Metabolite marker for predicting intrahepatic cholestasis in gestation period and adverse events related to intrahepatic cholestasis

Abstract

The invention discloses a metabolite marker for predicting intrahepatic cholestasis in a gestation period and adverse events related to the intrahepatic cholestasis, a prognosis risk model for the intrahepatic cholestasis in the gestation period based on the metabolite marker and the related adverse events and application of the prognosis risk model. Specifically, the metabolite marker provided by the invention comprises one or more of Preg17olS, PM6S, PregS, PMS, PM3S < + >, ALT, AST and TBA. The risk model established by combining the metabolite marker with clinical data can accurately predict the intrahepatic cholestasis in the gestation period and related adverse events of the intrahepatic cholestasis in the gestation period, and has an important value for timely intervening and improving the outcome of pregnant and lying-in women and newborns.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technologies, and particularly to a metabolite biomarker for predicting intrahepatic cholestasis of pregnancy and related adverse events. Background Art

[0002] Pregnancy requires complex physiological adaptations to support the health of the mother and fetus, and the function of the liver plays an important role therein. However, disruption of liver function can lead to potentially life-threatening complications. Among them, intrahepatic cholestasis of pregnancy (ICP) is the most common liver disease, affecting 0.2% to 28% of pregnancies globally, depending on race, geography, and seasonality. The recurrence rate of ICP in subsequent pregnancies is 45% to 90%. The occurrence of ICP is associated with an increased risk of poor maternal and neonatal outcomes, including preterm birth, neonatal respiratory distress syndrome, and even stillbirth. For the mother, the most distressing symptom is severe itching, which usually starts in the third trimester of pregnancy and mainly affects the palms and soles of the feet (but can still occur anywhere on the body), and this itching usually persists until delivery, being most severe at night, resulting in sleep deprivation and depressive episodes in pregnant women. Despite the significant prevalence and impact of ICP, research on its early prediction and effective treatment remains limited, mainly due to insufficient understanding of its underlying mechanisms.

[0003] In addition to the complex pathology, a more pressing problem is the lack of reliable tests to predict the onset of ICP in early pregnancy and related adverse outcomes. Currently, clinical diagnosis usually occurs around 33 weeks of pregnancy, and the median onset time of complications such as preterm birth is 34 weeks. Although there are treatment methods such as ursodeoxycholic acid (UDCA), late diagnosis does not provide sufficient time to mitigate disease progression and prevent severe complications including stillbirth. Therefore, formulating an early prognosis prediction strategy is crucial for improving maternal and child outcomes. These limitations highlight the urgent need for reliable biomarkers to achieve prognostic prediction of ICP and provide an accurate prognostic risk model. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention has conducted research. By high-throughput screening of 1,490 compounds for those that can activate the putative pruritus receptor hMRGPRX4 (abbreviated as hX4), several sulfonated progesterone metabolites were finally identified as effective activators of hX4. Utilizing the significant difference in sulfonated progesterone levels between ICP patients and healthy pregnant women, the present invention has developed a prognostic risk model for predicting adverse events related to intrahepatic cholestasis of pregnancy according to different gestational stages. These models can predict the onset and related adverse consequences several months before the clinical symptoms appear. The research results of the present invention not only provide convincing evidence that sulfonated progesterone can activate hX4 to induce ICP-related pruritus symptoms, but also emphasize the early prediction of ICP and the prognosis of adverse events, providing a transformative opportunity to improve the prognosis of pregnant women and infants.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] The first aspect of the present invention provides the use of metabolite markers in the prognosis prediction of intrahepatic cholestasis of pregnancy, and the metabolite markers include one or more of Preg17olS, PM6S, PregS, PMS, PM3S+, ALT, AST, and TBA.

[0007] In the present invention, metabolites refer to intermediate and final products of metabolism, and are sometimes also referred to as small molecules or analytes with a molecular weight of less than 1,500 daltons. Metabolites are classified as primary metabolites that are directly involved in normal growth, development, and reproduction. Secondary metabolites are not directly involved in the latter processes, but can have important ecological functions (e.g., antibiotics, pigments). Metabolite markers can be measured individually, or in some embodiments, can be measured simultaneously, for example, using chip or microarray technology. In some embodiments, the metabolite markers include one or more of Preg17olS (fully named 17-hydroxypregnenolone sulfate), PM6S (fully named (3α, 5β)-pregnanolone sulfate), PM3S+, PregS (fully named Pregnenolone sulfate), PMS, ALT (fully named Alanine aminotransferase), AST (fully named Aspartate aminotransferase), and TBA (fully named Total bile acids).

[0008] Furthermore, the metabolite markers are a combination of Preg17olS, PM6S, PregS, PMS, PM3S+, ALT, AST, and TBA.

[0009] Further, the PMS is a combination of three isomers: PM4S, PM5S, and PM7S;

[0010] In some embodiments, PMS includes PM4S (full name: (3α, 5α)-pregnanolone sulfate), PM5S (full name: (3β, 5α)-pregnanolone sulfate), and PM7S (full name: (3β, 5β)-pregnanolonesulfate) (the three are chiral isomers), belonging to pregnanone (pregnenolone) derivatives, and PM3S+ includes PM3S and 3β5α-diolS (the two are chiral isomers), belonging to pregnanediol derivatives.

[0011] Further, the PM3S+ is a combination of two isomers: PM3S (full name: (3α, 5β)-pregnanediol sulfate) and 3β5α-diolS (full name: (3β, 5α)-pregnanediol sulfate).

[0012] Further, the metabolite markers are a combination of Preg17olS, PM6S, PM3S+, PregS, PMS, ALT, AST, and TBA;

[0013] In a second aspect of the present invention, a prognostic risk model for intrahepatic cholestasis of pregnancy is provided, and the prognostic risk model evaluates the prognostic risk of a subject based on the metabolite markers described in the first aspect of the present invention in a test sample.

[0014] Specifically, the risk refers to the risk of developing intrahepatic cholestasis of pregnancy and related adverse events.

