Analysis of Differential Metabolites in Serum Metabolomics of Patients with Aortic Dissection

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Analysis of differential metabolites in serum provides new methods for exploring the early diagnosis and treatment of aortic dissection Objectives This study examined affected metabolic pathways to assess the diagnostic value of metabolomics biomarkers in clients with AD. Method The serum from 30 patients with AD and 30 healthy people was collected. The most diagnostic metabolite markers were determined using metabolomic analysis and related metabolic pathways were explored. Results A total of 71 differential metabolites were identified. The altered metabolic pathways included reduced phospholipid catabolism and four different metabolites considered of most diagnostic value including N2-gamma-glutamylglutamine, PC(phocholines) (20:4(5Z,8Z,11Z,14Z)/15:0), propionyl carnitine, and taurine. These four predictive metabolic biomarkers accurately classified AD patient and healthy control (HC) samples with an area under the curve (AUC) of 0.9875. Based on the value of the four different metabolites, a formula was created to calculate the risk of aortic dissection. Risk score = N2-gamma-glutamylglutamine × -0.684 ་ PC(20:4(5Z,8Z,11Z,14Z)/15:0) × 0.427 ་ propionyl carnitine × 0.523 ་ taurine × -1.242. An additional metabolic pathways model related to aortic dissection was explored. Conclusion Metabolomics can help to explore the metabolic disorders of AD and aid a further search for potential metabolic biomarkers. Aortic dissection differential metabolites metabolic biomarkers metabolic pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Aortic dissection (AD) is a life-threatening condition caused by a tear in the inner layer of the aorta, which results in a separation of the layers of the aortic wall and subsequent formation of a true lumen and a false lumen 1, 2 . Current methods of diagnosing AD include the D-dimer test and contrast-enhanced computed tomography (CT) of the aorta. D-Dimers have high sensitivity, but extremely poor specificity, and enhanced CT requires expensive equipment and the rays cause potential harm to the patients 3 . The search for early diagnosis and more effective therapeutic targets is critical to ensure timely treatment of AD and help to guide its therapy. Metabolomics is an effective and widely used technology that has been increasingly employed to comprehensively profile disease progression 4 . Serum is frequently considered as a pool of metabolites and the analysis of serum metabolomics has been an efficient tool to identify potential metabolic biomarkers of diseases, that may improve early diagnosis, prognostic prediction, and personalized therapy 5 . Although metabolomics has made great progress in the diagnosis and treatment of diseases, there are few studies on metabolites of aortic dissection. This study was designed to explore the metabolites and metabolic pathways of patients with aortic dissection and search for potential biomarkers. Methods Patients and study design. Patients were enrolled from Shenzhen People’s Hospital between October 2019 and April 2021. Inclusion criteria were a diagnosis of acute aortic dissection, of less than two weeks duration, confirmed by aortic vascular enhanced CT. Patients were excluded with intramural hematoma of the aorta, aortic aneurysm, embolism, malignant tumor, severe infectious disease, trauma, a recent surgical procedure, and severe heart failure with left ventricular ejection fraction less than 20%. Informed consent was obtained from all patients. This study was performed under the guidance of the Helsinki Declaration and was approved by all centers. Serum samples were collected before the aortic surgery and were immediately frozen at -40°C for metabolomic analysis. Chemical and sample preparation All chemicals and solvents were of analytical or HPLC (High Performance Liquid Chromatography) grade. Water, methanol, acetonitrile, and formic acid were purchased from CNW technologies GmbH (Dusseldorf Germany). The serum was separated after − 4°C refrigeration for 60 min, and stored at -80°C for metabolite analysis. Samples stored at -80 C were thawed at room temperature and 200 µL of the sample was added to a 1.5 mL Eppendorf tube with 10 µL of 2-chloro-l-phenylalanine (0.3 mg/mL) dissolved in methanol as internal standard. Then, the tube was vortexed for 10 s, and a 15 uL ice-cold mixture of methanol and acetonitrile was added to the sample. The mixtures were vortexed for 1 min, ultrasonicated at an ambient temperature of 25–28°C for 10 min, and stored at -20°C for 30 min. The extract was centrifuged at 13,000 rpm at 4°C for 15 min, 1.0mL of supernatant in a brown and glass vial was dried in a freeze concentration centrifugal dryer and 15 µL mixture of methanol and water were added to each sample, vortexed for 30 s, and then kept at 4°C for 2 min. Samples were centrifuged at 1300 rpm, 4°C for 5 min. Then, 100 µL supernatant aliquots from each tube were collected using crystal syringes, filtered through 0.22 pm microflora and transferential vials. These were stored at -80°C for liquid chromatography-mass spectrometry (LC-MS) analysis. Quality control (QC) samples were prepared by pooling aliquots of all samples and injected every 10 samples throughout the analytical run to provide a set of data from which repeatability could be assessed. Data preprocessing and statistical analysis: We analyze the LC-MS raw data by the orogenesis QI software (Waters Corporation, Milord, USA), with the parameters was set in 5 ppm (parts per million) ,10 ppm and 0.02 min for the precursor tolerance (PT), fragment tolerance (FT) and retention time (RT). According to three-dimensional data sets including m/Z, peaks RT and peak intensities. We acquired the Excel file which obtained filtered data. The internal standard was used for data QC reproducibility. Metabolites were identified by progenesis QI Data Processing Software, based on public databases and self-built databases. We acquired combined data from combined positive and negative data, and imported it into the R ropls package. We conducted Principle component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (O) PLS-DA to compare different of metabolic alterations among experimental groups. Overall contribution of each variable was ranked in the OPLS-DA model. Those variables with a VIP > 1 are considered relevant for group discrimination. In this study, the default seven-round cross-validation was applied with one-seventh of the samples excluded from the material model inch round, to guard against overfitting. The deferential metabolite was selected based on the combination of the statistically significant threshold of variable influence on projection (VIP) values obtained from the OPLS- DÁ model and p-values from a two-tailed Student's test on the normalized peak areas, where metabolites with VIP values larger than 1.0 and p < 0.05 were considered a deferential metabolite. Result This study followed-up 44 patients who were suspected of having AD and 30 patients diagnosed with acute AD were enrolled (see Fig. 1 ). Sixty samples of human serum, 30 from AD patients and 30 from healthy controls (HC), were collected for LC-MS analysis. Nontargeted profiling was performed to obtain the serum metabolic characteristics as comprehensively as possible. In this study, by employing nontargeted LC-MS platforms, the metabolic profiles of ADs and HCs could be analyzed in detail and the related disordered metabolism pathway underlying AD development investigated. A novel biomarker panel was subsequently identified and validated for differentiating ADs and HCs and its clinical practicability assessed (Fig. 1 ). Baseline clinical characteristics of the AD and HC groups were compared in Table 1 . Compared with HC subjects, AD patients had higher levels of heart rate and D-Dimers. There were no significant differences in age, blood pressure levels on admission, body mass index(BMI), whether hypertension and diabetes were present, cTnI, NT-pro BNP, total cholesterol, low- and high-density lipoprotein cholesterol and triglyceride levels. To reveal AD pathogenesis serum metabolites, the pairwise comparisons