Identification of serum lipids and amino acids biomarkers in acute aortic dissection using tissue-informed metabolomics methods

Identification of serum lipids and amino acids biomarkers in acute aortic dissection using tissue-informed metabolomics methods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of serum lipids and amino acids biomarkers in acute aortic dissection using tissue-informed metabolomics methods Yi Liu, Hongyu Ye, Kejun Liu, Sihao Zhou, Chuan Yuan, Yingmeng Wu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9064932/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: Type A aortic dissection represents a critical cardiovascular emergency with high mortality and disability rates and a rising incidence. In addition to clinical management, efforts are directed towards identifying high-risk populations for prompt intervention. While some researchers have utilized blood metabolomics to investigate metabolic indicators of type A aortic dissection, the approach is hindered by significant variability, numerous confounding factors, inconsistent outcomes, and limited reproducibility. In this study, the feasibility and effectiveness of a tissue-informed oriented metabolomics approach which refered to oncological minimal residual disease (MRD) detection was investigated. METHODS: Serum and ascending aortic whole layer tissue were collected from 20 patients diagnosed with type A aortic dissection and 20 organ donor volunteers, serving as the experimental and control groups, respectively. Tissue-informed metabolomics included two steps: The metabolic profiles of ascending aortic tissues from patients with and without dissection were examined using a non-targeted metabolomics approach to identify tissue-specific metabolic differences(step 1). Following this, a targeted metabolomics approach was utilized to confirm and screen metabolites in blood samples from both patient groups based on the tissue metabolomic findings(step 2). RESULTS: The metabolomic profiles of aortic tissues obtained from patients diagnosed with type A aortic dissection were analyzed and found to exhibit significant divergence from those of aortic tissues sourced from individuals without any known health conditions. Disturbances in metabolic pathways, including central carbon metabolism, purine metabolism, and ascorbic acid metabolism, were particularly prominent in this study. Tissues were analyzed for 850 distinct metabolites, with amino acids and their derivatives being the most abundant at 147. Subsequently, the focus shifted to the plasma amino acid composition. Following comparative validation, three metabolites, namely kynurenine, homocysteine, and N-acetylneuraminic acid, were identified as significantly different. CONCLUSION: The findings of this study demonstrate the feasibility and effectiveness of utilizing tissue-informed metabolomics in the identification ofpatient-specific metabolites in type A aortic dissection. Tissue-informed metabolomics identified significant abnormalities in certain metabolic pathways and metabolite levels in patients diagnosed with type A aortic dissection. aortic dissection targeted metabolomics serum metabolites high-risk population biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights 1. This study represents a novel approach(tissue-informed metabolomics) in the identification ofpotential biomarkers in patients with type A aortic dissection through the integration of tissue and blood metabolomics. Idea of this novel approach was refered to minimal residual disease (MRD) detection in oncology. 2. Furthermore, the results of this study demonstrate the feasibility and effectiveness of utilizing tissue-informed metabolomics in the identification ofpatient-specific metabolites. Tissue-informed metabolomics can identified significant abnormalities in certain metabolic pathways and metabolite levels in patients diagnosed with type A aortic dissection. 1. Background A-type aortic dissection is an extremely aggressive cardiovascular disease with a high mortality rate, which can reach 57% despite surgical treatment in the hospital [ 1,2]. The disease lacks specific clinical symptoms [3], with a mortality rate of 1–2% per hour in patients who are untreated after the onset [4]. Throughout the consultation process, significant medical expenses create a substantial burden on patients, their families, and society. Nevertheless, the identification and early intervention of high-risk groups pose a significant challenge in managing this disease. In summary, there is a need to identify an efficient and expeditious screening method for at-risk populations and implement early intervention strategies for individuals at risk of developing aortic dissection. In recent years, research on blood metabolomics in the diagnosis of diseases has received great attention and has been used in the diagnosis of many diseases [6,8]. Numerous scholars have conducted research on type A aortic dissection utilizing blood metabolomics to discern potential specific metabolic markers for the disease. Nevertheless, these approaches were hindered by various limitations, such as high variability, multiple confounding factors, inconsistent outcomes, and limited reproducibility[9]. This study used a tissue-informed metabolomics approach which refered to oncological minimal residual disease (MRD) detection. The feasibility and effectiveness of this approach was investigated in this study. And this study also attempts to screen specific metabolic markers for patients with type A aortic dissection, preliminarily exploring the mechanism of the occurrence and development of aortic dissection, and providing new methods and ideas for screening and identifying high-risk populations of the disease. 2. methods (see annex for Serum metabolomics study methods and results) 2.1 Participants and sample collection This study was approved by the Ethics Committee. ( Zhongshan People's Hospital Clinical Research and Animal Experiment Ethic Committee ; Ethics number: 2024-073) Informed consent was obtained from each patient with aortic dissection and the donor’s immediate family. The studies were all conducted in accordance with the relevant guidelines / regulations, did not obtain any organs / tissues from the prisoners, and complied with the Declaration of Helsinki.All tissues used in the study were obtained by cardiothoracic surgeons at Zhongshan People's Hospital.Twenty aortic vascular tissue samples from patients with type A aortic dissection and 20 plasma samples from healthy organ donor volunteers were collected from September 2021 to July 2023. Twenty vascular tissue samples from patients with type A aortic dissection and 20 plasma samples from healthy organ donor volunteers were selected from the endothelial tissues of the aorta ofpatients who underwent surgery within 48 h of the onset of the disease (experimental group), while aortic tissue specimens were collected from healthy organ donor volunteers (control group); all specimens were cleared of surrounding connective tissues, and the blood was washed with saline. Plasma samples were collected within 24 h of disease onset; blood samples were collected and centrifuged at 3,000 rpm for 10 min at room temperature. The supernatant was divided into 1.5-ml centrifuge tubes (0.2 ml per tube) and immediately stored in a refrigerator at − 80°C for freezing, and refrigerated dry ice for transportation. 2.2 Sample preparation Methanol and acetonitrile were purchased from Merck (Kenyworth, NJ, USA) Acetic acid was purchased in Ron (Shanghai, China) Ammonium formate, ammonia, and formic acid were purchased in Aladdin (Shanghai, China). The mass spectrometer was purchased from SCIEX Corporation (Foster City, CA, USA). The ultra-efficient liquid chromatograph was purchased at Shimadzu (Japan). Centrifuge was purchased at Eppendorf (Hamburg, Germany). The constant temperature metal mixing instrument was purchased from Hangzhou Milan Instrument Co., LTD. (Hangzhou, China). One / 100,000 electronic balance was purchased from Mettoldo Instruments Co., Ltd. (Zurich, Switzerland). The centrifugal concentrator was purchased at LABCONCO (Missouri Kansas, USA). The vortex mixer was purchased at Kyllin-Bell (Haimen, China). The ultrasonic cleaning instrument was purchased from Kunshan Ultrasonic Instrument Co., Ltd. (Kunshan, China). Pipettes were purchased at Eppendorf (Hamburg, Germany). Automation workstations were purchased at Beckman Coulter (California, USA). The membrane sealwas purchased from Monad (Suzhou, China). The sample was removed from the − 80°C refrigerator and thawed on ice until it could be cut. Chop the sample and mix well,Samples were taken from multiple locations in the well-mixed sample, weighed 20 mg (± 1 mg), and added to the corresponding numbered centrifuge tubes . Subsequently, the sample was homogenized for 20 s on a ball-mill (30 HZ), before centrifuging at 3,000 r/min for 30 s at 4°C. After centrifugation, 400 µL of 70% methanol-water extract of the internal standard was added and oscillated at 1500 r/min for 5 min, before leaving on ice for 15 min. After centrifugation, 400 µL of 70% methanol internal standard extract was added, shaken at 1500 rpm for 5 min, and allowed to stand on ice for 15 min. Subsequently, the sample was centrifuged at 12000 rpm for 10 min at 4°C, before transferring 300 µL of supernatant to another tube of the corresponding number, and allowing to stand in the refrigerator at − 20°C for 30 min. Next, the sample was centrifuged at 12000 rpm for 3 min at 4°C, Pipette 200 µL of supernatant into the corresponding centrifuge tube for on-line analysis. 2.3 Chromatography mass spectrometry acquisition conditions High-performance liquid chromatography (HPLC): Two LC/MS methods were used for all samples. Analyses were performed using positive ion conditions and from column T3 (Water Acquisition Premier HSS T3 column 1.8 µm, 2. 1 mm × 100 mm) using 0. 1% formic acid in water solvent and 0. 1% formic acid in acetonitrile solvent B in the following gradient: 5–20% for 2 min, increasing to 60%, increasing to 99% for 1 min and 1.5 min, then back to 5% mobile phase B witnessed for 0. 1 min and held for 2.4 min. The analytical conditions were as follows: column temperature 40ºC; flow rate, 0.4 mL/min; injection volume 4 µL; and for the other sample, negative ion conditions were used with the same elution gradient as in the positive mode. Mass spectrometry conditions: Data were acquired using information-dependent acquisition using analyst Tfet 1.7. 1 software (Concord, ON, Canada) (IDA) mode. The source parameters were set as follows: ion source gas 1 (GAS1), 50 psi; ion source gas 2 (GAS2), 50 psi; curtain gas (CUR), 25 psi; temperature (TEM), 550ºC; aggregation potential (DP) in positive mode, 60 V or 60 V in negative mode; ion spray (ISVF), 5000 V or − 4000 V. The TOF MS scan parameters were set as follows: mass range, 50–1000 Da; accumulation time, 200 ms; and dynamic background subtract, on. The product ion scan parameters were set as follows: mass range, 25–1000 Da; accumulation time, 40 ms; positive and negative mode collision energies, 30 or − 30 V; collision energy diffusion, 15; resolution, UNIT; charge state, 1 to 1; intensity, 100 cps; exclusion of isotopes within 4 Da; mass tolerance, 50 ppm; maximum number of candidates to be monitored per cycle, and number of ions = 18. 