A panel of molecular markers for assessing preoperative perfusion impairment in type a acute aortic dissection

Abstract

The present invention relates to a set of molecular markers for assessing preoperative perfusion impairment in acute aortic dissection type A, said serum markers being: L-Kynurenine (Kyn), or L-Tryptophan (Try), or the ratio Kyn / Try (KTR).

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of medical biotechnology, in particular, to a group of molecular markers for evaluating preoperative perfusion malperfusion of type A acute aortic dissection. BACKGROUND

[0002] Aortic dissection (AD) is an acute separation of the aortic intima structure or dysfunction under the action of external mechanical factors, accompanied by intimal tear, aortic blood flow into the middle layer of the aortic wall through the intimal tear, and propagation along the antegrade and retrograde to form a false lumen. Compared with type B dissection, type A dissection is often associated with complications such as coronary artery involvement, aortic valve insufficiency, and aortic main branch involvement. Compared with chronic dissection (symptom onset time ≥ 14d), acute dissection (symptom onset time < 14d) is more dangerous and has a higher mortality rate.

[0003] Type A acute aortic dissection (TA-AAD) is a cardiovascular critical illness, and the 48-hour natural mortality rate can reach 68%. In recent years, with the increasing incidence of AD, surgical popularization has increased the survival rate of patients, but patients are still in a high-risk state, with a perioperative mortality rate of up to 25%, and a 3-year mortality rate of up to 10% after discharge. A number of studies have confirmed that TA-AAD is more likely to be associated with multiple organ perfusion malperfusion, often manifested as related organ dysfunction or circulatory failure. Studies have shown that preoperative perfusion malperfusion is an independent risk factor for increased mortality after TA-AAD. Some treatment teams or clinical studies have shown that improving the treatment strategy for patients with perfusion malperfusion, delaying surgical repair, and first restoring organ blood perfusion, and then performing surgical repair of the aorta after a period of metabolic and hemodynamic compensation, can effectively improve surgical outcomes and reduce mortality. Therefore, the effectiveness of surgical treatment of TA-AAD patients is largely influenced by the degree of preoperative organ ischemia. Accurate assessment of the degree of preoperative perfusion malperfusion is crucial for the development of surgical strategies, the implementation of postoperative care, and the prediction of postoperative mortality risk.

[0004] Currently, the evaluation of perfusion malperfusion in clinical practice mainly relies on imaging examination and clinical manifestations, and lacks convenience and timeliness in clinical application. The Penn score is based on the evaluation of TA-AAD postoperative high-risk patients with organ ischemia manifestations, but the Penn score may still underestimate the surgical risk of patients with severe organ-specific ischemia (such as mesenteric ischemia, persistent cerebral ischemia, and coronary artery ischemia).

[0005] Lactic acid is an existing marker of poor perfusion, however, the specificity of diagnosis is low. Therefore, it is of great clinical significance to find a more convenient, accurate and rapid method for evaluating preoperative poor perfusion in TA-AAD patients. A new reliable and effective risk stratification system based on biomarkers helps to optimize clinical decision-making, improve surgical success rate and improve patient prognosis.

[0006] Metabolomics changes are the most direct reflection of the body's response to disease states, 20% of which are composed of amino acids (AA) and their metabolites in the human body. Amino acids maintain the homeostasis of cell energy metabolism, endothelial function and vascular structure, and play an important regulatory role in various cardiovascular diseases. Detecting changes in amino acid metabolites has important value for disease diagnosis and risk stratification. Studies have confirmed that amino acid metabolism plays an important role in vascular homeostasis balance, and the circulating amino acid metabolic profile changes significantly in AD patients, which has the potential to be an AD biomarker. Therefore, it is of great clinical significance to find amino acid metabolites that can accurately evaluate the preoperative poor perfusion state of TA-AAD patients. SUMMARY

[0007] The present application first relates to the use of a group of serological diagnostic markers in the preparation of a kit for determining the preoperative poor perfusion of a patient with type A acute aortic dissection (TA-AAD), said serum markers being: L-kynurenine (Kyn), or L-tryptophan (Try), or the ratio of Kyn / Try (KTR);

[0008] Preferably, when (1) the serum concentration of L-kynurenine is increased; or (2) the serum concentration of L-tryptophan is decreased; or (3) the ratio of Kyn / Try (KTR) is increased, it is determined that the TA-AAD patient has a high risk of preoperative poor perfusion.