[0015] Further, the adverse events of intrahepatic cholestasis of pregnancy include premature birth, neonatal jaundice, and neonatal respiratory distress syndrome.

[0016] Further, the input variables of the prognostic risk model are the relative expression levels of the metabolite markers described in the first aspect of the present invention in the test sample and the clinical characteristic data of the subject corresponding to the test sample.

[0017] Further, the input variables of the prognostic risk model are the plasma levels of Preg17olS, PM6S, PregS, PMS, PM3S+, ALT, AST, and TBA.

[0018] Further, the clinical characteristic data includes the gestational stage of the subject.

[0019] Further, the formulas of the prognostic risk model are respectively:

[0020] The formula for predicting the risk of intrahepatic cholestasis of pregnancy in the first trimester of pregnancy is 8.55*[Preg17olS] + 4.50*[PM6S] - 1.72*[PregS] + 1.92*[PMS] + 0.37*[PM3S+] - 0.01*[ALT] + 0.01*[AST] - 0.02*[TBA] - 0.09. Those with a value above the cut-off value are determined to be at high risk, and those with a value below the cut-off value are determined to be at low risk. The cut-off value is 0.45.

[0021] The formula for predicting the risk of preterm birth related to intrahepatic cholestasis of pregnancy in the first trimester of pregnancy is 8.59*[Preg17olS] + 3.52*[PM6S] - 1.96*[PregS] + 2.07*[PMS] + 0.36*[PM3S+] + 0.18. Those with a value above the cut-off value are determined to be at high risk, and those with a value below the cut-off value are determined to be at low risk. The cut-off value is 0.18.

[0022] The formula for predicting the risk of neonatal jaundice related to intrahepatic cholestasis of pregnancy in the first trimester of pregnancy is -33.28*[Preg17olS] + 1.64*[PM6S] - 2.23*[PregS] + 1.44*[PMS] - 0.09*[PM3S+] + 2.48. Those with a value above the cut-off value are determined to be at high risk, and those with a value below the cut-off value are determined to be at low risk. The cut-off value is 0.35.

[0023] The formula for predicting the risk of neonatal respiratory distress syndrome related to intrahepatic cholestasis of pregnancy in the first trimester of pregnancy is 33.56*[Preg17olS] - 2.38*[PM6S] + 2.11*[PregS] + 0.16*[PMS] - 0.56*[PM3S+] - 4.46. Those with a value above the cut-off value are determined to be at high risk, and those with a value below the cut-off value are determined to be at low risk. The cut-off value is 0.14.

[0024] The formula for predicting the risk of intrahepatic cholestasis of pregnancy in the second trimester of pregnancy is -12.98*[Preg17olS] - 0.24*[PM6S] - 1.14*[PregS] + 1.69*[PMS] + 0.54*[PM3S+] + 0.01*[ALT] - 0.001*[AST] + 0.02*[TBA] - 0.98. Those with a value above the cut-off value are determined to be at high risk, and those with a value below the cut-off value are determined to be at low risk. The cut-off value is 0.40.

[0025] The formula for predicting the risk of preterm birth associated with intrahepatic cholestasis of pregnancy in the second trimester of pregnancy is 0.94*[Preg17olS] - 0.29*[PM6S] - 2.28*[PregS] + 1.47*[PMS] + 0.65*[PM3S+] + 0.42. Those with values above the cut-off value are determined to be at high risk, and those with values below the cut-off value are determined to be at low risk. The cut-off value is 0.19.

[0026] The formula for predicting the risk of neonatal jaundice associated with intrahepatic cholestasis of pregnancy in the second trimester of pregnancy is -19.63*[Preg17olS] + 2.67*[PM6S] - 2.38*[PregS] + 0.72*[PMS] - 0.02*[PM3S+] + 2.43. Those with values above the cut-off value are determined to be at high risk, and those with values below the cut-off value are determined to be at low risk. The cut-off value is 0.44.

[0027] The formula for predicting the risk of neonatal respiratory distress syndrome associated with intrahepatic cholestasis of pregnancy in the second trimester of pregnancy is 72.42*[Preg17olS] + 2.72*[PM6S] - 1.53*[PregS] - 1.11*[PMS] - 0.45*[PM3S+] + 0.85. Those with values above the cut-off value are determined to be at high risk, and those with values below the cut-off value are determined to be at low risk. The cut-off value is 0.27.

[0028] In the present invention, the term "cut-off value" refers to a value that is statistically relevant to a specific outcome when compared with the analysis result. In a preferred embodiment, the cut-off value is determined based on the statistical conclusions of studies comparing ICP patients and healthy pregnant women. Some such studies are shown in the Examples section herein, but studies from the literature and the experience of users of the methods described herein can also be used to generate or adjust the cut-off value. The cut-off value can also be determined by considering the patient's genetic background, clinical characteristics, working environment, and the situation and results of other relevant factors.

[0029] Furthermore, the method for constructing the prognostic risk model includes the following steps: obtaining the plasma level data of the metabolite markers and the clinical follow-up data described in the first aspect of the present invention, and constructing a prognostic risk model based on the level data of the metabolite markers and the clinical follow-up data.

[0030] Furthermore, the prognostic risk model is determined using one or more algorithms selected from the following: generalized linear model, principal component analysis, logistic regression analysis, LASSO regression analysis, nearest neighbor analysis, support vector machine, neural network model, random forest model.

[0031] Furthermore, the prognostic risk model is determined using the generalized linear model.

[0032] The third aspect of the present invention provides the use of a reagent for detecting the level of the metabolite marker described in the first aspect of the present invention in a sample in the preparation of a prognostic prediction product for intrahepatic cholestasis of pregnancy, and the product includes a kit, a chip, a test strip, or a device, equipment, computer-readable storage medium.

[0033] Further, the reagent for detecting the level of the metabolite marker described in the first aspect of the present invention in the sample includes reagents used in one or more of the following methods: mass spectrometry, nuclear magnetic resonance spectroscopy, liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry, surface-enhanced Raman spectroscopy, matrix-assisted laser desorption / ionization mass spectrometry, quantitative mass spectrometry imaging technology, surface-assisted laser desorption / ionization mass spectrometry.