2.4 Quality control The reproducibility of metabolite extraction and detection was determined by analyzing the overlapping display of total ion chromatogram (TIC) plots analyzed by mass spectrometry detection of different quality control samples. The empirical cumulative distribution function (ECDF) was used to analyze the frequency of coefficient of variation (CV) less than the reference value to determine the stability of the experimental data. Blank samples were interspersed throughout the experiment, and the peaks of the extracted ion chromatogram (EIC) were used to reflect cross-contamination between substances. Overall sample principal component pre-analysis: Principal component analysis (PCA) was performed on peaks extracted from all experimental and QC samples to determine whether the metabolites differed between groups and whether the experimental samples had good reproducibility. 2.5 Data processing The raw data were converted to mzXML format by ProteoWizard, and the XCMS program was used for peak extraction, alignment, and retention time correction. The peak area was corrected using the “SVR” method, and the peaks with a missing rate > 50% were filtered in each group of samples. The corrected and filtered peaks were searched in the laboratory’s own database and integrated with public libraries, AI prediction libraries, and the metDNA method to obtain metabolite identification information. The data were first standardized (unit variance scaling UV), and then multivariate statistical analyses, including PCA and orthogonal partial least squares discriminant analysis (OPLS-DA), were performed using the MetaboAnalystR package OPLSR.The quality of the model was tested using cross-validation to verify the model using R2 and Q2. The validity of the model was further examined using 200 random permutation experiments. 2.6 Differential metabolite screening annotation A combination of univariate and multivariate statistical analyses was used to screen for differential metabolites. We used univariate statistical analyses such as hypothesis testing and fold change(FC) of difference analysis, and performed screening using P value and FC values of univariate analyses combined with multivariate analyses such as PCA and OPLS-DA. A VIP > 1, P value 1 were used as screening criteria for the major metabolites. The metabolites interacted with each other in the organism to form different pathways, and the KEGG database was used to annotate the differential metabolites, Identify metabolic pathways with significant differences. Finally, the ability to identify differential metabolites and their sensitivity and specificity were evaluated using the receiver operating characteristic (ROC) curve and area under curve (AUC). 2.7 Comparison of Differences After initial derivation of metabolites with differential properties using untargeted tissue metabolism analysis, serum was subjected to targeted metabolomics analysis using a fixed species amino acid assay kit.The number of detectors was 94, which overlapped with 11 of the 850 metabolites screened. Meaningful differential metabolites were screened after targeting analysis and compared with the 11 metabolites described earlier to select identical metabolites(Fig. 1 ). 3. Results Among the 40 participants included in this study, there were no significant differences between the aortic dissection and control groups in terms of sex, age, height, weight, LDL, HDL, and BNP (Table 1 ). However, there were differences in presence of hypertension, P = 0.001. Table 1 Clinical information ofthe intercalated and control groups Factor Aortic dissection (n = 20) Control group (n = 20) P value Sex 0.632 Male 18 (90%) 17 (85%) Female 2 ( 10%) 3 ( 15%) Age (years) 52.20 ± 9.68 44.00 ± 10.68 0.663 Height (cm) 170.35 ± 5.38 169.10 ± 5.41 0.733 Weight (kg) 73.00 ± 7.39 67.35 ± 5.45 0.721 Hypertension 0.001 Yes 16 (80%) 4 (20%) No 4 (20%) 16 (80%) Diabetes 0.633 Yes 3 ( 15%) 2 ( 10%) No 17 (85%) 18 (90%) Low density lipoprotein (mmol/L) 2.22 ± 0.55 2.02 ± 1.04 0.469 High density lipoprotein (mmol/L) 0.82 ± 0.25 0.85 ± 0.33 0.371 Cardio-cerebrovascular disease 0.698 Yes 2 ( 10%) 3 ( 15%) No 18 (90%) 17 (85%) Brain Natriuretic Peptide (pg/ml) 326.69 ± 183.90 300.95 ± 244.45 0.469 The TIC plot and substance CV values were used to show that the data analysis had good reproducibility and stability (Fig. 2 a-d), and the EIC plot suggested that the substance cross-contamination was within the controllable range during the assay (Fig. 2 e-f). The principal component pre-analysis of the overall samples revealed variability and good reproducibility among the metabolite groups (Fig. 3 a-b). Non-targeted metabolomics analysis was first performed on vascular tissues, and after normalizing the data, PCA was performed on grouped samples (Fig. 3 c). Based on the detected ion peaks in each sample, the model monitors the QC samples through the PCA established above, which shows that the instrument state is stable (Fig. 3 e).However, the PCA method was not sensitive to the variables with small correlation. To solve this problem, we analyzed the samples again with OPLS-DA (Fig. 3 d).After multivariate statistical analysis ofthe samples, the scatter plots of the PCA and OPLS-DA scores suggested that the samples were significantly different in the distribution of the two sample groups. Hierarchical cluster analysis was used to derive a hierarchical clustering tree ofthe samples (Fig. 4 a), indicating that the experimental and control groups were well clustered. The clustering heat map also indicated that the two groups of metabolites were differentiated (Fig. 4 c). The variable importance in projection (VIP) value indicates the strength of the effect ofthe intergroup differences ofthe corresponding metabolite in the categorization discrimination of each group of samples in the model, and it is generally considered that metabolites with VIP > 1 are significantly different. The S-plot derived on the basis of OPLS-DA was used to visualize the metabolites with significant differences between groups (Fig. 4 b). After the initial screening of metabolites with significant differences between aortic tissues, VIP > 1, log2fc > 1, and P < 0.05 were used as screening conditions(Fig. 1 ), and 850 meaningful differential metabolites were selected (Fig. 5 a),and violin plot was drawn to show their distribution (Fig. 5 b), which is shown here in order ofVIP value size for the top 50 species only.Based on the differential metabolite results, the KEGG database was utilized to annotate the differential metabolites (Fig. 5 c),Combined with the Differential Abundance Score (DA Score) (Fig. 5 d), it can be seen that there are changes in the main metabolic pathways such as central carbon metabolism, purine metabolism, ascorbate and aldehyde metabolism, glyoxylate and dicarboxylate metabolism, and glucagon signaling pathway. Targeted serum metabolomics analysis of the amino acid metabolites (see Appendix for specific analyses) yielded 21 metabolites with differentials (Fig. 6 a ),and violin plot was drawn to show their distribution (Fig. 6 b). After validation and comparison with the differential metabolites identified in the metabolic analysis of aortic tissues, three metabolites with significant differences were screened, namely homocysteine, kynurenine, and N-acetylneuraminic acid.AUC of homocysteine was 0.96, specificity (0.91),sensitivity ( 1); AUC of kynurenine was 0.81, specificity (0.67),sensitivity (0.95); and AUC ofN-acetylneuraminic acid was 0.69, specificity (0.52), sensitivity (0.85)(Fig. 7 a-c).N-acetylneuraminic acid has an AUC value between 0.5–0.7, which has an average discriminatory ability, kynurenine has an AUC value between 0.7–0.9, which has a better discriminatory ability, and homocysteine has an AUC value > 0.9, which has a very good discriminatory value. 4. Discussion 4. 1 Significance of the tissue-informed metabolomics methods Type A aortic dissection is complex and has a high mortality rate of 50% -68% within 48 h after onset and 90% within 3 months [ 10]. In China, patients with aortic dissection are characterized by a gradually younger age of onset, a higher proportion of males, a wide variation in treatment options, and a gradual increase in incidence in recent years [ 11,12]. Currently, the mechanism of the occurrence and development of type A aortic dissection remains unclear; despite diagnostic means, most cases are only diagnosed after the appearance of symptoms at the clinic and there remains a lack of a convenient and quick method to screen out high-risk populations and to intervene in the high-risk patients as early as possible to slow the development of the disease. In recent years, peripheral blood has been used to search for biological markers. Previous studies have suggested that AD is related to D-dimer, MMPs, elastin, and inflammatory markers [ 13, 14]. However, these substances change to varying degrees in different diseases and so lack specificity. Pathological changes in the organism can cause some corresponding changes in type and level of the metabolites, and the use of metabolomics to analyze these disease-induced metabolites can help people better understand the process of disease and the metabolic pathways of substances in the organism, thereby providing new methods and ideas for the diagnosis and treatment of the disease [ 15]. Yong et al[ 16]. used blood metabolomics to study patients with AD and found that metabolites such as AFMK, glycerophosphocholine, and ergothioneine differed in patients with AD. These substances are mainly involved in amino acid, lipid, and choline metabolism[ 17]. Using the same method, Hao[ 18] showed that 38 metabolites differed between patients with type A aortic dissection and patients with non-type A aortic dissection who had hypertension, with plasma hydrocortisone and dimethylglycine differing more significantly. These metabolites are mainly involved in lipid metabolism, carbohydrate metabolism, and membrane transport, and serve to regulate the progression of AAD . which shows that the process of aortic dissection development and progression is accompanied by metabolic abnormalities, The use of metabolomics to explore the processes involved in the development and progression of the disease is feasible. However, as shown by their findings, the same methodology yielded different differential metabolites in the blood of different patients with aortic dissection, It can be seen that blood metabolomics results were hindered by high variability, multiple confounding factors, inconsistent outcomes, and limited reproducibility. In this study, a tumour-informed approach that has emerged in recent years in oncology research was drawn upon. This approach involves initially utilizing vascular tissue for untargeted metabolomics analysis, followed by serum for targeted metabolomics analysis to confirm the initial identification of differential metabolites in vascular tissue, with the aim of mitigating the aforementioned limitations. 