Early diagnosis or prediction biomarker for acute pancreatitis infection complications

Abstract

The invention relates to a biomarker for early diagnosis or prediction of acute pancreatitis infection complications, the prediction capability of plasma KA on IPN of acute pancreatitis patients is evaluated, and the result shows that the AUC of IPN of KA prediction verification set patients reaches 0.805 and exceeds the AUC of APACHE II and IL-10. The results show that the plasma KA can be used as a high-efficiency biomarker to predict the occurrence of the IPN of the acute pancreatitis patient in the early stage.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology, and particularly relates to a biomarker for early diagnosis or prediction of infectious complications of acute pancreatitis. Background Art

[0002] Acute pancreatitis (AP) is a digestive system emergency characterized by pancreatic inflammation, manifested as acute abdominal pain and elevated serum pancreatic enzymes. It is characterized by abnormal activation of pancreatic enzymes leading to autodigestion of the pancreas, often accompanied by infectious complications. AP can be divided into mild AP (MAP), moderate severe AP (MSAP) and severe AP (SAP) according to whether there is organ failure within 48 hours and its degree. SAP patients often have a long course of disease and many complications, including multiple organic dysfunction syndrome (MODS) during the acute inflammatory period and infectious complications that are prone to occur during hospitalization, such as sepsis and infected pancreatic necrosis (IPN) caused by bacterial infection of the peripancreatic tissue, which pose a serious threat to the patient's life. MSAP or SAP patients are prone to infectious complications such as secondary infection or infected pancreatic necrosis (IPN) during hospitalization, which greatly increases the mortality rate and length of hospital stay of patients during hospitalization. Among them, IPN can increase the mortality rate of AP to 32%, and it is also the main cause of death in SAP patients in the later stage. Therefore, effective prediction of the prognosis of IPN is crucial for the clinical management of patients.

[0003] At present, the indicators used in clinical practice to assess the severity of AP include APACHE II score, BISAP score and serum procalcitonin (PCT). These scoring systems can effectively predict organ failure and mortality, but cannot predict the occurrence of infectious complications of AP such as IPN. The Pancreatitis Activity Scoring System (PASS) was first proposed by Pedram Paragomi et al., which introduced indicators of analgesic use and pain scores. It was later verified by Mao Wenjian et al. in the Chinese population and can be used to predict the occurrence of IPN in necrotizing pancreatitis. However, the use of the PASS scoring system to predict infectious complications requires judgment and complex calculations by experienced clinicians, and has limited effect on early identification and prediction of infectious complications. Therefore, early prediction of the occurrence of IPN is still a problem, and an effective and convenient biomarker is urgently needed.

[0004] For AP patients, intestinal microorganisms can affect the disease process directly or indirectly by producing certain metabolites to regulate the intestinal barrier and immune response. In previous studies, there have been many studies on the changes in the intestinal flora of AP patients, but the research on intestinal metabolites is still insufficient.

[0005] The diagnostic and therapeutic value of intestinal microbiota in critical diseases such as AP is becoming increasingly prominent. However, there are still some shortcomings in current related research. First, although the changing trends of intestinal-related species have been preliminarily elucidated, the changes in intestinal metabolites and their role in the disease process have not been fully studied. At present, the research on metabolites is more limited to SCFA metabolites. Secondly, for SAP, the impact of changes in the intestinal microenvironment on the occurrence of infectious complications in patients has not been fully explored. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a biomarker for early diagnosis or prediction of infectious complications of acute pancreatitis.

[0007] The present invention provides an application of a biomarker in preparing a diagnostic or prognostic reagent for acute pancreatitis. The biomarker comprises kynurenic acid KA.

[0008] The present invention provides an application of a biomarker in preparing a reagent for diagnosing or early predicting the occurrence of infectious complications of acute pancreatitis. The biomarker comprises kynurenic acid KA.

[0009] The kynurenic acid KA is plasma kynurenic acid KA.

[0010] The optimal cut-off value of the biomarker kynurenic acid KA in the preparation of diagnosis or early prediction of IPN occurrence in patients with acute pancreatitis is 25.96 ng / ml, at which time the detection sensitivity and specificity are 80% and 85.71% respectively, and the area under the receiver operating characteristic curve is 0.807.

[0011] The biomarkers also include serotonin metabolites and indole metabolites.

[0012] The biomarkers also include microbial biomarkers, and the microbial marker is Escherichia-Shigella.

[0013] The serotonin metabolites are serotonin and 5-hydroxyindoleacetic acid (5-HIAA); the indole metabolite is indoleacetic acid.

[0014] The present invention provides a use of a reagent for detecting any of the biomarkers in preparing a reagent or a kit for prognosis, diagnosis or prediction of infectious complications of acute pancreatitis.

[0015] The reagents or kits include reagents or kits for detecting the content of metabolites.

[0016] The reagents or kits include reagents or kits for detecting the content of metabolites by chromatography and / or mass spectrometry.

