The mechanism and application of UDCA series bile acids in preventing intestinal inflammatory damage caused by sepsis

Abstract

The present invention provides a mechanism of action of a UDCA series of bile acids in preventing intestinal inflammatory damage caused by sepsis and its application. By studying serum bile acid metabolites in children with sepsis, TBA and other substances are identified as sepsis-related biomarkers and a joint prediction model is constructed. The UDCA series of bile acids are studied in depth, and their important role is verified in animal models and intestinal cell monolayer models. UDCA can improve the disease phenotype of septic mice and the expression of colon tissue-related factors, and improve the survival rate; it can also improve LPS-induced intestinal cell monolayer damage, improve barrier function, tight junction protein expression levels, and reduce the expression of pro-inflammatory factors. This invention provides new directions and potential targets for the diagnosis and treatment of intestinal cell inflammatory damage caused by sepsis in children, and is expected to improve patient prognosis.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical diagnosis and treatment, and in particular, relates to an action mechanism of UDCA series bile acids in preventing intestinal inflammatory damage caused by sepsis and an application thereof. Background Art

[0002] Sepsis is a life-threatening organ dysfunction caused by an imbalanced host response to infection, and it affects children extensively. Every year, there are a large number of neonatal and pediatric sepsis cases worldwide, and the mortality rate of severe sepsis is as high as 9% - 20%. Currently, sepsis treatment mainly focuses on antibiotics, fluid resuscitation, and organ function protection, but it also brings about problems such as pathogen resistance, drug allergies, and toxic reactions.

[0003] Compared with healthy children, children with sepsis have significant intestinal flora imbalance, including loss of important flora, loss of intestinal flora diversity, changes in flora ecological functions, and massive reproduction of pathogens. Intestinal flora imbalance mainly increases the susceptibility to sepsis by changing the host's metabolites and initiating inflammatory immune responses, thereby promoting disease progression and multiple organ damage.

[0004] Healthy intestinal flora can promote the development and maturation of children's immune system, enhance the host's resistance to pathogens, regulate the balance of the immune system, inhibit the colonization of exogenous pathogens, produce beneficial metabolites, enhance intestinal barrier function, regulate cell epigenetics and coordinate inflammatory response, etc. Children's intestinal flora is highly plastic and easily affected by environmental factors. The pathophysiological state of sepsis and exposure to drugs such as antibiotics can easily lead to intestinal flora imbalance and increase the burden of disease.

[0005] However, there are also some problems. For example, probiotics may lead to poor therapeutic effects, colonization of drug-resistant bacteria, bacteremia and other adverse consequences; the effectiveness of selective gastrointestinal decontamination needs to be further confirmed, and its actual clinical application is relatively small; fecal microbiota transplantation is challenging in children; enteral and parenteral nutrition management needs to be adjusted according to different situations, and current research is still limited; blood phosphorus level management is important but the actual operation is complicated; methods of regulating intestinal flora such as drug therapy are still insufficiently studied in children with sepsis with underlying diseases.

[0006] As a metabolite of intestinal flora, bile acid plays an important physiological role in the digestive system. At the same time, it binds to a variety of bile acid receptors as a hormone, significantly affecting biological processes such as metabolism, inflammation, and immune balance. As the understanding of the role of the intestinal microbiome in sepsis deepens, the complex relationship between bile acid metabolism and sepsis has gradually attracted attention.

[0007] Therefore, it is urgent to find the mechanism of action and application of UDCA series bile acids in preventing intestinal inflammatory damage in sepsis. UDCA series bile acids may play an important role in preventing intestinal inflammatory damage in sepsis by regulating the expression level of intestinal cell tight junction proteins, reducing the expression level of intestinal cell proinflammatory cytokines, and improving intestinal cell monolayer barrier function. Through in-depth research on UDCA series bile acids, we hope to find a safer and more effective treatment for sepsis, improve the prognosis of children with sepsis, and treat intestinal inflammatory damage in sepsis. Summary of the invention

[0008] In order to solve the problems existing in the prior art, the present invention provides a mechanism of action of UDCA series bile acids in preventing intestinal inflammatory damage caused by sepsis and its application. Through in-depth research on serum bile acid metabolites in children with sepsis, the important role of UDCA series bile acids in sepsis is determined, and its key role is verified in animal models and intestinal cell monolayer models. Specifically, UDCA can improve the disease phenotype of septic mice, regulate the expression of colon tissue-related factors, and improve the survival rate; it can also improve LPS-induced intestinal cell monolayer damage, improve barrier function, tight junction protein expression level and reduce the expression of pro-inflammatory factors, providing new ideas and potential targets for the treatment of intestinal cell inflammatory damage caused by sepsis, and has broad application prospects.

[0009] In order to achieve the above object, the present invention adopts the following scheme:

[0010] Sepsis is a serious disease with rapid clinical deterioration and high mortality rate, manifested as acute organ dysfunction caused by systemic infection, characterized by multiple organ failure and high short-term mortality rate. It is understandable that the short-term in the "risk of death in patients with sepsis in the short term" here is not an absolutely fixed time node. Although a commonly used time point such as "mortality rate of patients with sepsis within 90 days" is commonly used in clinical practice, this is only a time node summarized based on clinical experience. With the progress of research or improvement of treatment methods, this time node or duration may change, for example, within 90 days or 100 days, or within a year, 360 days, or within half a year, 180 days, or within 50 days, or within 20 days, within 4 weeks, within 3 weeks, within 2 weeks, within 1 week, etc., it is possible to develop from sepsis to death.

[0011] Existing sepsis-related scoring systems are often based on relatively small clinical cohorts, making it difficult to obtain more adequate validation. A more accurate and effective sepsis prognosis test is still an unmet clinical need. In this study, metabolomics analysis was performed on serum samples from several patients, and the samples were randomly assigned as discovery and validation sets. In the discovery set, 702 children with sepsis and 269 non-infected controls were first selected for study, and the basic information, infection sites, pathogens, and complications of the children were analyzed. Then, the children with sepsis were grouped and clinical indicators were analyzed. The enrolled children were divided into a sepsis survival group, a sepsis death group, and a non-infected control group. The preoperative indicators of the non-infected control group were used as the baseline, and the laboratory indicators of sepsis patients at different stages were analyzed, and the clinical indicators of the sepsis survival group and the death group were compared. Multivariate logistic regression analysis was used to determine the prognostic protective factors and risk factors at different stages of sepsis, and the significantly different biomarkers in the early and severe stages of sepsis were screened and verified by ROC curve. On this basis, a joint prediction model of Lac, TBA, and septic shock was established. Then, in the validation set, new samples were collected for the same analysis process, and the patient data in the validation set were substituted into the joint prediction model established in the test set, and the ROC curve was redrawn to calculate the AUC value, sensitivity, and specificity of the model. After analysis, the prediction results in the validation set were highly consistent with those in the discovery set, proving that the joint prediction model has high accuracy and reliability in predicting the short-term mortality risk of patients with sepsis.

[0012] In the best joint prediction model constructed above, TBA performed particularly well in predicting the probability of death in patients with sepsis. Therefore, in subsequent experiments, the necessity of further exploring the role of different types of bile acids in sepsis was explored, and the UDCA series of bile acids were studied in depth as biomarkers to construct a prediction model for short-term mortality in children with sepsis, and its role in predicting short-term mortality in children with sepsis was verified through animal models. At the same time, the application of UDCA series of bile acids in intestinal cell inflammatory damage in children with sepsis was studied, and a Caco-2 intestinal cell monolayer model of LPS inflammatory damage was constructed to verify the improvement of UDCA on intestinal cell monolayer damage.

[0013] Studies have shown that the sepsis group significantly changed the serum metabolome. Many metabolites are significantly associated with the mortality rate in the sepsis group within a specific time and the risk of progression to sepsis in the non-sepsis group within a specific time. This study trained and verified a machine learning algorithm for predicting the mortality rate of sepsis within a specific time (e.g., mortality within 90 days). The present invention uses liquid chromatography-mass spectrometry (LCMS) to perform targeted analysis of selected metabolites, and the constructed model can be used clinically, providing a powerful tool for clinicians to identify high-risk patients early, take targeted treatment measures in a timely manner, and improve the survival rate of patients. It also lays a solid foundation for the future development of more accurate diagnostic methods and treatment strategies for pediatric sepsis.

[0014] On the one hand, the present invention provides a use of a biomarker for preparing a reagent for predicting whether a sepsis patient has a risk of death, characterized in that the biomarker includes one or more of the following: serum lactate, platelet count, total bile acid, blood urea nitrogen, D-dimer, TNF-α, pH value, glucose, albumin, creatine kinase isoenzyme, procalcitonin, and septic shock.

[0015] Furthermore, the predicting whether a sepsis patient has a risk of death refers to predicting whether the sepsis patient has a risk of death in the short term.

[0016] The "short term" here can refer to whether the sepsis patient has a risk of death within 90 days, 180 days, 360 days, etc., and of course it can also be less than 90 days, such as 50 days, 40 days, 30 days or a shorter time, or it can be within 1 year, 11 months, 10 months, 9 months, 8 months, 7 months, 6 months, 5 months, 4 months, 3 months, 2 months, 1 month, etc.

[0017] Because the short-term here is difficult to predict in advance, it is a time node summarized based on current clinical experience. With the passage of time or the improvement of other treatment methods, this time node or the length of time may change. It may also change according to the length of the patient's hospitalization.

[0018] The present invention collects serum samples of sepsis patients at admission through non-targeted metabolomics research, and divides them into a sepsis survival group and a sepsis death group according to whether they survive in the short term. The serum samples of the sepsis survival group and the sepsis death group at admission are analyzed by UPLC-MS / MS ultra-high performance liquid chromatography-tandem mass spectrometry, and a series of biomarkers that can predict the risk of death in sepsis patients in the short term are found. Machine learning, statistical analysis and pathway enrichment analysis (including random forest, FDR test, t-test) are used to determine the candidate biomarkers in the discovery set and determine their priorities, and screen significantly different metabolites, and finally obtain 12 biomarkers: serum lactate, platelet count, total bile acid, blood urea nitrogen, D-dimer, TNF-α, pH value, glucose, albumin, creatine kinase isoenzyme, and procalcitonin, which can be used to efficiently predict the risk of death in sepsis patients in the short term.

[0019] At the same time, joint prediction models for predicting the probability of death in sepsis patients in the short term (90 days) were constructed. The best prediction model was obtained by screening the logistic regression equation and comprehensively considering the predictive ability, stability and feasibility of clinical application of each indicator. The regression equation of the best prediction model was P=1 / (1+e(-2.333+0.147X1+0.012X2+1.068X3)), where X1, X2 and X3 were Lac, TBA and whether septic shock occurred; when P was greater than 0.204, the risk of death in sepsis patients within 90 days was predicted to be high; when p was less than 0.204, the risk of death in sepsis patients within 90 days was predicted to be low. In this best prediction model, further analysis found that TBA (total bile acid) performed particularly well in predicting the probability of death in sepsis patients.