4.2 Abnormalities in metabolic pathways In the first half of the study, by analyzing tissue untargeted metabolomics, After enriching differential metabolites into metabolic pathways we initially found that multiple metabolic pathways were abnormal in patients with type A aortic dissection, Among them, changes in metabolic pathways such as central carbon, ascorbic acid, and purine were more pronounced. Central carbon metabolism, a crucial metabolic system in organisms, includes the core processes of glycolysis, pentose phosphate, and the tricarboxylic acid cycle. It is the main conduit for energy for the organism, as well as a provider of precursor substances needed for other metabolic processes. When glucose metabolism is disturbed, it may lead to an imbalance in blood glucose levels, and blood glucose abnormalities promote arterial endothelial cell damage and accelerate the process of atherosclerosis [ 19,20].It has been found that vascular endothelial cells can utilize glycolysis to produce adenosine-5'-triphosphate ATP[21], Cardiovascular cells have high glycolytic activity, which has been shown to be their main source of ATP. Increased glycolysis following atherosclerosis in blood vessels as a means of maintaining the energy requirements of endothelial cells. However, prolonged excessive increase in glycolysis leads to inhibition of mitochondrial respiration, which increases reactive oxygen species and produces excess lactic acid, which increases the activity of proteases that destroy the extracellular matrix, leading to vascular inflammation and disruption of the vascular wall[22–24]. In the pentose phosphate pathway, reduced coenzyme II has a role in synthesizing lipids, nucleotides, and nitric oxide, thereby enhancing endothelial angiogenic activity [25], If overexpressed, it causes deposition of vascular lipids, leading to the formation of atherosclerosis. Succinate, an intermediate metabolite of the tricarboxylic acid cycle, is a ligand for the G protein-coupled receptor GPR91 (also known as SUCNR1). It has been shown that succinic acid utilizes the SUCNR1 signaling pathway to modulate the renin-angiotensin system and thus blood pressure[26]. It has been reported that the absence of macrophage citrate lyase in atherosclerotic plaques stabilizes atherosclerotic plaques[27]. It can be seen that abnormalities in the central carbon metabolism pathway can affect the vasculature in a number of ways, and its abnormalities may have a correlation with type A aortic dissection. Ascorbic acid is also known as vitamin C, Its metabolism plays multiple important roles in the body, one of which is to promote the synthesis of vascular collagen, which in turn enhances the elasticity of blood vessels and maintains their permeability[28]. About 30% of the body's protein is collagen, a key component that plays an important role in maintaining cell structure and function. If ascorbic acid intake is insufficient, collagen synthesis will be impaired, which in turn affects the formation of intercellular junction channels[29,30]. Collagen not only affects the connections between cells, but also regulates the structure and elasticity of the arterial and venous blood vessels in the body to some extent. When there is an inadequate supply of ascorbic acid, resulting in decreased collagen synthesis, the elasticity of the blood vessel wall decreases, which increases the risk of blood vessel rupture in the presence of elevated blood pressure. In the present study, an overall trend of decreased ascorbic acid metabolism was observed, which is thought to be another important factor contributing to decreased aortic wall elasticity. Thus, there may be a link between abnormalities in the ascorbic acid metabolic pathway and type A aortic dissection. In addition, abnormal purine metabolism is a noteworthy pathway. Disorders of purine metabolism in the body are associated with hyperuricemia, and prolonged hyperuricemia can also lead to atherosclerosis[31]. Untimely treatment of gout patients will lead to a continuous elevation of blood uric acid, which will damage the endothelium of blood vessels, thus leading to atherosclerosis and increasing the probability of cardiovascular and cerebrovascular diseases [32]. The P2X7 receptor, a receptor for purines, is able to influence endothelium-dependent diastolic function by reducing nitric oxide (NO) production upon activation [33]. This mechanism of action is crucial for vascular function.When endothelium-dependent diastolic function is diminished, blood vessels may become stiff and lose their normal elasticity, leading to an increase in vascular resistance and facilitating the progression of hypertension[34]. The increased level of purine metabolism in this study suggests that abnormalities in purine metabolic pathways may be associated with the development of type A aortic dissection. From the first half of the study, it can be concluded that the tissue metabolic profiles of patients with type A aortic dissection have obvious differences, and a preliminary understanding of the possible influence of certain metabolic pathway abnormalities on the development and progression of the disease has been established, which lays the foundation for the next step of exploring the changes of certain specific metabolic substances in the disease and searching for biomarkers. 4.3 Significantly different metabolites In the second half of the study, homocysteine, kynurenine and N-acetylneuraminic acid were screened for significant variability using a combination oftissue and blood metabolomics. Multiple studies have shown [35,36], Homocysteine may lead to reduced vascular elasticity and elevated pressure on blood vessels through factors such as pro-atherosclerosis, damage to vascular endothelial cells in response to inflammation, and remodeling of extracellular matrix components to degrade elastin and collagen. It also promotes platelet adhesion to vascular endothelial cells with elevated levels of procoagulant factors and other factors leading to thrombosis. It has been suggested[37] that the addition of homocysteine assays to the Framingham Risk Score (FRS) significantly improves the risk classification of cardiovascular disease. And in recent years, homocysteine has been used as a routine clinical test for cardiovascular disease. The results of this study showed that homocysteine levels in vascular tissues and blood of patients with type A aortic dissection were significantly higher than those of normal subjects, which suggests that there is a correlation between the development of type A aortic dissection and its metabolic abnormality, which may be a factor in the formation of aortic dissection. Kynurenine is produced by the tryptophan metabolic pathway and indoleamine 2,3-dioxygenase IDO is the rate-limiting enzyme for its metabolism, IDO deficiency may exacerbate the accumulation of macrophages and T cells in atherosclerotic plaques[38], It can further reduce the elasticity of blood vessels that were already suffering from atherosclerotic plaques. It has also been shown that[39], Kynurenine has a significant attenuation of coronary artery contractile response to thromboxane analog U-46619, It also reduces blood pressure by activating adenylate cyclase, which is a vasorelaxing enzyme that leads to vasodilation. Reduced levels of kynurenine, which can cause increased aortic atherosclerosis along with increased blood pressure, may cause the development of type A aortic dissection in the presence of both factors. The serum and tissue levels of kynurenine were significantly lower in the patients with type A aortic dissection in this study, and there is good evidence that kynurenine plays an important role in the pathogenesis of type A aortic dissection. N-acetylneuraminic acid is widely distributed in serum, cell membranes, mucous glands, and is also present in large quantities on the surface of vascular endothelial cells. Early studies have shown that[40], N-acetylneuraminic acid has been detected at elevated levels in patients with atherosclerosis, but the exact mechanism of action is unclear. In recent years, it has been reported that adequate salivation of the vascular endothelium determines its susceptibility to atherosclerotic plaque formation, and that desialylation of LDL causes the accumulation of cholesterol and lipids in the arterial wall. From the final results, the screened homocysteine and kynurenine were reported to have a possible correlation with the occurrence and development of aortic dissection, This demonstrates the feasibility of using a tissue-based information-directed assay to explore biomarkers in patients with type A aortic dissection in this study, This provides a new way of thinking and approaching the study of the occurrence and development of the disease. N-acetylneuraminic acid has been reported to be strongly associated with atherosclerosis, However, it has not yet been proposed to have a correlation with the occurrence of type A aortic dissection. In this study, it was found to have significant changes in type A aortic dissection, but the exact mechanism of action is still unclear and further studies are needed. 5. Limitations In this study, we failed to use a one-to-one assay kit for serum targeting analysis, but instead used a certain fixed type of kit. Therefore, when targeting the serum, the substances detected in the kit overlapped less with the substances we screened for, This will lead us to end up with fewer meaningful metabolites. The effectiveness of this method has been confirmed, and my colleagues will continue to expand the detection scope, hoping to detect more specific metabolites. Because the samples for the study were obtained after the onset of the disease, the causal relationship between the development of the disease and the metabolites has not been conclusively established. What can be determined, however, is that there is a relationship between them, which lays the groundwork for research to find biomarkers in patients with type A aortic dissection. 6. Conclusion As validated in this study, the use of tissue-informed metabolomics is feasible and effective in studies screening for patient-specific metabolites in type A aortic dissection. Analysis of the metabolic profiles of patients with type A aortic dissection revealed significant changes in the pathways of central carbon metabolism, purine metabolism, and ascorbic acid metabolism, mainly, and the development of the disease may be related to them. Of all the differential metabolites, homocysteine, kynurenine, and N-acetylneuraminic acid metabolites were the most distinctive, Potential biomarkers for screening people at high risk for type A aortic dissection. Abbreviations AAD acute aortic dissection AFMK formyl-N-acetyl-5-methoxykynurenamine AUC area under curve BNP brain natriuretic peptide CE collision energy CG curtain gas CV coefficient of variation DAS differential abundance score DP declustering potential ECDF empirical cumulative distribution function ESI electrospray ionization FC fold change FRS framingham risk score GC gas chromatography IDO indoleamine 2,3-dioxygenase KEGG kyoto encyclopedia of genes and genomes LC liquid chromatography LPC lyso-phosphatidylcholine MMPs matrix metallo proteinases MRM multiple reaction monitoring MS mass spectrometry MWDB metware database NMR nuclear magnetic resonance OPLS-DA orthogonal partial least squares-discriminant analysis PCA principal component analysis ROC receiver operating characteristic curve SVR support vector regression TIC total ion chromatogram UV unit variance scaling VIP variable importance in projection Declarations Clinical trial number Not applicable. Ethics approval and consent to participate This study was approved by the Ethics Committee.( Zhongshan People's Hospital Clinical Research and Animal Experiment Ethic Committee ; Ethics number: 2024-073). Informed consent was obtained from each patient with aortic dissection and the donor’s immediate family. The studies were all conducted in accordance with the relevant guidelines / regulations, did not obtain any organs / tissues from the prisoners, and complied with the Declaration of Helsinki.All tissues used in the study were obtained by cardiothoracic surgeons at Zhongshan People's Hospital. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This work was supported by Zhongshan city social public welfare and basic research project(2023B1023). Author contributions WZH and BFL conceived the study. KJL, CYand SHZ performed the database search, literature review, study selection, quality evaluation, and data collection. YMW and YL performed statistical analysis and interpreted the results. YL and HYY drafted the manuscript. All authors have contributed to the manuscript and approved the submitted version. Acknowledgements Not applicable. Author details WZH, BFL, YL, YMW, HYY, KJL, CY, and YL all belong to Zhongshan People's Hospital;The SHZ belongs to the The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 461 Hospital. Additional information Attachment added the serum metabolomics study methods and results References Levy D, GoyalA, Grigorova Y, et al. Aortic Dissection[J]. In: StatPearls. Treasure Island (FL): StatPearls Publishing; April 23, 2023. Pape LA, Awais M, Woznicki EM, et al. 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Amino Acids. 2023;10.1007/s00726-023-03339-5. Lis DM, Baar K. Effects of Different Vitamin C-Enriched Collagen Derivatives on Collagen Synthesis[J]. Int J Sport Nutr Exerc Metab. 2019;29(5):526–531. Shaw G, Lee-Barthel A, Ross ML, et al. Vitamin C-enriched gelatin supplementation before intermittent activity augments collagen synthesis[J]. Am J Clin Nutr. 2017;105( 1):136–143. Chu C, Dai Y, Mu J, et al. Associations of risk factors in childhood with arterial stiffness 26 years later: the Hanzhong adolescent hypertension cohort[J]. J Hypertens. 2017;35 Suppl 1:S10-S15. Fang JI, Wu JS, Yang YC, et al. High uric acid level associated with increased arterial stiffness in apparently healthy women[J]. Atherosclerosis. 2014;236(2):389–393. Komalavilas P, Luo W, Guth C M, et al.Vascular surgical stretch injury leads to activation of P2X7 receptors and impaired endothelial function[J].PLoS One,2017, 12 ( 11): e0188069. Schiffrin E L.How Structure, Mechanics, and Function of the Vasculature Contribute to Blood Pressure Elevation in Hypertension[J].Can J Cardiol,2020, 36 (5): 648–658. Aléssio AC, Santos CX, Debbas V, et al. Evaluation of mild hyperhomocysteinemia during the development of atherosclerosis in apolipoprotein E-deficient and normal mice[J]. Exp Mol Pathol. 2011;90( 1):45–50. Deng J, Liu J, Cao L, et al. The Association between Hyperhomocysteinemia and Thoracoabdominal Aortic Aneurysms in Chinese Population[J]. Biomed Res Int. 2020;2020:4691026. Veeranna V, Zalawadiya SK, Niraj A, et al. Homocysteine and reclassification of cardiovascular disease risk[J]. JAm Coll Cardiol. 2011;58( 10):1025–1033. Stone TW, Darlington LG. Endogenous kynurenines as targets for drug discovery and development[J]. Nat Rev Drug Discov. 2002;1(8):609–620. Wang Y, Liu H, McKenzie G, et al. Kynurenine is an endothelium-derived relaxing factor produced during inflammation [J]. Nat Med. 2010;16(3):279–285. Gokmen SS, Kilicli G, Ozcelik F, et al. Association between serum total and lipid-bound sialic acid concentration and the severity of coronary atherosclerosis[J]. J Lab Clin Med. 2002;140(2):110 − 11 Additional Declarations No competing interests reported. Supplementary Files serummetabolomics.pdf 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9064932","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605911624,"identity":"52483e5c-af72-4a13-aa2a-71fca3baa9ef","order_by":0,"name":"Yi Liu","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liu","suffix":""},{"id":605911626,"identity":"ddfdfc5a-46e3-420f-b085-d99b30d9b3fc","order_by":1,"name":"Hongyu Ye","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Ye","suffix":""},{"id":605911629,"identity":"1802c2f6-b014-4ca9-ace1-cd94d652aeb3","order_by":2,"name":"Kejun Liu","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kejun","middleName":"","lastName":"Liu","suffix":""},{"id":605911631,"identity":"f92b525b-9050-47ce-be12-7ad123096349","order_by":3,"name":"Sihao Zhou","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sihao","middleName":"","lastName":"Zhou","suffix":""},{"id":605911632,"identity":"b7706716-14ef-407a-992b-49d6c3dfa96c","order_by":4,"name":"Chuan Yuan","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chuan","middleName":"","lastName":"Yuan","suffix":""},{"id":605911634,"identity":"cabb2639-b6b1-4449-a590-726dd1b08fb9","order_by":5,"name":"Yingmeng Wu","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingmeng","middleName":"","lastName":"Wu","suffix":""},{"id":605911636,"identity":"a1cfdff2-5530-452f-b10b-dd8b4a223562","order_by":6,"name":"Yi Liang","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liang","suffix":""},{"id":605911640,"identity":"aa2bb89f-55a8-4c0a-9eab-1dd772c8ed38","order_by":7,"name":"Binfei Li","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Binfei","middleName":"","lastName":"Li","suffix":""},{"id":605911644,"identity":"77255f23-808d-447f-b3dc-f47ff6360491","order_by":8,"name":"Weizhao Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYLCCBAYGOfvjPWA2Dx+xWowZzpxhYDgA1MJGrEWJDTdywFoYCGqRdz/7dMPDHbWJjTPfHnz8McdOho2B+eGjG3i0GJ5JN7uReOa4cbN0XrLBwW3JQIexGRvn4NPSkMZ2I7HtmGybdI6ZxMFtzEAtPGzSeLX0PwNrYeyRPAPSUk9Yi7wE2JYaxRkSPCAthwlrMZAA23LA2IAnx9jg7LbjPGzMBPwi35/GdvNnW52cAfsZwweV26rt+dmbHz7Ga8sBMHUYSYgZj3KwLQ1gqo6AslEwCkbBKBjRAABPjkrifv/PQQAAAABJRU5ErkJggg==","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Weizhao","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-03-08 14:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9064932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9064932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105034251,"identity":"4304802f-075e-438a-9b80-73684698b0e7","added_by":"auto","created_at":"2026-03-20 07:22:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":384926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening map（a） and statistical flow chart（b） of the differential metabolites. ROC \u003c/strong\u003ereceiver operating characteristic curve; \u003cstrong\u003efc \u003c/strong\u003efold change; \u003cstrong\u003elog2fc \u003c/strong\u003eThe fc in log2fc is fold change, which is log2fc after taking the logarithm with 2 as the base; \u003cstrong\u003eVIP \u003c/strong\u003evariable importance in projection; \u003cstrong\u003ePCA \u003c/strong\u003eprincipal component analysis; \u003cstrong\u003eOPLS-DA \u003c/strong\u003eorthogonal partial least squares-discriminant analysis;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9064932/v1/cf335da57a516fd140053df7.png"},{"id":104874968,"identity":"55a9e499-41c5-4ac2-a103-3be400add6cd","added_by":"auto","created_at":"2026-03-18 08:35:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStability of the experimental data. a-b \u003c/strong\u003eThe superposition ofthe TIC graph shows high overlap ofthe curves ofthe total ion flow for metabolite detection, the retention times and peak intensities are the same, which indicates that the mass spectrometry has good signal stability when detecting the same samples at different times. \u003cstrong\u003ec-d \u003c/strong\u003eThe horizontal coordinate represents the CV value, and the vertical coordinate represents the proportion of substances smaller than the corresponding CV value to the total number of substances, where different colors represent different groups of samples; QC denotes the quality control sample, in which the two reference lines perpendicular to the X-axis correspond to the CV value of 0.3 and 0.5, and the two reference lines parallel to the X-axis correspond to the substances that accounted for 75 and 85% ofthe total number of substances. e\u003cstrong\u003e-f \u003c/strong\u003eNo obvious peaks were detected in the blank samples ofthe EIC graph, which indicates that there is less residual substance, and that the cross contamination between the samples is within the controllable range.(Figure a\u003cstrong\u003e, c, e \u003c/strong\u003eare the negative ion mode, and Figure \u003cstrong\u003eb, d, f \u003c/strong\u003eare the positive ion mode.)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9064932/v1/d3d832b7b28fa919c53120be.png"},{"id":105033745,"identity":"216b9828-40a7-4337-a657-dcbdf8d77aa4","added_by":"auto","created_at":"2026-03-20 07:21:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOtherness in the QC samples. a-b \u003c/strong\u003eThe QC samples in the positive-ion and negative-ion modes are tightlyclustered together. \u003cstrong\u003ec \u003c/strong\u003ePC1 denotes the first principal component, PC2 denotes the second principal component, andthe percentage denotes the explanation rate ofthe variance ofthe principal component on the dataset; each point in the graph denotes a sample, samples in the same group are denoted using the same color. \u003cstrong\u003ed \u003c/strong\u003eThe horizontal coordinate indicates the score ofthe predictive component, and the direction ofthe horizontal coordinate shows the gap between groups; the vertical coordinate indicates the score ofthe orthogonal component, and the direction of the vertical coordinate shows the gap within groups; the percentage indicates the explanation rate ofthe component on the dataset. \u003cstrong\u003ee \u003c/strong\u003eThe abscissa is the order of sample detection, the ordinate reflects the PC1 values, and the yellow and red lines define the positive or negative 2 and three standard deviation ranges, respectively. Green dots represent the QC QC samples, and black dots represent test samples.The PC1 Scores of general QC samples isin the normal range within plus or minus 3 standard deviations.(Figure a\u003cstrong\u003e \u003c/strong\u003eare the negative ion mode, and Figure b are the positive ion mode.)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9064932/v1/8d06fd3298c96ac5616d8f67.png"},{"id":105033996,"identity":"9c7c689f-f988-4e3c-b6eb-7f4443608bf2","added_by":"auto","created_at":"2026-03-20 07:22:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":242870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe samples showed significant otherness \u003c/strong\u003e. a\u003cstrong\u003e-b \u003c/strong\u003eEach row in the figure represents one sample. Samples with high similarity to each other are clustered under the same cluster. Right image :The horizontal coordinate issample information and the vertical coordinate is differential metabolite information, with red representing high levels and green representing low levels. The clustering lines on the left side ofthe figure are metabolite clusteringlines, and the clustering lines on the top ofthe figure are sample clustering lines. \u003cstrong\u003ec \u003c/strong\u003eHorizontal coordinates indicate the covariance ofprincipal components and metabolites. Vertical coordinates indicate the correlation coefficient between principal components and metabolites, the closer to the upper right and lower left corners of the metabolites the more significant their differences; the red VIP value is \u0026gt; 1, and the green VIP value is ≤ 1.