[0017] Beneficial Effects

[0018] In the present invention, it was found that only plasma KA was significantly positively correlated with the length of ICU stay of patients. In addition, KA levels were also significantly positively correlated with IL-10 levels, PCT, and APAHCE II scores of patients, indicating that KA has the potential to predict infection and poor prognosis in patients with acute pancreatitis.

[0019] The present invention evaluates the predictive ability of plasma KA for the occurrence of IPN in patients with acute pancreatitis. The results show that the AUC of KA for predicting the occurrence of IPN in the validation set of patients reached 0.805, exceeding APACHE II (AUC = 0.749) and IL-10 (AUC = 0.735). These results indicate that plasma KA can be used as an efficient biomarker to predict the occurrence of IPN in patients with acute pancreatitis at an early stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Changes in intestinal flora in patients with acute pancreatitis; (A) Shannon diversity index comparison, Student's t test, box plots are displayed with medians and quartiles. (B) LefSe analysis reflects the difference in species from phylum to genus, and species with LDA>4 and p<0.05 are displayed. (C) Spearman correlation interaction network of species in the intestines of AP patients, blue lines represent significant negative correlations, red lines represent significant positive correlations, the transparency of the lines reflects the associated R value, and the depth of the squares represents the abundance of species. AP (n=47), HC (n=19), *P<0.05;

[0021] Figure 2 Alterations in intestinal flora of mice with acute pancreatitis induced by sodium taurocholate; (A) PCoA analysis, each point represents a sample, and the differences between groups were tested by ANOSIM. (B) Shannon diversity index comparison, Student's t test, box plots are displayed with medians and quartiles. (C) LefSe analysis reflects the difference in species from phylum to genus, and species with LDA>4 and p<0.05 are displayed. AP (n=13), HC (n=6), *P<0.05;

[0022] Figure 3Figure 1 shows the association between intestinal flora and clinical characteristics in patients with acute pancreatitis. (A) Spearman correlation analysis heat map, the color of each square represents the degree of association between clinical factors and species (Spearman r), and 3 patients who died during hospitalization were excluded when calculating the association with hospitalization duration. (B) Shannon diversity comparison, using Student's test statistics, the results showed no statistical difference. (C-D) PCoA analysis, each point represents a sample, the difference between groups was tested by ANOSIM, the results showed no statistical difference. (E.) Comparison of g_Escherichia-Shigella abundance between AP-infection and AP-non-infection groups, using Wilcoxon test statistics. (F) Shannon diversity index of intestinal flora in AP patients with hyperlipidemia (HLP) and patients without hyperlipidemia (non-HLP) was tested by Student's t test. Box plots are presented with medians and quartiles, and bar graphs represent means and standard deviations. AP-infection (n=24), AP-non-infection (n=23); AP-IPN (n=8); AP-non-IPN (n=39); HLP (n=33); non-HLP (n=14)*P<0.05, **P<0.01, ***P<0.001;

[0023] Figure 4 Alterations in intestinal metabolism in patients with acute pancreatitis; (A) Volcano plot of metabolite distribution, each point represents a metabolite, VIP>1, P<0.05 is judged as a differential metabolite, red represents metabolites significantly enriched in AP, and blue represents metabolites significantly reduced in AP. (B) KEGG enrichment analysis bubble plot, the figure shows the top 10 enrichment pathways of differential metabolites after Fisher exact test, and the size of the bubble is the number of enriched metabolites. (C) Spearman correlation analysis heat map, the color of each square represents the degree of association between the metabolite and the species (Spearman r). AP (n=33); HC (n=19), *P<0.05, **P<0.01, ***P<0.001;

[0024] Figure 5The association between intestinal metabolites and clinical characteristics in patients with acute pancreatitis; (A) Spearman correlation analysis heat map, the color of each square represents the degree of correlation between clinical factors and relative abundance of metabolites (Spearman r), blue markers are tryptophan metabolites, orange markers are tyrosine metabolites, n = 31, and 2 patients who died during hospitalization were excluded when calculating the association with hospitalization duration. (B) KEGG enrichment analysis bubble chart, the figure shows the top 5 enrichment pathways of differential metabolites after Fisherexact test, the size of the bubble is the number of enriched metabolites, and the differential metabolites are obtained by comparing AP-infection (n = 16) and AP-non-infection (n = 17), and AP-IPN (n = 5) and AP-non-IPN (n = 28). *P < 0.05, **P < 0.01, ***P < 0.001;