between the AD and HC groups were conducted. Based on the partial least squares (PLS-DA) method, the data showed significant separation between the two groups of samples (Fig. 2 A) and the permutation test showed that the PLS-DA score were dependable without overfitting with R 2 = 0 to 0.44 and Q2 = 0 to 0.455 (Supplementary materials, Fig. 2 ) Table 1 Baseline of Patients with Aortic Dissection and Healthy Controls Aortic dissection (n = 30) Heath Control (n = 30) p Value Male (n[%]) 27 (90.0) 25 (83.3) ＞0.05 Age (years) 52.93 ± 12.11 53.87 ± 12.22 ＞0.05 HR (bmp) 74.47 ± 19.18 83.43 ± 13.43 ＜0.05 SBP (mmHg) 147.40 ± 35.61 136.10 ± 32.44 ＞0.05 DBP (mmHg) 83.77 ± 20.65 84.30 ± 19.23 ＞0.05 BMI (kg/m2) 25.77 ± 4.04 24.12 ± 3.06 ＞0.05 Hypertension (n[%]) 26 (86.7) 23 (76.7) ＞0.05 Diabetes (n[%]) 3 (10.0) 4 (13.3) ＞0.05 Triglycerides(mmol/L) 1.60 ± 0.75 1.42 ± 0.95 ＞0.05 TC(mmol/L) 4.22 ± 1.23 4.01 ± 1.15 ＞0.05 LDL-C(mmol/L) 2.46 ± 1.09 2.36 ± 0.96 ＞0.05 HDL-C(mmol/L) 1.06 ± 0.33 1.08 ± 0.31 ＞0.05 cTnI(ng/dL) 0.07 ± 1.09 1.87 ± 0.62 ＞0.05 NTpro-BNP(pg/ml) 4017.34 ± 14098.03 2557.83 ± 5094.34 ＞0.05 D-Dimer(ug/L) 8970.75 ± 18508.39 594.64 ± 720.81 ＜0.05 A total of 1221 metabolites, including 426 downregulated and 795 upregulated metabolites were screened by LC-MS analysis. The screening criteria were that the variable’s importance in projection (VIP) > 1 and a t -test where p < 0.05. Once the metabolite fulfilled this condition, it was considered a potential biomarker. The AD and HC samples were compared to identify and characterize specific metabolites and underlying metabolic pathways. A total of 1221 metabolites, including 426 downregulated and 795 upregulated metabolites, were screened by LC-MS analysis and focused on 71 differential metabolites between two groups, Visualizing the p-value, VIP, and fold change values was helpful for screening differential metabolites, as shown in Fig. 3 . There were 13 specific metabolic biomarkers for distinguishing AD from HC (Table 2 ). A regression algorithm of least absolute shrinkage and selection operator (LASSO) was used to further identify four predictive metabolic biomarkers, N2-gamma-Glutamylglutamine, PC (20:4(5Z,8Z,11Z,14Z)/15:0), propionyl carnitine and taurine as shown in Fig. 4 , which served as a molecular diagnostic signature to predict whether individuals were suffering from AD or not. The training and test sets were set according to the concentration of metabolites and these four predictive metabolic biomarkers accurately classified the AD and HC samples with an AUC of 0.9875. Based on the value of the four different metabolites, a formula was developed to calculate the risk of aortic dissection: Risk score = N2-gamma-glutamylglutamine × -0.684 ་ PC(20:4(5Z,8Z,11Z,14Z)/15:0) × 0.427 ་ propionyl carnitine × 0.523 ་ taurine × -1.242. Table 2 Statistical Analysis of Diagnostic Biomarkers: Discovery Phase Metabolites log2(FC) Retention time (min) VIP P-value adj.P-value FC Dolichyl b-D-glucosyl phosphate 1.2323 4.9621 2.5299 0.000213 0.00424 2.3505 LysoPC(20:1(11Z)) -0.5924 12.3464 2.1241 8.21E-05 0.0024 0.66333 LysoPC(24:1(15Z)) 0.2348 12.5863 1.2708 0.00223 0.01696 1.1768 PC(20:4(5Z,8Z,11Z,14Z)/15:0) 0.9031 14.104 2.4255 0.000332 0.005473 1.8701 SM(d18:0/16:1(9Z)) -0.9505 13.0766 19.0271 3.86E-05 0.001593 0.5174 Sphingosine 1-phosphate -0.5039 10.3455 1.0297 4.92E-06 0.000451 0.7052 Taurine -1.0405 0.68757 1.1709 5.74E-07 0.000128 0.4862 Ursocholic acid -2.2665 9.5403 1.0286 0.01302 0.05564 0.2078 N2-gamma-Glutamylglutamine -1.4247 0.6876 1.5007 3.49E-06 0.000364 0.3725 L-Norleucine 0.23482 1.3388 3.6752 0.04904 0.137118 1.1768 Propionylcarnitine 1.2261 1.3976 2.3843 0.000976 0.010334 2.3394 2-Tetradecanone 0.3418 8.8515 1.4916 0.00018 0.00387 1.2671 Tryptophyl-Phenylalanine -1.4872 5.4163 1.0591 2.42E-07 7.86E-05 0.3567 To reveal the metabolic processes of metabolites and analyze the pathways in which these metabolites were involved, 71 differential metabolites were introduced into three Kyoto encyclopedia of genes and genomes (KEGG) databases HMDB, Lipimaps, and Metlin. The results suggested that the differential metabolites after screening were enriched in these three metabolic pathways choline metabolism in cancer, neuroactive ligand-receptor interaction and glycerophospholipid metabolism (Fig. 2 B). The study found that of the metabolic biomarkers, PC (20:4(5Z,8Z,11Z,14Z)/15:0) played a key role in the glycerophospholipid metabolism pathway (Fig. 5 ) and PC (20:4(5Z,8Z,11Z,14Z)/15:0) activated the glycerophospholipid metabolism pathway downstream of phosphitylated, including cell-cycle progression, proliferation migration angiogenesis, and actin reorganization. Discussion This study found that a metabolomic strategy could be used to identify potential metabolic markers of AD and to explore the metabolic pathways related to its occurrence. We carried out a comprehensive metabolomic evaluation of 30 AD patients. Metabolic phenotypes revealed significant pattern differences between patients with AD and HC, suggesting that AD may involve some metabolic disturbance, such as phospholipid metabolism disorder. Patients with AD have a higher heart rate and D-dimer 6 . Except for heart rate at admission and D-dimer levels, the clinical baseline data between the two groups of patients was the same. Analysis of metabolic pathways enriched by 71 different metabolites showed that the different metabolites were involved in five pathways, including choline metabolism in cancer, neuroactive ligand-receptor interaction, glycerophospholipid metabolism, sphingolipid metabolism, and ether lipid metabolism. KEGG analysis showed oxidative stress and lipid transport and metabolism were significantly implicated. Compared to HCs, patients with AD had down-regulated N2-gamma-glutamylglutamine(γ-glu) and taurine, both of which play a key role in amino acid and fat metabolism. especially taurine. There is evidence that it affects mitochondrial bioenergetics, counteracts lipid peroxidation and even increases cellular antioxidant defense in response to inflammation 7 . The patients with AD also had up-regulated propionyl carnitine and PC(20:4(5Z,8Z,11Z,14Z)/15:0). Propionyl carnitine is a free radical that can produce positive effects on endothelial function and protect endothelia from oxidative stress 8 . The increase in propionyl carnitine may be related to the repair of endometria after aortic dissection tear. The PC(20:4(5Z,8Z,11Z,14Z)/15:0) is the precursor of phospholipids, which can decholine to produce phospholipids through a series of metabolic reactions in the body. The phospholipid metabolism disorder may be closely related to the occurrence of AD. The diagnosis of acute AD relies on imaging such as vascular enhanced CT. 9 For community hospitals that lack relevant equipment, this is not conducive for the early diagnosis of acute aortic dissection, so finding a high-sensitivity metabolite of AD would be important for rescuing these patients. This study identified and validated the signature consisting of four differential metabolites known to be related to AD pathogenesis that can accurately distinguish AD patients from healthy controls. Despite limitations such as the small sample size, evidence showed that metabolites may be adopted as markers for the diagnosis of AD. Aortic dissection is caused by a tear in the intimal layer of the aorta, resulting in the separation of the layers of the aortic wall 10 . Extracellular matrix degradation and inflammation may contribute to the occurrence of the disease, but the precise trigger of aortic dissections is still unknown 2 . In the metabolic pathways enriched with differential metabolites, this study constructed a simple metabolic pathway model, where choline promoted the increase of phospholipids through transport proteins and second messengers. Phospholipids activated downstream pathways including cell-cycle progression, proliferation migration angiogenesis and actin reorganization, but PC(20:4(5Z,8Z,11Z,14Z)/15:0) controls and regulates other pathways by negative feedback. Previous studies considered that AD would activate cell-cycle progression 11, 12 and this is consistent with these results. It is considered that the inflammatory response was