[0034] Further, the reagent detects the level of the metabolite marker in the sample by one or several of targeted or non-targeted nuclear magnetic resonance methods, chromatography methods, spectroscopy methods, mass spectrometry methods, chromatography-mass spectrometry methods.

[0035] Further, the chromatography method includes gas chromatography, capillary electrophoresis, liquid chromatography, high-performance liquid chromatography, ultra-high performance liquid chromatography.

[0036] Further, the spectroscopy method includes ultraviolet-visible spectroscopy, infrared spectroscopy, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy.

[0037] Further, the mass spectrometry method includes, for example, tandem mass spectrometry, matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry, MALDI-TOF-TOF mass spectrometry, MALDI quadrupole-time-of-flight (Q-TOF) mass spectrometry, electrospray ionization (ESI) TOF mass spectrometry, ESI-Q-TOF, ESI-TOF-TOF, ESI-ion trap mass spectrometry, ESI triple quadrupole mass spectrometry, ESI Fourier transform mass spectrometry (FTMS), MALDI-FTMS, MALDI-ion trap-TOF, and ESI-ion trap TOF.

[0038] Further, the product further includes a sample pretreatment reagent.

[0039] In the present invention, a test sample for detecting the metabolite biomarker refers to a composition obtained from or derived from a subject (such as an individual of interest), comprising cellular entities and / or other molecular entities characterized and / or identified based on physical, biochemical, chemical, and / or physiological characteristics. The test sample can be from the subject's blood and other fluid samples and tissue samples of biological origin, such as biopsy tissue samples or tissue cultures or cells derived therefrom. The source of the tissue sample can be solid tissue, such as from fresh, frozen, and / or preserved organ or tissue samples, biopsy tissues, or aspirates; blood or any blood component; body fluid; cells at any time during an individual's pregnancy or development; or plasma. The term test sample includes biological samples that have been processed in any way after their acquisition, such as treated with reagents, stabilized, or enriched for certain components (such as proteins or polynucleotides), or embedded in a semi-solid or solid matrix for sectioning purposes. The cells are animal cells, such as cells well known in the art derived from humans, chimpanzees, monkeys, horses, cows, sheep, pigs, donkeys, camels, dogs, rabbits, cats, rats, mice, fish, birds, or insects.

[0040] Furthermore, the subject refers to any individual of interest. Preferably, the subject refers to a living organism suffering from or suspected of suffering from intrahepatic cholestasis of pregnancy and related adverse events, including humans and other mammals, preferably primates, and particularly preferably humans.

[0041] Furthermore, the sample includes serum, plasma, urine, saliva, cerebrospinal fluid, lymph fluid, amniotic fluid, follicular fluid, synovial fluid, milk, tears, semen, feces, intestinal extracts, and cell or tissue extracts.

[0042] Furthermore, the sample is plasma.

[0043] The fourth aspect of the present invention provides a prognosis assessment system for intrahepatic cholestasis of pregnancy. The prognosis assessment system includes an analysis unit that uses the prognosis risk model described in the second aspect of the present invention for analysis and scoring.

[0044] Furthermore, the prognosis assessment system further includes an input unit and an output unit.

[0045] Furthermore, the input unit is used to input the absolute level data of the metabolite biomarker described in the first aspect of the present invention in the test sample.

[0046] Furthermore, the input unit is also used to input the clinical characteristic data of the subject corresponding to the test sample.

[0047] Furthermore, the clinical characteristic data includes the pregnancy stage of the subject.

[0048] The pregnancy stages include the first trimester (or early pregnancy, which in the present invention refers to the first "three - month" period of pregnancy, usually 1 - 15 weeks, corresponding to months 1 - 3), the second trimester (or mid - pregnancy, which in the present invention refers to the second "three - month" period of pregnancy, usually 13 - 25 weeks, corresponding to months 4 - 6.5), and the third trimester (or late pregnancy, which in the present invention refers to the third "three - month" period of pregnancy, usually 26 - 40 weeks, corresponding to months 6.5 - 10).

[0049] Further, the output unit is used to output the risk value of the subject corresponding to the sample to be tested for having intrahepatic cholestasis of pregnancy and related adverse events.

[0050] The fifth aspect of the present invention provides a prognostic evaluation device for intrahepatic cholestasis of pregnancy, and the prognostic evaluation device includes a memory and a processor.

[0051] The memory is used to store program instructions.

[0052] The processor is used to execute the program instructions. When the program instructions are executed, it is used to perform the following operations: obtain the relative expression level data of the metabolite markers described in the first aspect of the present invention in the sample to be tested and the clinical characteristic data of the subject corresponding to the sample to be tested, and input the relative expression level data of the metabolite markers described in the first aspect of the present invention and the clinical characteristic data of the subject corresponding to the sample to be tested into the prognostic risk model described in the second aspect of the present invention to obtain the prognostic evaluation result of the sample to be tested.

[0053] Further, the prognostic evaluation result is the risk value of the subject corresponding to the sample to be tested for having intrahepatic cholestasis of pregnancy and related adverse events.

[0054] Further, the computer device may have: a display device for displaying information to the user; and a keyboard and a pointing device (such as a mouse), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (such as visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including voice input, speech input, or tactile input).

[0055] The sixth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following method is implemented: obtaining relative expression level data of the metabolite markers described in the first aspect of the present invention in a sample to be tested and clinical characteristic data of the subject corresponding to the sample to be tested, and inputting the relative expression level data of the metabolite markers described in the first aspect of the present invention and the clinical characteristic data of the subject corresponding to the sample to be tested into the prognostic risk model described in the second aspect of the present invention to obtain a prognostic evaluation result of the sample to be tested.

[0056] Further, the prognostic evaluation result is the risk value of the subject corresponding to the sample to be tested developing intrahepatic cholestasis of pregnancy and related adverse events.