[0009] Said determination of the risk of preoperative poor perfusion of a TA-AAD patient refers to the evaluation of the preoperative poor perfusion state and severity.

[0010] Said perfusion refers to the perfusion of blood flow from the main branch vessels of the aorta to the corresponding organs to maintain their normal physiological functions, and the related organs are: brain, spinal cord, myocardium, mesentery, kidney, upper or lower limb skeletal muscle.

[0011] Said poor perfusion refers to the decrease in blood flow perfusion from the main branch vessels of the aorta to the corresponding organs, resulting in dysfunction / failure, including: stroke, paraplegia caused by spinal cord ischemia, myocardial infarction, liver dysfunction / failure, intestinal necrosis, renal dysfunction / failure, and upper or lower limb hemiplegia / paresthesia.

[0012] Preferably, the calculation method of KTR is: [serum concentration of Kyn (μmol / L) / serum concentration of Try (μmol / L)] * 1000, and the unit of KTR is μmol / mmol.

[0013] More preferably, when

[0014] when (1) the serum concentration of kynurenine (L-Kynurenine) is > 2.131 μM;

[0015] or (2) the serum concentration of tryptophan (L-Tryptophan) is < 55.3 μM;

[0016] or (3) KTR > 30.54 μmol / mmol, the calculation method of KTR is: serum concentration of Kyn / serum concentration of Try * 1000.

[0017] it is determined that the TA-AAD patient has a high risk of poor preoperative perfusion.

[0018] The present application also relates to a diagnostic kit for determining the poor preoperative perfusion of a type A acute aortic dissection (TA-AAD) patient, which comprises: detection reagents for detecting the concentration of kynurenine and / or tryptophan in the serum of a TA-AAD patient.

[0019] Preferably, the diagnostic kit is a high-performance liquid chromatography-mass spectrometry (UPLC-MS / MS) kit.

[0020] The present application has the advantages of:

[0021] (1) Kynurenine, tryptophan and the ratio of kynurenine to tryptophan can be used as diagnostic markers for poor preoperative perfusion of TA-AAD patients, which helps to optimize clinical decision-making, improve surgical success rate and improve patient prognosis.

[0022] (2) Compared with the existing diagnostic marker lactic acid (AUC for distinguishing poor perfusion is 0.67), the use of kynurenine, tryptophan and KTR as diagnostic markers can significantly improve the accuracy of determining poor perfusion, and the AUC for distinguishing poor perfusion using kynurenine, tryptophan and KTR is 0.72, 0.63 and 0.80, respectively.

[0023] (3) Compared with patients without poor perfusion, the circulating kynurenine level of patients with poor perfusion is significantly increased (P < 0.001), the tryptophan level is significantly decreased (P < 0.001), and the kynurenine / tryptophan ratio is significantly increased (P < 0.001). BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 , Differential metabolic AA screening flowchart. In the screening set of 10 TA-AAD patients and 10 healthy controls, the AA associated with preoperative poor perfusion of TA-AAD was screened by multiple comparison between groups (screening criteria: FDR corrected P value <0.05; FC>1.5) and machine learning (LASSO regression and random forest) methods.

[0025] Figure 2 , Volcano plot of multiple comparison screening AA. In the screening set of 10 TA-AAD patients and 10 healthy controls, the AA associated with preoperative poor perfusion of TA-AAD was screened by multiple comparison between groups (screening criteria: FDR corrected P value <0.05; FC>1.5), and 6 AA were screened by multiple comparison screening: L-Tryptophan, L-Kynurenine, L-Histidine, L-Glutamine, L-Tyrosine, L-Serine. In the figure, the vertical dotted line indicates the difference ratio 1.0; the dark gray dot indicates the down-regulated amino acid; the black dot indicates the up-regulated amino acid; the light gray dot indicates the amino acid with no significant difference in expression.

[0026] Figure 3 , LASSO regression screening AA. LASSO regression screening obtained 2 AA with non-zero coefficients: L-Tryptophan, L-Kynurenine (3A: variable fitting plot: each curve corresponds to each independent variable in the original model, and as the value of λ changes, the coefficient of each independent variable changes, the optimal λ value makes the mean square error in the model minimum, and 2 prediction variables are selected; 3B: non-zero variable fitting plot after cross-validation: 5-fold cross-validation is performed to select the optimal λ value. Select the λ value that minimizes the mean square error to screen the variables.