important for AD 13–15 , leading to smooth muscle cells (SMCs) expression of secretory molecules, including cytokines and extracellular matrix (ECM) molecules and prompting the proliferative capacity 16 . Inflammatory cells, such as lymphocytes and macrophages, secrete multiple inflammatory cytokines to promote vascular adhesion molecule expression 17 , eventually leading to proliferation migration angiogenesis. Inflammatory cells also contribute to the apoptosis of SMCs in the aortic artery and lead to medial degradation, exacerbating intimal tears 18 . Actin is an important protein, playing a critical role in many cellular functions and the interaction of actin with myosin forms the basis of muscle contraction 19 . In human aortic smooth muscle cells, smooth muscle α-actin (α-SMA) participated in filamentous actin formation and its expression can facilitate stress fiber formation and cell contraction in human aortic smooth muscle cells 20 . During the development of AD, smooth muscle cells may switch occurrence of phenotype 21 ,lead to actin reorganization and cause smooth muscle cell apoptosis 21 , eventually causing the occurrence and progression of aortic dissection. This study had some limitations. It failed to functionally verify the differential metabolites further. The sample size was small and could be further expanded in the future. Conclusion 1.Patients with AD had down-regulated N2-gamma-Glutamylglutamine(γ-glu) and taurine, up-regulated propionyl carnitine and PC(20:4(5Z,8Z,11Z,14Z)/15:0). It may become a marker for diagnosing aortic dissection. 2. There are a multiple of metabolic pathway disorders in patients with AD. Phospholipids play a key role in the pathway, leading to cell-cycle progression, proliferation migration angiogenesis, and actin reorganization. Abbreviations AD aortic dissection CT computed tomography HPLC High Performance Liquid Chromatography LC-MS liquid chromatography-mass spectrometry QC Quality control RT retention time PCA Principle component analysis (O) PLS-DA orthogonal partial least-squares-discriminant analysis HC healthy controls BMI body mass index SMCs smooth muscle cells ECM extracellular matrix α-SMA smooth muscle α-actin Declarations Acknowledgements We thank the Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People’s Hospital （The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology）data. Author contributions Yun Gong , Tangzhiming Li Qiyun Liu, Xiaoyu Wang , Zixian Deng conceptualized and designed the study, carried out the initial analyses, drafted the initial manuscript and reviewed and revised the manuscript; Huadong Liu, Biao Yu, Lixin Cheng critically reviewed and revised the manuscript; and all authors approved the final manuscript as submitted. Ethics approval and consent to participate The research ethics committee of the Shenzhen People’s Hospital.in China approved this study and waived informed consent. All the participants agreed with this study. The written consents were taken from patients before participation. The study was carried out in accordance with Helsinki Declaration Competing interests The authors declare that they have no competing interests. Ethics approval and consent to participate All methods were carried out in accordance with the declaration of Helsinki. Consent for publication Not applicable Data Availability statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Funding Not applicable) References Silaschi M, Byrne J and Wendler O. Aortic dissection: medical, interventional and surgical management. Heart (British Cardiac Society) . 2017;103:78-87. Nienaber CA, Clough RE, Sakalihasan N, Suzuki T, Gibbs R, Mussa F, Jenkins MP, Thompson MM, Evangelista A, Yeh JS, Cheshire N, Rosendahl U and Pepper J. Aortic dissection. Nat Rev Dis Primers . 2016;2:16053. Pape LA, Awais M, Woznicki EM, Suzuki T, Trimarchi S, Evangelista A, Myrmel T, Larsen M, Harris KM, Greason K, Di Eusanio M, Bossone E, Montgomery DG, Eagle KA, Nienaber CA, Isselbacher EM and O'Gara P. Presentation, Diagnosis, and Outcomes of Acute Aortic Dissection: 17-Year Trends From the International Registry of Acute Aortic Dissection. Journal of the American College of Cardiology . 2015;66:350-8. Sabatine MS, Liu E, Morrow DA, Heller E, McCarroll R, Wiegand R, Berriz GF, Roth FP and Gerszten RE. Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation . 2005;112:3868-75. Tang WH, Wang Z, Cho L, Brennan DM and Hazen SL. Diminished global arginine bioavailability and increased arginine catabolism as metabolic profile of increased cardiovascular risk. Journal of the American College of Cardiology . 2009;53:2061-7. Nazerian P, Mueller C, Soeiro AM, Leidel BA, Salvadeo SAT, Giachino F, Vanni S, Grimm K, Oliveira MT, Jr., Pivetta E, Lupia E, Grifoni S and Morello F. Diagnostic Accuracy of the Aortic Dissection Detection Risk Score Plus D-Dimer for Acute Aortic Syndromes: The ADvISED Prospective Multicenter Study. Circulation . 2018;137:250-258. Seidel U, Huebbe P and Rimbach G. Taurine: A Regulator of Cellular Redox Homeostasis and Skeletal Muscle Function. Molecular nutrition & food research . 2019;63:e1800569. Andreozzi GM. Propionyl l-carnitine: intermittent claudication and peripheral arterial disease. Expert opinion on pharmacotherapy . 2009;10:2697-707. Nienaber CA and Clough RE. Management of acute aortic dissection. The Lancet . 2015;385:800-811. Hagan PG, Nienaber CA, Isselbacher EM, Bruckman D, Karavite DJ, Russman PL, Evangelista A, Fattori R, Suzuki T, Oh JK, Moore AG, Malouf JF, Pape LA, Gaca C, Sechtem U, Lenferink S, Deutsch HJ, Diedrichs H, Marcos y Robles J, Llovet A, Gilon D, Das SK, Armstrong WF, Deeb GM and Eagle KA. The International Registry of Acute Aortic Dissection (IRAD): new insights into an old disease. JAMA . 2000;283:897-903. Wang L, Zhang J, Fu W, Guo D, Jiang J and Wang Y. Association of smooth muscle cell phenotypes with extracellular matrix disorders in thoracic aortic dissection. J Vasc Surg . 2012;56:1698-709, 1709.e1. Müller BT, Modlich O, Prisack HB, Bojar H, Schipke JD, Goecke T, Feindt P, Petzold T, Gams E, Müller W, Hort W and Sandmann W. Gene expression profiles in the acutely dissected human aorta. Eur J Vasc Endovasc Surg . 2002;24:356-64. Son BK, Sawaki D, Tomida S, Fujita D, Aizawa K, Aoki H, Akishita M, Manabe I, Komuro I, Friedman SL, Nagai R and Suzuki T. Granulocyte macrophage colony-stimulating factor is required for aortic dissection/intramural haematoma. Nature communications . 2015;6:6994. Anzai A, Shimoda M, Endo J, Kohno T, Katsumata Y, Matsuhashi T, Yamamoto T, Ito K, Yan X, Shirakawa K, Shimizu-Hirota R, Yamada Y, Ueha S, Shinmura K, Okada Y, Fukuda K and Sano M. Adventitial CXCL1/G-CSF expression in response to acute aortic dissection triggers local neutrophil recruitment and activation leading to aortic rupture. Circulation research . 2015;116:612-23. Tieu BC, Lee C, Sun H, Lejeune W, Recinos A, 3rd, Ju X, Spratt H, Guo DC, Milewicz D, Tilton RG and Brasier AR. An adventitial IL-6/MCP1 amplification loop accelerates macrophage-mediated vascular inflammation leading to aortic dissection in mice. The Journal of clinical investigation . 2009;119:3637-51. Hayashi-Hori M, Aoki H, Matsukuma M, Majima R, Hashimoto Y, Ito S, Hirakata S, Nishida N, Furusho A, Ohno-Urabe S and Fukumoto Y. Therapeutic Effect of Rapamycin on Aortic Dissection in Mice. International journal of molecular sciences . 2020;21. Lindholt JS and Shi GP. Chronic inflammation, immune response, and infection in abdominal aortic aneurysms. Eur J Vasc Endovasc Surg . 2006;31:453-63. Luo F, Zhou XL, Li JJ and Hui RT. Inflammatory response is associated with aortic dissection. Ageing research reviews . 2009;8:31-5. Dominguez R and Holmes KC. Actin structure and function. Annual review of biophysics . 2011;40:169-86. Sun Y, Yang Z, Zheng B, Zhang XH, Zhang ML, Zhao XS, Zhao HY, Suzuki T and Wen JK. A Novel Regulatory Mechanism of Smooth Muscle α-Actin Expression by NRG-1/circACTA2/miR-548f-5p Axis. Circulation research . 2017;121:628-635. Clément M, Chappell J, Raffort J, Lareyre F, Vandestienne M, Taylor AL, Finigan A, Harrison J, Bennett MR, Bruneval P, Taleb S, Jørgensen HF and Mallat Z. Vascular Smooth Muscle Cell Plasticity and Autophagy in Dissecting Aortic Aneurysms. Arteriosclerosis, thrombosis, and vascular biology . 