[0057] Any combination of one or more computer-readable media may be employed. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, which can be used by or in connection with an instruction execution system, apparatus, or device.

[0058] Further, more specific examples of the computer-readable storage medium include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disk read-only memory (CD ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0059] Specifically, the system / computer-readable storage medium is a method for distinguishing different components, elements, parts, portions, or assemblies of different levels. However, if other words can achieve the same purpose, the words may be replaced by other expressions. Those skilled in the art of the present invention are familiar with the fact that the present invention can be implemented as a device, a method, or a computer program product. Therefore, the content disclosed in the present invention can be specifically implemented in the following forms, that is, it can be completely hardware, can also be completely software (including firmware, resident software, microcode, etc.), or can also be a form combining hardware and software. In addition, in some specific embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, which contains computer-readable program code.

[0060] The seventh aspect of the present invention provides the application of the prognosis risk model described in the second aspect of the present invention, the prognosis assessment system described in the fourth aspect of the present invention, the prognosis assessment device described in the fifth aspect of the present invention, and / or the computer-readable storage medium described in the sixth aspect of the present invention in the preparation of a prognosis prediction product for intrahepatic cholestasis of pregnancy.

[0061] Advantages and beneficial effects of the present invention: The present invention provides a metabolite marker for predicting intrahepatic cholestasis of pregnancy and related adverse events, and provides a prognosis risk model constructed based on the metabolite marker. This model can accurately assess the risks of intrahepatic cholestasis of pregnancy and related adverse events for subjects. According to the ROC curve drawn, the AUCs for different pregnancy stages are 0.71 and 0.73 respectively, with relatively high accuracy. Brief Description of the Drawings

[0062] Figure 1 It is a result diagram for high-throughput screening to determine the activation of hMRGPRX4 receptor by sulfonated progesterone. Among them, A is a flow chart for determining candidate pruritogens through the hX4 receptor; B is a schematic diagram of high-throughput screening for pruritogenic agents and structural diagrams of nine best pruritogenic metabolites; in C, the left figure is a schematic diagram of the sulfonation effect of SULT enzyme in the liver, and the right figure is a schematic diagram of the relative activation of the hX4 receptor by sulfonated progesterone and its corresponding non-sulfonated precursor; D is a dose-response curve of DCA, PM5S, PM3S, and PregS and their corresponding non-sulfonated precursors activating hX4; E is a schematic diagram of the relative activation of the hX4, hX1, hX2, and hX3 receptors induced by the indicated compounds (100 μM).

[0063] Figure 2 It is a result diagram for sulfonated progesterone to activate DRG neurons expressing hX4 and induce itching. Among them, A is a schematic flow chart for culturing, transfecting WT DRG neurons and performing calcium imaging; B-D show calcium imaging results of PM5S, PM3S, and PM5S with the hX4 antagonist (1-55). From left to right, they are representative images, representative fluorescence, and dose-response curves of hX4-P2A-RFP (hX4 + ) or control (hX4 - ) in DRG neurons; E is a schematic flow chart for constructing hX4 humanized rats; F is a quantification diagram of scratching episodes induced by subcutaneous injection of PM5S (left) or PM3S (right) at a specified dose (dissolved in 50 μl of solvent) into the nape of the neck of hX4 humanized rats; G is a quantification diagram of scratching episodes induced by subcutaneous injection of 300 μg of PM5S (left) or PM3S (right) into the nape of the neck of hX4 humanized rats; H is a quantification diagram of scratching episodes induced by 300 μg of PM5S, 300 μg of PM3S, and 500 μg of 5-HT in WT rats.

[0064] Figure 3Results graph of sulfonated progesterone-induced human pruritus; among which, A is a schematic diagram of the double-blind study process of the itching-inducing ability of various compounds (25 μL of solvent was injected into each arm); B is a graph of the time course of the perceived itching intensity induced by PM5S or the vehicle; C is a violin plot showing the schematic diagram of the peak value of the perceived itching intensity induced by PM5S or the vehicle at different time stages in Figure B; D is a summary graph of the area under the curve (AUC) of the 30-minute trajectory curve obtained from individual subjects; E-G are similar to B-D, but the results of male and female subjects are distinguished; H is a representative image of the injection site and a statistical graph of the erythema area measured 30 minutes after injection; I-J are graphs of the results of the time course (I) and AUC (J) of the perceived itching intensity induced by PM5S or the vehicle after pretreatment with a topical antihistamine.

[0065] Figure 4 Results graph of the correlation between sulfonated progesterone levels and the intensity of ICP pruritus. Among which, A is a schematic diagram of the strategy process for detecting the effect of sulfonated progesterone in ICP-related pruritus; the upper part of Figure B is a representative total ion chromatogram of various sulfonated progesterones separated and identified by HPLC-MS / MS, and the lower part is a summary graph of the levels of different sulfonated progesterones in 35 ICP patients and 27 control subjects at the designated time points; C is a graph of the correlation analysis between the pruritus intensity and blood drug concentration of ICP patients in the third trimester of pregnancy and control subjects.