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9064932/v1/00a307a35e01edde587898b3.png"},{"id":105034181,"identity":"4ffb695c-c1b7-4ced-97a2-c4d8427679b3","added_by":"auto","created_at":"2026-03-20 07:22:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":304488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential metabolites and changes in metabolic pathways in tissues. a \u003c/strong\u003eEach dot in the volcano plot\u003c/p\u003e\n\u003cp\u003erepresents a metabolite, where green dots represent downregulated differential metabolites, red dots represent upregulated differential metabolites, and grey represents metabolites that are detected but with non-significant differences; the horizontal coordinates represent the logarithm ofthe multiplicity of differences in the relative content (log2FC), and the size ofthe dots represents the VIP value. \u003cstrong\u003eb \u003c/strong\u003eHorizontal coordinates are sample groupings, and vertical coordinates are relative contents of differential metabolites (raw peak areas). The box shape in the center represents the interquartile range, and the thin black line extending from it represents the 95% confidence interval. The black horizontal line right in the middle is the median, and the outer shape represents the distribution density ofthe data. \u003cstrong\u003ec \u003c/strong\u003eThe horizontal coordinate indicates the corresponding Rich Factor of each pathway, the vertical coordinate is the pathway name (sorted by P value), and the color of the dot is the size ofthe P value, where the redder the dot, the more significant the enrichment. The size ofthe dots represents the number of differentially enriched metabolites. \u003cstrong\u003ed \u003c/strong\u003eThe vertical coordinate represents the name of the differential pathway (sorted by P value), and the horizontal coordinate represents the differential abundance score (DA score). The DAscore reflects the overall change of all metabolites in the metabolic pathway, and a score of 1 indicates that the expression of all identified metabolites in the pathway tends to be upregulated, while a score of −1 indicates that the expression of all metabolites in the pathway tends to be downregulated. The length ofthe line segment indicates the absolute value of the DA score, and the size of the dot at the end ofthe line segment indicates the number of different metabolites in the pathway, where the longer the dot segment, the greater the trend. The colorsofthe line and dots reflect the P value size, The color ofthe line segments and dots reflect the size ofthe P-value, the darker the red color, the smaller the P-value, the darker the purple color, the larger the P-value.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9064932/v1/3012d8f818e29dd964ef8a01.png"},{"id":105034306,"identity":"2a2f0752-7a37-4196-8ea2-72eaafc6c63a","added_by":"auto","created_at":"2026-03-20 07:23:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":178733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential metabolites of amino acid species in the serum. a \u003c/strong\u003eThe larger the absolute value ofthe horizontal coordinate, the greater the fold difference in expression between the two samples; the larger the value of the vertical coordinate, the more significant the differential expression. The green dots represent downregulation ofdifferentially expressed metabolites, the red dots represent upregulation of differentially expressed metabolites, and the grey dots represent metabolites that were detected but with no significant difference. \u003cstrong\u003eb \u003c/strong\u003eHorizontal coordinates are sample groupings and vertical coordinates are relative levels of differential metabolites (raw peak areas). The box shape in the center represents the interquartile range, the thin black line extending from it represents the 95 % confidence interval, the black horizontal line right in the middle is the median, and the outershape represents the density ofthe distribution ofthe data.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9064932/v1/3a4539620f59e061553d086f.png"},{"id":105034120,"identity":"361d6c3a-2dd9-4861-a222-d26ab0df0830","added_by":"auto","created_at":"2026-03-20 07:22:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":124893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe most significantly different metabolites. \u003c/strong\u003eThe text marked in red in the graph is the AUC value and 95% confidence interval corresponding to the curve; the text marked in black is the best critical value, and the specificity and sensitivity are shown in parentheses.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9064932/v1/200d02426dad90506b14c433.png"},{"id":109165984,"identity":"b1abcc77-f8ec-49f2-aa38-0f7e9f29d630","added_by":"auto","created_at":"2026-05-13 08:15:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1752805,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9064932/v1/a1eb57d6-33b7-4c86-8b31-694a30e4456e.pdf"},{"id":105034055,"identity":"b6d09112-c507-48b0-8f4f-aae0fc59192c","added_by":"auto","created_at":"2026-03-20 07:22:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":992678,"visible":true,"origin":"","legend":"","description":"","filename":"serummetabolomics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9064932/v1/bb4380921e95ee48559cf892.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of serum lipids and amino acids biomarkers in acute aortic dissection using tissue-informed metabolomics methods","fulltext":[{"header":"Highlights","content":"\u003cp\u003e1. This study represents a novel approach(tissue-informed metabolomics) in the identification ofpotential biomarkers in patients with type A aortic dissection through the integration of tissue and blood metabolomics. Idea of this novel approach was refered to minimal residual disease\u003c/p\u003e\u003cp\u003e(MRD) detection in oncology.\u003c/p\u003e\u003cp\u003e2. Furthermore, the results of this study demonstrate the feasibility and effectiveness of utilizing tissue-informed metabolomics in the identification ofpatient-specific metabolites.\u003c/p\u003e\u003cp\u003eTissue-informed metabolomics can identified significant abnormalities in certain metabolic pathways and metabolite levels in patients diagnosed with type A aortic dissection.\u003c/p\u003e"},{"header":"1. Background","content":"\u003cp\u003eA-type aortic dissection is an extremely aggressive cardiovascular disease with a high mortality rate, which can reach 57% despite surgical treatment in the hospital [ 1,2]. The disease lacks specific clinical symptoms [3], with a mortality rate of 1\u0026ndash;2% per hour in patients who are untreated after the onset [4]. Throughout the consultation process, significant medical expenses create a substantial burden on patients, their families, and society. Nevertheless, the identification and early intervention of high-risk groups pose a significant challenge in managing this disease.\u003c/p\u003e\n\u003cp\u003eIn summary, there is a need to identify an efficient and expeditious screening method for at-risk populations and implement early intervention strategies for individuals at risk of developing aortic dissection. In recent years, research on blood metabolomics in the diagnosis of diseases has received great attention and has been used in the diagnosis of many diseases [6,8].\u003c/p\u003e\n\u003cp\u003eNumerous scholars have conducted research on type A aortic dissection utilizing blood metabolomics to discern potential specific metabolic markers for the disease. Nevertheless, these approaches were hindered by various limitations, such as high variability, multiple confounding factors, inconsistent outcomes, and limited reproducibility[9]. This study used a tissue-informed metabolomics approach which refered to oncological minimal residual disease (MRD) detection. The feasibility and effectiveness of this approach was investigated in this study. And this study also attempts to screen specific metabolic markers for patients with type A aortic dissection, preliminarily exploring the mechanism of the occurrence and development of aortic dissection, and providing new methods and ideas for screening and identifying high-risk populations of the disease.\u003c/p\u003e"},{"header":"2. methods (see annex for Serum metabolomics study methods and results)","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Participants and sample collection\u003c/h2\u003e\n \u003cp\u003eThis study was approved by the Ethics Committee. ( Zhongshan People\u0026apos;s Hospital Clinical Research and Animal Experiment Ethic Committee ; Ethics number: 2024-073) Informed consent was obtained from each patient with aortic dissection and the donor\u0026rsquo;s immediate family. The studies were all conducted in accordance with the relevant guidelines / regulations, did not obtain any organs / tissues from the prisoners, and complied with the Declaration of Helsinki.All tissues used in the study were obtained by cardiothoracic surgeons at Zhongshan People\u0026apos;s Hospital.Twenty aortic vascular tissue samples from patients with type A aortic dissection and 20 plasma samples from healthy organ donor volunteers were collected from September 2021 to July 2023. Twenty vascular tissue samples from patients with type A aortic dissection and 20 plasma samples from healthy organ donor volunteers were selected from the endothelial tissues of the aorta ofpatients who underwent surgery within 48 h of the onset of the disease (experimental group), while aortic tissue specimens were collected from healthy organ donor volunteers (control group); all specimens were cleared of surrounding connective tissues, and the blood was washed with saline. Plasma samples were collected within 24 h of disease onset; blood samples were collected and centrifuged at 3,000 rpm for 10 min at room temperature. The supernatant was divided into 1.5-ml centrifuge tubes (0.2 ml per tube) and immediately stored in a refrigerator at \u0026minus;\u0026thinsp;80\u0026deg;C for freezing, and refrigerated dry ice for transportation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Sample preparation\u003c/h2\u003e\n \u003cp\u003eMethanol and acetonitrile were purchased from Merck (Kenyworth, NJ, USA) Acetic acid was purchased in Ron (Shanghai, China) Ammonium formate, ammonia, and formic acid were purchased in Aladdin (Shanghai, China). The mass spectrometer was purchased from SCIEX Corporation (Foster City, CA, USA). The ultra-efficient liquid chromatograph was purchased at Shimadzu (Japan). Centrifuge was purchased at Eppendorf (Hamburg, Germany). The constant temperature metal mixing instrument was purchased from Hangzhou Milan Instrument Co., LTD. (Hangzhou, China). One / 100,000 electronic balance was purchased from Mettoldo Instruments Co., Ltd. (Zurich, Switzerland). The centrifugal concentrator was purchased at LABCONCO (Missouri Kansas, USA). The vortex mixer was purchased at Kyllin-Bell (Haimen, China). The ultrasonic cleaning instrument was purchased from Kunshan Ultrasonic Instrument Co., Ltd. (Kunshan, China). Pipettes were purchased at Eppendorf (Hamburg, Germany). Automation workstations were purchased at Beckman Coulter (California, USA). The membrane sealwas purchased from Monad (Suzhou, China).\u003c/p\u003e\n \u003cp\u003eThe sample was removed from the \u0026minus;\u0026thinsp;80\u0026deg;C refrigerator and thawed on ice until it could be cut. Chop the sample and mix well,Samples were taken from multiple locations in the well-mixed sample, weighed 20 mg (\u0026plusmn;\u0026thinsp;1 mg), and added to the corresponding numbered centrifuge tubes .\u003c/p\u003e\n \u003cp\u003eSubsequently, the sample was homogenized for 20 s on a ball-mill (30 HZ), before centrifuging at 3,000 r/min for 30 s at 4\u0026deg;C. After centrifugation, 400 \u0026micro;L of 70% methanol-water extract of the internal standard was added and oscillated at 1500 r/min for 5 min, before leaving on ice for 15 min. After centrifugation, 400 \u0026micro;L of 70% methanol internal standard extract was added, shaken at 1500 rpm for 5 min, and allowed to stand on ice for 15 min. Subsequently, the sample was centrifuged at 12000 rpm for 10 min at 4\u0026deg;C, before transferring 300 \u0026micro;L of supernatant to another tube of the corresponding number, and allowing to stand in the refrigerator at \u0026minus;\u0026thinsp;20\u0026deg;C for 30 min.