[0027] Figure 4 , Random forest screening AA. The random forest screening results are sorted according to the variable importance, and the top 5 AA are selected: L-Kynurenine, L-Tryptophan, 3-Aminoisobutyric acid, L-Glutamine, Lysine (4A: Random forest model stability change; 4B: Random forest variable feature importance sorting).

[0028] Figure 5 , AA metabolite correlation heat map in screening data set. In order to evaluate the multiple collinearity of each AA level in the screening data, a correlation heat map was drawn using the Spearman correlation coefficient.

[0029] Figure 6, the changes of kynurenine and tryptophan in the discovery dataset were found. Compared with the patients without perfusion abnormality, the level of kynurenine in the patients with perfusion abnormality was significantly increased (P<0.001) (6A), the level of tryptophan was significantly decreased (P<0.001) (6B), and the level of kynurenine / tryptophan ratio was significantly increased (P<0.001) (6C).

[0030] Figure 7 , the changes of kynurenine and tryptophan in the discovery dataset were found. Compared with the patients without perfusion abnormality, the level of kynurenine in the patients with perfusion abnormality was significantly increased (P<0.001) (6A), the level of tryptophan was significantly decreased (P<0.001) (6B), and the level of kynurenine / tryptophan ratio was significantly increased (P<0.001) (6C). DETAILED DESCRIPTION

[0031] The experimental methods used in the following examples are conventional methods unless otherwise specified.

[0032] The materials, reagents, etc. used in the following examples can be obtained from commercial channels unless otherwise specified.

[0033] Select 424 TA-AAD patients, randomly divide them into screening dataset (n=60), discovery dataset (n=253) and validation dataset (n=111), group the patients according to the perfusion abnormality evaluation standard in the screening dataset, use UPLC-MS / MS to detect the expression changes of amino acid metabolites in the serum samples of the patients, and screen and verify the amino acid markers related to preoperative perfusion abnormality.

[0034] Experimental reagents:

[0035] Boric acid, N-ethylmaleimide (NEM), 4-tert-butyl thiophenol (tBBT), dimethyl sulfoxide (DMSO), 5-aminoisoquinoline (5AIQ), N,N'-disuccinimidyl carbonate (DSC), ascorbic acid (Vc), ethylenediaminetetraacetic acid (EDTA) and tris(2-carboxyethyl)phosphine (TCEP) were purchased from Sigma-Aldrich (Shanghai) Trading Co., Ltd.;

[0036] Analytically pure potassium phosphate dibasic trihydrate (K2HPO4·3H2O) and sodium phosphate monobasic dihydrate (NaH2PO4·2H2O) were purchased from China National Pharmaceutical Group Chemical Reagent Co., Ltd. in Shanghai, China;

[0037] Formic acid for mass spectrometry analysis, HPLC pure acetonitrile and methanol were purchased from Thermo Fisher (China) Co., Ltd.

[0038] Phosphate buffer solution (pH = 7.0) was prepared by 0.1 M K2H P O4·3H2O and NaH2PO4·2H2O, 10 mM ascorbic acid and 10 mM EDTA. Borate buffer solution (pH = 8.8) was prepared by 0.2 M boric acid, 20 mM TCEP and 1 mM ascorbic acid.

[0039] 5-Aminoisoquinolinyl-N-hydroxysuccinimidyl carbamate (5-AIQC) was synthesized from 5AIQ and DSC in acetonitrile solution.

[0040] 123 kinds of amino metabolite standard list is shown in Table 1 below

[0041] Table 1, amino metabolite standard

[0042]

[0043]

[0044] Note: AS: sugar amine; PAA: protein amino acid; NT: monoamine neurotransmitter; N-PAA: non-protein amino acid; ALA: aliphatic amine; ARA: aromatic amine; S-AA: sulfur-containing amino metabolite; SP: small peptide; MAA: post-translational modification amino acid.

[0045] Example 1, blood sample processing and analysis

[0046] 1.1, sample preparation:

[0047] (1) Accurately pipette 20 μL of plasma sample, add 60 μL of methanol for protein precipitation;

[0048] (2) Centrifuge (12000 rpm, 4℃, 10 min) and take 10 μL of supernatant, add 80 μL of 2.5 mM NEM phosphate buffer solution, vortex, and react for about 1 min.