[0066] Figure 5Sulfated progesterone levels measured at different gestational stages can predict the outcomes of ICP. The results are shown in the figures. Figure A is a schematic diagram of the protocol for generating a predictive model for ICP in a retrospective cohort. Figures B1 - B4 are the results for predicting the risk of ICP in the first trimester of pregnancy. Among them, Figures B1 - B2 show the ROC curves of a single sulfated progesterone metabolite (B1), a combination of sulfated progesterone metabolites (PS - 1st model), and classical biomarkers (TBA, ALT, and AST) used to predict ICP (B2). Figure B3 shows the ROC curves of the PS - 1st model and individual classical biomarkers for predicting ICP. Figure B4 is a summary graph of the AUC of the ROC curves. Figures C1 - C4 are the same as B1 - B4, showing the results for predicting the risk of ICP in the second trimester of pregnancy. Figures D - E are summary graphs of the number of participants corresponding to the predicted risk of ICP according to the ICP - PS - 1st model (first trimester) (D) and the ICP - PS - 2nd model (second trimester) (E), with their optimal cut - off values being 0.45 (D) and 0.40 (E) respectively. In Figure F, the upper graph shows the summary of the predicted risk of ICP for ICP patients and healthy control individuals at specified time points during pregnancy (using the ICP - PS - 1st and ICP - PS - 2nd models), and the lower graph shows the proportion of ICP patients successfully predicted in the first trimester and clinically diagnosed in the third trimester, with a median difference of 163 days in the successful prediction weeks. Figures G1 - G4 are for predicting preterm birth (PTB) based on measurements taken in the first trimester of pregnancy. Figure G1 shows the ROC curves of classical biomarkers used to predict PTB and the PTB - PS - 1 st model (combining all five sulfated progesterone metabolites). Figure G2 is a summary of the AUC of the ROC curves. Figure G3 shows the sensitivity of using the PTB - PS - 1 st model and TBA to predict PTB at different times during the gestational stage. Figure G4 shows the proportion of women in labor with high or low predicted PTB risk according to the PTB - PS - 1 st model. Figures H1 - H4 are the same as G1 - G4, used to predict PTB in the second trimester of pregnancy.

[0067] Figure 6 Results of using sulfated progesterone levels to predict adverse events related to intrahepatic cholestasis of pregnancy. Among them, Figures A - B show the correlation between sulfated progesterone levels in the first and second trimesters of pregnancy and the gestational age at delivery. Figures C - D show the results of sulfated progesterone levels in the PTB prediction model based on GLM. Figures E - F show the AUC for prediction in the first and second trimesters of pregnancy. Figures G - H show the correlation between TBA, AST, and ALT and PTB. Figures I - J show the AUC of sulfated progesterone levels measured in the first trimester of pregnancy for predicting neonatal jaundice and NRDS. Figures K - L show the AUC of sulfated progesterone levels measured in the second trimester of pregnancy for predicting neonatal jaundice and NRDS. Detailed implementation methods

[0068] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0069] Example

[0070] I. Patients

[0071] This retrospective study (NCT06366659) has been approved by the Medical Science Research Ethics Committee of Peking University Third Hospital (M2024049) and the Medical Ethics Committee of West China Second Hospital, Sichuan University (2022YFC2704703 and 2024068). All participants were fully informed and understood the detailed information related to the study and provided written informed consent. Samples not used in routine clinical procedures could be used without obtaining informed consent from the subjects. Participants could choose to withdraw from the study at any time. Subjects with skin lesions such as pruritus accompanied by eczema, pruritus caused by other liver-related diseases such as primary biliary cholestasis, and pruritus lasting at least 4 - 6 weeks after childbirth were excluded. Plasma samples were collected from participants recruited from Peking University Third Hospital in the late pregnancy and the pruritus intensity was recorded. Data from the discovery cohort and the retrospective multicenter cohort were collected from multiple medical centers in Sichuan, China, during the period from 2019 to 2023, and the plasma samples were leftovers from routine clinical operations.

[0072] II. Experimental Materials

[0073] 1) Animals: All wild-type (WT) animals were purchased from Charles River Laboratories (Beijing, China). Male and female rats (6 - 8 weeks old, 200 - 300 g) were housed (3 - 5 rats per cage) under a standard 12-hour light / 12-hour dark cycle. Behavioral experiments were conducted during the light cycle. All animal experiments were approved by the Animal Care and Use Committee of the School of Life Sciences, Peking University.

[0074] 2) Stable cell lines: The stable cell lines used in this study were constructed as described in Elife (MRGPRX4 is a bile acid receptor for human cholestatic itch) published by H. S. Yu et al. in 2019. Briefly, the DNA encoding human MRGPRXI, MRGPRX2, MRGPRX3, and MRGPRX4 was subcloned into the PiggyBac transposon vector and co-transfected with the highly active PiggyBac transposase into HEK293T cells using polyethyleneimine (PEI). Positive cells were cultured in DMEM containing 10% fetal bovine serum (FBS), 1 μg / ml puromycin, 100 μg / ml penicillin, and 100 μg / ml streptomycin at 37°C in a 5% CO 2 humidified atmosphere.

[0075] III. Experimental methods

[0076] 1) FLIPR assay: Twenty-four hours before the assay, HEK293T cells stably expressing human MRGPRX were seeded in 96-well plates at a density of 50,000 cells per well. The next day, the medium was removed, and the cells were loaded with Fluo-8 AM (AAT Bio) for 1 hour and then washed with Hanks' balanced salt solution containing 5 mM HEPES (pH 7.4) (referred to as "HHBS"). Before adding the drug, the baseline signal of Fluo-8 was recorded as F 0 . Then, the test compound was added to the wells at different concentrations, and the Fluo-8 signal was immediately measured using the FLIPR TETRA system (Molecular Devices). The peak signal was defined as F, and the activation response was calculated as (F - F 0 ) / F 0 .

[0077] 2) TGFα shedding assay: An AP-TGF-α stable cell line expressing hX4 was used. The isolated cell pellet was resuspended in 10 ml of PBS by centrifugation (190 g, 5 min) and incubated at room temperature for 10 min. The cells were then recentrifuged and resuspended in 4 ml of HHBS, and the recovered cells were seeded in 96-well plates (90 μl / well) and incubated at 37°C in a 5% CO 2 humidified atmosphere for 30 min. After 30 min, 10 μl of a 10× compound stock solution was added to each well. The plates were then incubated for 1 hour, and the alkaline phosphatase activity (using a substrate) in the conditioned medium and the cells was measured.

[0078] 3) Protein-ligand docking: Using the web version of CB-Dock2 ( https: / / cadd.labshare.cn / cb-dock2 / php / index.php ) Perform cavity search and automatic blind docking on ligands PM5 and PM3 using hX4 protein.