[0025] Figure 6Figure 1 shows the changes in tryptophan metabolism in acute pancreatitis; (A) Heat map of the mean difference in abundance of tryptophan metabolites in fecal non-targeted metabolomics, AP (n=33), HC (n=19); (B) Heat map of the mean difference in abundance of tryptophan metabolites in plasma non-targeted metabolomics, AP (n=34), HC (n=10); the color of the squares in (AB) represents the relative abundance, the green box represents the indole pathway metabolites, the blue box represents the serotonin pathway metabolites, and the red box represents the kynurenine metabolites. Wilcoxon test was used for statistical analysis. (C) The abundance of plasma KA in AP patients (n=31) was significantly negatively correlated with the abundance of 5-HIAA, serotonin and 3-methyl-oxindole in feces, and Spearman correlation analysis showed that the association was statistically significant; (D) Comparison of plasma and fecal tryptophan (Trp) concentrations between AP mice and HC mice. (E) Comparison of relative abundance of various tryptophan metabolites in plasma and feces of AP mice (n=11-14) and HC mice (n=5-6), calculated as metabolite concentration / Trp concentration. (DE) Student's t test was used, and only substances with significant statistical differences among all metabolites are presented in E. 3-HK, 3-Hydroxy-DL-kynurenine; XA, Xanthurenic acid; KA, Kynurenic acid; IArA, Indole-3-acrylic acid; IPA, Indole-3-propionic acid; NAS, N-Acetyl serotonin; ILA, Indolelacticacid. *P<0.05; **P<0.01; ***P<0.001;

[0026] Figure 7 Figure 2: The relationship between tryptophan metabolism changes and infectious complications of acute pancreatitis; (A) The difference in the abundance of KA in the plasma of patients with acute pancreatitis with IPN (n=6) and without IPN (n=28), statistical analysis was performed using the Wilcoxon test. (BC) Spearman correlation heat map shows the association between the relative abundance of tryptophan metabolites in feces (n=30) and plasma (n=31) of patients with acute pancreatitis and indicators of clinical severity. The color of the square represents the direction, and the depth represents the degree of association. (When analyzing the length of ICU stay, patients who died during hospitalization were excluded.) *P<0.05; **P<0.01; ***P<0.001;

[0027] Figure 8The plasma tryptophan metabolic profile of patients in the validation set of acute pancreatitis; (A) The composition of patients with acute pancreatitis in the validation set. (B) Differential tryptophan metabolites in patients with acute pancreatitis (n=61) compared with healthy controls (n=14). (C) Differential tryptophan metabolites in patients with acute pancreatitis with secondary infection (n=27) and without secondary infection (n=34). (D) Differential tryptophan metabolites in patients with acute pancreatitis with IPN (n=7) and without IPN (n=54). The graph is presented in a volcano plot, p<0.05, and the statistical test is Welch's t test, P<0.05 for differential metabolites, red points are upregulated metabolites relative to healthy controls or uninfected patients, and blue points represent downregulated metabolites;

[0028] Fig. 9 KA can be used as a predictive marker for IPN in acute pancreatitis; (A) Spearman analysis of the association between plasma tryptophan metabolites and ICU length of stay, SOFA score, serum PCT, and APACHE II score in the validation set of acute pancreatitis patients. (B) Pearson correlation analysis between plasma KA (ng / mL, log) and patients' plasma IL-10, ICU length of stay, serum PCT, and APACHE II score. n = 60 in AB, one patient who died during hospitalization was excluded. (C) Difference in relative abundance of KA between sepsis patients (n = 27) and healthy people (n = 10) in plasma non-target metabolism. (D) Difference in relative abundance of KA between the death group (n = 4) and the survival group (n = 23) in sepsis patients during hospitalization. CD used Wilcoxon test. (E) Spearman correlation analysis between relative abundance of plasma tryptophan metabolites and patients' ICU length of stay, APACHE II score, and SOFA score in sepsis patients (n = 23). (F) ROC curves of plasma KA concentration, APACHE II score, and plasma IL-10 level in the validation set of patients with acute pancreatitis as independent predictors of IPN in patients (n=61). *P<0.05; **P<0.01; ***P<0.001, ns, not significant. DETAILED DESCRIPTION

[0029] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0030] Example 1

[0031] 1. Inclusion criteria and status of enrolled patients

[0032] For AP patients, acute pancreatitis can be diagnosed if any two of the following three points are met: (1) acute and persistent severe upper abdominal pain that can radiate to the back; (2) serum amylase or lipase levels are higher than three times the upper limit of normal; (3) abdominal imaging examinations reveal pancreatic edema or peripancreatic exudate. The cause of admission, clinical sequential organ failure assessment (SOFA), APACHE II score, serum PCT, modified Marshall score, whether infectious complications occurred during hospitalization, the site of infection, the isolated pathogen, the occurrence of IPN, the length of ICU stay, and the mortality rate within 90 days were collected. The criteria for SOFA score are shown in Table 1; APACHE score is acute physiology score (APS) is shown in Table 2; the criteria for modified Marshall score are shown in Table 3; the clinical characteristics of the enrolled AP patients are shown in Table 4.