[0020] The study showed that the TBA level is of great significance for the prediction of disease prognosis, as can be seen from the neural network model. High TBA levels indicate that patients have an increased risk of poor prognosis. For the mortality risk prediction model for sepsis patients, when the serum TBA level is ≥17.95 µmol / L, the mortality rate of children with sepsis is significantly increased, with a hazard ratio of 7.658 (95% CI: 5.575-10.520), P<0.05. In addition, the dynamic changes of serum TBA in children with sepsis during hospitalization and the correlation analysis with various laboratory indicators have confirmed the importance of TBA, a key indicator in the optimal joint prediction model. Based on this, it was decided to explore the role of different types of bile acids in sepsis in subsequent experiments. Finally, based on the in-depth study of different types of bile acids, it was confirmed that the UDCA series of bile acids are biomarkers that can effectively predict the short-term mortality risk of childhood sepsis, providing a new direction for the division of sepsis subgroups.

[0021] At the same time, since the importance of UDCA series bile acids in sepsis was discovered in previous studies, a series of experiments were conducted to further clarify its mechanism of action. A Caco-2 enterocyte monolayer model of LPS inflammatory injury was constructed, confirming the application of UDCA series bile acids in enterocyte inflammatory injury in children with sepsis, providing new ideas and experimental basis for in-depth research on the treatment strategy of sepsis.

[0022] In some embodiments, the biomarker reagent for predicting whether a sepsis patient has a risk of death or a probability of survival is a detection reagent prepared with the biomarker as the detection target, such as a sample pretreatment reagent, an antigen or an antibody, and other biological reagents and kits suitable for the detection of the biomarker; it can also be developed into a standardized reagent or kit suitable for LC-UV or LC-MS detection of the biomarker.

[0023] Furthermore, the biomarkers include serum lactate, total bile acid, and septic shock.

[0024] Furthermore, the reagent is used to detect biomarkers in blood, serum, plasma or whole blood samples of sepsis patients.

[0025] Further, the reagent is used to detect the presence or absence of biomarkers in the sample, or the relative abundance or relative concentration, or the degree or amplitude of the change in the content or quantity of the marker. The presence or absence of the marker or the level of the content here is a relative concept. For example, for patients who died from sepsis and those who survived, the content of these specific markers is compared with the baseline of patients who died from sepsis or survived. It may be that for some markers, the content in patients who died from sepsis or had a high probability of death is higher than that in patients who survived sepsis, and this high has a statistical difference, such as a significant or extremely significant increase. Therefore, when these markers are judged, if it is a single marker, if the probability of the marker occurring at a certain risk increases, the content of the marker changes. The change here may be a relative increase or a relative decrease. This relative increase or relative decrease has a significant difference, and of course it can also be an extremely significant difference. Therefore, no matter what means are used for detection, a predetermined value can be used as a standard (cut-off value). If it is higher than this value, it is considered that the content has changed. Such results can be used as predictive or diagnostic value.

[0026] Therefore, in some aspects, the markers described in the present invention can be obtained by detecting the marker content in the sample by any existing known method, such as liquid chromatography, gas chromatography, mass spectrometry, LC-MS, GC-MS, CC-MS, LC-MS-MS, NMR, immunochromatography test paper, immunoreaction chip, capillary electrophoresis, infrared spectroscopy, etc. As long as they can be used to detect the marker content in the sample, they can be used to predict whether the sepsis patient has a risk of death in the short term. As long as the content in the sample can be detected, it can be used to predict or diagnose the probability of a certain disease. It can be understood that the detection here is to detect the individual sample, and then compare it with the pre-set standard, and the comparison result is used to judge or predict the occurrence state of the disease, for example, it can be used to predict whether the sepsis patient will die or the probability of death, and it can also predict whether the sepsis patient will survive or the probability of survival. This prediction or diagnosis is whether it will occur within a certain period of time. Of course, such detection can be continuous detection, and the progress of the disease can be inferred as the content of certain substances changes.

[0027] In some embodiments, the relative abundance is the peak area of ​​the biomarker in the detection spectrum obtained by high performance liquid chromatography-tandem mass spectrometry. For example, if the average peak area of ​​a biomarker measured in the control sample (sepsis patient short-term survival group) is 500, and the average peak area measured in the sample of the sepsis patient short-term death group is 3000, then the abundance of the biomarker in the sample is considered to be 6 times that in the control sample.

[0028] Furthermore, the predicting whether a sepsis patient has a risk of death or a probability of survival refers to predicting whether a sepsis patient has a risk of death or a probability of survival within 90 days.

[0029] On the other hand, the present invention provides a kit for predicting whether a sepsis patient has a risk of death, the kit comprising a detection reagent for a biomarker for the use as described in any one of the above technical solutions.

[0030] Furthermore, the detection reagent of the biomarker includes a standard of the biomarker.

[0031] In another aspect, the present invention provides a biomarker combination for predicting whether a patient with sepsis has a risk of death, comprising the following biomarkers: serum lactate, platelet count, total bile acid, blood urea nitrogen, D-dimer, TNF-α, pH value, glucose, albumin, creatine kinase isoenzyme, procalcitonin, and septic shock.

[0032] In another aspect, the present invention provides a system for predicting whether a sepsis patient has a risk of death, the system comprising a data analysis module; the data analysis module is used to analyze the detection value of a biomarker, wherein the biomarker comprises one or more of the following: serum lactate, platelet count, total bile acid, blood urea nitrogen, D-dimer, TNF-α, pH value, glucose, albumin, creatine kinase isoenzyme, procalcitonin, and septic shock.

[0033] Furthermore, the biomarkers include serum lactate, total bile acid, and septic shock.

[0034] Furthermore, the detection value of the biomarker is to detect the biomarker in the blood, serum, plasma or whole blood sample of the sepsis patient, and is used to detect the presence or relative abundance or relative concentration of the biomarker in the sample, or the change in relative content.

[0035] Furthermore, the data analysis module calculates a predicted value for predicting whether a sepsis patient has a risk of death by substituting the detection value of the biomarker into a regression equation, thereby evaluating the risk of death of the sepsis patient.

[0036] The present invention can analyze the detection value of the biomarker by any method (such as by computer software, or manual experience evaluation, etc.), obtain certain equations, and then bring the test results into the equations to automatically obtain the predicted result values. It is used to calculate the predicted value of whether a sepsis patient has a risk of death. These equations or computer programs are generally generated by analyzing between certain groups, and then verifying the accuracy of the equation calculation results between the certain groups. For example, select the death group and the survival group of sepsis patients for equation calculation training, and then continue to select other death groups and survival groups of sepsis patients for verification to see the accuracy of the results, so as to obtain the ideal equation for calculation. It can be understood that the more data obtained, the more the equation can be continuously revised or the AI ​​can self-learn and self-adjust, so that the prediction accuracy is higher and more in line with the actual situation. Therefore, no matter which model or equation can be applied to the invention to automatically output the prediction results. Therefore, there are many computer algorithms to choose from, such as mixed discriminant analysis (mda); gradient boosting (gbm); logistic regression (plr); generalized linear model (glmnet); random forest (ranger); naïve Bayes; neural network (avNNet); support vector machine (svmRadial) and other computing methods, all of which can be used for the markers or combinations of markers of the present invention for prediction or diagnosis.

[0037] Furthermore, the predicting whether a sepsis patient has a risk of death refers to predicting whether the sepsis patient has a risk of death in the short term.

[0038] The "short term" here can refer to whether the sepsis patient has a risk of death within 90 days, 180 days, 360 days, etc., and of course it can also be less than 90 days, such as 50 days, 40 days, 30 days or a shorter time, or it can be within 1 year, 11 months, 10 months, 9 months, 8 months, 7 months, 6 months, 5 months, 4 months, 3 months, 2 months, 1 month, etc.

[0039] Because the short-term here is difficult to predict in advance, it is a time node summarized based on current clinical experience. With the passage of time or the improvement of other treatment methods, this time node or the length of time may change. It may also change according to the length of the patient's hospitalization.

[0040] Furthermore, the regression equation is a logistic regression equation.

[0041] Furthermore, the logistic regression equation is constructed and trained by the detection values ​​of biomarkers of known samples.

[0042] Furthermore, the training is performed by constructing a training set and using a training method to train the training set so as to obtain a logistic regression equation; the training set is serum samples of sepsis patients.

[0043] Furthermore, the training method is to divide the training set into a survival group and a death group according to whether the subjects survive in the short term, detect the biomarker levels of the survival group and the death group respectively, obtain the detection values, perform logistic regression based on the detection values ​​of the biomarkers, analyze the relationship between the detection values ​​of the survival group and the death group, and construct a logistic regression equation.

[0044] The training set described in the present invention can be serum samples of sepsis patients collected under any circumstances, that is, it can be serum samples of sepsis patients collected at different times, different regions, different numbers, different genders, different ages, etc.

[0045] Furthermore, the data analysis module substitutes the detection value of the biomarker into the logistic regression equation to calculate the predicted value for predicting the risk of death of sepsis patients within 90 days, thereby evaluating the risk of death of sepsis patients within 90 days.

[0046] Furthermore, the logistic regression equation is:

[0047] P=1 / (1+e(-2.333+0.147X1+0.012X2+1.068X3)).

[0048] Furthermore, p represents the predicted value of the risk of death within 90 days in patients with sepsis; X1, X2, and X3 represent Lac, TBA, and whether septic shock occurs.

[0049] Furthermore, when P is greater than 0.204, it is predicted that the risk of death within 90 days in patients with sepsis is high; when p is less than 0.204, it is predicted that the risk of death within 90 days in patients with sepsis is low.

[0050] Furthermore, the system also includes a data storage module, a data input interface and a data output interface; the data storage module is used to store the detection values ​​of biomarkers; the data input interface is used to input the detection values ​​of biomarkers, and the data output interface is used to output the prediction results.

[0051] In another aspect, the present invention provides a use of a biomarker for preparing a reagent for predicting the short-term mortality rate of patients with sepsis, wherein the biomarker comprises one or more of the following: 3β-ursodeoxycholic acid, 3β-chenodeoxycholic acid, allolithocholic acid, 7-ketolithocholic acid, ursodeoxycholic acid, glycoursodeoxycholic acid, orthocholic acid, and glycocholic acid.

[0052] Furthermore, the biomarkers include 3β-ursodeoxycholic acid and ursodeoxycholic acid.

[0053] Furthermore, the reagent is used to detect biomarkers in blood, serum, plasma or whole blood samples of sepsis patients; the reagent is used to detect the presence or relative abundance or relative concentration of biomarkers in the sample, or changes in relative content.

[0054] In yet another aspect, the present invention provides a kit for predicting the short-term mortality of patients with sepsis, the kit comprising a detection reagent for a biomarker for the purpose described in any one of the above technical solutions.

[0055] Furthermore, the detection reagent of the biomarker includes a standard of the biomarker.

[0056] Furthermore, the predicting of the short-term mortality rate of sepsis patients refers to predicting the mortality rate of sepsis patients within 90 days.

[0057] In another aspect, the present invention provides a biomarker combination for predicting the short-term mortality of sepsis patients, comprising the following biomarkers: 3β-ursodeoxycholic acid and ursodeoxycholic acid.

[0058] On the other hand, the present invention provides a system for predicting the short-term mortality rate of patients with sepsis, the system comprising a data analysis module; the data analysis module is used to analyze the detection value of biomarkers, and the biomarkers include one or more of the following: 3β-ursodeoxycholic acid, 3β-chenodeoxycholic acid, allolithocholic acid, 7-ketolithocholic acid, ursodeoxycholic acid, glycoursodeoxycholic acid, orthocholic acid, and glycocholic acid.

[0059] Furthermore, the biomarkers include 3β-ursodeoxycholic acid and ursodeoxycholic acid.