[0079] 4) Isolation, electroporation and culture of rat dorsal root ganglion neurons

[0080] Collect adult rat DRG tissue, cut it into pieces smaller than 1 mm, and then digest it in a solution containing 5 mg / ml dispase and 1 mg / ml collagenase at 37°C for 1 h. After mechanical grinding and centrifugation, wash the cells with 15% (w / v) BSA solution and resuspend them in DMEM / F12 supplemented with 10% FBS. Then, electroporate DRG neurons using the P3 Primary Cell 4D Nucleofector X Kit L (Lonza) according to the manufacturer's protocol. Then seed the cells on glass coverslips pre-coated with poly-D-lysine and laminin and culture for another 72 h to allow transgene expression.

[0081] 5) Confocal Ca 2+ imaging

[0082] In the Ca 2+ imaging experiment, cells are loaded with 5 μM Fluo-8 AM (AAT Bio) at 37˚C for 1 h and then rinsed to remove the dye. Various compounds are perfused onto the cells in the chamber using a custom-built 6-channel perfusion valve control system, and real-time fluorescence images are recorded using a Nikon A1 confocal microscope.

[0083] 6) Construction of hX4 humanized rats

[0084] To construct hX4 humanized rats, we used CRISPR / Cas9 gene editing technology to modify the rat MrgA gene. Replace the nucleotide sequence of 28 amino acids before the stop codon of exon 2 with the hX4-3*Flag-P2A-iCre-WPRE-polyA sequence, and insert the CAG-loxP-Frt-Stop-loxP-Frt-hX4-3*Flag-iP2A-mScarlet-WPRE-polyA construct into the ROSA26 locus using CRISPR / Cas9. Screen the Fl offspring by tail biopsy to confirm the presence of gene modification. Finally, establish hX4-expressing humanized rats by breeding MrgA::hX4-Cre rats with Rosa26::loxP-STOP-loxP-hX4-3*flag mScarlet (Rosa26::LSL-hX4) rats.

[0085] 7) Animal behavior studies

[0086] The behavioral tests were conducted and analyzed by an experimenter who was blind to the genotype and the compounds used. Before the experiment, the animals were placed in the experimental chamber for 1 hour per day for 5 consecutive days. On the 6th day, the animals were first acclimated to the experimental chamber, and the basal scratching or wiping behavior was recorded for 60 min using a camera. Then the test compound (dissolved in 50 μl of a vehicle consisting of 91% saline and 9% Tween-80) was injected intradermally into the nape or cheek of the animals, and the scratching or wiping behavior was recorded again for 60 min. For antagonist pretreatment, 1-55 was injected intradermally into the nape, and 20 min later the test compound was injected at the same site. Scratching or wiping was defined as the consecutive movement of the ipsilateral hind or forepaw toward the injection site, respectively. The scratching or wiping behavior was quantified by counting the number of episodes during the 60-min observation period before and after injection.

[0087] 8) Human pruritus test

[0088] This study (NCT06364969) was approved by the Human and Animal Protection Committee of the Department of Psychology, Peking University (2021-06-02) and was conducted as described in the aforementioned article by H. S. Yu et al. The volunteers were students and teachers from Peking University, regardless of gender. All subjects received the experimental protocol and provided written informed consent. All compounds were administered subcutaneously using the INJEX 30 needle-free injection system (INJEX Pharma GmbH, Berlin, Germany).

[0089] First, each test compound was dissolved in saline containing 8% Tween-80 (Sigma-Aldrich) to the final concentration. The injection site was cleaned by alcohol wiping, and 25 μl of each solution was injected intradermally on the volar surface of both arms. Pruritus was defined as the desire to scratch during the experiment, and the subjects were instructed to rate the perceived intensity of pruritus caused by the given stimulus using a generalized labeled scale during the 30-min observation period. After these 30 min, the area of the red flare around the injection site was measured.

[0090] For the antihistamine pretreatment experiment, approximately 1.5 g of topical antihistamine cream (doxepin hydrochloride cream, Chongqing Huabang Pharmaceutical Co., Ltd.) or placebo (cold cream, Avene) was applied 2 hours before injection; all unabsorbed cream was removed by alcohol wiping. PM5S and histamine were prepared and injected as described above. Then the subjects were instructed to rate the pruritus sensation as described above.

[0091] 9) Blood sample preparation

[0092] Take whole blood samples from ICP patients and healthy pregnant women and place them in anticoagulant tubes. Plasma was separated from the blood by centrifugation at 12,000 g for 10 min at 4°C and stored at -80°C. For protein precipitation, 100 μl of each plasma sample was mixed with 425 μl of methanol and then vortexed for 10 minutes. After centrifugation at 14,000 g for 10 minutes, the supernatant was transferred to a new tube, stored at -80°C, and then subjected to HPLC-MS / MS analysis.

[0093] 10) HPLC-MS / MS analysis

[0094] HPLC-MS / MS analysis was performed using an Agilent UPLC 1290-MS / MS 6495 system and an API5000 tandem mass spectrometer. 5 μl of each sample was injected into the system. A sulfonated progesterone was separated using an EC-Cl8 column (Agilent Poroschell 120, 50×4.6 mm, 2.7 μm particle size, Agilent Technologies). The mobile phase was LC-MS grade water and methanol, supplemented with 0.01% formic acid and 10 mM ammonium acetate. The gradient was 70% methanol for 1.2 min; changed to 75% methanol within 6.8 min; changed to 100% methanol within 0.2 min, maintained 100% methanol for 2.2 min, returned to 70% methanol within 0.25 min, and then maintained 70% methanol for 2.35 min for re-equilibration. The flow rate was maintained at 0.3 ml / min and the HPLC column was maintained at 20°C.

[0095] For MS analysis, the negative electrospray ionization mode was used, the ionization voltage was 5500 V, and multiple reaction monitoring was combined.