2019;39:1149-1159. Supplementary Material Supplementary Material is not available with this version Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Apr, 2024 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 27 Nov, 2023 Reviews received at journal 12 Nov, 2023 Reviewers agreed at journal 30 Oct, 2023 Reviews received at journal 09 Sep, 2023 Reviewers agreed at journal 20 Aug, 2023 Reviewers agreed at journal 14 Aug, 2023 Reviewers invited by journal 14 Aug, 2023 Editor assigned by journal 14 Aug, 2023 Editor invited by journal 30 Jul, 2023 Submission checks completed at journal 30 Jul, 2023 First submitted to journal 02 Jul, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3133220","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":222692445,"identity":"7b917871-6d00-4710-ba0d-dcee49f97cde","order_by":0,"name":"Yun Gong","email":"","orcid":"","institution":"Shenzhen People’s Hospital （The Second Clinical Medical College, Jinan University; ）","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Gong","suffix":""},{"id":222692446,"identity":"52d3e69a-be36-4cd7-9f38-35c0e5b1fc33","order_by":1,"name":"Tangzhiming Li","email":"","orcid":"","institution":"Shenzhen People’s Hospital （The Second Clinical Medical College, Jinan University; ）","correspondingAuthor":false,"prefix":"","firstName":"Tangzhiming","middleName":"","lastName":"Li","suffix":""},{"id":222692447,"identity":"ee6f2ba3-58c5-45ef-a47a-32e250ace06d","order_by":2,"name":"Qiyun Liu","email":"","orcid":"","institution":"Shenzhen People’s Hospital （The Second Clinical Medical College, Jinan University; ）","correspondingAuthor":false,"prefix":"","firstName":"Qiyun","middleName":"","lastName":"Liu","suffix":""},{"id":222692448,"identity":"00427971-779d-4aad-9dc1-c3ed84f91184","order_by":3,"name":"Xiaoyu Wang","email":"","orcid":"","institution":"Shenzhen People’s Hospital （The Second Clinical Medical College, Jinan University; ）","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Wang","suffix":""},{"id":222692449,"identity":"a4ab493d-c3e7-445a-8d63-e22b6aa8c73f","order_by":4,"name":"Zixian Deng","email":"","orcid":"","institution":"Shenzhen People’s Hospital （The Second Clinical Medical College, Jinan University; ）","correspondingAuthor":false,"prefix":"","firstName":"Zixian","middleName":"","lastName":"Deng","suffix":""},{"id":222692450,"identity":"cecc142d-bd8d-46a8-bd06-d194d7391a38","order_by":5,"name":"Huadong Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPgkGw8c//9nIsbG3HyBOC5sEg7ExA1uaMR/PmQSitZhJM7AdTpwn4WBApBbp5g3SBTxp6W0SDAkMPyq2EaFF5liB8QwJm9w26cYDjD1nbhPjsByDBB6DtNw2mQMJzIxtRGo5wJNwOJ1NIsGAaC2GzTwHDieQoiWtmHFmQ5phGzCQDxLlF36J5O0/PjbYyMu3tx988KOCCC0o4ACJ6kfBKBgFo2AU4AIA7+E3yYlCHa4AAAAASUVORK5CYII=","orcid":"","institution":"Shenzhen People’s Hospital （The Second Clinical Medical College, Jinan University; ）","correspondingAuthor":true,"prefix":"","firstName":"Huadong","middleName":"","lastName":"Liu","suffix":""},{"id":222692451,"identity":"da2f8056-8fd2-49f5-b1bb-82f80cd4d3c8","order_by":6,"name":"Biao Yu","email":"","orcid":"","institution":"Luohu People’s Hospital (Shenzhen Luohu Hospital Group, The Third Affiliated Hospital of Shenzhen University)","correspondingAuthor":false,"prefix":"","firstName":"Biao","middleName":"","lastName":"Yu","suffix":""},{"id":222692452,"identity":"4ca8cd02-406b-47ff-adaf-2437014ca019","order_by":7,"name":"Lixin Cheng","email":"","orcid":"","institution":"Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology,","correspondingAuthor":false,"prefix":"","firstName":"Lixin","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2023-07-02 16:29:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3133220/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3133220/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-024-03798-y","type":"published","date":"2024-04-25T21:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":41089341,"identity":"ded00c1f-744b-48a3-95da-4b362a7e447c","added_by":"auto","created_at":"2023-08-04 17:26:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":268311,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Design: 30 AD Patients and 30 Healthy Controls\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3133220/v1/1d8949c89c94dbc9c78564f7.png"},{"id":41090259,"identity":"ace76677-4e8e-474c-83f0-033b95e163f1","added_by":"auto","created_at":"2023-08-04 17:34:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":520839,"visible":true,"origin":"","legend":"\u003cp\u003ePLS-DA Score Plots and Metabolic Pathways\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3133220/v1/e1ad8d4a8748c0b4306c89fe.png"},{"id":41090260,"identity":"f268de93-9193-4a06-8e34-e2d6f0411ca1","added_by":"auto","created_at":"2023-08-04 17:34:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":327512,"visible":true,"origin":"","legend":"\u003cp\u003eThirteen specific metabolic biomarkers for distinguishing AD from HC\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3133220/v1/ce664811aa1f6f6deac85229.png"},{"id":41088349,"identity":"764704ba-c37d-469a-baa8-1873bb3d1a94","added_by":"auto","created_at":"2023-08-04 17:18:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":145174,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic Outcomes and Prediction Accuracies: The diagnostic outcomes are shown in the receiver-operating characteristic (ROC) curves for comparison between ADs and HCs. The prediction accuracies by the biomarkers in the training and test sets were compared between two groups.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3133220/v1/74ef9b79ab698f6c56ce10e3.png"},{"id":41088346,"identity":"9d8b4c75-6e32-4f4a-9376-46cfc9e176f9","added_by":"auto","created_at":"2023-08-04 17:18:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":889433,"visible":true,"origin":"","legend":"\u003cp\u003eProposed Mechanisms of Metabolite Phosphitylated on Pathogenesis of AD\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3133220/v1/2b9cbeff2188b9503197b19a.png"},{"id":55689377,"identity":"4448bf20-4189-4bcf-9448-c4bbdeb98965","added_by":"auto","created_at":"2024-05-01 21:57:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2729224,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3133220/v1/5891ce96-9a7d-4307-be43-004e8014f3e7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Differential Metabolites in Serum Metabolomics of Patients with Aortic Dissection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAortic dissection (AD) is a life-threatening condition caused by a tear in the inner layer of the aorta, which results in a separation of the layers of the aortic wall and subsequent formation of a true lumen and a false lumen\u003csup\u003e1, 2\u003c/sup\u003e. Current methods of diagnosing AD include the D-dimer test and contrast-enhanced computed tomography (CT) of the aorta. D-Dimers have high sensitivity, but extremely poor specificity, and enhanced CT requires expensive equipment and the rays cause potential harm to the patients\u003csup\u003e3\u003c/sup\u003e. The search for early diagnosis and more effective therapeutic targets is critical to ensure timely treatment of AD and help to guide its therapy.\u003c/p\u003e \u003cp\u003eMetabolomics is an effective and widely used technology that has been increasingly employed to comprehensively profile disease progression\u003csup\u003e4\u003c/sup\u003e. Serum is frequently considered as a pool of metabolites and the analysis of serum metabolomics has been an efficient tool to identify potential metabolic biomarkers of diseases, that may improve early diagnosis, prognostic prediction, and personalized therapy\u003csup\u003e5\u003c/sup\u003e. Although metabolomics has made great progress in the diagnosis and treatment of diseases, there are few studies on metabolites of aortic dissection. This study was designed to explore the metabolites and metabolic pathways of patients with aortic dissection and search for potential biomarkers.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003ePatients and study design.