\u003c/p\u003e\n \u003cp\u003eNext, the sample was centrifuged at 12000 rpm for 3 min at 4\u0026deg;C, Pipette 200 \u0026micro;L of supernatant into the corresponding centrifuge tube for on-line analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Chromatography mass spectrometry acquisition conditions\u003c/h2\u003e\n \u003cp\u003eHigh-performance liquid chromatography (HPLC): Two LC/MS methods were used for all samples. Analyses were performed using positive ion conditions and from column T3 (Water Acquisition Premier HSS T3 column 1.8 \u0026micro;m, 2. 1 mm \u0026times; 100 mm) using 0. 1% formic acid in water solvent and 0. 1% formic acid in acetonitrile solvent B in the following gradient: 5\u0026ndash;20% for 2 min, increasing to 60%, increasing to 99% for 1 min and 1.5 min, then back to 5% mobile phase B witnessed for 0. 1 min and held for 2.4 min. The analytical conditions were as follows: column temperature 40\u0026ordm;C; flow rate, 0.4 mL/min; injection volume 4 \u0026micro;L; and for the other sample, negative ion conditions were used with the same elution gradient as in the positive mode.\u003c/p\u003e\n \u003cp\u003eMass spectrometry conditions: Data were acquired using information-dependent acquisition using analyst Tfet 1.7. 1 software (Concord, ON, Canada) (IDA) mode. The source parameters were set as follows: ion source gas 1 (GAS1), 50 psi; ion source gas 2 (GAS2), 50 psi; curtain gas (CUR), 25 psi; temperature (TEM), 550\u0026ordm;C; aggregation potential (DP) in positive mode, 60 V or 60 V in negative mode; ion spray (ISVF), 5000 V or \u0026minus;\u0026thinsp;4000 V. The TOF MS scan parameters were set as follows: mass range, 50\u0026ndash;1000 Da; accumulation time, 200 ms; and dynamic background subtract, on. The product ion scan parameters were set as follows: mass range, 25\u0026ndash;1000 Da; accumulation time, 40 ms; positive and negative mode collision energies, 30 or \u0026minus;\u0026thinsp;30 V; collision energy diffusion, 15; resolution, UNIT; charge state, 1 to 1; intensity, 100 cps; exclusion of isotopes within 4 Da; mass tolerance, 50 ppm; maximum number of candidates to be monitored per cycle, and number of ions\u0026thinsp;=\u0026thinsp;18.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Quality control\u003c/h2\u003e\n \u003cp\u003eThe reproducibility of metabolite extraction and detection was determined by analyzing the overlapping display of total ion chromatogram (TIC) plots analyzed by mass spectrometry detection of different quality control samples. The empirical cumulative distribution function (ECDF) was used to analyze the frequency of coefficient of variation (CV) less than the reference value to determine the stability of the experimental data. Blank samples were interspersed throughout the experiment, and the peaks of the extracted ion chromatogram (EIC) were used to reflect cross-contamination between substances.\u003c/p\u003e\n \u003cp\u003eOverall sample principal component pre-analysis: Principal component analysis (PCA) was performed on peaks extracted from all experimental and QC samples to determine whether the metabolites differed between groups and whether the experimental samples had good reproducibility.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Data processing\u003c/h2\u003e\n \u003cp\u003eThe raw data were converted to mzXML format by ProteoWizard, and the XCMS program was used for peak extraction, alignment, and retention time correction. The peak area was corrected using the \u0026ldquo;SVR\u0026rdquo; method, and the peaks with a missing rate\u0026thinsp;\u0026gt;\u0026thinsp;50% were filtered in each group of samples. The corrected and filtered peaks were searched in the laboratory\u0026rsquo;s own database and integrated with public libraries, AI prediction libraries, and the metDNA method to obtain metabolite identification information.\u003c/p\u003e\n \u003cp\u003eThe data were first standardized (unit variance scaling UV), and then multivariate statistical analyses, including PCA and orthogonal partial least squares discriminant analysis (OPLS-DA), were performed using the MetaboAnalystR package OPLSR.The quality of the model was tested using cross-validation to verify the model using R2 and Q2. The validity of the model was further examined using 200 random permutation experiments.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Differential metabolite screening annotation\u003c/h2\u003e\n \u003cp\u003eA combination of univariate and multivariate statistical analyses was used to screen for differential metabolites. We used univariate statistical analyses such as hypothesis testing and fold change(FC) of difference analysis, and performed screening using P value and FC values of univariate analyses combined with multivariate analyses such as PCA and OPLS-DA. A VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Student\u0026rsquo;s t test), and log2fc\u0026thinsp;\u0026gt;\u0026thinsp;1 were used as screening criteria for the major metabolites. The metabolites interacted with each other in the organism to form different pathways, and the KEGG database was used to annotate the differential metabolites, Identify metabolic pathways with significant differences. Finally, the ability to identify differential metabolites and their sensitivity and specificity were evaluated using the receiver operating characteristic (ROC) curve and area under curve (AUC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Comparison of Differences\u003c/h2\u003e\n \u003cp\u003eAfter initial derivation of metabolites with differential properties using untargeted tissue metabolism analysis, serum was subjected to targeted metabolomics analysis using a fixed species amino acid assay kit.The number of detectors was 94, which overlapped with 11 of the 850 metabolites screened. Meaningful differential metabolites were screened after targeting analysis and compared with the 11 metabolites described earlier to select identical metabolites(Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eAmong the 40 participants included in this study, there were no significant differences between the aortic dissection and control groups in terms of sex, age, height, weight, LDL, HDL, and BNP (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). However, there were differences in presence of hypertension, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical information ofthe intercalated and control groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAortic\u003c/p\u003e\n \u003cp\u003edissection\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 ( 10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 ( 15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.20\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170.35\u0026thinsp;\u0026plusmn;\u0026thinsp;5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.00\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.35\u0026thinsp;\u0026plusmn;\u0026thinsp;5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 ( 15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 ( 10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow density lipoprotein (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh density lipoprotein (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardio-cerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 ( 10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 ( 15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrain Natriuretic Peptide (pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e326.69\u0026thinsp;\u0026plusmn;\u0026thinsp;183.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300.95\u0026thinsp;\u0026plusmn;\u0026thinsp;244.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe TIC plot and substance CV values were used to show that the data analysis had good reproducibility and stability (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea-d), and the EIC plot suggested that the substance cross-contamination was within the controllable range during the assay (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee-f). The principal component pre-analysis of the overall samples revealed variability and good reproducibility among the metabolite groups (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea-b). Non-targeted metabolomics analysis was first performed on vascular tissues, and after normalizing the data, PCA was performed on grouped samples (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). Based on the detected ion peaks in each sample, the model monitors the QC samples through the PCA established above, which shows that the instrument state is stable (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ee).However, the PCA method was not sensitive to the variables with small correlation. To solve this problem, we analyzed the samples again with OPLS-DA (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed).After multivariate statistical analysis ofthe samples, the scatter plots of the PCA and OPLS-DA scores suggested that the samples were significantly different in the distribution of the two sample groups. Hierarchical cluster analysis was used to derive a hierarchical clustering tree ofthe samples (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea), indicating that the experimental and control groups were well clustered. The clustering heat map also indicated that the two groups of metabolites were differentiated (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec). The variable importance in projection (VIP) value indicates the strength of the effect ofthe intergroup differences ofthe corresponding metabolite in the categorization discrimination of each group of samples in the model, and it is generally considered that metabolites with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 are significantly different. The S-plot derived on the basis of OPLS-DA was used to visualize the metabolites with significant differences between groups (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003eAfter the initial screening of metabolites with significant differences between aortic tissues, VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, log2fc\u0026thinsp;\u0026gt;\u0026thinsp;1, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used as screening conditions(Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), and 850 meaningful differential metabolites were selected (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea),and violin plot was drawn to show their distribution (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb), which is shown here in order ofVIP value size for the top 50 species only.Based on the differential metabolite results, the KEGG database was utilized to annotate the differential metabolites (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec),Combined with the Differential Abundance Score (DA Score) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed), it can be seen that there are changes in the main metabolic pathways such as central carbon metabolism, purine metabolism, ascorbate and aldehyde metabolism, glyoxylate and dicarboxylate metabolism, and glucagon signaling pathway.