[0060] Furthermore, the detection value of the biomarker is to detect the biomarker in the blood, serum, plasma or whole blood sample of the sepsis patient, and is used to detect the presence or relative abundance or relative concentration of the biomarker in the sample, or the change in relative content.

[0061] Furthermore, the data analysis module calculates and predicts the short-term mortality rate of sepsis patients by substituting the detection value of the biomarker into the regression equation, thereby evaluating whether the sepsis patients will die in the short term.

[0062] Furthermore, predicting the short-term mortality rate of sepsis patients refers to predicting whether sepsis patients have a risk of death in the short term.

[0063] The "short term" here can refer to whether the sepsis patient has a risk of death within 90 days, 180 days, 360 days, etc., and of course it can also be less than 90 days, such as 50 days, 40 days, 30 days or a shorter time, or it can be within 1 year, 11 months, 10 months, 9 months, 8 months, 7 months, 6 months, 5 months, 4 months, 3 months, 2 months, 1 month, etc.

[0064] Because the short-term here is difficult to predict in advance, it is a time node summarized based on current clinical experience. With the passage of time or the improvement of other treatment methods, this time node or the length of time may change. It may also change according to the length of the patient's hospitalization.

[0065] Furthermore, the regression equation is a logistic regression equation.

[0066] Furthermore, the logistic regression equation is constructed and trained by the detection values ​​of biomarkers of known samples.

[0067] Furthermore, the training is performed by constructing a training set and using a training method to train the training set so as to obtain a logistic regression equation; the training set is serum samples of sepsis patients.

[0068] Furthermore, the training method is to divide the training set into a survival group and a death group according to whether the subjects survive in the short term, detect the biomarker levels of the survival group and the death group respectively, obtain the detection values, perform logistic regression based on the detection values ​​of the biomarkers, analyze the relationship between the detection values ​​of the survival group and the death group, and construct a logistic regression equation.

[0069] The training set described in the present invention can be serum samples of sepsis patients collected under any circumstances, that is, it can be serum samples of sepsis patients collected at different times, different regions, different numbers, different genders, different ages, etc.

[0070] Furthermore, the data analysis module substitutes the detection value of the biomarker into the logistic regression equation to calculate the predicted value for predicting the risk of death of sepsis patients within 90 days, thereby evaluating the risk of death of sepsis patients within 90 days.

[0071] Furthermore, the logistic regression equation is:

[0072] P=1 / (1+e(0.648-0.004X1-0.003X2)).

[0073] Further, p represents the predicted value for predicting the risk of death within 90 days in patients with sepsis, X1 represents the serum concentration of 3β-UDCA, and X2 represents the serum concentration of UDCA.

[0074] Furthermore, when P is greater than 0.4633394, it is predicted that the risk of death within 90 days in patients with sepsis is high; when p is less than 0.4633394, it is predicted that the risk of death within 90 days in patients with sepsis is low;

[0075] Furthermore, the system also includes a data storage module, a data input interface and a data output interface; the data storage module is used to store the detection values ​​of biomarkers; the data input interface is used to input the detection values ​​of biomarkers, and the data output interface is used to output the prediction results.

[0076] In another aspect, the present invention provides a method for predicting whether a sepsis patient has a risk of death, by analyzing the detection value of a biomarker, for predicting whether a sepsis patient has a risk of death; the biomarker comprises one or more of the following: serum lactate, platelet count, total bile acid, blood urea nitrogen, D-dimer, TNF-α, pH value, glucose, albumin, creatine kinase isoenzyme, procalcitonin, septic shock.

[0077] Furthermore, the biomarkers include serum lactic acid, total bile acid, and septic shock, and the total bile acid includes ursodeoxycholic acid series bile acids.

[0078] Furthermore, the detection value of the biomarker is to detect the biomarker in the blood, serum, plasma or whole blood sample of the sepsis patient, and is used to detect the presence or relative abundance or concentration of the biomarker in the sample.

[0079] Furthermore, the method calculates the predicted value for predicting whether a sepsis patient has a risk of death by substituting the detection value of the biomarker into a regression equation, thereby evaluating the risk of death in sepsis patients.

[0080] Furthermore, the predicting whether a sepsis patient has a risk of death refers to predicting whether the sepsis patient has a risk of death in the short term.

[0081] The "short term" here can refer to whether the sepsis patient has a risk of death within 90 days, 180 days, 360 days, etc., and of course it can also be less than 90 days, such as 50 days, 40 days, 30 days or a shorter time, or it can be within 1 year, 11 months, 10 months, 9 months, 8 months, 7 months, 6 months, 5 months, 4 months, 3 months, 2 months, 1 month, etc.

[0082] Because the short-term here is difficult to predict in advance, it is a time node summarized based on current clinical experience. With the passage of time or the improvement of other treatment methods, this time node or the length of time may change. It may also change according to the length of the patient's hospitalization.

[0083] Furthermore, the regression equation is a logistic regression equation.

[0084] Furthermore, the logistic regression equation is constructed and trained by the detection values ​​of biomarkers of known samples.

[0085] Furthermore, the training is performed by constructing a training set and using a training method to train the training set so as to obtain a logistic regression equation; the training set is serum samples of sepsis patients.

[0086] Furthermore, the training method is to divide the training set into a survival group and a death group according to whether the subjects survive in the short term, detect the biomarker levels of the survival group and the death group respectively, obtain the detection values, perform logistic regression based on the detection values ​​of the biomarkers, analyze the relationship between the detection values ​​of the survival group and the death group, and construct a logistic regression equation.

[0087] The training set described in the present invention can be serum samples of sepsis patients collected under any circumstances, that is, it can be serum samples of sepsis patients collected at different times, different regions, different numbers, different genders, different ages, etc.

[0088] Furthermore, the data analysis module substitutes the detection value of the biomarker into the logistic regression equation to calculate the predicted value for predicting the risk of death of sepsis patients within 90 days, thereby evaluating the risk of death of sepsis patients within 90 days.

[0089] On the other hand, the present invention provides a use of bile acid for preparing an agent for preventing intestinal inflammatory damage caused by sepsis, wherein the bile acid comprises one or more of the following: primary glycine and taurine conjugated bile acid, sulfated bile acid, secondary unconjugated bile acid, glycine conjugated secondary bile acid, and primary unconjugated bile acid.

[0090] In the present invention, the role of different types of bile acids in sepsis was analyzed through an in-depth study of serum bile acid metabolites in children with sepsis.

[0091] Studies have found that in patients with sepsis, intestinal flora imbalance is obvious, and bile acids, as important substances in the body's physiological metabolism and immune system, may play an important role in preventing intestinal inflammatory damage caused by sepsis. Through the analysis of a large number of serum samples, it was determined that a variety of bile acids, including primary glycine and taurine-conjugated bile acids, sulfated bile acids, secondary unconjugated bile acids, glycine-conjugated secondary bile acids, and primary unconjugated bile acids, have potential uses in preventing intestinal inflammatory damage caused by sepsis.

[0092] In some embodiments, it is found that the mechanism of action of bile acid in preventing intestinal inflammatory damage mainly includes the following aspects: First, bile acid can regulate the expression level of tight junction proteins in intestinal cells. Tight junction proteins are essential for maintaining the integrity of the intestinal mucosal barrier. Bile acids enhance the intestinal barrier function by regulating the expression of these proteins, preventing pathogens and inflammatory factors from invading intestinal cells, thereby preventing intestinal inflammatory damage; secondly, bile acids can reduce the expression level of proinflammatory cytokines in intestinal cells. In the state of sepsis, the overexpression of proinflammatory cytokines will aggravate the intestinal inflammatory response. Bile acids can reduce the inflammatory response by inhibiting the production of proinflammatory cytokines and playing a protective role on the intestine; in addition, bile acids may also prevent intestinal inflammatory damage by regulating the expression of related factors in the colon tissue of septic mice. For example, it regulates the expression of factors related to immune response, intestinal barrier function, etc., improves the microenvironment of colon tissue, and reduces the risk of intestinal inflammation.

[0093] Further, the bile acid includes one or more of the following: 3β-ursodeoxycholic acid, 3β-chenodeoxycholic acid, allolithocholic acid, 7-ketolithocholic acid, ursodeoxycholic acid, glycoursodeoxycholic acid, orthocholic acid, glycocholic acid.

[0094] Furthermore, the bile acid includes UDCA series bile acid.

[0095] Furthermore, the UDCA series bile acids include 3β-ursodeoxycholic acid, ursodeoxycholic acid, and glycoursodeoxycholic acid.

[0096] In yet another aspect, the present invention provides a use of bile acid for preparing an agent for regulating the expression level of intestinal cell tight junction proteins in enterogenic sepsis.

[0097] Further, the bile acid includes one or more of the following: 3β-ursodeoxycholic acid, 3β-chenodeoxycholic acid, allolithocholic acid, 7-ketolithocholic acid, ursodeoxycholic acid, glycoursodeoxycholic acid, orthocholic acid, glycocholic acid.

[0098] Furthermore, the bile acid includes UDCA series bile acid.

[0099] Furthermore, the UDCA series bile acids include 3β-ursodeoxycholic acid, ursodeoxycholic acid, and glycoursodeoxycholic acid.

[0100] In yet another aspect, the present invention provides a use of bile acid for preparing an agent for reducing the expression level of pro-inflammatory cytokines in intestinal cells in enterogenic sepsis.

[0101] In yet another aspect, the present invention provides a use of bile acid for preparing an agent for improving the barrier function of intestinal cell monolayer in enterogenic sepsis.

[0102] In yet another aspect, the present invention provides a use of bile acid for preparing a reagent for regulating the expression of colon tissue-related factors in septic mice.

[0103] In another aspect, the present invention provides a use of bile acid for preparing an agent for preventing the early onset of enterogenic sepsis, wherein the bile acid comprises one or more of the following: primary glycine and taurine conjugated bile acids, sulfated bile acids, secondary unconjugated bile acids, glycine conjugated secondary bile acids, and primary unconjugated bile acids.

[0104] Further, the bile acid includes one or more of the following: 3β-ursodeoxycholic acid, 3β-chenodeoxycholic acid, allolithocholic acid, 7-ketolithocholic acid, ursodeoxycholic acid, glycoursodeoxycholic acid, orthocholic acid, glycocholic acid.

[0105] Furthermore, the bile acid includes UDCA series bile acid.

[0106] Furthermore, the UDCA series bile acids include 3β-ursodeoxycholic acid, ursodeoxycholic acid, and glycoursodeoxycholic acid.

[0107] In another aspect, the present invention provides a use of a biomarker for preparing a reagent for predicting whether a sepsis patient has a risk of death in the short term, wherein the biomarker comprises one or more of the following: serum lactate, platelet count, total bile acid, blood urea nitrogen, D-dimer, TNF-α, pH value, glucose, albumin, creatine kinase isoenzyme, procalcitonin, septic shock, 3β-ursodeoxycholic acid, 3β-chenodeoxycholic acid, allolithocholic acid, 7-ketolithocholic acid, ursodeoxycholic acid and glycoursodeoxycholic acid, NorCA, glycocholic acid.

[0108] Further, the biomarker includes serum lactic acid, total bile acid, septic shock, or the biomarker includes 3β-ursodeoxycholic acid and ursodeoxycholic acid.

[0109] Furthermore, the reagent is used to detect biomarkers in blood, serum, plasma or whole blood samples of sepsis patients; the reagent is used to detect the presence or relative abundance or relative concentration of biomarkers in the sample, or changes in relative content.