\u003c/p\u003e\u003cp\u003ePatients were enrolled from Shenzhen People’s Hospital between October 2019 and April 2021. Inclusion criteria were a diagnosis of acute aortic dissection, of less than two weeks duration, confirmed by aortic vascular enhanced CT. Patients were excluded with intramural hematoma of the aorta, aortic aneurysm, embolism, malignant tumor, severe infectious disease, trauma, a recent surgical procedure, and severe heart failure with left ventricular ejection fraction less than 20%. Informed consent was obtained from all patients. This study was performed under the guidance of the Helsinki Declaration and was approved by all centers.\u003c/p\u003e\u003cp\u003eSerum samples were collected before the aortic surgery and were immediately frozen at -40°C for metabolomic analysis.\u003c/p\u003e\u003cp\u003eChemical and sample preparation\u003c/p\u003e\u003cp\u003eAll chemicals and solvents were of analytical or HPLC (High Performance Liquid Chromatography) grade. Water, methanol, acetonitrile, and formic acid were purchased from CNW technologies GmbH (Dusseldorf Germany). The serum was separated after − 4°C refrigeration for 60 min, and stored at -80°C for metabolite analysis. Samples stored at -80 C were thawed at room temperature and 200 µL of the sample was added to a 1.5 mL Eppendorf tube with 10 µL of 2-chloro-l-phenylalanine (0.3 mg/mL) dissolved in methanol as internal standard. Then, the tube was vortexed for 10 s, and a 15 uL ice-cold mixture of methanol and acetonitrile was added to the sample. The mixtures were vortexed for 1 min, ultrasonicated at an ambient temperature of 25–28°C for 10 min, and stored at -20°C for 30 min. The extract was centrifuged at 13,000 rpm at 4°C for 15 min, 1.0mL of supernatant in a brown and glass vial was dried in a freeze concentration centrifugal dryer and 15 µL mixture of methanol and water were added to each sample, vortexed for 30 s, and then kept at 4°C for 2 min. Samples were centrifuged at 1300 rpm, 4°C for 5 min. Then, 100 µL supernatant aliquots from each tube were collected using crystal syringes, filtered through 0.22 pm microflora and transferential vials. These were stored at -80°C for liquid chromatography-mass spectrometry (LC-MS) analysis.\u003c/p\u003e\u003cp\u003eQuality control (QC) samples were prepared by pooling aliquots of all samples and injected every 10 samples throughout the analytical run to provide a set of data from which repeatability could be assessed.\u003c/p\u003e\u003cp\u003eData preprocessing and statistical analysis:\u003c/p\u003e\u003cp\u003eWe analyze the LC-MS raw data by the orogenesis QI software (Waters Corporation, Milord, USA), with the parameters was set in 5 ppm (parts per million) ,10 ppm and 0.02 min for the precursor tolerance (PT), fragment tolerance (FT) and retention time (RT). According to three-dimensional data sets including m/Z, peaks RT and peak intensities. We acquired the Excel file which obtained filtered data. The internal standard was used for data QC reproducibility.\u003c/p\u003e\u003cp\u003eMetabolites were identified by progenesis QI Data Processing Software, based on public databases and self-built databases. We acquired combined data from combined positive and negative data, and imported it into the R ropls package. We conducted Principle component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (O) PLS-DA to compare different of metabolic alterations among experimental groups. Overall contribution of each variable was ranked in the OPLS-DA model. Those variables with a VIP \u0026gt; 1 are considered relevant for group discrimination.\u003c/p\u003e\u003cp\u003eIn this study, the default seven-round cross-validation was applied with one-seventh of the samples excluded from the material model inch round, to guard against overfitting.\u003c/p\u003e\u003cp\u003eThe deferential metabolite was selected based on the combination of the statistically significant threshold of variable influence on projection (VIP) values obtained from the OPLS- DÁ model and p-values from a two-tailed Student's test on the normalized peak areas, where metabolites with VIP values larger than 1.0 and p \u0026lt; 0.05 were considered a deferential metabolite.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eThis study followed-up 44 patients who were suspected of having AD and 30 patients diagnosed with acute AD were enrolled (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sixty samples of human serum, 30 from AD patients and 30 from healthy controls (HC), were collected for LC-MS analysis. Nontargeted profiling was performed to obtain the serum metabolic characteristics as comprehensively as possible. In this study, by employing nontargeted LC-MS platforms, the metabolic profiles of ADs and HCs could be analyzed in detail and the related disordered metabolism pathway underlying AD development investigated. A novel biomarker panel was subsequently identified and validated for differentiating ADs and HCs and its clinical practicability assessed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBaseline clinical characteristics of the AD and HC groups were compared in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared with HC subjects, AD patients had higher levels of heart rate and D-Dimers. There were no significant differences in age, blood pressure levels on admission, body mass index(BMI), whether hypertension and diabetes were present, cTnI, NT-pro BNP, total cholesterol, low- and high-density lipoprotein cholesterol and triglyceride levels.\u003c/p\u003e \u003cp\u003eTo reveal AD pathogenesis serum metabolites, the pairwise comparisons between the AD and HC groups were conducted. Based on the partial least squares (PLS-DA) method, the data showed significant separation between the two groups of samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and the permutation test showed that the PLS-DA score were dependable without overfitting with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0 to 0.44 and Q2\u0026thinsp;=\u0026thinsp;0 to 0.455 (Supplementary materials, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline of Patients with Aortic Dissection and Healthy Controls\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=\"char\" char=\".\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAortic dissection (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeath Control (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (n[%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.93\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.87\u0026thinsp;\u0026plusmn;\u0026thinsp;12.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (bmp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.47\u0026thinsp;\u0026plusmn;\u0026thinsp;19.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.43\u0026thinsp;\u0026plusmn;\u0026thinsp;13.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＜0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147.40\u0026thinsp;\u0026plusmn;\u0026thinsp;35.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136.10\u0026thinsp;\u0026plusmn;\u0026thinsp;32.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.77\u0026thinsp;\u0026plusmn;\u0026thinsp;20.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.30\u0026thinsp;\u0026plusmn;\u0026thinsp;19.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (n[%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (76.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (n[%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecTnI(ng/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNTpro-BNP(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4017.34\u0026thinsp;\u0026plusmn;\u0026thinsp;14098.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2557.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5094.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＞0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-Dimer(ug/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8970.75\u0026thinsp;\u0026plusmn;\u0026thinsp;18508.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e594.64\u0026thinsp;\u0026plusmn;\u0026thinsp;720.