[0033] 2. Animal Experiments

[0034] The AP animal model uses a retrograde injection model of sodium taurocholic acid (TCA) using 8-week-old male C57BL / 6 mice. The modeling method is as follows: prepare 2% (mass / volume) TCA solution (dissolved in saline) in advance, stain with 0.1% methylene blue, filter and sterilize, draw with a 10mL syringe, take a disposable infusion extension tube with an infusion needle, fix it on a micropump, and exhaust the air bubble. After intraperitoneal anesthesia of mice that have been fasting for 12 hours, remove the abdominal hair, prepare the skin, and use iodine to disinfect the operation site. Use surgical scissors and forceps that have been sterilized in advance to cut the skin and peritoneum 2cm in the midline of the upper abdomen of the mouse, use sterile cotton swabs and forceps to find the duodenum of the mouse, gently pull out the pancreas, and clamp the hepatic portal end of the pancreaticobiliary duct with a microvascular clamp. Find the opening of the duodenal bile duct, carefully insert the infusion needle into the common bile duct and fix it, and start pumping TCA solution at a flow rate of 5μL / min. The injection time is 10min for every 20g of mouse body weight. After the injection is completed, pull out the infusion needle, remove the microvascular clamp, stuff the intestinal segment back into the abdominal cavity, use 5-0 sutures to suture the abdominal cavity and skin, and wait for the mouse to wake up. For mice in the healthy control group, only the abdominal cavity was opened, the pancreas and duodenum were turned over, and the abdomen was closed. After 24h, the cecal contents of the mice were taken and immediately stored at -80℃, and then used for 16S RNA sequencing to detect intestinal flora.

[0035] 3. Plasma non-targeted metabolomics

[0036] For the AP patients collected in the first part, the plasma samples of the patients at the time of admission were retained, collected with EDTA anticoagulant tubes, placed in a 4°C refrigerator overnight, centrifuged to obtain the plasma supernatant, and frozen in a -80°C refrigerator. After excluding some hemolyzed samples, a total of 34 plasma samples from patients with acute pancreatitis were collected, and the plasma samples of 10 healthy people were used as controls for non-target metabolomics analysis of the patients' plasma.

[0037] The main processing flow of specimen pretreatment and LC-MS / MS is as follows: 100 μL of liquid sample was placed in a 1.5 mL centrifuge tube, 400 μL of extract containing 0.02 mg / mL L-2-chlorophenylalanine (acetonitrile: methanol = 1:1) was added, the sample was shaken and mixed, and low-temperature ultrasonic extraction was performed for 30 min (5°C, 40 kHz). The sample was allowed to stand at -20°C for 30 min, then centrifuged for 15 min (4°C, 13000g), and dried with nitrogen. Then 100 μL of acetonitrile: water = 1:1 solution was used for re-dissolution. After repeating the above extraction and centrifugation process, the supernatant was transferred to the injection bottle for machine analysis. All sample metabolites were mixed in equal volumes as QC samples. During the instrument analysis process, a QC sample was inserted into every 5-10 samples to examine the repeatability of the entire analysis process.

[0038] The samples were analyzed by LC-MS / MS using the UHPLC-Q Exactive system of Thermo Fisher Scientific. The chromatographic conditions were as follows: 3 μL of sample was separated by HSS T3 column (100 mm × 2.1 mm id, 1.8 μm) and then entered mass spectrometry detection. Mobile phase A was 95% water + 5% acetonitrile (containing 0.1% formic acid), and mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid). The flow rate was 0.40 mL / min, and the column temperature was 40 °C. Under mass spectrometry conditions, the sample mass spectrometry signal was collected in positive and negative ion scanning mode, and the mass scanning range was 70-1050 m / z. The sheath gas flow rate was 40 psi, the auxiliary gas flow rate was 10 psi, the ion source heating temperature was 400 °C, the positive and negative mode ion spray voltages were set to 3500 V and -2800 V, respectively, and the collision energy was 20-40-60 V. The primary mass spectrometer resolution was 70000, the secondary mass spectrometer resolution was 17500, and the data were collected in DDA mode.

[0039] The LC-MS raw data were imported into the metabolomics processing software Progenesis QI (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, and peak alignment, and finally a data matrix of retention time, mass-to-charge ratio, and peak intensity was obtained. At the same time, the MS and MSMS mass spectrometry information was matched with the metabolic public database HMDB and Metlin and the Meiji self-built library to obtain metabolite information. Finally, the searched data matrix was uploaded to the Meiji cloud platform (cloud.majorbio.com) for analysis. Before analysis, the data matrix was first preprocessed: the 80% rule was used to remove missing values, fill in vacancies (fill in vacancies with the minimum value in the original matrix), and the response intensity of the sample mass spectrometry peak was normalized by the sum normalization method to obtain the normalized data matrix. At the same time, the variables with a relative standard deviation of >30% of the QC samples were deleted, and log10 logarithmic processing was performed to obtain the final data matrix for subsequent analysis.

[0040] 4. Tryptophan targeted metabolome analysis

[0041] The sample pretreatment, liquid chromatography-mass spectrometry (LC-MS / MS) analysis, and data preprocessing process were as follows: First, 33 tryptophan pathway substance standards were accurately weighed and single standard solutions were prepared with 50% methanol. Subsequently, appropriate amounts of various single standard solutions were mixed into mixed standards and diluted with ultrapure water to form mixed standard solutions of different concentrations. At the same time, the isotope standard Trp-D5 was accurately weighed and a single standard solution was prepared with methanol, which was then diluted with ultrapure water to form an isotope internal standard working solution with a concentration of 4000 ng / mL.