[0110] Furthermore, predicting whether a sepsis patient has a risk of death in the short term includes predicting whether the sepsis patient has a risk of death within 90 days.

[0111] In yet another aspect, the present invention provides a kit for predicting whether a sepsis patient has a risk of death, the kit comprising a detection reagent for a biomarker for the use described in any one of the above technical solutions.

[0112] Furthermore, the detection reagent of the biomarker includes a standard of the biomarker.

[0113] In another aspect, the present invention provides a biomarker combination for predicting whether a sepsis patient has a risk of death, comprising the following biomarkers: serum lactate, platelet count, total bile acid, blood urea nitrogen, D-dimer, TNF-α, pH value, glucose, albumin, creatine kinase isoenzyme, procalcitonin, septic shock, 3β-ursodeoxycholic acid, 3β-chenodeoxycholic acid, allolithocholic acid, 7-ketolithocholic acid, ursodeoxycholic acid and, glycoursodeoxycholic acid, NorCA, glycocholic acid.

[0114] In another aspect, the present invention provides a system for predicting whether a sepsis patient has a risk of death, the system comprising a data analysis module; the data analysis module is used to analyze the detection value of a biomarker, wherein the biomarker comprises one or more of the following: serum lactate, platelet count, total bile acid, blood urea nitrogen, D-dimer, TNF-α, pH value, glucose, albumin, creatine kinase isoenzyme, procalcitonin, septic shock, 3β-ursodeoxycholic acid, 3β-chenodeoxycholic acid, allolithocholic acid, 7-ketolithocholic acid, ursodeoxycholic acid and, glycoursodeoxycholic acid, NorCA, glycocholic acid.

[0115] Further, the biomarker includes serum lactic acid, total bile acid, septic shock, or the biomarker includes 3β-ursodeoxycholic acid and ursodeoxycholic acid.

[0116] Furthermore, the data analysis module calculates a predicted value for predicting whether a sepsis patient has a risk of death by substituting the detection value of the biomarker into a regression equation, thereby evaluating the risk of death of the sepsis patient.

[0117] 1. The present invention has screened out 12 new biomarkers that can predict whether sepsis patients have a risk of death at an early stage; in particular, biomarkers that predict the risk of death in sepsis patients in the short term. Through the study of bile acids, bile acids have been established as an effective biomarker for predicting sepsis mortality, which makes up for the shortcomings of existing diagnostic standards in prognosis screening. By testing bile acid levels in the early stages of the disease, high-risk children can be detected in time, and time can be gained for taking active treatment measures as soon as possible, thereby improving the prognosis of death and achieving early diagnosis and intervention. At the same time, it helps doctors to develop personalized and more accurate treatment plans for children based on bile acid levels and related changes, thereby improving treatment effects.

[0118] 2. Three biomarkers were screened and a short-term mortality risk prediction model for patients with sepsis was constructed. The prediction performance was excellent, with the highest AUC value reaching 0.761. At the same time, two bile acids, 3β-UDCA and UDCA, were used to construct a joint prediction model for the short-term mortality rate of patients with sepsis, with the highest AUC value reaching 0.828.

[0119] 3. For the first time, it was found that the serum bile acid profile of children with sepsis changed significantly, especially the concentration of UDCA bile acids decreased significantly. It was found for the first time that the concentration of UDCA series bile acids was significantly negatively correlated with inflammation and coagulation indicators, but significantly positively correlated with immune indicators. It was found for the first time that UDCA series bile acids in the human body can improve the intestinal tissue and intestinal barrier function damaged by inflammation, and inhibit the inflammatory response.

[0120] 4. The present invention provides strong support for the use of UDCA series bile acids as biomarkers for predicting the short-term mortality of children with sepsis. Its potential applications are wide-ranging: (1) Prediction of sepsis-related death: By detecting the serum UDCA series bile acid concentration in children with sepsis, the high-risk population for sepsis-related death can be screened out, so that they can receive active treatment guidance and intervention at the early stage of the disease, thereby improving the prognosis of death; (2) Low-invasive diagnosis of sepsis-related death: Only a single blood draw upon admission can provide strong support for clinicians to judge the condition of children with sepsis and provide a basis for formulating more accurate treatment plans; (3) Improvement of sepsis-related death: Through clinical drug UDCA intervention, the UDCA content of children with sepsis can be increased to achieve an improvement in sepsis-related death.

[0121] 5. The present invention reveals the mechanism of action of bile acid in reducing the mortality rate of sepsis, and provides a key basis for in-depth exploration of the pathogenesis of sepsis: During sepsis, the release of inflammatory mediators inhibits the expression of liver transport protein genes, resulting in impaired bile acid transport and metabolism, causing hyperbilirubinemia and cholestasis. Elevated circulating bile acids will isolate nuclear FXR / RXR heterodimers, affect the feedback inhibition of bile acid synthesis, activate the NLRP inflammasome signaling pathway, accelerate disease progression and high mortality. Based on the mechanism of action of bile acid, it provides direction and targets for the development of new drugs for the treatment of sepsis, and promotes drug innovation.

[0122] 6. The present invention provides a bile acid mechanism of action in predicting sepsis mortality and its application, clarifies the regulatory effect of bile acid on intestinal flora, helps maintain the balance of intestinal microecology, and thus indirectly improves the condition of children with sepsis; at the same time, it effectively increases the medical community's attention and awareness of childhood sepsis as a serious disease, and promotes the development of multidisciplinary collaboration and comprehensive treatment strategies.

[0123] 7. In the present invention, a completely new mechanism of action of UDCA series bile acids in preventing intestinal inflammatory damage caused by sepsis is discovered. UDCA series bile acids effectively prevent and treat intestinal inflammatory damage caused by sepsis through a multi-faceted mechanism of action, such as regulating the expression level of intestinal cell tight junction proteins, reducing the expression level of intestinal cell proinflammatory cytokines, improving intestinal cell monolayer barrier function, regulating the expression of colon tissue-related factors in septic mice, and regulating the balance of intestinal flora. It helps to improve the intestinal inflammatory state of sepsis patients, especially children with sepsis, maintain the balance of intestinal microecology, and thus provide new approaches and methods for the comprehensive treatment of sepsis, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0124] Figure 1 The figure is a flow chart of the experimental study of children with sepsis and non-infection controls in the present invention.

[0125] Figure 2 Figure 2 shows the age distribution and gender ratio of children with sepsis.

[0126] Figure 3 This is a statistical chart of the distribution of infection sites in children with sepsis.

[0127] Figure 4 Figure 2 shows the types of pathogens that infect children with sepsis.

[0128] Figure 5 This is a comparison chart of the differences in complications between the survival group and the death group of children with sepsis.

[0129] Figure 6 This is a comparison chart of various indicators between the sepsis group and the non-infection control group.

[0130] Figure 7 This is a comparison chart of the differences in clinical indicators between the sepsis survival group and the death group.

[0131] Figure 8 Figure 3 shows the multivariate logistic regression analysis of the early stage of sepsis.

[0132] Fig. 9 Figure 2 shows the multivariate logistic regression analysis of the severity of sepsis.

[0133] Fig.10 Figure 2 is a binary logistic analysis chart of sepsis complications.

[0134] Fig.11 This is the ROC curve analysis diagram of indicators in the early stage of sepsis.

[0135] Fig.12 This is the ROC curve analysis diagram of sepsis complications in predicting sepsis mortality.

[0136] Fig.13 This is the ROC curve analysis diagram of the indicators of severe stage of sepsis.

[0137] Fig.14 This is the ROC curve analysis diagram of the combined prediction of sepsis mortality by serum lactate, total bile acid levels and septic shock in the early stage of sepsis.

[0138] Fig.15 A neural network model was developed to evaluate the importance of various clinical indicators for sepsis prognosis according to the severity of sepsis.

[0139] Fig.16 Figure 3 shows the relationship between the risk of death and baseline TBA level in patients with sepsis.

[0140] Fig.17 This is a graph showing the dynamic changes of serum TBA in children with sepsis during hospitalization.

[0141] Fig.18 This is a correlation analysis chart between TBA and various laboratory indicators in patients with sepsis.

[0142] Fig.19 This is the PLS-DA analysis diagram of the overall distribution of serum bile acids in the sepsis group and the control group.

[0143] Fig. 20 The figure shows the relative abundance of the top 10 bile acids in serum samples.

[0144] Fig.21 and Fig. 22 Volcano plot of differential bile acid in serum samples from children with sepsis and controls.

[0145] Fig.23 This is a logistic regression analysis of the difference in bile acid between children with sepsis and controls.

[0146] Fig.24 Figure 2 is the ROC analysis of the difference in bile acid levels between serum samples of children with sepsis and controls.

[0147] Fig.25 This is a graph showing the difference in bile acid between the survival group and the death group.

[0148] Fig.26 This is the analysis diagram of the combined prediction model of 3β-UDCA and UDCA.

[0149] Fig. 27 This is a correlation analysis chart between UDCA series bile acids and clinical indicators.

[0150] Fig.28 This is a comparison of serum bile acid profiles between children with sepsis and the control group.

[0151] Fig.29 This is a comparison chart of serum UDCA series bile acid concentrations in children with sepsis survival group and death group.

[0152] Fig.30 This is a heat map showing the correlation between serum UDCA series bile acids and clinical indicators in children with sepsis.

[0153] Fig.31 ROC curves for 3β-UDCA and UDCA in differentiating children in the sepsis group from the control group.

[0154] Fig.32 ROC curves for 3β-UDCA, UDCA, and GUDCA in predicting short-term mortality in patients with sepsis.

[0155] Fig.33 Schematic diagram of the grouping of UDCA intervention in LPS mouse model.

[0156] Fig.34 Figure 2 shows the changes in disease phenotype in septic mice after UDCA intervention.

[0157] Fig.35 This figure shows the effect of UDCA intervention on the expression levels of pro-inflammatory cytokines, autophagy proteins and tight junction proteins (qPCR detection) in colon tissue of septic mice.

[0158] Fig.36 This figure shows the effect of UDCA intervention on the expression levels of pro-inflammatory cytokines, autophagy proteins and tight junction proteins (WB detection) in colon tissue of septic mice.

[0159] Fig.37 Schematic diagram of the grouping of UDCA intervention in CLP mouse model.

[0160] Fig.38 Figure 2 shows the changes in disease phenotype in sepsis mice after UDCA intervention (CLP model).

[0161] Fig.39 This is the effect of UDCA intervention on the mortality rate of septic mice (survival curve).

[0162] Fig.40 This figure shows the effect of UDCA intervention on the expression levels of pro-inflammatory, anti-inflammatory cytokines and tight junction proteins (qPCR detection, CLP model) in colon tissue of septic mice.

[0163] Fig.41 This figure shows the effect of UDCA intervention on the expression levels of proinflammatory cytokines, autophagy proteins and tight junction proteins (WB detection, CLP model) in colon tissue of septic mice.

[0164] Fig.42 This is a line graph showing the changes in TEER values ​​of Caco-2 cell monolayer over time (different treatment groups).

[0165] Fig.43 Comparison of FD-4 permeability in Caco-2 cell monolayer (different treatment groups).

[0166] Fig.44 The figure shows the effect of UDCA on the expression level of tight junction proteins in Caco-2 cells induced by inflammation (qPCR and WB detection).