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e＜0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 1221 metabolites, including 426 downregulated and 795 upregulated metabolites were screened by LC-MS analysis. The screening criteria were that the variable\u0026rsquo;s importance in projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1 and a \u003cem\u003et\u003c/em\u003e-test where p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Once the metabolite fulfilled this condition, it was considered a potential biomarker. The AD and HC samples were compared to identify and characterize specific metabolites and underlying metabolic pathways. A total of 1221 metabolites, including 426 downregulated and 795 upregulated metabolites, were screened by LC-MS analysis and focused on 71 differential metabolites between two groups, Visualizing the p-value, VIP, and fold change values was helpful for screening differential metabolites, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThere were 13 specific metabolic biomarkers for distinguishing AD from HC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A regression algorithm of least absolute shrinkage and selection operator (LASSO) was used to further identify four predictive metabolic biomarkers, N2-gamma-Glutamylglutamine, PC (20:4(5Z,8Z,11Z,14Z)/15:0), propionyl carnitine and taurine as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which served as a molecular diagnostic signature to predict whether individuals were suffering from AD or not. The training and test sets were set according to the concentration of metabolites and these four predictive metabolic biomarkers accurately classified the AD and HC samples with an AUC of 0.9875. Based on the value of the four different metabolites, a formula was developed to calculate the risk of aortic dissection:\u003c/p\u003e \u003cp\u003eRisk score\u0026thinsp;=\u0026thinsp;N2-gamma-glutamylglutamine \u0026times; -0.684 ་ PC(20:4(5Z,8Z,11Z,14Z)/15:0) \u0026times; 0.427 ་ propionyl carnitine \u0026times; 0.523 ་ taurine \u0026times; -1.242.\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\u003eStatistical Analysis of Diagnostic Biomarkers: Discovery Phase\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=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elog2(FC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRetention time (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eadj.P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDolichyl b-D-glucosyl phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.9621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.3505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysoPC(20:1(11Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.5924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.3464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.21E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysoPC(24:1(15Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.5863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC(20:4(5Z,8Z,11Z,14Z)/15:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.8701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM(d18:0/16:1(9Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.9505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.0766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.0271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.86E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingosine 1-phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.5039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.3455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.92E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaurine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.0405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.74E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrsocholic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.2665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.5403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2-gamma-Glutamylglutamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.4247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.49E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Norleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.6752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.137118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropionylcarnitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.3843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.3394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Tetradecanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.8515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTryptophyl-Phenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.4872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.4163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.42E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.86E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo reveal the metabolic processes of metabolites and analyze the pathways in which these metabolites were involved, 71 differential metabolites were introduced into three Kyoto encyclopedia of genes and genomes (KEGG) databases HMDB, Lipimaps, and Metlin. The results suggested that the differential metabolites after screening were enriched in these three metabolic pathways choline metabolism in cancer, neuroactive ligand-receptor interaction and glycerophospholipid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The study found that of the metabolic biomarkers, PC (20:4(5Z,8Z,11Z,14Z)/15:0) played a key role in the glycerophospholipid metabolism pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and PC (20:4(5Z,8Z,11Z,14Z)/15:0) activated the glycerophospholipid metabolism pathway downstream of phosphitylated, including cell-cycle progression, proliferation migration angiogenesis, and actin reorganization.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study found that a metabolomic strategy could be used to identify potential metabolic markers of AD and to explore the metabolic pathways related to its occurrence. We carried out a comprehensive metabolomic evaluation of 30 AD patients. Metabolic phenotypes revealed significant pattern differences between patients with AD and HC, suggesting that AD may involve some metabolic disturbance, such as phospholipid metabolism disorder.\u003c/p\u003e \u003cp\u003ePatients with AD have a higher heart rate and D-dimer\u003csup\u003e6\u003c/sup\u003e. Except for heart rate at admission and D-dimer levels, the clinical baseline data between the two groups of patients was the same.\u003c/p\u003e \u003cp\u003eAnalysis of metabolic pathways enriched by 71 different metabolites showed that the different metabolites were involved in five pathways, including choline metabolism in cancer, neuroactive ligand-receptor interaction, glycerophospholipid metabolism, sphingolipid metabolism, and ether lipid metabolism. KEGG analysis showed oxidative stress and lipid transport and metabolism were significantly implicated.\u003c/p\u003e \u003cp\u003eCompared to HCs, patients with AD had down-regulated N2-gamma-glutamylglutamine(γ-glu) and taurine, both of which play a key role in amino acid and fat metabolism. especially taurine. There is evidence that it affects mitochondrial bioenergetics, counteracts lipid peroxidation and even increases cellular antioxidant defense in response to inflammation\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe patients with AD also had up-regulated propionyl carnitine and PC(20:4(5Z,8Z,11Z,14Z)/15:0). Propionyl carnitine is a free radical that can produce positive effects on endothelial function and protect endothelia from oxidative stress\u003csup\u003e8\u003c/sup\u003e. The increase in propionyl carnitine may be related to the repair of endometria after aortic dissection tear.\u003c/p\u003e \u003cp\u003eThe PC(20:4(5Z,8Z,11Z,14Z)/15:0) is the precursor of phospholipids, which can decholine to produce phospholipids through a series of metabolic reactions in the body. The phospholipid metabolism disorder may be closely related to the occurrence of AD.\u003c/p\u003e \u003cp\u003eThe diagnosis of acute AD relies on imaging such as vascular enhanced CT.\u003csup\u003e9\u003c/sup\u003e For community hospitals that lack relevant equipment, this is not conducive for the early diagnosis of acute aortic dissection, so finding a high-sensitivity metabolite of AD would be important for rescuing these patients.