[0049] (3) Add 10 μL of tBBT DMSO solution to the reaction solution of the previous step, vortex, react for 3 min, and remove excess NEM.

[0050] (4) Add another 700 μL of borate buffer solution and vortex for 30 s.

[0051] (5) Finally, add 200 μL of 5-AIQC acetonitrile solution, vortex, derivatize, react for 10 min in a 55℃ water bath, and after the reaction is completed, add 10 μL of formic acid to the solution. Centrifuge (12000 rpm, 4℃, 10 min) and take the supernatant, filter with a 0.22 μM filter, and UPLC-MS / MS for testing.

[0052] 1.2. UPLC-MS / MS system detection

[0053] The sample to be tested was detected using Agilent 1290 UPLC coupled with Agilent 6470 triple quadrupole mass spectrometer (Agilent, USA).

[0054] The injection volume was 1 μL, the chromatographic column was ZORBAX Eclipse Plus C18 column (2.1 x 100 mm, 1.8 μm, Agilent, USA), and the column temperature was 50 °C.

[0055] The mobile phase A was ultrapure water, and the mobile phase B was methanol containing 0.1% HCOOH.

[0056] The elution gradient was set as follows:

[0057] From 2 min to 13.5 min, 99% A decreased to 20% A;

[0058] From 13.51 min to 16 min, 5% A and 95% B were used to flush the chromatographic column.

[0059] The flow rate was 0.5 ml / min;

[0060] The multiple reaction monitoring (MRM) method was used in the positive ion mode (ESI+),

[0061] Ion source conditions: dry gas flow 10 L / min, dry gas temperature 315 °C, atomizer pressure 50 psi, sheath gas temperature 350 °C, sheath gas flow 10 L / min, nozzle voltage 500 V, capillary voltage 4000 V. MassHunter software (Agilent, USA) was used for data acquisition and analysis. After the spectrum was imported into the MassHunter software, the retention time and MRM ion pair were compared with the standard (Table 1) for qualitative analysis, and the external standard method was used for quantitative analysis.

[0062] Example 2, statistical analysis of detection results

[0063] (1) In the screening set of 10 TA-AAD patients and 10 healthy controls, the AA related to poor preoperative perfusion of TA-AAD was screened by multiple test comparison between groups (screening criteria: FDR corrected P value <0.05; FC>1.5) and machine learning (LASSO regression and random forest) methods, and the screening process is shown in Figure 1 .

[0064] (2) Six AAs were screened by multiple test comparison: tryptophan, kynurenine, histidine, glutamine, tyrosine, and serine, as shown in Figure 2 .

[0065] (3) LASSO regression screening obtained 2 AA with non-zero coefficients: tryptophan, kynurenine, see Figure 3 .

[0066] (4) Random forest screening results were sorted according to variable importance, and the top 5 AA were selected: kynurenine, tryptophan, 3-aminoisobutyric acid, glutamine, lysine, see Figure 4 .

[0067] (5) The intersection of the screening results obtained 2 AA: kynurenine and tryptophan.

[0068] (6) Evaluate the multiple collinearity of each AA level in the screening set, use Spearman correlation coefficient to draw a correlation heat map, it can be seen that there is correlation between multiple amino acids, see Figure 5 .

[0069] (7) It was found that in the data set, compared with patients without perfusion adverse events, the level of kynurenine in patients with perfusion adverse events was significantly increased (P<0.001), and the level of tryptophan was significantly decreased (P=0.001). Ratio analysis of kynurenine and tryptophan showed that compared with patients without perfusion adverse events, the ratio of kynurenine / tryptophan in patients with perfusion adverse events was significantly increased (P<0.001), see Figure 6 , Table 5.

[0070] (8) Logistics regression results showed that the proportion of patients with perfusion adverse events gradually increased with the increase of kynurenine tertile stratification, and gradually decreased with the increase of tryptophan tertile stratification. After correction of risk factors in multivariate analysis, the increased risk of perfusion adverse events in TA-AAD patients was independently associated with increased kynurenine levels, decreased tryptophan levels, and increased kynurenine / tryptophan ratio. And the linear trend test was statistically different, see Table 2.