[0167] Fig.45 This is the effect of UDCA on the expression levels of proinflammatory cytokines in inflammation-induced Caco-2 cells (qPCR detection).

[0168] Fig.46The figure shows the effect of UDCA on the expression level of proinflammatory cytokines in inflammation-induced Caco-2 cells (WB and ELISA detection).

[0169] (1) Diagnosis or testing

[0170] The diagnosis or detection here, prediction refers to the detection or analysis of biomarkers in the sample, or the content of the target biomarker, such as the absolute content or relative content, and then the presence or amount of the target marker is used to explain whether the individual providing the sample may have or suffer from a certain disease, or the possibility of having a certain disease. The meanings of diagnosis and detection here are interchangeable. The result of such a test or diagnosis cannot be directly used as a direct result of illness, but is an intermediate result. If a direct result is obtained, other auxiliary means such as pathology or anatomy are required to confirm that a certain disease is present. For example, the present invention provides a variety of new biomarkers that are associated with sepsis patients, or the death or survival of sepsis patients. The changes in the content of these markers are directly related to whether sepsis is present and whether sepsis patients die.

[0171] (2) Association between markers or biomarkers and sepsis

[0172] Markers and biomarkers have the same meaning in the present invention. The connection here means that the appearance or change in the content of a certain biomarker in a sample has a direct correlation with a specific disease or the progression of the disease, such as a relative increase or decrease in the content, indicating that the possibility of suffering from the disease is higher than that of healthy people, or that the progression of the disease has become more serious or has progressed from one stage to another. For example, a single marker or a combination of marker substances of the multiple new markers of the present invention can be used to predict whether a sepsis patient will die or not.

[0173] If multiple different markers appear in a sample at the same time or the relative changes in their contents indicate that the probability of the person suffering from the disease is higher than that of a healthy person. That is to say, among the types of markers, some markers are strongly associated with the disease, some markers are weakly associated with the disease, or some are even not associated with a specific disease. One or more of those markers with strong correlations can be used as markers for diagnosing the disease, and those markers with weak correlations can be combined with strong markers to diagnose a disease, thereby increasing the accuracy of the test results. The disease here can be the process or progression of the disease, such as the development from a relatively good stage of a disease to a more malignant or serious stage, or even death.

[0174] For the numerous biomarkers in serum discovered by the present invention, these markers can be used to distinguish non-sepsis patients from septic patients, or predict whether non-sepsis will develop into sepsis; they can also be used to diagnose or predict the probability or possibility of whether a septic patient will develop or die. The markers here can be used as a single marker for direct detection or diagnosis. The selection of such a marker indicates that the relative change in the content of the marker is strongly correlated with the development of non-sepsis patients into septic patients, or whether septic patients develop or die. Of course, it is understandable that one or more markers that are related to whether a septic patient develops or dies, or whether a non-sepsis patient develops into a septic patient, can be selected for simultaneous detection. It is normal to understand that in some ways, selecting a biomarker with a strong correlation for detection or diagnosis can achieve a certain standard of accuracy, such as 60%, 65%, 70%, 80%, 85%, 90% or 95% accuracy, which can indicate that these markers can obtain the intermediate value of diagnosing a certain disease, but it does not mean that a certain disease can be directly confirmed.

[0175] Of course, you can also choose the difference marker with the larger ROC value as the diagnostic marker. The so-called strong and weak are generally calculated and confirmed by some algorithms, such as the contribution rate or weight analysis of the marker and sepsis, or the probability of death from sepsis. Such a calculation method can be a significance analysis ( p Multivariate statistical analysis mainly includes principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA).

[0176] (3) Definition of disease terms

[0177] Sepsis patients are defined as those diagnosed according to the 2024 Phoenix Sepsis Criteria, which require a "Phoenix Sepsis Score" of 2 or more (indicating potentially life-threatening organ dysfunction in the respiratory, cardiovascular, coagulation, and / or neurological systems) in children with suspected or confirmed infection.

[0178] (4) Characteristics of sepsis:

[0179] 1. Non-specific early symptoms

[0180] Fever or hypothermia: Sepsis in children is often accompanied by abnormal body temperature, manifested as high fever (>38.5°C) or hypothermia (<36°C).

[0181] Fatigue, lethargy: Children may appear unusually tired, lethargic, or less active, symptoms that are often mistaken for a common infection.

[0182] Loss of appetite: Infants and young children are particularly prone to refusing to eat or eating less.

[0183] 2. Rapidly progressive clinical symptoms

[0184] Dyspnea: Tachypnea (increased respiratory rate) is a common manifestation of sepsis in children, sometimes accompanied by hypoxemia.

[0185] Tachycardia: An increased heart rate is one of the early signs of sepsis, but it may also be mistaken for a physiological response to fever.

[0186] Skin changes: Cool, cyanotic, or mottled skin may indicate peripheral hypoperfusion.

[0187] 3. Organ dysfunction

[0188] Respiratory: Acute respiratory distress syndrome (ARDS) may occur, manifested by hypoxemia and respiratory distress.

[0189] Cardiovascular: Decreased blood pressure or hypotension, which is of particular concern in children who may be normotensive but have decreased peripheral perfusion in the early stages of septic shock.

[0190] Nervous system: Changes in consciousness, such as drowsiness, slow reaction, and coma in severe cases.

[0191] Renal function: Decreased urine output (oliguria or anuria) and elevated serum creatinine indicate impaired renal function.

[0192] Coagulation system: Decreased platelet count may lead to increased risk of bleeding.

[0193] 4. Multiple Organ Dysfunction Syndrome (MODS)

[0194] As the disease progresses, sepsis may lead to dysfunction of multiple organ systems, known as multiple organ dysfunction syndrome, which is one of the main causes of sepsis mortality.

[0195] 5. Changes in biomarkers

[0196] Elevated C-reactive protein (CRP) and procalcitonin (PCT): These inflammatory markers are often used to assess the severity of sepsis but cannot be used alone to diagnose it.

[0197] Acid-base balance and electrolyte disorders: such as lactic acidosis and metabolic acidosis indicate that the condition is serious.

[0198] 6. Source of infection and characteristics of pathogens

[0199] There are many types of pathogens, including bacteria, viruses, fungi, etc. The source of infection of childhood sepsis is often difficult to diagnose quickly, and a comprehensive judgment is required clinically.

[0200] 7. Age dependence of onset

[0201] Newborns and infants: Symptoms are more subtle and may manifest as crying, refusal to feed, or unstable body temperature, so high vigilance is required.

[0202] Adolescents: Symptoms are more similar to those in adults, but manifestations of organ dysfunction may differ due to physiological differences.

[0203] (5) Gold standard for diagnosis of sepsis:

[0204]

[0205] Note: PSS: Phoenix Sepsis Score; FEU: fibrinogen equivalent unit; IMV: invasive mechanical ventilation; PaO2 / FiO2: ratio of partial pressure of arterial oxygen to fraction of inspired oxygen; SpO2: pulse oximetry. SI conversion factor: To convert lactate from mmol / L to mg / dL, divide by 0.111. a. Scores can be calculated in the absence of certain variables (e.g., cardiovascular scores can be determined even if lactate levels are not measured and vasopressors are not used). The medical team will obtain laboratory tests and other measurements at their own discretion based on clinical judgment. Unmeasured variables will not affect the score. b. The SpO2 / FiO2 ratio is calculated only when SpO2 is 97% or less. c. Respiratory dysfunction with a score of 1 can be assessed in any child receiving oxygen, high-flow, noninvasive positive pressure ventilation, or IMV respiratory support, including children who are not receiving IMV support and have a PaO2 / FiO2 ratio <200 mmHg and SpO2 / FiO2 ratio <220. For children with a PaO2 / FiO2 ratio <200 mmHg and SpO2 / FiO2 ratio <220 who are receiving IMV support, please refer to the criteria for scores 2 and 3. d. Vasoactive drugs include any dose of epinephrine, norepinephrine, dopamine, dobutamine, milrinone, and / or vasopressin (for shock). e. The reference range for lactate is 0.5 to 2.2 mmol / L. Lactate can be arterial or venous. f. Measured mean arterial pressure (such as invasive arterial or non-invasive oscillatory blood pressure) is preferred. If measured mean arterial pressure is not available, calculated mean arterial pressure (1 / 3× systolic pressure + 2 / 3× diastolic pressure) can be used as an alternative. g. Age will not be adjusted for prematurity. These standards do not apply to premature infants (<37 weeks) and neonates hospitalized during the perinatal period or patients 18 years and older. h. Reference ranges for coagulation function indicators: platelets: (150~450)×103 / μL; D-dimer: <0.5 mg / L FEU; fibrinogen: 180~410 mg / dL. The international normalized ratio reference range is based on the local reference prothrombin time. i. The neurological dysfunction subscore was validated in children who received sedation and those who did not receive sedation, and in children who received or did not receive IMV support. j. Glasgow Coma Scale (Glasgow Coma Scale) scores the child's level of consciousness based on his or her language, eye, and motor responses, ranging from 3 to 15. The higher the score, the better the neurological function. DETAILED DESCRIPTION

[0206] The present invention is described in detail below in conjunction with embodiments. It should be pointed out that the embodiments described below are intended to facilitate the understanding of the present invention and do not have any limiting effect on the present invention.

[0207] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0208] Unless otherwise specified, the materials and reagents used in the following examples can be obtained from commercial sources.

[0209] Glossary:

[0210] 1. Prognostic protective factors: refers to factors associated with a good prognosis of a disease. In the present invention, platelet count and urea nitrogen are identified as prognostic protective factors, which means that when a patient has a higher level of platelet count and an appropriate level of urea nitrogen, it may indicate that the patient has a relatively good disease outcome, such as a lower risk of death, a faster recovery speed, and fewer serious complications.

[0211] 2. Prognostic risk factors: refers to factors that can independently increase the risk of poor prognosis of the disease. In the present invention, total bile acid, lactic acid, D-dimer and increased TNF-α are identified as independent prognostic risk factors. This shows that even when considering multiple other factors, the increase or abnormality of these factors will still significantly increase the possibility of poor prognosis in patients, such as higher mortality, more severe organ dysfunction, etc.

[0212] Example 1 Preliminary screening of biomarkers using metabolomics

[0213] Sepsis has high morbidity and mortality in children, and there is currently a lack of effective markers for early diagnosis and prognostic assessment. Global epidemiological data show that the incidence of sepsis in young children is extremely high, and the mortality rate of severe sepsis cases is as high as 50%. Sepsis in children often presents with nonspecific symptoms and progresses rapidly, so early identification and timely treatment are crucial.

[0214] Intestinal flora plays an important regulatory role in host metabolism and immunity. As a metabolite of intestinal flora, the role of bile acid in sepsis is still unclear. In view of the serious harm of sepsis and the shortcomings of existing diagnostic and prognostic evaluation methods, considering the importance of bile acid in physiological processes and its association with intestinal flora, the inventors speculate that bile acid metabolites may be closely related to the occurrence, development and mortality of childhood sepsis, and bile acid may become a potential biomarker for childhood sepsis. Therefore, in this embodiment, an in-depth study of bile acid metabolites in the serum of children with sepsis was carried out, and the expression of D-dimer, CRP, PCT and bile acid in children with sepsis was analyzed respectively, and their effects on disease severity and prognosis were evaluated, and the correlation between changes in these biomarkers and the condition of sepsis in children was analyzed, in order to provide a more accurate reference for clinical diagnosis and treatment. Among them, D-dimer is a fibrin degradation product, reflecting the marker of activation of the body's coagulation and fibrinolytic systems; CRP, or C-reactive protein, is a protein that rises sharply in plasma when the body is infected or tissue is damaged; PCT refers to procalcitonin, and its level in plasma increases in severe bacterial, fungal, and parasitic infections, as well as sepsis and multiple organ failure; bile acid is an important component of bile and plays an important role in fat metabolism. It also participates in regulating the body's metabolism, inflammation, and immune balance.