[0042] For intestinal content or tissue samples, take 25 mg of sample, add 10 μL of internal standard working solution (4000 ng / mL), and add 190 μL of extraction solution (methanol: water = 4:1). The sample was ground by a freeze grinder for 6 min (-10 ° C, 50 Hz), followed by low-temperature ultrasound for 30 min (5 ° C, 40 KHz), -20 ° C for 30 min, 4 ° C 13000 rcf centrifugation for 15 min, and 150 μL of supernatant was blown dry with nitrogen. Then add 75 μL of 1% acetonitrile water (containing 0.1% formic acid) to dissolve, vortex mix for 30 s, low-temperature ultrasound for 15 min (5 ° C, 40 KHz), 4 ° C 13000 rcf centrifugation for 15 min, and take the supernatant for detection. For plasma samples, take 100 μL of sample, add 10 μL of isotope internal standard working solution (Trp-D5, 4000 ng / mL), and then add 390 μL of extraction solution (methanol). The sample was vortexed for 30 seconds, sonicated at low temperature for 30 minutes (5°C, 40KHz), left at -20°C for 30 minutes, centrifuged at 13000rcf at 4°C for 15 minutes, and 400μL of the supernatant was blown dry with nitrogen. Then 100μL of 1% acetonitrile water (containing 0.1% formic acid) was added for redissolution, vortexed for 30 seconds, sonicated at low temperature for 15 minutes (5°C, 40KHz) again, centrifuged at 13000rcf at 4°C for 15 minutes, and the supernatant was taken for instrument testing. In order to ensure the accuracy of the analysis, a QC sample was inserted into every 5-10 samples during the instrument analysis process to examine the repeatability of the entire analysis process.

[0043] This experiment used a Nexera Series LC-40 liquid phase system and The samples were analyzed by LC-MS / MS using a 6500+ mass spectrometer. The chromatographic conditions were as follows: 2 μL of the sample was separated by an ACQUITY UPLC HSS T3 (2.1×150 mm, 1.8 μm) liquid chromatography column and then entered the mass spectrometry detection. Mobile phase A was 0.1% formic acid in water, and mobile phase B was 0.1% formic acid in acetonitrile. The separation gradient was set as follows: 0.0-2.5min, mobile phase B increased from 1% to 11%; 2.5-5.5min, mobile phase B maintained at 11%; 5.5-6.5min, mobile phase B increased from 11% to 28%; 6.5-7.5min, mobile phase B maintained at 28%; 7.5-12.5min, mobile phase B increased from 28% to 50%; 12.5-13.5min, mobile phase B increased from 50% to 95%; 13.5-15.5min, mobile phase B maintained at 95%; 15.5-15.6min, mobile phase B decreased from 95% to 1%; 15.6-18min, mobile phase B maintained at 1%. The flow rate was 0.35mL / min, and the column temperature was 40°C. The mass spectrometry conditions were that the samples were detected in positive and negative modes. The curtain gas was 35 psi, the collision gas was Medium, the ion source spray gas was 50 psi, the auxiliary heating gas was 50 psi, the spray voltage was 5500 V / -4500 V, and the ion source heating temperature was 550°C.

[0044] The LC-MS raw data were imported into the Sciex quantitative software OS, and the default parameters were used to automatically identify and integrate each ion fragment, and assisted with manual inspection. Finally, the concentration results were calculated by substituting the mass spectrum peak area of ​​the sample analyte into the linear equation of the standard.

[0045] Specimens were collected from 51 AP patients admitted to the emergency ICU of Ruijin Hospital from September 2019 to December 2021. After excluding unqualified specimens and sequencing results, a total of 47 AP patients' intestinal flora data and 33 AP patients' fecal metabolomics data were collected. These patients were all MSAP or SAP patients with organ failure. The results were compared with those of 19 healthy controls (HC) to clarify the characteristics of AP's flora and metabolic changes.

[0046] result:

[0047] 1. Changes in intestinal flora in acute pancreatitis

[0048] like Figure 1 Compared with healthy controls, patients with acute pancreatitis had significantly lower levels of Shannon diversity ( Figure 1A). The AP group had high abundance of Proteobacteria, Enterococcus, and Klebsiella, which are common opportunistic pathogens. In addition, AP patients had significantly lower abundance of Lachnospiraceae, especially Blautia and Eubacterium hallii group, which are also considered to be protective commensal bacteria producing SCFAs ( Figure 1 B). By analyzing the microbial interaction network in AP patients, it was found that Enterococcus was significantly negatively correlated with most species, while Blautia was significantly positively correlated with most species ( Figure 1 C).

[0049] Since there are many confounding factors in the comparison between clinical AP patients and healthy people, the changes in the intestinal flora of the AP mouse model induced by TCA retrograde bile duct injection were also evaluated. At 24 hours after modeling, the intestinal flora structure of AP mice had changed significantly, and the Shannon diversity level showed a downward trend ( Figure 2 AB). AP mice also had Proteobacteria in their intestines, an increase in the abundance of Enterococcus, and a decrease in the abundance of Blautia in the Lachnospiraceae family, which is consistent with the findings in clinical patients ( Figure 2 C). In addition, AP mice were also found to have an increase in the abundance of conditional pathogens such as Enterobacteriaceae and Escherichia-Shigella, and a decrease in the abundance of Firmicutes and Eubacterium fissicatena group. Based on the above results, it can be clearly seen that the intestinal flora characteristics of acute pancreatitis are: decreased flora diversity, increased abundance of conditional pathogens such as Enterococcus, and decreased abundance of protective commensal bacteria such as Brautia.