\u003c/p\u003e\n\u003cp\u003eTargeted serum metabolomics analysis of the amino acid metabolites (see Appendix for specific analyses) yielded 21 metabolites with differentials (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea ),and violin plot was drawn to show their distribution (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). After validation and comparison with the differential metabolites identified in the metabolic analysis of aortic tissues, three metabolites with significant differences were screened, namely homocysteine, kynurenine, and N-acetylneuraminic acid.AUC of homocysteine was 0.96, specificity (0.91),sensitivity ( 1); AUC of kynurenine was 0.81, specificity (0.67),sensitivity (0.95); and AUC ofN-acetylneuraminic acid was 0.69, specificity (0.52), sensitivity (0.85)(Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea-c).N-acetylneuraminic acid has an AUC value between 0.5\u0026ndash;0.7, which has an average discriminatory ability, kynurenine has an AUC value between 0.7\u0026ndash;0.9, which has a better discriminatory ability, and homocysteine has an AUC value\u0026thinsp;\u0026gt;\u0026thinsp;0.9, which has a very good discriminatory value.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e4. 1 Significance of the tissue-informed metabolomics methods\u003c/p\u003e \u003cp\u003eType A aortic dissection is complex and has a high mortality rate of 50% -68% within 48 h after onset and 90% within 3 months [ 10]. In China, patients with aortic dissection are characterized by a gradually younger age of onset, a higher proportion of males, a wide variation in treatment options, and a gradual increase in incidence in recent years [ 11,12]. Currently, the mechanism of the occurrence and development of type A aortic dissection remains unclear; despite diagnostic means, most cases are only diagnosed after the appearance of symptoms at the clinic and there remains a lack of a convenient and quick method to screen out high-risk populations and to intervene in the high-risk patients as early as possible to slow the development of the disease.\u003c/p\u003e \u003cp\u003eIn recent years, peripheral blood has been used to search for biological markers. Previous studies have suggested that AD is related to D-dimer, MMPs, elastin, and inflammatory markers [ 13, 14]. However, these substances change to varying degrees in different diseases and so lack specificity. Pathological changes in the organism can cause some corresponding changes in type and level of the metabolites, and the use of metabolomics to analyze these disease-induced metabolites can help people better understand the process of disease and the metabolic pathways of substances in the organism, thereby providing new methods and ideas for the diagnosis and treatment of the disease [ 15]. Yong et al[ 16]. used blood metabolomics to study patients with AD and found that metabolites such as AFMK, glycerophosphocholine, and ergothioneine differed in patients with AD. These substances are mainly involved in amino acid, lipid, and choline metabolism[ 17]. Using the same method, Hao[ 18] showed that 38 metabolites differed between patients with type A aortic dissection and patients with non-type A aortic dissection who had hypertension, with plasma hydrocortisone and dimethylglycine differing more significantly. These metabolites are mainly involved in lipid metabolism, carbohydrate metabolism, and membrane transport, and serve to regulate the progression of AAD .\u003c/p\u003e \u003cp\u003ewhich shows that the process of aortic dissection development and progression is accompanied by metabolic abnormalities, The use of metabolomics to explore the processes involved in the development and progression of the disease is feasible. However, as shown by their findings, the same methodology yielded different differential metabolites in the blood of different patients with aortic dissection, It can be seen that blood metabolomics results were hindered by high variability, multiple confounding factors, inconsistent outcomes, and limited reproducibility. In this study, a tumour-informed approach that has emerged in recent years in oncology research was drawn upon. This approach involves initially utilizing vascular tissue for untargeted metabolomics analysis, followed by serum for targeted metabolomics analysis to confirm the initial identification of differential metabolites in vascular tissue, with the aim of mitigating the aforementioned limitations.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Abnormalities in metabolic pathways\u003c/h2\u003e \u003cp\u003eIn the first half of the study, by analyzing tissue untargeted metabolomics, After enriching differential metabolites into metabolic pathways we initially found that multiple metabolic pathways were abnormal in patients with type A aortic dissection, Among them, changes in metabolic pathways such as central carbon, ascorbic acid, and purine were more pronounced.\u003c/p\u003e \u003cp\u003eCentral carbon metabolism, a crucial metabolic system in organisms, includes the core processes of glycolysis, pentose phosphate, and the tricarboxylic acid cycle. It is the main conduit for energy for the organism, as well as a provider of precursor substances needed for other metabolic processes. When glucose metabolism is disturbed, it may lead to an imbalance in blood glucose levels, and blood glucose abnormalities promote arterial endothelial cell damage and accelerate the process of atherosclerosis [ 19,20].It has been found that vascular endothelial cells can utilize glycolysis to produce adenosine-5'-triphosphate ATP[21], Cardiovascular cells have high glycolytic activity, which has been shown to be their main source of ATP. Increased glycolysis following atherosclerosis in blood vessels as a means of maintaining the energy requirements of endothelial cells. However, prolonged excessive increase in glycolysis leads to inhibition of mitochondrial respiration, which increases reactive oxygen species and produces excess lactic acid, which increases the activity of proteases that destroy the extracellular matrix, leading to vascular inflammation and disruption of the vascular wall[22\u0026ndash;24]. In the pentose phosphate pathway, reduced coenzyme II has a role in synthesizing lipids, nucleotides, and nitric oxide, thereby enhancing endothelial angiogenic activity [25], If overexpressed, it causes deposition of vascular lipids, leading to the formation of atherosclerosis. Succinate, an intermediate metabolite of the tricarboxylic acid cycle, is a ligand for the G protein-coupled receptor GPR91 (also known as SUCNR1). It has been shown that succinic acid utilizes the SUCNR1 signaling pathway to modulate the renin-angiotensin system and thus blood pressure[26]. It has been reported that the absence of macrophage citrate lyase in atherosclerotic plaques stabilizes atherosclerotic plaques[27]. It can be seen that abnormalities in the central carbon metabolism pathway can affect the vasculature in a number of ways, and its abnormalities may have a correlation with type A aortic dissection.\u003c/p\u003e \u003cp\u003eAscorbic acid is also known as vitamin C, Its metabolism plays multiple important roles in the body, one of which is to promote the synthesis of vascular collagen, which in turn enhances the elasticity of blood vessels and maintains their permeability[28]. About 30% of the body's protein is collagen, a key component that plays an important role in maintaining cell structure and function. If ascorbic acid intake is insufficient, collagen synthesis will be impaired, which in turn affects the formation of intercellular junction channels[29,30]. Collagen not only affects the connections between cells, but also regulates the structure and elasticity of the arterial and venous blood vessels in the body to some extent. When there is an inadequate supply of ascorbic acid, resulting in decreased collagen synthesis, the elasticity of the blood vessel wall decreases, which increases the risk of blood vessel rupture in the presence of elevated blood pressure. In the present study, an overall trend of decreased ascorbic acid metabolism was observed, which is thought to be another important factor contributing to decreased aortic wall elasticity. Thus, there may be a link between abnormalities in the ascorbic acid metabolic pathway and type A aortic dissection.\u003c/p\u003e \u003cp\u003eIn addition, abnormal purine metabolism is a noteworthy pathway. Disorders of purine metabolism in the body are associated with hyperuricemia, and prolonged hyperuricemia can also lead to atherosclerosis[31]. Untimely treatment of gout patients will lead to a continuous elevation of blood uric acid, which will damage the endothelium of blood vessels, thus leading to atherosclerosis and increasing the probability of cardiovascular and cerebrovascular diseases [32]. The P2X7 receptor, a receptor for purines, is able to influence endothelium-dependent diastolic function by reducing nitric oxide (NO) production upon activation [33]. This mechanism of action is crucial for vascular function.When endothelium-dependent diastolic function is diminished, blood vessels may become stiff and lose their normal elasticity, leading to an increase in vascular resistance and facilitating the progression of hypertension[34]. The increased level of purine metabolism in this study suggests that abnormalities in purine metabolic pathways may be associated with the development of type A aortic dissection. From the first half of the study, it can be concluded that the tissue metabolic profiles of patients with type A aortic dissection have obvious differences, and a preliminary understanding of the possible influence of certain metabolic pathway abnormalities on the development and progression of the disease has been established, which lays the foundation for the next step of exploring the changes of certain specific metabolic substances in the disease and searching for biomarkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Significantly different metabolites\u003c/h2\u003e \u003cp\u003eIn the second half of the study, homocysteine, kynurenine and N-acetylneuraminic acid were screened for significant variability using a combination oftissue and blood metabolomics.\u003c/p\u003e \u003cp\u003eMultiple studies have shown [35,36], Homocysteine may lead to reduced vascular elasticity and elevated pressure on blood vessels through factors such as pro-atherosclerosis, damage to vascular endothelial cells in response to inflammation, and remodeling of extracellular matrix components to degrade elastin and collagen. It also promotes platelet adhesion to vascular endothelial cells with elevated levels of procoagulant factors and other factors leading to thrombosis. It has been suggested[37] that the addition of homocysteine assays to the Framingham Risk Score (FRS) significantly improves the risk classification of cardiovascular disease. And in recent years, homocysteine has been used as a routine clinical test for cardiovascular disease. The results of this study showed that homocysteine levels in vascular tissues and blood of patients with type A aortic dissection were significantly higher than those of normal subjects, which suggests that there is a correlation between the development of type A aortic dissection and its metabolic abnormality, which may be a factor in the formation of aortic dissection. Kynurenine is produced by the tryptophan metabolic pathway and indoleamine 2,3-dioxygenase IDO is the rate-limiting enzyme for its metabolism, IDO deficiency may exacerbate the accumulation of macrophages and T cells in atherosclerotic plaques[38], It can further reduce the elasticity of blood vessels that were already suffering from atherosclerotic plaques. It has also been shown that[39], Kynurenine has a significant attenuation of coronary artery contractile response to thromboxane analog U-46619, It also reduces blood pressure by activating adenylate cyclase, which is a vasorelaxing enzyme that leads to vasodilation. Reduced levels of kynurenine, which can cause increased aortic atherosclerosis along with increased blood pressure, may cause the development of type A aortic dissection in the presence of both factors. The serum and tissue levels of kynurenine were significantly lower in the patients with type A aortic dissection in this study, and there is good evidence that kynurenine plays an important role in the pathogenesis of type A aortic dissection.