[0215] The specific analysis steps are as follows:

[0216] 1. Selection and characteristics of research subjects

[0217] First, 702 children with sepsis and 269 non-infected controls were selected for the study. Figure 1 The basic information of children with sepsis, such as gender, age, infection site, pathogens, complications, etc., was shown in detail: Among children with sepsis, 401 patients were male (accounting for 57.12%). The average age was 4.21 years old, and 65.8% of the cases occurred in children under 5 years old ( Figure 2 The longest hospital stay was 168 days, and the median was 14 days. The infection sites of sepsis patients included 260 cases of respiratory tract infection (37.04%), 111 cases of multi-site infection (15.81%), 104 cases of gastrointestinal infection (14.81%), 97 cases of blood-borne infection (13.82%), 82 cases of intracranial infection (11.68%), and 27 cases of urinary tract infection (3.85%) ( Figure 3 Pathogens were isolated from 52% of patients, with viruses having the highest detection rate, Staphylococcus aureus being the most commonly isolated bacteria, and Candida albicans being the main fungus ( Figure 4In the complication statistics, septic shock, electrolyte imbalance, respiratory failure, coagulopathy, septic encephalopathy, heart failure, multiple organ dysfunction syndrome (MODS), and gastrointestinal bleeding showed significant statistical differences between the survival group and the death group ( Figure 5 ).

[0218] 2. Grouping and Clinical Index Analysis

[0219] According to the prognosis of childhood sepsis, all enrolled children were divided into three groups: sepsis survival group (538 children, 76.64%), sepsis death group (164 children, 23.36%), and non-infection control group (269 children). In the control group, there were 144 males (53.53%) and 125 females (46.47%), with an average age of 3.74 years. There was no significant difference in age and gender between the non-infection control group and the sepsis group (P>0.05). Figure 6 As shown, all patients in the sepsis death group died within 90 days.

[0220] This experiment used the preoperative laboratory indicators of the non-infected control group as the baseline and compared the laboratory results of patients with sepsis at different stages. Among the laboratory indicators collected during the patient's hospitalization, the indicators on the day of sepsis diagnosis and the most severe stage were finally selected for analysis. There was no significant difference in age, gender ratio and body mass index (BMI) between the case group and the control group (P>0.05) ( Figure 6 ). Abnormal laboratory test results are common in children with sepsis. Blood gas and electrolyte analysis showed that the sepsis group had significant differences in pH, sodium, potassium, and blood glucose compared with the control group ( Figure 6 Hematology and biochemical parameters (such as WBC, HB, PLT, CRP, ALB, GLB, bilirubin, TBA, ALT, AST, SCR, BUN, UA, Ca 2+ , P 2+ Mg 2+ , LDH, CK and CK-MB) and D-dimer and inflammatory markers showed significant differences ( Figure 6 ). Subsequently, a comparative analysis of clinical indicators between the sepsis survival group and the death group showed that there were differences in age and gender ratio between the two subgroups ( Figure 7 All other clinical indicators with significant differences (P<0.05) were included in the logistic regression analysis.

[0221] 3. Determination of prognostic protective factors and prognostic risk factors

[0222] Clinical indicators of the early stage of sepsis, including age, sex ratio, pH value, Na +, glucose (Glu), hemoglobin (HB), lactic acid (Lac), platelet count (Plt), albumin (Alb), globulin (Glb), direct bilirubin (DBIL), indirect bilirubin (IBIL), total bile acid (TBA), serum creatinine (Scr), blood urea nitrogen (BUN), uric acid (UA), lactate dehydrogenase (LDH), creatine kinase isoenzyme (CK-MB), calcium ion (Ca 2+ ), phosphorus ion (P 3+ ), magnesium ion (Mg 2+ ), D-dimer, PCT, IL-2, IL-6, IL-10 and TNF-α levels were analyzed by multivariate logistic regression analysis ( Figure 8 ) showed that platelet count and urea nitrogen were prognostic protective factors, but total bile acid, lactic acid, D-dimer and increased TNF-α were independent prognostic risk factors (when performing binary regression analysis in SPSS, the β value indicates the direction and degree of influence of the independent variable on the dependent variable. If the β value is negative (the β values ​​of urea nitrogen and platelets here are negative), it means that when other variables are kept constant, the probability of the dependent variable taking "1" (that is, the probability of a sepsis death event) will decrease for every unit increase in the independent variable, which means that an increase in urea nitrogen and platelets reduces the risk of death). In the multivariate logistic regression analysis of severe sepsis ( Fig. 9 ), pH, albumin and creatine kinase isoenzyme were prognostic protective factors, while glucose level, total bile acid, PCT and TNF-α were independent prognostic risk factors. Fig.10 ), respiratory failure, septic shock, gastrointestinal bleeding, sepsis-related encephalopathy, MODS, heart failure, and coagulopathy are predictors of in-hospital mortality in critically ill children with sepsis.

[0223] 4. Screening of biomarkers with significant differences between the early and severe stages of sepsis

[0224] Early stage of sepsis ( Fig.11 ) serum lactate (Lac), platelet count (PLT), total bile acid (TBA), blood urea nitrogen (BUN), D-dimer and TNF-α levels to predict the prognosis and complications of sepsis ( Fig.12) of respiratory failure, septic shock, gastrointestinal bleeding, sepsis-associated encephalophathy, multiple organ dysfunction syndrome (MODS), heart failure, coagulation dysfunction, and severe stage ( Fig.13 ), pH value, glucose (Glu), albumin (ALB), CK-MB, PCT and TNF-α levels, and were verified by ROC curve, showing significant differences. The results showed that the Lac and TBA levels in the early stage of sepsis and septic shock had the largest AUC in the model, but were still below 0.7, as shown in Table 1 ( Fig.11 , Fig.12 and Fig.13 The AUC values ​​and p-values ​​are in brackets).

[0225] Table 1. Ranking of AUC values ​​of different indicators in the early and severe stages of sepsis

[0226]

[0227] 5. Construction of a joint prediction model for predicting the probability of death in sepsis patients within the short term (90 days)

[0228] Therefore, in order to further improve the prediction accuracy, a combined prediction model of Lac, TBA and septic shock was established in this example ( Fig.14 ), a multivariate logistic regression equation was established to predict the risk of short-term (90-day) mortality in patients with sepsis, as shown below:

[0229] The markers: Lac, TBA and septic shock were selected to jointly construct a model, and the model-related parameters are as follows:

[0230] The model equation is:

[0231] P=1 / (1+e(-2.333+0.147X1+0.012X2+1.068X3)).

[0232] In the model equation, P represents the predicted value of the risk of death within 90 days in patients with sepsis; X1, X2 and X3 represent Lac, TBA and whether septic shock occurs, respectively; the names of "Lac, TBA, septic shock" represent the true concentration values ​​of the corresponding biomarkers; the coefficients before the names of "X1, X2 and X3" represent the model coefficients of the corresponding biomarkers; -2.333 represents the intercept value of the logistic regression equation. The AUC value of the model is 0.761, the sensitivity is 69.8%, the specificity is 69.1%, the positive predictive value is 68.6%, and the negative predictive value is 80.8%, indicating that the model can well predict the risk of death in sepsis; and when P is greater than 0.204, the risk of death in patients with sepsis within 90 days is predicted to be high; when p is less than 0.204, the risk of death in patients with sepsis within 90 days is predicted to be low.

[0233] At the same time, in subsequent experiments, the above results were further verified. First, a batch of samples from sepsis patients were collected as a validation set. A total of 500 serum samples from pediatric sepsis patients were collected, of which 250 were in the sepsis survival group and 250 were in the sepsis death group (death within 90 days). The serum lactate (Lac), platelet count (PLT), total bile acid (TBA), blood urea nitrogen (BUN), D-dimer, TNF-α, pH value, glucose (Glu), albumin (ALB), CK-MB, PCT and septic shock levels in samples from different groups were also tested (specific indicators are described in Table 1), and the test results were compared and analyzed with the data from the above test set. The patient data in the validation set (Lac, TBA and septic shock levels in serum, such as the Lac and TBA concentrations and septic shock status of each patient) were substituted into the test set model, and the ROC curve was redrawn. The AUC value of the model was calculated to be 0.763, with a sensitivity of 69.5%, a specificity of 69.4%, a positive predictive value of 69.0%, and a negative predictive value of 80.9%.

[0234] In summary, the prediction results in the validation set are highly consistent with those in the discovery set, which once again proves that the joint prediction model based on serum lactate (Lac), total bile acid (TBA) and septic shock has high accuracy and reliability in predicting the risk of death in sepsis patients in the short term (90 days). This model can effectively divide sepsis patients into high-risk and low-risk groups, providing a strong basis for clinicians to identify high-risk patients early and take timely intervention measures. At the same time, the results of the validation set also further show that these biomarkers play an important role in the occurrence and development of sepsis, and have important clinical value for the diagnosis and prognosis evaluation of sepsis.

[0235] Therefore, in this embodiment, the biomarkers: Lac, TBA and septic shock are used to construct a joint prediction model for predicting the probability of death in sepsis patients in the short term (90 days), and the model has the best diagnostic efficacy.

[0236] VI. Importance of TBA, a key indicator in the optimal joint prediction model

[0237] Among the best joint prediction models constructed above, TBA performed particularly well in predicting the probability of death in patients with sepsis. Fig.15 It can be seen from the neural network model that TBA level is of great significance for predicting the prognosis of the disease. High TBA level indicates that the patient has an increased risk of poor prognosis. Therefore, early monitoring of serum TBA level can provide important reference value for the prognosis of sepsis patients. In SPSS statistical analysis software, the inventors used the neural network radial basis function to obtain the results of the importance of each indicator for the independent variable in predicting the prognosis of sepsis. Through the independent variable importance diagram ( Fig.15 ), the influence of different independent variables on the prediction of sepsis death can be analyzed. This figure shows that serum total bile acid has the greatest impact on the prognosis of sepsis. Therefore, when the serum total bile acid level increases, the mortality rate of sepsis increases. Independent variable importance diagram: The lower line value is the relative importance coefficient of each variable, and the sum of all indicators is 1. The upper line is the standardized importance coefficient, with the most important indicator as the control (set to 100%), and the relative percentage value obtained by comparing other indicators with it.

[0238] Further analysis showed that for the mortality risk prediction model for sepsis patients, the study found that the baseline TBA level of sepsis patients was closely related to the risk of death. When the serum TBA level was ≥17.95 µmol / L, the mortality rate of children with sepsis increased significantly, with a hazard ratio of 7.658 (95% CI: 5.575-10.520), P<0.05; this provides an important reference threshold for clinicians to quickly identify high-risk patients ( Fig.16 Cox hazard model).

[0239] In addition, a study on the dynamic changes of serum TBA in children with sepsis during hospitalization showed that the TBA levels of children who died of sepsis were higher and fluctuated more, while the TBA levels of surviving children were relatively stable. Dynamic monitoring of TBA change trends is crucial for accurately predicting the prognosis of patients with sepsis, and can provide a strong basis for timely clinical adjustment of treatment plans, which is conducive to early intervention to improve patient prognosis ( Fig.17 ).