\u003c/p\u003e \u003cp\u003eThis study identified and validated the signature consisting of four differential metabolites known to be related to AD pathogenesis that can accurately distinguish AD patients from healthy controls. Despite limitations such as the small sample size, evidence showed that metabolites may be adopted as markers for the diagnosis of AD.\u003c/p\u003e \u003cp\u003eAortic dissection is caused by a tear in the intimal layer of the aorta, resulting in the separation of the layers of the aortic wall\u003csup\u003e10\u003c/sup\u003e. Extracellular matrix degradation and inflammation may contribute to the occurrence of the disease, but the precise trigger of aortic dissections is still unknown\u003csup\u003e2\u003c/sup\u003e. In the metabolic pathways enriched with differential metabolites, this study constructed a simple metabolic pathway model, where choline promoted the increase of phospholipids through transport proteins and second messengers. Phospholipids activated downstream pathways including cell-cycle progression, proliferation migration angiogenesis and actin reorganization, but PC(20:4(5Z,8Z,11Z,14Z)/15:0) controls and regulates other pathways by negative feedback.\u003c/p\u003e \u003cp\u003ePrevious studies considered that AD would activate cell-cycle progression\u003csup\u003e11, 12\u003c/sup\u003e and this is consistent with these results. It is considered that the inflammatory response was important for AD\u003csup\u003e13\u0026ndash;15\u003c/sup\u003e, leading to smooth muscle cells (SMCs) expression of secretory molecules, including cytokines and extracellular matrix (ECM) molecules and prompting the proliferative capacity\u003csup\u003e16\u003c/sup\u003e. Inflammatory cells, such as lymphocytes and macrophages, secrete multiple inflammatory cytokines to promote vascular adhesion molecule expression\u003csup\u003e17\u003c/sup\u003e, eventually leading to proliferation migration angiogenesis. Inflammatory cells also contribute to the apoptosis of SMCs in the aortic artery and lead to medial degradation, exacerbating intimal tears\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eActin is an important protein, playing a critical role in many cellular functions and the interaction of actin with myosin forms the basis of muscle contraction\u003csup\u003e19\u003c/sup\u003e. In human aortic smooth muscle cells, smooth muscle α-actin (α-SMA) participated in filamentous actin formation and its expression can facilitate stress fiber formation and cell contraction in human aortic smooth muscle cells\u003csup\u003e20\u003c/sup\u003e. During the development of AD, smooth muscle cells may switch occurrence of phenotype\u003csup\u003e21\u003c/sup\u003e,lead to actin reorganization and cause smooth muscle cell apoptosis\u003csup\u003e21\u003c/sup\u003e, eventually causing the occurrence and progression of aortic dissection.\u003c/p\u003e \u003cp\u003eThis study had some limitations. It failed to functionally verify the differential metabolites further. The sample size was small and could be further expanded in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e1.Patients with AD had down-regulated N2-gamma-Glutamylglutamine(\u0026gamma;-glu) and taurine, up-regulated propionyl carnitine and PC(20:4(5Z,8Z,11Z,14Z)/15:0). It may become a marker for diagnosing aortic dissection.\u003c/p\u003e\n\u003cp\u003e2. There are a multiple of metabolic pathway disorders in patients with AD. Phospholipids play a key role in the pathway, leading to cell-cycle progression, proliferation migration angiogenesis, and actin reorganization.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAD \u0026nbsp; \u0026nbsp; \u0026nbsp;aortic dissection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCT \u0026nbsp; \u0026nbsp; \u0026nbsp;computed tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHPLC \u0026nbsp; \u0026nbsp;High Performance Liquid Chromatography\u003c/p\u003e\n\u003cp\u003eLC-MS \u0026nbsp; liquid chromatography-mass spectrometry\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQC \u0026nbsp; \u0026nbsp; \u0026nbsp;Quality control\u003c/p\u003e\n\u003cp\u003eRT \u0026nbsp; \u0026nbsp; \u0026nbsp; retention time\u003c/p\u003e\n\u003cp\u003ePCA \u0026nbsp; \u0026nbsp; \u0026nbsp;Principle component analysis\u003c/p\u003e\n\u003cp\u003e(O) PLS-DA \u0026nbsp; orthogonal partial least-squares-discriminant analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; healthy controls\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp;body mass index\u003c/p\u003e\n\u003cp\u003eSMCs \u0026nbsp; \u0026nbsp;smooth muscle cells\u003c/p\u003e\n\u003cp\u003eECM \u0026nbsp; \u0026nbsp; extracellular matrix\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026alpha;-SMA \u0026nbsp; smooth muscle \u0026alpha;-actin\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank the\u0026nbsp;Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People\u0026rsquo;s Hospital\u0026nbsp;（The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology）data.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eYun Gong\u003csup\u003e\u0026nbsp;\u003c/sup\u003e, Tangzhiming Li\u003csup\u003e\u0026nbsp;\u0026nbsp;\u003c/sup\u003eQiyun Liu, Xiaoyu Wang\u003csup\u003e, \u0026nbsp;\u003c/sup\u003eZixian Deng\u0026nbsp;conceptualized and designed the study, carried out the initial analyses, drafted the initial manuscript and reviewed and revised the manuscript;\u0026nbsp;Huadong Liu, Biao Yu, Lixin Cheng\u0026nbsp;critically reviewed and revised the manuscript; and all authors approved the final manuscript as submitted.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe research ethics committee of the Shenzhen People\u0026rsquo;s Hospital.in China approved this study and waived informed consent. All the participants agreed with this study. The written consents were taken from patients before participation. The study was carried out in accordance with Helsinki Declaration\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with the declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Consent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data Availability statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable)\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSilaschi M, Byrne J and Wendler O. Aortic dissection: medical, interventional and surgical management. \u003cem\u003eHeart (British Cardiac Society)\u003c/em\u003e. 2017;103:78-87.\u003c/li\u003e\n\u003cli\u003eNienaber CA, Clough RE, Sakalihasan N, Suzuki T, Gibbs R, Mussa F, Jenkins MP, Thompson MM, Evangelista A, Yeh JS, Cheshire N, Rosendahl U and Pepper J. Aortic dissection. \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e. 2016;2:16053.\u003c/li\u003e\n\u003cli\u003ePape LA, Awais M, Woznicki EM, Suzuki T, Trimarchi S, Evangelista A, Myrmel T, Larsen M, Harris KM, Greason K, Di Eusanio M, Bossone E, Montgomery DG, Eagle KA, Nienaber CA, Isselbacher EM and O\u0026apos;Gara P. Presentation, Diagnosis, and Outcomes of Acute Aortic Dissection: 17-Year Trends From the International Registry of Acute Aortic Dissection. \u003cem\u003eJournal of the American College of Cardiology\u003c/em\u003e. 2015;66:350-8.\u003c/li\u003e\n\u003cli\u003eSabatine MS, Liu E, Morrow DA, Heller E, McCarroll R, Wiegand R, Berriz GF, Roth FP and Gerszten RE. Metabolomic identification of novel biomarkers of myocardial ischemia. \u003cem\u003eCirculation\u003c/em\u003e. 2005;112:3868-75.\u003c/li\u003e\n\u003cli\u003eTang WH, Wang Z, Cho L, Brennan DM and Hazen SL. Diminished global arginine bioavailability and increased arginine catabolism as metabolic profile of increased cardiovascular risk. \u003cem\u003eJournal of the American College of Cardiology\u003c/em\u003e. 2009;53:2061-7.\u003c/li\u003e\n\u003cli\u003eNazerian P, Mueller C, Soeiro AM, Leidel BA, Salvadeo SAT, Giachino F, Vanni S, Grimm K, Oliveira MT, Jr., Pivetta E, Lupia E, Grifoni S and Morello F. Diagnostic Accuracy of the Aortic Dissection Detection Risk Score Plus D-Dimer for Acute Aortic Syndromes: The ADvISED Prospective Multicenter Study. \u003cem\u003eCirculation\u003c/em\u003e. 2018;137:250-258.