[0050] 2. Association between intestinal flora and clinical features of acute pancreatitis

[0051] The microbiota-related parameters of AP patients were correlated with the relevant indicators reflecting the severity of the patients. The results showed that there was no significant correlation between the two ( Figure 3 A). Considering the role of intestinal flora in the late infectious complications of AP, the intestinal flora characteristics were compared between patients who developed secondary infection in the future (AP-infection) and those who did not develop secondary infection (AP-non-infection), and between patients who developed IPN in the future and those who did not develop IPN (AP-non-IPN). The results showed that there was no significant difference in the intestinal flora species diversity and the flora structure shown by PCoA between the groups ( Figure 3 BD). It is worth noting that the abundance of Escherichia-Shigella was significantly higher in AP patients who were subsequently infected, suggesting that the abundance of Escherichia-Shigella on the day of admission may affect the risk of future infection in AP patients ( Figure 3 E). Interestingly, the association between microbial structure and AP predisposition was also compared, and it was found that AP patients with concomitant hyperlipidemia had significantly lower levels of Shannon microbial diversity and Chao abundance index ( Figure 3 F). The above results indicate that the intestinal flora of patients with acute pancreatitis at admission is not closely related to the prognosis of the patients.

[0052] 3. Intestinal metabolic changes in acute pancreatitis

[0053] Comparing the intestinal metabolomes of AP patients and HC patients, a total of 455 differential metabolites were annotated, of which 419 (92.1%) were metabolites missing in AP patients, reflecting the weakened metabolic activity of the AP intestine ( Figure 4 A). These 455 metabolites were annotated to the KEGG database for enrichment analysis, and it was found that these metabolites were mainly derived from amino acid metabolism pathways, including tryptophan metabolism, tyrosine metabolism, arginine and proline metabolism ( Figure 4 B). Tryptophan and tyrosine have benzene ring structures and belong to the same aromatic amino acids as phenylalanine. The purine metabolism pathway also showed significant differences between the two groups. The main functional difference metabolites were associated with the main species in the intestine. The results showed that four bacterial genera were most closely associated with multiple metabolites, including Enterococcus, Pseudomonas, Blautia and Romboutsia ( Figure 4 C) The association trends of Enterococcus and Pseudomonas with metabolites were completely opposite to those of Blautia and Lonbacteria.

[0054] 4. Association between intestinal metabolism and clinical features of acute pancreatitis

[0055] The main functional difference metabolites of AP patients were correlated with relevant indicators reflecting the severity of the patients. The results showed that multiple metabolites were significantly correlated with the severity of the patients' prognosis, including multiple tryptophan metabolites, such as: 5-hydroxyindoleacetic acid (5-Hydroxyindoleacetic acid), 5-hydroxyindoleacetalaldehyde (5-Hydroxyindoleacetalehyde), indoleacetic acid (Indoleacetic acid), serotonin (Serotonin), etc.; and multiple tyrosine metabolites, such as: Tyrosol (Tyrosol), dopaquinone (Dopaquinone), hydroxyphenylacetylglycine (Hydroxyphenylacetylglycine), levodopa (L-Dopa). The abundance of these metabolites was significantly negatively correlated with the length of ICU stay of patients, suggesting that the weakened ability of aromatic amino acid metabolism is related to the poor prognosis of AP patients ( Figure 5 A).

[0056] In addition, the metabolite abundance of AP patients who did or did not develop infectious complications was compared, and the results showed that there was a significant difference in tryptophan metabolism between patients who developed infectious complications or IPN in the future and those who did not on the day of admission ( Figure 5 B). The differential tryptophan metabolites are shown in Table 5. The results show that kynurenine and N'-formylkynurenine are significantly enriched in the intestines of patients with secondary infection, while the remaining tryptophan metabolites are significantly reduced in the intestines of patients with secondary infection or IPN. The above results indicate that abnormal intestinal tryptophan metabolism at admission is closely related to the prognosis of patients with acute pancreatitis and the risk of infection-related complications in the future.

[0057] In summary, the intestinal flora characteristics of AP patients and mouse models were clarified, with the increase of Enterococcus and the decrease of Blautia as the core. The changes in the abundance of these two bacteria were closely related to the abundance of multiple species and differential metabolites. However, the changes in the early intestinal flora could not effectively predict the prognosis of AP and the occurrence of future infections. The intestinal metabolic characteristics of AP patients are manifested by the reduction of two aromatic amino acid metabolites, tryptophan and tyrosine. In addition, unlike the flora results, tryptophan and tyrosine metabolism, especially tryptophan metabolism, are closely related to the length of ICU stay of AP patients and the occurrence of future infectious complications.

