolism of L-sorbose in the rat and the effect of the intestinal microflora on its utilization both in the rat and in the human. Nutr Metab. 1979; 23(3):145-55.\u003c/li\u003e\n\u003cli\u003eYu Q, Fan L. Understanding the combined effect and inhibition mechanism of 4-hydroxycinnamic acid and ferulic acid as tyrosinase inhibitors. Food Chem. 2021; 352:129369.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Congenital heart disease, Severity, Metabolomics, Differential metabolites","lastPublishedDoi":"10.21203/rs.3.rs-2464935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2464935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCongenital heart disease (CHD) is characterized by various phenotypes, however, differences in metabolic profiles associated with CHD of various severity have not been elucidated. In this study, differences in metabolite concentrations among mild, moderate, and severe forms of CHD were explored, providing novel clues for our understanding of the mechanism of CHD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMaternal amniotic fluid samples from fetuses with mild (n\u0026thinsp;=\u0026thinsp;15), moderate (n\u0026thinsp;=\u0026thinsp;7), and severe (n\u0026thinsp;=\u0026thinsp;29) CHD lesions were analyzed by GC-TOF/MS. PCA, PLS-DA, and differential metabolite analysis among these three groups were conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePCA and PLS-DA models showed that metabolic profiles were comparable among CHD of different severity. Significant differences between mild and moderate CHD lesions were observed in the levels of gluconolactone, ornithine, threonine, sorbose, pentadecanoic acid, and the uric acid/xanthine ratio. Of these six differential metabolites, gluconolactone (r\u0026thinsp;=\u0026thinsp;0.469, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), sorbose (r\u0026thinsp;=\u0026thinsp;0.577, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and the uric acid/xanthine ratio (r\u0026thinsp;=\u0026thinsp;0.438, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) were positively correlated with moderate CHD lesions, while ornithine (r=-0.531, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), threonine (r=-0.546, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), and pentadecanoic acid (r=-0.454, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) were negatively associated. We found 9 differential metabolites between mild and severe CHD lesions, among which the alpha-ketoisovaleric acid/valine ratio (r=-0.383, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), gluconolactone (r\u0026thinsp;=\u0026thinsp;0.391, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), and 4-hydroxycinnamic acid (r\u0026thinsp;=\u0026thinsp;0.342, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) were correlated with severe CHD lesions. Only sorbose showed significant differences between moderate and severe CHD lesions, and was negatively associated with severe CHD lesions (r=-0.341, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCompared with mild CHD, specific differences were observed in metabolites or metabolite ratios in moderate and severe CHD lesions of CHD, several of which were significantly correlated with CHD severity. These results can help to understand the metabolic status of the affected fetus and provide new possibilities for exploring the pathological mechanism of CHD.\u003c/p\u003e","manuscriptTitle":"Untargeted metabolomics analysis of differences in metabolite levels in congenital heart disease of varying severity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-08 15:29:49","doi":"10.21203/rs.3.rs-2464935/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":"d5cf4ac6-0927-49b9-ab26-69f16d0c04ba","owner":[],"postedDate":"February 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-06-07T10:29:30+00:00","versionOfRecord":[],"versionCreatedAt":"2023-02-08 15:29:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2464935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2464935","identity":"rs-2464935","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}