\u003c/li\u003e\n\u003cli\u003eSeidel U, Huebbe P and Rimbach G. Taurine: A Regulator of Cellular Redox Homeostasis and Skeletal Muscle Function. \u003cem\u003eMolecular nutrition \u0026amp; food research\u003c/em\u003e. 2019;63:e1800569.\u003c/li\u003e\n\u003cli\u003eAndreozzi GM. Propionyl l-carnitine: intermittent claudication and peripheral arterial disease. \u003cem\u003eExpert opinion on pharmacotherapy\u003c/em\u003e. 2009;10:2697-707.\u003c/li\u003e\n\u003cli\u003eNienaber CA and Clough RE. Management of acute aortic dissection. \u003cem\u003eThe Lancet\u003c/em\u003e. 2015;385:800-811.\u003c/li\u003e\n\u003cli\u003eHagan PG, Nienaber CA, Isselbacher EM, Bruckman D, Karavite DJ, Russman PL, Evangelista A, Fattori R, Suzuki T, Oh JK, Moore AG, Malouf JF, Pape LA, Gaca C, Sechtem U, Lenferink S, Deutsch HJ, Diedrichs H, Marcos y Robles J, Llovet A, Gilon D, Das SK, Armstrong WF, Deeb GM and Eagle KA. The International Registry of Acute Aortic Dissection (IRAD): new insights into an old disease. \u003cem\u003eJAMA\u003c/em\u003e. 2000;283:897-903.\u003c/li\u003e\n\u003cli\u003eWang L, Zhang J, Fu W, Guo D, Jiang J and Wang Y. Association of smooth muscle cell phenotypes with extracellular matrix disorders in thoracic aortic dissection. \u003cem\u003eJ Vasc Surg\u003c/em\u003e. 2012;56:1698-709, 1709.e1.\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller BT, Modlich O, Prisack HB, Bojar H, Schipke JD, Goecke T, Feindt P, Petzold T, Gams E, M\u0026uuml;ller W, Hort W and Sandmann W. Gene expression profiles in the acutely dissected human aorta. \u003cem\u003eEur J Vasc Endovasc Surg\u003c/em\u003e. 2002;24:356-64.\u003c/li\u003e\n\u003cli\u003eSon BK, Sawaki D, Tomida S, Fujita D, Aizawa K, Aoki H, Akishita M, Manabe I, Komuro I, Friedman SL, Nagai R and Suzuki T. Granulocyte macrophage colony-stimulating factor is required for aortic dissection/intramural haematoma. \u003cem\u003eNature communications\u003c/em\u003e. 2015;6:6994.\u003c/li\u003e\n\u003cli\u003eAnzai A, Shimoda M, Endo J, Kohno T, Katsumata Y, Matsuhashi T, Yamamoto T, Ito K, Yan X, Shirakawa K, Shimizu-Hirota R, Yamada Y, Ueha S, Shinmura K, Okada Y, Fukuda K and Sano M. Adventitial CXCL1/G-CSF expression in response to acute aortic dissection triggers local neutrophil recruitment and activation leading to aortic rupture. \u003cem\u003eCirculation research\u003c/em\u003e. 2015;116:612-23.\u003c/li\u003e\n\u003cli\u003eTieu BC, Lee C, Sun H, Lejeune W, Recinos A, 3rd, Ju X, Spratt H, Guo DC, Milewicz D, Tilton RG and Brasier AR. An adventitial IL-6/MCP1 amplification loop accelerates macrophage-mediated vascular inflammation leading to aortic dissection in mice. \u003cem\u003eThe Journal of clinical investigation\u003c/em\u003e. 2009;119:3637-51.\u003c/li\u003e\n\u003cli\u003eHayashi-Hori M, Aoki H, Matsukuma M, Majima R, Hashimoto Y, Ito S, Hirakata S, Nishida N, Furusho A, Ohno-Urabe S and Fukumoto Y. Therapeutic Effect of Rapamycin on Aortic Dissection in Mice. \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e. 2020;21.\u003c/li\u003e\n\u003cli\u003eLindholt JS and Shi GP. Chronic inflammation, immune response, and infection in abdominal aortic aneurysms. \u003cem\u003eEur J Vasc Endovasc Surg\u003c/em\u003e. 2006;31:453-63.\u003c/li\u003e\n\u003cli\u003eLuo F, Zhou XL, Li JJ and Hui RT. Inflammatory response is associated with aortic dissection. \u003cem\u003eAgeing research reviews\u003c/em\u003e. 2009;8:31-5.\u003c/li\u003e\n\u003cli\u003eDominguez R and Holmes KC. Actin structure and function. \u003cem\u003eAnnual review of biophysics\u003c/em\u003e. 2011;40:169-86.\u003c/li\u003e\n\u003cli\u003eSun Y, Yang Z, Zheng B, Zhang XH, Zhang ML, Zhao XS, Zhao HY, Suzuki T and Wen JK. A Novel Regulatory Mechanism of Smooth Muscle \u0026alpha;-Actin Expression by NRG-1/circACTA2/miR-548f-5p Axis. \u003cem\u003eCirculation research\u003c/em\u003e. 2017;121:628-635.\u003c/li\u003e\n\u003cli\u003eCl\u0026eacute;ment M, Chappell J, Raffort J, Lareyre F, Vandestienne M, Taylor AL, Finigan A, Harrison J, Bennett MR, Bruneval P, Taleb S, J\u0026oslash;rgensen HF and Mallat Z. Vascular Smooth Muscle Cell Plasticity and Autophagy in Dissecting Aortic Aneurysms. \u003cem\u003eArteriosclerosis, thrombosis, and vascular biology\u003c/em\u003e. 2019;39:1149-1159.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Material","content":"\u003cp\u003eSupplementary Material is not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aortic dissection, differential metabolites, metabolic biomarkers, metabolic pathways","lastPublishedDoi":"10.21203/rs.3.rs-3133220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3133220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePathogenesis and diagnostic biomarkers of aortic dissection (AD) can be classified by analysis of the serum differential metabolites. Analysis of differential metabolites in serum provides new methods for exploring the early diagnosis and treatment of aortic dissection\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study examined affected metabolic pathways to assess the diagnostic value of metabolomics biomarkers in clients with AD.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe serum from 30 patients with AD and 30 healthy people was collected. The most diagnostic metabolite markers were determined using metabolomic analysis and related metabolic pathways were explored.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 71 differential metabolites were identified. The altered metabolic pathways included reduced phospholipid catabolism and four different metabolites considered of most diagnostic value including N2-gamma-glutamylglutamine, PC(phocholines) (20:4(5Z,8Z,11Z,14Z)/15:0), propionyl carnitine, and taurine. These four predictive metabolic biomarkers accurately classified AD patient and healthy control (HC) samples with an area under the curve (AUC) of 0.9875. Based on the value of the four different metabolites, a formula was created to calculate the risk of aortic dissection. Risk score\u0026thinsp;=\u0026thinsp;N2-gamma-glutamylglutamine \u0026times; -0.684 ་ PC(20:4(5Z,8Z,11Z,14Z)/15:0) \u0026times; 0.427 ་ propionyl carnitine \u0026times; 0.523 ་ taurine \u0026times; -1.242. An additional metabolic pathways model related to aortic dissection was explored.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMetabolomics can help to explore the metabolic disorders of AD and aid a further search for potential metabolic biomarkers.\u003c/p\u003e","manuscriptTitle":"Analysis of Differential Metabolites in Serum Metabolomics of Patients with Aortic Dissection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-04 17:17:58","doi":"10.21203/rs.3.rs-3133220/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2023-11-27T10:50:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-11-13T01:09:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79856237-fa17-43bd-88aa-30bb542827b3","date":"2023-10-30T09:55:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-09T11:41:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ffb21fd3-d840-4775-a729-63bfc5958054","date":"2023-08-20T10:26:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54ebc3ef-be27-4f52-a182-228e8ae2b772","date":"2023-08-14T12:10:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-08-14T10:14:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-14T10:11:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-07-30T21:50:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-07-30T21:44:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2023-07-02T16:26:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b2129ab-9cfb-4101-88d6-00b02d149545","owner":[],"postedDate":"August 4th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-01T21:57:30+00:00","versionOfRecord":{"articleIdentity":"rs-3133220","link":"https://doi.org/10.1186/s12872-024-03798-y","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2024-04-25 21:57:30","publishedOnDateReadable":"April 25th, 2024"},"versionCreatedAt":"2023-08-04 17:17:58","video":"","vorDoi":"10.1186/s12872-024-03798-y","vorDoiUrl":"https://doi.org/10.1186/s12872-024-03798-y","workflowStages":[]},"version":"v1","identity":"rs-3133220","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3133220","identity":"rs-3133220","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}