[0058] Table 5: Changes in intestinal tryptophan metabolites in complications of AP infection

[0059]

[0060]

[0061] 5. Changes in tryptophan metabolism in acute pancreatitis

[0062] In part 4, intestinal tryptophan dysregulation in patients with acute pancreatitis was found, such as Figure 6 As shown in A, compared with the healthy control, the abundance of various tryptophan metabolites in the feces of the AP group changed, as shown in: indoleacetic acid and 2-methylaminobenzoyl acetate in the indole pathway; serotonin, 5-HIAA and its derivatives in the serotonin pathway, etc., decreased significantly in abundance during AP, while Kyn and its precursor NFK levels in the kynurenine pathway showed an upward trend. The tryptophan metabolism changes in the non-target metabolomics of the patient's plasma were further analyzed, and a total of 8 tryptophan metabolites were annotated, among which the downstream substance of the kynurenine pathway, kynurenic acid (KA), increased significantly in AP ( Figure 6 B). These results indicate that there is a significant imbalance in tryptophan metabolism in the feces and plasma of patients with acute pancreatitis, which is reflected in the increase of kynurenine pathway metabolites and the decrease of indole and serotonin metabolites. In addition, it was found that the plasma KA of patients was significantly negatively correlated with the decrease of various indole and serotonin metabolites in the feces, suggesting that there may be competition among various metabolic pathways for limited tryptophan resources under the AP state ( Figure 6 C).

[0063] In view of the influence of clinical treatment and individual differences, the TCA-induced AP animal model was used for verification, and the mouse plasma and feces were collected 24 hours after modeling for targeted tryptophan metabolomics analysis. First, it was found that compared with healthy controls, AP had a depletion of tryptophan resources in both plasma and feces ( Figure 6 D) Further, the ratio of each metabolite to the total amount of tryptophan was calculated to reflect the activity of each metabolic pathway. Figure 6 E. In plasma, kynurenine pathway metabolites such as 3-HK, XA, and KA were significantly increased in AP mice, and indole pathway metabolites such as IArA and IPA were significantly reduced in the plasma of AP mice. In feces, serotonin pathway metabolites such as serotonin and NAS were significantly reduced, and the indole pathway metabolite tryptol was significantly reduced, while nicotinamide, as the end product of kynurenine metabolism, was significantly increased. Interestingly, ILA, as a metabolite of the indole pathway, was significantly increased in the feces of mice. The above results are consistent with clinical findings, indicating that tryptophan consumption increases during acute pancreatitis, the kynurenine pathway becomes its main metabolic pathway, and the metabolic pathways that produce serotonin or indole derivatives are weakened.

[0064] 6. Association between tryptophan metabolism imbalance and infectious complications of acute pancreatitis

[0065] In Part 4, the differences in intestinal tryptophan metabolism between AP patients with and without infectious complications were compared. The results showed that patients with infectious complications and IPN had decreased indole metabolites, decreased serotonin metabolites, and increased kynurenine metabolites. Furthermore, the plasma tryptophan metabolism levels of the patients were also analyzed, and the results showed that in patients with IPN, the plasma KA level was significantly higher ( Figure 7 A). In addition, most intestinal tryptophan metabolites, such as IAA, IPA, serotonin, 5-HIAA and their derivatives, were negatively correlated with the length of ICU stay, while plasma KA levels were significantly positively correlated with the length of ICU stay and serum PCT ( Figure 7 B, C). Combining the results of plasma tryptophan metabolism with those of intestinal tryptophan metabolism further verified that the bias of tryptophan metabolism toward the kynurenine pathway was closely related to infectious complications and poor prognosis of acute pancreatitis.

[0066] 7. Plasma tryptophan markers for predicting infectious complications in acute pancreatitis

[0067] After clarifying the association between tryptophan metabolic imbalance and infectious complications of acute pancreatitis, we hypothesized that tryptophan metabolites may be used as diagnostic markers to predict infectious complications of acute pancreatitis. Plasma from 61 AP patients was collected as a validation group for targeted tryptophan metabolome analysis, and plasma from 14 healthy subjects with consistent age and gender distribution was collected as a control group ( Figure 8 A).

[0068] First, compared with healthy controls, the tryptophan level in AP patients was significantly reduced, while the concentrations of kynurenine metabolites such as Kyn, KA, and 3-HK were significantly increased ( Figure 8 B). Secondly, the association between tryptophan metabolites and infectious complications was analyzed, and it was found that patients with AP who developed secondary infections had higher concentrations of KA and 3-HK; patients with IPN also had higher abundances of substances such as KA and QA ( Figure 8 CD). These results suggest that the increase of kynurenine metabolites is associated with an increased risk of infectious complications in patients.