[0096] 11) Standard compounds and calibration curves

[0097] Stock solutions of all standard compounds were prepared using 100% methanol at a concentration of 1 mg / ml. Working solutions containing different concentrations were prepared by diluting the stock solutions to the following final concentrations using charcoal-stripped blank human plasma: concentrations of PM4S (Steraloids Inc.), PM5S (Toronto Research Chemicals), PM6S (Steraloids Inc.), and PM7S (Steraloids Inc.) were 0.64, 1.28, 2.57, 5.13, 10.27, 20.53, 41.07, 82.14, 164.27, 657.09, and 2628.38 ng / ml; concentration of Preg17olS (Steraloids Inc.) was 2.5, 5.0, 10.1, 20.1, 40.3, 80.6, 161.1, 644.6, 2578.4, and 10313.5 ng / ml (Steraloids Inc.); concentrations of PM3S and 3β5α-diolS (Steraloids Inc.) were 0.61, 1.22, 2.44, 4.89, 9.78, 19.56, 39.12, 78.24, 156.47, 625.89, and 2503.56 ng / ml; concentration of PregS (Toronto Research Chemicals) was 0.64, 1.28, 2.55, 5.11, 10.21, 20.42, 40.84, 81.69, 163.38, 653.52, and 2614.06 ng / ml. Calibration curves were obtained by plotting the MS response of the standard solutions against the concentration. All solutions were stored at -80°C.

[0098] 12) Generation and evaluation of prediction models

[0099] The R package caret (version 6.0 - 94) was used to generate models for predicting ICP and related adverse events. Prediction models were established using plasma sulfated progesterone levels in the first (early) and second (mid) “trimesters” of pregnancy. The data were carefully examined and incomplete values were removed. To avoid multicollinearity, we used the “findCorrelation” function in R to exclude highly correlated variables, with a cut - off threshold of 0.8. To avoid multicollinearity, we used a generalized linear model (GLM) with a binomial family (“1” for positive and “0” for negative) to indicate the presence or absence of CP. The model was trained using the R package caret and a ten - fold cross - validation procedure was used to ensure reusability and reliability. Specifically, in each iteration of cross - validation, the data were divided into 10 groups, 9 groups were used to train the GLM model, and the 10th group was used as a test set to evaluate the performance of the model. The model was evaluated using metrics such as ROC, AUC, accuracy (calculated using the formula [(TP + TN) / (P + N)]), specificity (true negative rate calculated using the formula TN / (TN + FP)), and sensitivity (true positive rate calculated using the formula [TP / (TP + FN)]) to assess the performance of patients, where T represents true, F represents false, P represents positive, and N represents negative. The ROC curve of the sulfated progesterone biomarker was calculated and implemented using the R program or the ROC module in IBM SPSS Statistics (version 27).

[0100] 13) Statistical analysis

[0101] Statistical analysis was performed using OriginPro 2020. Inter - group data were analyzed using Student’s t - test, one - way ANOVA, or Mann - Whitney U - test, with statistical significance set at p < 0.05. Unless otherwise stated, all summary data are presented as mean ± SEM.

[0102] IV. Experimental results

[0103] 1) High - throughput assays showed that sulfated progesterone highly specifically activates the pruritus receptor hX4

[0104] Considering the increased intensity of ICP - related pruritus during pregnancy and the possible role of the pruritus receptor hX4 in this symptom, the inventors attempted to identify an endogenous metabolite that changes in parallel with pruritus intensity and activates hX4. First, through a literature survey, 28 endogenous metabolites were identified that increase during pregnancy and decrease postpartum ( Figure 1A) in. More than 40% of the metabolites are steroids. Therefore, to increase the likelihood of identifying hits, we incorporated the commercial steroid library of Steraloids Inc. (https: / / www.steraloids.com / ) and obtained a total of 1490 potential candidate compounds for screening as possible pruritogens ( Figure 1 A) in. Then, we performed high-throughput screening using a fluorescence imaging reader (FLIPR) calcium assay to determine whether any of these candidates could activate hX4 in a stable HEK293T cell line expressing hX4. Deoxycholic acid (DCA), a known hX4 ligand, was used as a positive control. After screening 1490 compounds, the inventors identified 22 metabolites that strongly activated hX4, each inducing a calcium response at least 0.85-fold higher than that induced by DCA ( Figure 1 B) in. These metabolites could be classified into five categories based on their structures, namely sulfonated progesterone (i.e., sulfonated progestin derivatives), bile acid derivatives, androgen derivatives, estrogen derivatives, and "others" ( Figure 1 B) in. Progesterone is an essential hormone for pregnancy to term, and its levels gradually increase during pregnancy and then decrease at parturition. Given the similarity between this time course and the progression of ICP-related pruritus, the present invention focused on nine sulfonated progesterones by further examining their ability to activate hX4 and induce ICP-related pruritus ( Figure 1 B) in.

[0105] These nine sulfonated progesterones could be further classified into three categories based on their non-sulfonate precursors: pregnanolone derivatives, pregnanediol derivatives, and pregnenolone derivatives ( Figure 1 B) in. To quantify the ability of the compounds to activate hX4, we developed an α-index. The higher the α-index, the more effective the activation of hX4, and DCA was defined as having an α-index of 1.0. Among the nine sulfonated progesterones tested, PM4S and PM3S were the most effective hX4 activators, while PregS was the least effective activator. Among these pruritogens, the levels of PM5S, PM3S, and PM3DiS were significantly elevated in ICP patients. Given the higher endogenous levels of PM5S and PM3S in ICP, the possible role of sulfonated progesterones in ICP-related pruritus was further investigated using these two compounds.