[0240] At the same time, the correlation analysis of various laboratory indicators in patients with sepsis showed that ( Fig.18), TBA levels are positively correlated with multiple indicators, including lactate, bilirubin, transaminase, lactate dehydrogenase, troponin, inflammatory factors, etc., and are closely related to liver function and inflammatory response. High TBA levels may lead to hemoglobin and thrombocytopenia, which in turn affects disease progression and prognosis.

[0241] 7. The need to explore the role of different types of bile acids in sepsis

[0242] Although the key role of TBA in the joint prediction model of sepsis has been clarified in the above examples, sepsis is an important issue in global public health and there are still many aspects that deserve in-depth discussion. Sepsis is an important issue in global public health and often presents non-specific symptoms in children. Unlike adult sepsis, pediatric sepsis progresses rapidly and is prone to develop multiple organ failure, shock, and death, so early identification and timely treatment of pediatric sepsis are crucial. The Phoenix Sepsis Criteria focuses mainly on four organ systems, but in pediatric sepsis, other organ dysfunctions are also important. The time series of organ dysfunction and death does not necessarily indicate a causal relationship, and physiological dynamic measurements may better reflect the patient's condition deterioration than static or single-point assessments.

[0243] Biomarkers are quantifiable indicators of disease progression and individual status that can help identify patients and serve as precise diagnostic tools. Current research focuses on diagnostic biomarkers for early detection, but there is still a need to develop markers that can predict adverse outcomes and divide sepsis subgroups.

[0244] Abnormal liver function is common in ICU patients, with 33.9% of patients having liver damage. Bile acid levels exceed the normal range when liver function is impaired, leading to cytotoxic effects and abnormal increases in biochemical indicators.

[0245] Bile acids play an important role in the immune system, promoting tissue repair, resisting new infections, and clearing existing infections. In the intestine, bile acid absorption sites can trigger T cell-mediated inflammation, and certain bile acids can regulate immune responses by inhibiting TH17 cell function. Long-term exposure to bile acids can cause damage to the immune system and inflammation, which in turn leads to adverse consequences. Different types of bile acids have different effects on the intestinal barrier, and unbalanced intestinal homeostasis may lead to multiple organ failure or even death. In addition, bile acids have antimicrobial effects on intestinal pathogens.

[0246] At the same time, bile acids are correlated with cardiac function. In patients with cirrhosis, heart failure is closely related to increased serum bile acid concentrations. In a mouse model of bile duct injury, increased bile acid levels in hepatocytes are associated with coagulation and platelet activation. In children with sepsis, bile acids are also correlated with platelets and D-dimers, and these indicators are closely related to the severity of the disease.

[0247] The results of the present invention show that the TBA level in children who died of sepsis was higher, and there was a significant correlation between the TBA level and clinical indicators related to organ function.

[0248] Therefore, in subsequent studies, the inventors further explored the role of different types of bile acids in sepsis. At the same time, they conducted in-depth research on the specific relationship between bile acids and the functions of various organs in patients with sepsis, in order to provide more targeted strategies for the precise treatment of sepsis and to find specific types of bile acids that may serve as markers for dividing sepsis subgroups.

[0249] Example 2 In-depth study of UDCA series bile acids as biomarkers in sepsis

[0250] 1. Construction of a prediction model for short-term mortality in children with sepsis using UDCA series bile acids

[0251] In the previous study of biomarkers of pediatric sepsis, we preliminarily confirmed the importance of bile acid in pediatric sepsis, and concluded that TBA plays a key role in the combined prediction model of sepsis and the necessity of exploring different types of bile acids. Therefore, in this example, the serum bile acid level was further tested, and the bile acid metabolites in the serum of pediatric sepsis patients were studied in depth.

[0252] First, 61 bile acids were accurately quantitatively analyzed in 165 serum samples by liquid chromatography-tandem mass spectrometry ( Fig.19 ), the overall distribution of 61 bile acids in the sepsis group and the control group was compared by PLS-DA analysis. The results showed that the distribution of serum bile acids in the sepsis group was significantly different from that in the control group (p < 0.05) ( Fig. 20 The results of the volcano plot difference analysis showed that 3β-UDCA (3β- ursodeoxycholic acid), UDCA (ursodeoxycholic acid), CDCA (chenodeoxycholic acid), HCA (Chinese name: hyocholic acid), NorCA (Chinese name: norcholic acid) and DCA (deoxycholic acid) were significantly reduced in children with sepsis ( Fig.21 , Fig. 22). Biomarkers were further screened by ROC analysis, and the results showed that 3β-UDCA (AUC: 0.782, 95% CI: 0.713-0.851), CDCA (AUC: 0.749, 95% CI: 0.674-0.823) and UDCA (AUC: 0.741, 95% CI: 0.666-0.815) could be used as independent factors to distinguish between non-infected controls and sepsis patients. When 3β-UDCA was combined with UDCA for ROC analysis of binary logistic regression, the area under the curve was 0.783, and the 95% confidence interval was 0.714-0.853 ( Fig.23 , Fig.24 ).

[0253] According to the prognosis of children with sepsis, they were divided into a survival group and a death group (death within 90 days), and the serum bile acids of children in different subgroups were compared to screen out differential bile acids. The results showed that 3β-UDCA (3β- ursodeoxycholic acid), 3β-CDCA (3β- chenodeoxycholic acid), alloLCA (allolithocholic acid), 7-ketoLCA (7-ketolithocholic acid), UDCA (ursodeoxycholic acid) and GUDCA (glycoursodeoxycholic acid) were significantly enriched in the survival group, while NorCA (Chinese name: orthocholic acid) and GCA (glycocholic acid) were significantly enriched in the death group ( Fig.25 ). Further multivariate binary logistic regression analysis showed that 7-ketoLCA (OR=0.569, 95% CI: 0.338-0.957, p=0.034), 3β-UDCA (OR=0.353, 95% CI: 0.138-0.906, p=0.030) and UDCA (OR=0.579, 95% CI: 0.353-0.950, p=0.031) were protective factors for sepsis survival. ROC curve analysis showed that 3β-UDCA (AUC: 0.825, 95% CI: 0.738-0.912) was statistically significant in predicting sepsis survival.

[0254] Therefore, in order to further improve the accuracy of predicting the survival rate or mortality rate of sepsis, a joint prediction model of 3β-UDCA and UDCA was established in this example, and a multivariate logistic regression equation for predicting the short-term (90-day) mortality risk of sepsis patients was established, as shown below:

[0255] The model equation is:

[0256] P=1 / (1+e(0.648-0.004X1-0.003X2)).

[0257] Where X1 represents the serum concentration of 3β-UDCA, and X2 represents the serum concentration of UDCA (the corresponding model formula needs to be supplemented);

[0258] In the model equation, P represents the predicted value for predicting the risk of death within 90 days in patients with sepsis; X1 represents the serum concentration of 3β-UDCA, and X2 represents the serum concentration of UDCA; the coefficient before the name of "3β-UDCA and UDCA" represents the model coefficient of the corresponding biomarker; 0.648 represents the intercept value of the logistic regression equation. The AUC value of the model is 0.828, the 95% CI is 0.741-0.915, the sensitivity is 0.894, and the specificity is 0.79, indicating that the model can well predict the mortality rate within 90 days in patients with sepsis; and when P is greater than 0.4633394, it is predicted that the risk of death within 90 days in patients with sepsis is high; when p is less than 0.4633394, it is predicted that the risk of death within 90 days in patients with sepsis is low ( Fig.26 ).

[0259] At the same time, in subsequent experiments, the above results were further verified. First, an additional batch of independent serum samples were collected as a validation set. A total of 200 serum samples from children with sepsis were collected, of which 100 were in the sepsis survival group and 100 were in the sepsis death group (death within 90 days). Liquid chromatography-tandem mass spectrometry was also used to perform precise quantitative analysis of 61 bile acids in these 200 serum samples.

[0260] Secondly, PLS-DA analysis was performed to compare the overall distribution of 61 bile acids in the new sepsis group and the control group using the same analysis process as the test set; the top 10 bile acids in relative abundance were analyzed; Volcano Plot and binary logistic regression analysis were used to identify the bile acids with large and statistically significant differences in serum samples between children with sepsis and the control group; ROC analysis was performed to screen biomarkers; patients were divided into survival and death groups according to their prognosis, and serum bile acids in different subgroups of children were compared to screen out differential bile acids; multivariate binary logistic regression analysis was performed to determine the protective factors for sepsis survival; and the combined prediction model of 3β-UDCA and UDCA was verified.

[0261] Finally, the patient data in the validation set (serum 3β-UDCA and UDCA concentrations) were substituted into the joint prediction model established in the test set, the ROC curve was redrawn, and the AUC value, sensitivity, and specificity of the model were calculated. After analysis, it was found that the AUC value of the model in the validation set was 0.832, the sensitivity was 0.885, and the specificity was 0.791, which were similar to the test set results. This shows that the joint prediction model based on 3β-UDCA and UDCA also has good predictive ability in the new sample set and can effectively distinguish the survival and death of patients with sepsis.

[0262] In summary, the prediction results in the validation set are highly consistent with those in the discovery set, which once again proves that the joint prediction model constructed based on 3β-UDCA and UDCA has high accuracy and reliability in predicting the risk of death in patients with sepsis in the short term (90 days). This model provides a powerful tool for clinicians to evaluate the prognosis of patients with sepsis, helps to identify high-risk patients early, take targeted treatment measures in time, and improve the survival rate of patients. At the same time, it further emphasizes the important role of 3β-UDCA and UDCA in the occurrence and development of sepsis, and provides an important reference for in-depth research on the pathological mechanism of sepsis and the development of new treatments. At the same time, this example also analyzes the correlation between UDCA series bile acids and UDCA precursor CDCA bile acids and clinical indicators in children with sepsis. The results showed that the UDCA series bile acids were significantly negatively correlated with coagulation and inflammatory indicators, and positively correlated with immune indicators ( Fig. 27 ).

[0263] 2. In-depth validation of UDCA series of bile acids in predicting short-term mortality in children with sepsis

[0264] Based on the conclusions of previous studies, further in-depth research was conducted on the bile acid metabolites in the serum of children with sepsis. The specific research steps are as follows:

[0265] Serum bile acid levels were tested in 166 girls (85 sepsis patients and 81 controls). Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) was used to detect the bile acid spectrum of clinical serum samples. At the same time, after intervening in bile acid metabolism in two classic sepsis mouse models, LPS and CLP, the disease phenotype and survival rate of mice were observed to further verify the importance of bile acid in sepsis from the animal model level. The specific results are as follows: Figure 28-Figure 32 As shown. Among them, Fig.28 and Fig.29 Mean values ​​and SD were used for statistical analysis using the Mann-Whitney test; heat map Fig.30Spearman correlation analysis was used, and R means Spearman correlation coefficient; Fig.31 and Fig.32 This is the ROC analysis result diagram.

[0266] from Fig.28 It can be seen that UPLC-MS / MS detected the bile acid spectrum of clinical serum samples. The results showed that the serum UDCA series bile acid concentration in children with sepsis was significantly lower than that in the control group, which preliminarily revealed the abnormal changes of UDCA series bile acids in sepsis patients.