[0069] Further analysis of the association between various tryptophan metabolites and patient prognosis revealed that only plasma KA was significantly positively correlated with the patient's ICU length of stay ( Fig. 9 A). In addition, KA levels were significantly positively correlated with the patients' IL-10 levels, PCT, and APAHCE II scores, indicating that KA has the potential to predict infection and poor prognosis in patients with acute pancreatitis ( Fig. 9 B). KA also increased in the plasma of patients with sepsis, but had no significant correlation with death and ICU length of stay, suggesting that the role of plasma KA in predicting adverse prognosis may be specific to AP ( Fig. 9CE). Finally, the predictive ability of plasma KA for IPN in patients with acute pancreatitis was evaluated. When the KA cut off value was 25.96 ng / ml, it had the best diagnostic efficacy in predicting IPN in patients with acute pancreatitis, with a sensitivity and specificity of 80% and 85.71%, respectively, and an AUC of 0.805, which exceeded APACHE II (AUC = 0.749) and IL-10 (AUC = 0.735) ( Fig. 9 F). These results suggest that plasma KA can be used as an efficient biomarker to predict the occurrence of IPN in patients with acute pancreatitis at an early stage.

[0070] 8. Summary

[0071] Through AP patients and mouse models, the characteristics of the intestinal flora with an increase in Enterococcus and a decrease in Blautia as the core were clarified. The changes in the abundance of these two bacteria were closely related to the abundance of multiple species and differential metabolites. However, changes in the early intestinal flora could not effectively predict the prognosis of AP and the occurrence of future infections. The intestinal metabolic characteristics of AP patients are manifested as a decrease in two aromatic amino acid metabolites, tryptophan and tyrosine. In addition, unlike the results of the flora, tryptophan and tyrosine metabolism, especially tryptophan metabolism, are closely related to the length of ICU stay in AP patients and the occurrence of future infectious complications. In AP patients, changes in tryptophan metabolism lead to tryptophan depletion, an increase in kynurenine metabolites, and a decrease in serotonin and indole metabolites. The study found that the metabolite kynurenic acid (KA) can be used as a new early biomarker for AP to effectively predict the occurrence of future IPN.

[0072] Table 1: SOFA scoring criteria

[0073]

[0074]

[0075] Note: PaO2 / FiO2: the ratio of arterial oxygen partial pressure to inspired oxygen concentration, i.e. oxygenation index; GCS: Glasgow Coma Scale.

[0076] Table 2. APS points recognition standards

[0077]

[0078] Note: Rectal temperature: (℃); mean arterial pressure (mmHg); heart rate (times / min); respiratory rate (times / min); venous blood HCO3 (mmol / L); blood sodium (mmol / L); blood potassium (mmol / L); blood creatinine (μmol / L); blood urea nitrogen (mg / dL); HCT: hematocrit (%); WBC: peripheral blood white blood cell count (×10 9 / L)

[0079] Table 3. Modified Marshall Organ Failure Scoring Criteria

[0080]

[0081] Note: PaO2 / FiO2, mmHg; systolic blood pressure, mmHg; serum creatinine, μmol / L.

[0082] Table 4 Clinical characteristics of patients with acute pancreatitis

[0083] Clinical characteristics Acute pancreatitis (n=51) Age, years old 44.7±13.3 Gender, male / female 32 / 19 Causes Biliary 25(49.0％) Hyperlipidemia 36(70.6％) IPN 8(17.0％) Secondary infection 24(47.1％) Infection site lung 19(79.2％) Urinary tract 9(37.5％) Abdominal cavity 9(37.5％) blood flow 5(20.8％) SOFA score 2.74±2.04 APA HCE II score 7.12±4.24 Modified Marshall score 1.72±1.51 Serum procalcitonin, ng / mL 4.47±6.40 Mortality within 90 days 3(5.9％) Length of time in the ICU, days 23.9±31.8

[0084] Note: Measurement data are presented as mean ± standard deviation, and count data are presented as frequency (percentage).

Claims

1. Use of a biomarker in the preparation of a diagnostic or prognostic reagent for acute pancreatitis, characterized in that: The biomarkers include kynurenic acid KA.

2. Use of a biomarker in the preparation of a reagent for diagnosing or predicting infectious complications of acute pancreatitis, characterized in that: The biomarkers include kynurenic acid KA.

3. The application according to claim 2, characterized in that: The biomarker is used in preparing a reagent for diagnosing or predicting the occurrence of IPN in patients with acute pancreatitis.

4. The use according to any one of claims 1-2, characterized in that: The biomarkers also include serotonin metabolites and indole metabolites.

5. The use according to any one of claims 1-2, characterized in that: The biomarkers also include Escherichia-Shigella.

6. The use according to claim 4, characterized in that: The serotonin metabolites include serotonin and 5-hydroxyindoleacetic acid (5-HIAA); the indole metabolites include indoleacetic acid.

7. Use of a reagent for detecting the biomarker according to any one of claims 1 to 2 in the preparation of a reagent or kit for the prognosis, diagnosis or prediction of infectious complications of acute pancreatitis.

8. The use according to claim 7, characterized in that: The reagents or kits include reagents or kits for detecting the content of metabolites.

9. The use according to claim 8, characterized in that: The reagents or kits include reagents or kits for detecting the content of metabolites by chromatography and / or mass spectrometry.

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