\u003c/p\u003e \u003cp\u003eN-acetylneuraminic acid is widely distributed in serum, cell membranes, mucous glands, and is also present in large quantities on the surface of vascular endothelial cells. Early studies have shown that[40], N-acetylneuraminic acid has been detected at elevated levels in patients with atherosclerosis, but the exact mechanism of action is unclear. In recent years, it has been reported that adequate salivation of the vascular endothelium determines its susceptibility to atherosclerotic plaque formation, and that desialylation of LDL causes the accumulation of cholesterol and lipids in the arterial wall.\u003c/p\u003e \u003cp\u003eFrom the final results, the screened homocysteine and kynurenine were reported to have a possible correlation with the occurrence and development of aortic dissection, This demonstrates the feasibility of using a tissue-based information-directed assay to explore biomarkers in patients with type A aortic dissection in this study, This provides a new way of thinking and approaching the study of the occurrence and development of the disease. N-acetylneuraminic acid has been reported to be strongly associated with atherosclerosis, However, it has not yet been proposed to have a correlation with the occurrence of type A aortic dissection. In this study, it was found to have significant changes in type A aortic dissection, but the exact mechanism of action is still unclear and further studies are needed.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eIn this study, we failed to use a one-to-one assay kit for serum targeting analysis, but instead used a certain fixed type of kit. Therefore, when targeting the serum, the substances detected in the kit overlapped less with the substances we screened for, This will lead us to end up with fewer meaningful metabolites. The effectiveness of this method has been confirmed, and my colleagues will continue to expand the detection scope, hoping to detect more specific metabolites. Because the samples for the study were obtained after the onset of the disease, the causal relationship between the development of the disease and the metabolites has not been conclusively established. What can be determined, however, is that there is a relationship between them, which lays the groundwork for research to find biomarkers in patients with type A aortic dissection.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eAs validated in this study, the use of tissue-informed metabolomics is feasible and effective in studies screening for patient-specific metabolites in type A aortic dissection. Analysis of the metabolic profiles of patients with type A aortic dissection revealed significant changes in the pathways of central carbon metabolism, purine metabolism, and ascorbic acid metabolism, mainly, and the development of the disease may be related to them. Of all the differential metabolites, homocysteine, kynurenine, and N-acetylneuraminic acid metabolites were the most distinctive, Potential biomarkers for screening people at high risk for type A aortic dissection.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAAD acute aortic dissection\u003c/p\u003e\u003cp\u003eAFMK formyl-N-acetyl-5-methoxykynurenamine\u003c/p\u003e\u003cp\u003eAUC area under curve\u003c/p\u003e\u003cp\u003eBNP brain natriuretic peptide\u003c/p\u003e\u003cp\u003eCE collision energy\u003c/p\u003e\u003cp\u003eCG curtain gas\u003c/p\u003e\u003cp\u003eCV coefficient of variation\u003c/p\u003e\u003cp\u003eDAS differential abundance score\u003c/p\u003e\u003cp\u003eDP declustering potential\u003c/p\u003e\u003cp\u003eECDF empirical cumulative distribution function\u003c/p\u003e\u003cp\u003eESI electrospray ionization\u003c/p\u003e\u003cp\u003eFC fold change\u003c/p\u003e\u003cp\u003eFRS framingham risk score\u003c/p\u003e\u003cp\u003eGC gas chromatography\u003c/p\u003e\u003cp\u003eIDO indoleamine 2,3-dioxygenase\u003c/p\u003e\u003cp\u003eKEGG kyoto encyclopedia of genes and genomes\u003c/p\u003e\u003cp\u003eLC liquid chromatography\u003c/p\u003e\u003cp\u003eLPC lyso-phosphatidylcholine\u003c/p\u003e\u003cp\u003eMMPs matrix metallo proteinases\u003c/p\u003e\u003cp\u003eMRM multiple reaction monitoring\u003c/p\u003e\u003cp\u003eMS mass spectrometry\u003c/p\u003e\u003cp\u003eMWDB metware database\u003c/p\u003e\u003cp\u003eNMR nuclear magnetic resonance\u003c/p\u003e\u003cp\u003eOPLS-DA orthogonal partial least squares-discriminant analysis\u003c/p\u003e\u003cp\u003ePCA principal component analysis\u003c/p\u003e\u003cp\u003eROC receiver operating characteristic curve\u003c/p\u003e\u003cp\u003eSVR support vector regression\u003c/p\u003e\u003cp\u003eTIC total ion chromatogram\u003c/p\u003e\u003cp\u003eUV unit variance scaling\u003c/p\u003e\u003cp\u003eVIP variable importance in projection\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee.( Zhongshan People\u0026apos;s Hospital Clinical Research and Animal Experiment Ethic Committee ; Ethics number: 2024-073).\u0026nbsp;Informed consent was obtained from each patient with aortic\u0026nbsp;dissection and the donor\u0026rsquo;s immediate family. The studies were all conducted in accordance with the relevant guidelines / regulations, did not obtain any organs / tissues from the prisoners, and complied with the Declaration of Helsinki.All tissues used in the study were obtained by cardiothoracic surgeons at Zhongshan People\u0026apos;s Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;data\u0026nbsp;that\u0026nbsp;support\u0026nbsp;the\u0026nbsp;findings\u0026nbsp;of this\u0026nbsp;study\u0026nbsp;are\u0026nbsp;available\u0026nbsp;from\u0026nbsp;the\u0026nbsp;corresponding\u0026nbsp;author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing\u0026nbsp;interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;Zhongshan\u0026nbsp;city\u0026nbsp;social public welfare and basic research project(2023B1023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWZH \u0026nbsp;and \u0026nbsp;BFL \u0026nbsp; conceived \u0026nbsp;the \u0026nbsp;study. \u0026nbsp; KJL, \u0026nbsp; CYand \u0026nbsp;SHZ performed \u0026nbsp; the \u0026nbsp;database \u0026nbsp;search, \u0026nbsp;\u0026nbsp;literature \u0026nbsp;review, \u0026nbsp; study \u0026nbsp;selection, \u0026nbsp;quality \u0026nbsp; evaluation, \u0026nbsp;and \u0026nbsp;data collection. YMW and YL performed statistical analysis and interpreted the results. YL and HYY drafted the manuscript. All authors have contributed to the manuscript and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWZH, BFL, YL, YMW, HYY, KJL, CY, and YL all belong to Zhongshan People\u0026apos;s Hospital;The SHZ belongs to the The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 461 Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAttachment added the serum metabolomics study methods and results\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLevy D, GoyalA, Grigorova Y, et al. Aortic Dissection[J]. In: StatPearls. 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J Lab Clin Med. 2002;140(2):110\u0026thinsp;\u0026minus;\u0026thinsp;11\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":"aortic dissection, targeted metabolomics, serum metabolites, high-risk population, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-9064932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9064932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND: \u003c/strong\u003eType A aortic dissection represents a critical cardiovascular emergency with high mortality and disability rates and a rising incidence. In addition to clinical management, efforts are directed towards identifying high-risk populations for prompt intervention. While some researchers have utilized blood metabolomics to investigate metabolic indicators of type A aortic dissection, the approach is hindered by significant variability, numerous confounding factors, inconsistent outcomes, and limited reproducibility. In this study, the feasibility and effectiveness of a tissue-informed oriented metabolomics approach which refered to oncological minimal residual disease (MRD) detection was investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS: \u003c/strong\u003eSerum and ascending aortic whole layer tissue were collected from 20 patients diagnosed with type A aortic dissection and 20 organ donor volunteers, serving as the experimental and control groups, respectively. Tissue-informed metabolomics included two steps: The metabolic profiles of ascending aortic tissues from patients with and without dissection were examined using a non-targeted metabolomics approach to identify tissue-specific metabolic differences(step 1). Following this, a targeted metabolomics approach was utilized to confirm and screen metabolites in blood samples from both patient groups based on the tissue metabolomic findings(step 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS: \u003c/strong\u003eThe metabolomic profiles of aortic tissues obtained from patients diagnosed with type A aortic dissection were analyzed and found to exhibit significant divergence from those of aortic tissues sourced from individuals without any known health conditions. Disturbances in metabolic pathways, including central carbon metabolism, purine metabolism, and ascorbic acid metabolism, were particularly prominent in this study. Tissues were analyzed for 850 distinct metabolites, with amino acids and their derivatives being the most abundant at 147. Subsequently, the focus shifted to the plasma amino acid composition. Following comparative validation, three metabolites, namely kynurenine, homocysteine, and N-acetylneuraminic acid, were identified as significantly different.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSION: \u003c/strong\u003eThe findings of this study demonstrate the feasibility and effectiveness of utilizing tissue-informed metabolomics in the identification ofpatient-specific metabolites in type A aortic dissection. Tissue-informed metabolomics identified significant abnormalities in certain metabolic pathways and metabolite levels in patients diagnosed with type A aortic dissection.\u003c/p\u003e","manuscriptTitle":"Identification of serum lipids and amino acids biomarkers in acute aortic dissection using tissue-informed metabolomics methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 08:35:13","doi":"10.21203/rs.3.rs-9064932/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":"65b034cf-e4d3-412d-8225-1935b196abe0","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-13T07:54:54+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T08:13:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 08:35:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9064932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9064932","identity":"rs-9064932","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}