[0071] Table 2 Association analysis of amino acids and preoperative perfusion adverse events in the discovery set

[0072]

[0073]

[0074] (9) ROC curve was used to evaluate the diagnostic value of AA in the validation set. Lactate was used as the reference. The diagnostic value of kynurenine, tryptophan and the ratio of kynurenine / tryptophan was compared. The evaluation indexes included area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), negative likelihood ratio (-LR) and positive likelihood ratio (+LR). The results showed that the diagnostic value of the ratio of kynurenine / tryptophan was the best, and the AUC for distinguishing perfusion failure was 0.8 (0.74-0.86), as shown in Table 3.

[0075] Table 3 Evaluation of the diagnostic value of AA in the validation set

[0076]

[0077] Table: AUC: area under the curve; PPV: positive predictive value; NPV: negative predictive value; +LR: positive likelihood ratio; -LR: negative likelihood ratio; Kyn: kynurenine; Try: tryptophan; LA: lactate.

[0078] (10) The correlation between kynurenine, tryptophan and the ratio of kynurenine / tryptophan and perfusion failure was verified in the validation set. Compared with patients without perfusion failure, the level of kynurenine (concentration unit same as Table 2) in patients with perfusion failure was significantly increased (P<0.001), the level of tryptophan (concentration unit same as Table 2) was significantly decreased (P<0.001), and the level of the ratio of kynurenine / tryptophan (concentration unit same as Table 2) was significantly increased (P<0.001), as shown in Table 6. Figure 7 .

[0079] (11) ROC curve was used to evaluate the diagnostic value of AA in the validation set. Lactate was used as the reference. The diagnostic value of kynurenine, tryptophan and the ratio of kynurenine / tryptophan was compared. The evaluation indexes included area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), negative likelihood ratio (-LR) and positive likelihood ratio (+LR). The results showed that the diagnostic value of the ratio of kynurenine / tryptophan was the best, and the AUC for distinguishing perfusion failure was 0.80 (0.71-0.89), and the accuracy of diagnosis was 69.37%, as shown in Table 4.

[0080] Table 4 Evaluation of the diagnostic value of AA in the validation set

[0081]

[0082] Table: AUC: Area under the curve; Kyn: kynurenine; Try: tryptophan; LA: lactic acid.

[0083] Table 5 Findings dataset TA-AAD patients serum amino acid content and ratio (KTR = Kyn / Try*1000, unit: pmol / mmol)

[0084]

[0085]

[0086]

[0087]

[0088] Table 6 Validation dataset TA-AAD patients serum amino acid content and ratio (KTR = Kyn / Try*1000, unit: pmol / mmol)

[0089]

[0090]

[0091] The above results demonstrate that in patients with ischemic heart disease, multiple different kinds of plasma steroid hormones are important biomarkers for diagnosing ischemic heart failure, which helps to identify high-risk patients who need more aggressive treatment intervention.

Claims

1. Use of a group of serum diagnostic markers in the preparation of a kit for determining preoperative malperfusion in patients with type A acute aortic dissection (TA-AAD), wherein the serum markers are: kynurenine (Kyn), tryptophan (Try), or the Kyn / Try ratio (KTR); The determination of the risk of preoperative poor perfusion in TA-AAD patients refers to the assessment of the preoperative poor perfusion status and severity; The perfusion mentioned above refers to: the main branches of the aorta supply blood perfusion to the corresponding organs to maintain their normal physiological functions; The malperfusion mentioned above refers to the decrease in blood perfusion of the main branches of the aorta supplying the corresponding organs, resulting in functional impairment / failure.

2. The use according to claim 1, characterized in that when (1) Elevated serum concentration of kynurenine; or (2) decreased serum tryptophan concentration; or (3) The ratio of Kyn to Try, KTR, increases; The TA-AAD patients were judged to be at high risk of preoperative poor perfusion.

3. The use according to claim 1 or 2, characterized in that The calculation method of the KTR is: [Serum concentration of Kyn (μmol / L) / Serum concentration of Try (μmol / L)] × 1000, unit is μmol / mmol.

4. The use according to claim 2, characterized in that when When (1) the serum concentration of kynurenine is >2.131 μM; or (2) serum concentration of tryptophan <55.3 μM; or (3) when KTR>30.54 μmol / mmol; The TA-AAD patients were judged to be at high risk of preoperative poor perfusion.

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