[0267] from Fig.29 It can be seen that the serum UDCA series bile acid concentrations in children in the sepsis death group were significantly lower than those in the sepsis survival group, further emphasizing the close relationship between UDCA series bile acids and the prognosis of sepsis patients.

[0268] Fig.30 The heat map of Spearman correlation analysis showed that the reduced UDCA series bile acids in the serum of children with sepsis were significantly negatively correlated with sepsis-related Lac, inflammation (WBC, CRP, PCT), liver function (ALT, AST, TBA), cardiac function (LDH) and coagulation indicators (PT, APTT, TT, INR, D-dimer); and were significantly positively correlated with immune indicators (IgM), HB and Plt; the complex relationship between UDCA series bile acids and various clinical indicators was deeply explained, providing a new perspective for understanding the pathophysiological process of sepsis.

[0269] from Fig.31 The ROC curve analysis results showed that the reduction in the serum levels of 3β-UDCA and UDCA could effectively distinguish children in the sepsis group from those in the control group, with AUC values ​​of 0.782 and 0.741, respectively; this indicated that when a child's serum levels of 3β-UDCA and UDCA were significantly reduced, he or she was likely to suffer from sepsis.

[0270] from Fig.32 The ROC curve analysis results show that the reduction in the serum levels of 3β-UDCA, UDCA and GUDCA has a significant statistical significance in predicting whether sepsis patients will die in the short term, and their AUC values ​​are 0.825, 0.692 and 0.692, respectively. This indicates that when the serum levels of 3β-UDCA, UDCA and GUDCA in a sepsis patient are significantly reduced, he or she may die in the short term.

[0271] Therefore, it can be seen that in children with sepsis and children who died of sepsis, the level of serum ursodeoxycholic acid (UDCA) series bile acids showed a significant decrease. Further studies have shown that this series of bile acids is significantly positively correlated with inflammatory coagulation indicators, that is, as the inflammation and coagulation worsen, the level of UDCA series bile acids further decreases; at the same time, it is negatively correlated with immune indicators, which means that changes in immune function and changes in the content of UDCA series bile acids show an inverse trend. This change characteristic of the UDCA series bile acids makes it of great value in the diagnosis and prognosis evaluation of sepsis. On the one hand, its reduction can assist doctors in diagnosing sepsis, and by detecting the content of UDCA series bile acids in serum, it provides a new basis for judging whether the patient has sepsis. On the other hand, this series of bile acids can also predict the prognosis of patients with sepsis, including but not limited to predicting the risk of death in sepsis patients in the short term, the progression of the disease, etc. This opens up new ideas for the diagnosis and prognosis assessment of sepsis. The UDCA series of bile acids are expected to become potential biomarkers and provide important references for clinical treatment.

[0272] 3. Validation of UDCA series bile acids in predicting short-term mortality in children with sepsis in animal models

[0273] In order to more conclusively confirm the reliability of UDCA series bile acids as biomarkers for predicting short-term mortality in children with sepsis, this example conducted in-depth verification using an animal model.

[0274] 1. UDCA intervention in LPS mouse model

[0275] like Fig.33 As shown in the figure, in the experiment of UDCA intervention in LPS mouse model, the mice were divided into five groups: control group (NC), antibiotic interference with bile acid metabolism sepsis group (ABX+LPS), simple sepsis group (LPS), UDCA intervention antibiotic sepsis group (ABX+LPS+UDCA), and UDCA intervention sepsis group (LPS+UDCA). First, according to the MSS (Mouse Sepsis Score, mouse sepsis score, the higher the score, the more severe the disease phenotype of the mouse, and the lower the MSS score, the lower the severity of the disease in the septic mouse after UDCA intervention, that is, the disease phenotype has improved) score table, the disease phenotype of the mouse was evaluated from seven aspects: appearance, autonomous consciousness, autonomous activity, response to stimulation, eyes, respiratory rate and respiratory quality. The results showed that the disease phenotype of the septic mouse was significantly improved after UDCA intervention ( Fig.34At the same time, the expression levels of pro-inflammatory cytokines (IL-6, TNF-a), autophagy proteins (Beclin-1) and tight junction proteins (Claudin-1, Occludin) in the colon tissue of each group of mice were detected by qPCR. The results showed that UDCA intervention could improve the expression of pro-inflammatory cytokines and excessive autophagy proteins in the colon tissue of septic mice, and increase the expression of tight junction proteins ( Fig.35 ). Similarly, WB was used to detect the expression levels of inflammatory cytokines (IL-6, TNF-a), autophagy proteins (Beclin-1), and tight junction proteins (Claudin-1, Occludin) in the colon tissue of each group of mice. The results showed that UDCA intervention could improve the expression of pro-inflammatory cytokines and excessive autophagy proteins in the colon tissue of septic mice and increase the expression of tight junction proteins ( Fig.36 ).in, Fig.34 , Fig.35 , Fig.36 The data are shown as mean and SD, and statistical analysis was performed using Student's t test or Mann-Whitney test depending on whether the data conformed to normal distribution.

[0276] 2. UDCA intervention in CLP mouse model

[0277] like Fig.37 As shown in the figure, in the experiment of UDCA intervention in CLP mouse model, mice were also divided into five groups: control group (sham), antibiotic interference bile acid metabolism sepsis group (ABX+CLP), simple sepsis group (CLP), UDCA intervention antibiotic sepsis group (ABX+CLP+UDCA), and UDCA intervention sepsis group (CLP+UDCA). The disease phenotype of mice was evaluated by MSS score sheet (appearance, autonomous consciousness, autonomous activity, response to stimulation, eyes, respiratory rate and respiratory quality). The results showed that the disease phenotype of sepsis mice improved after UDCA intervention ( Fig.38 ). At the same time, from Fig.39 It can be seen that UDCA intervention can improve the mortality rate of septic mice. The expression levels of pro-inflammatory cytokines (IL-6, TNF-a) and tight junction proteins (Claudin-1, Occludin) in the colon tissue of each group of mice were detected by qPCR. The results showed that UDCA intervention can improve the expression of pro-inflammatory cytokines and excessive autophagy proteins in the colon tissue of septic mice, and increase the expression of anti-inflammatory cytokines and tight junction proteins ( Fig.40). Similarly, WB was used to detect the expression levels of inflammatory cytokines (IL-6, TNF-a), autophagy proteins (Beclin-1), and tight junction proteins (Claudin-1, Occludin) in the colon tissue of each group of mice. The results showed that UDCA intervention could improve the expression of pro-inflammatory cytokines and excessive autophagy proteins in the colon tissue of septic mice and increase the expression of tight junction proteins ( Fig.41 ).in, Fig.38 , Fig.40 and Fig.41 The data are mean and SD, and statistical analysis was performed using Student's t test or Mann-Whitney test, depending on whether the data conformed to normal distribution; Fig.39 For the survival curves, log-rank test was used for statistical analysis.

[0278] The experimental results of this example further verify the important role of UDCA series bile acids in sepsis. Combined with the previous study on the significant reduction of UDCA series bile acid levels in the serum of children with sepsis and children who died of sepsis, and the significant positive correlation with inflammation and coagulation, and negative correlation with immune indicators, it strongly supports the conclusion that UDCA series bile acids are metabolic biomarkers for predicting short-term mortality in children with sepsis. This not only provides new ideas and potential biomarkers for the diagnosis and prognosis assessment of sepsis, but also lays a solid foundation for the future development of more accurate diagnostic methods and treatment strategies for sepsis in children.

[0279] Example 3 Application of UDCA series bile acids in intestinal cell inflammatory injury in children with sepsis

[0280] In the aforementioned experiments, animal models were used to further verify that the UDCA series of bile acids are metabolic biomarkers for predicting short-term mortality in childhood sepsis. Studies have shown that after UDCA intervention in the mouse model of sepsis, the disease phenotype of the mice improved, and the expression levels of pro-inflammatory cytokines, autophagy proteins, and tight junction proteins in colon tissue were improved, and the survival rate of the mice was increased. The above results strongly support the important role of the UDCA series of bile acids in sepsis. This finding provides important clues for in-depth research on the pathogenesis and treatment strategies of sepsis. The occurrence and development of sepsis not only involves changes in the overall body, but its inflammatory damage to intestinal cells also plays a key role in the course of the disease.

[0281] Therefore, this example will focus on the application of UDCA series bile acids in inflammatory damage to enterocytes in children with sepsis. In order to further study the application value of UDCA series bile acids in inflammatory damage to enterocytes in children with sepsis, a Caco-2 enterocyte monolayer model of LPS inflammatory damage was constructed in this example. The experimental results showed that after LPS treatment, the Caco-2 cell monolayer showed obvious damage, and UDCA treatment could significantly improve LPS-induced cell monolayer damage. The specific results are as follows: Figure 42-Figure 46 shown.

[0282] from Fig.42 and Fig.43 It can be seen that the barrier function test of Caco-2 cell monolayer showed that the transepithelial electrical resistance (TEER) of Caco-2 cell monolayer in the LPS intervention group decreased significantly, and the permeability of FD-4 increased; after treatment with UDCA, the TEER value of Caco-2 cell monolayer model increased compared with the LPS group, and the permeability of FD-4 decreased. This shows that UDCA can improve the barrier function of intestinal cell monolayer.

[0283] from Fig.44 It can be seen that the expression levels of tight junction proteins (claudin, occludin and ZO-1) in Caco-2 cells were detected by qPCR and WB. The results showed that UDCA intervention can significantly improve the expression level of tight junction proteins in inflammation-induced intestinal cell monolayer.

[0284] from Fig.45 and Fig.46 It can be seen that the expression levels of proinflammatory cytokines (IL-6, IL-1β and TNF-α) in Caco-2 cells were detected by qPCR, WB and ELISA, all of which showed that UDCA intervention could significantly reduce the expression levels of proinflammatory cytokines in inflammation-induced intestinal cell monolayers.

[0285] in, Fig.42 For a line chart, Figure 43-Figure 46 Data are means and SD, and statistical analysis was performed using Student's t test.

[0286] In summary, this example deeply studies the application of UDCA series bile acids in intestinal cell inflammatory damage in children with sepsis by constructing a Caco-2 intestinal cell monolayer model of LPS inflammatory damage. The experimental results clearly show that UDCA can significantly improve LPS-induced intestinal cell monolayer damage, improve the barrier function of intestinal cell monolayer, improve the expression level of tight junction proteins and reduce the expression level of proinflammatory cytokines. It can be seen that the UDCA series of bile acids plays an important and positive role in coping with intestinal cell inflammatory damage in children with sepsis. This conclusion provides new ideas and directions for further exploring the treatment strategies of childhood sepsis, and also provides potential therapeutic targets and intervention methods for the clinical treatment of childhood sepsis, which is expected to play an important role in improving the prognosis of children with sepsis in future clinical practice.

[0287] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Anyone familiar with this technology can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the definition of the claims.

Claims

1. Use of a reagent for detecting bile acid in the preparation of a kit for predicting the short-term mortality rate of patients with sepsis, characterized in that: The bile acid consists of 3β-ursodeoxycholic acid and ursodeoxycholic acid.

2. The use according to claim 1, characterized in that The predicting the short-term mortality rate of sepsis patients includes predicting the mortality rate of sepsis patients within 90 days.

3. The use according to claim 2, characterized in that The predicting of the short-term mortality rate of patients with sepsis is predicting the short-term mortality rate of children with sepsis.

Images