[0106] 2) Sulfonation is required for progesterone metabolites to activate hX4

[0107] This sulfonate group is required for all of the above nine pruritogens to activate hX4 because their direct non-sulfonate precursors cannot activate hX4 ( Figure 1C and D in). To determine why the sulfonate group is crucial for hX4 activation, we attempted to dock sulfonated progesterone with the reported hX4 structure using CB-Dock2. The docking results showed that PM5S and PM3S were located in the orthosteric pocket of hX4, and specific residues in the pocket interacted with these sulfonated progesterones. Some of these residues had molecular interactions with hX4 agonists, and mutations of most of these residues significantly reduced or eliminated the activation of hX4 by PM5S and PM3S. Moreover, the hX4 binding pocket formed a positively charged environment, indicating that the negatively charged sulfonate group might increase the interaction between the metabolite and the receptor. In addition, some drugs modified to contain negatively charged phosphate groups had higher potency in activating hX4 than the unmodified counterparts. Based on the structural model, we predicted that the sulfonate group in sulfonated progesterone interacted with the positively charged arginine residue at position 82 (R82 in the hX4 binding pocket), and the same residue was reported to have a strong charge interaction with the phosphate group in the agonist. To further investigate this interaction, arginine was replaced with negatively charged aspartic acid at R82, which greatly reduced the potency of PM5S and PM3S in activating hX4. Therefore, based on the study of the structure-function relationship, it can be concluded that sulfonated progesterone activates the hX4 receptor by binding in the orthosteric binding pocket, and the sulfonamide group in the metabolite is essential for this interaction. In addition, sulfonated progesterone selectively activates the hX4 receptor and cannot activate hMRGPRX1, hMRGPRX2, or hMRGPRX3 (other members of the human MRGPRX family) even at a high concentration of 100 μM ( Figure 1 in E).

[0108] 3) Sulfonated progesterone activates DRG neurons via hX4

[0109] Considering that the itching sensation is mainly conducted by DRG neurons, we next investigated whether sulfonated progesterone could activate DRG neurons via hX4. hX4 is a primate-specific receptor, and no homologous gene has been found in rodents; therefore, for Ca 2+ in vitro imaging, we heterologously expressed hX4 in cultured rat DRG neurons by electroporation ( Figure 2 in A). We found that PM5S significantly increased the intracellular Ca 2+ in a dose-dependent manner in hX4-expressing (hX4 rat DRG neurons) cells, but had no effect on the intracellular Ca - in untransfected (hX4 2+ ) neurons ( Figure 2 in B). Similar results were obtained for PM3S ( Figure 2in C). Whether pretreatment of DRG neurons with hX4 antagonists can inhibit P5S-induced DRG neuron activation. The hX4 antagonist compound "1-55" (developed by Escient Pharmaceuticals company) can dose-dependently block PM5 from activating hX4. In a time-course experiment, we found that applying 10 μM PM5S to hX4 + DRG neurons induced a strong Ca 2+ signal, which was abolished in the presence of 1-55 ( Figure 2 in D). In addition, the non-sulfonated precursors of PM5S and PM3S failed to activate hX4 + DRG neurons, which is consistent with the concept that sulfonamide groups are required for hX4 activation.

[0110] 4) Sulfonated progesterone induces pruritic behavior by activating hX4

[0111] It was demonstrated that sulfonated progesterone activated DRG neurons expressing hX4, and then these metabolites were tested by the intrascapular injection model to see if they could induce hX4-dependent pruritus in vivo ( Figure 2 in E). Statistical results confirmed that in wild-type (WT) rats, sulfonated progesterone could not induce pruritic behavior ( Figure 2 in H); as a positive control, 5-HT (5-hydroxytryptamine, a potent pruritogen) caused a strong pruritic response in WT rats. In contrast, it has been reported that PM3S induces scratching behavior in mice by activating TGR5. Therefore, rats expressing hX4 (i.e., hX4 humanized) provide a valuable tool for studying the role of sulfonated progesterone in pruritus.

[0112] hX4 humanized rats were generated by crossing MrgA::hX4-Cre rats with Rosa26::LSL-hX4 rats ( Figure 2 in E). The results showed that injecting PM5S or PM3S into the intrascapular region of hX4 humanized rats induced a strong sense of pruritus (quantified by the number of scratching times) in a dose-dependent manner ( Figure 2 in F), but had no effect on WT rats ( Figure 2 in H). In addition, the non-sulfonated precursor of PM5S could not activate hX4 in vitro and failed to induce pruritus in hX4 humanized rats; similarly, PregS, which has the lowest α-index among all nine metabolites, also failed to cause a significant scratching response in humanized rats, which is consistent with its relatively low potency in activating hX4. As an additional control, in the cheek injection model, we measured the pain and pruritus levels of rats by quantifying cheek wiping and scratching respectively. PM5S had no effect, while injecting capsaicin induced a strong pain response in WT rats. Figure 4in E). Then we found that when injecting PM5S into the cheek of hX4 humanized rats, it induced a strong scratching response but no pain response.

[0113] To verify whether the pruritic response induced by sulfated progesterone is indeed mediated by the hX4 receptor, we pretreated hX4 humanized rats with the hX4 antagonist 1-55 (or vehicle), and then measured the ability of PM5S and PM3S to induce pruritus. Compared with vehicle-treated rats, sulfated progesterone failed to cause pruritus in the "1-55"-treated rats ( Figure 2 in G). Thus, pharmacologically inhibiting hX4 activation with an antagonist can prevent sulfated progesterone-induced pruritus, suggesting a potentially viable strategy for preventing ICP-related pruritus.

[0114] 5) Sulfated progesterone induces pruritus in humans

[0115] Next, we tested whether sulfated progesterone can act as a pruritogen and induce pruritus when injected intradermally into human subjects ( Figure 3 in A). We found that a single injection of PM5S rapidly induced a pruritic response, peaking within 10 minutes and then gradually declining within 30 minutes ( Figure 3 in B-D), similar to the pruritus caused by human bile acids. In addition. PM5S induced pruritus in both female and male subjects, with similar potency in terms of the time course or the degree of pruritic response ( Figure 3 in E-G).

[0116] To test whether the typical pruritogen histamine is involved in sulfated progesterone-induced pruritus, we examined the allergic reactions of human subjects ( Figure 3 in A). We found that, unlike histamine, which causes a strong allergic reaction as shown by the "flushed" area at the injection site, PM5S did not trigger an allergic reaction ( Figure 3 in H). In addition, pretreatment with antihistamines failed to prevent PM5S-induced pruritus ( Figure 3 in I-J). These results indicate that sulfated progesterone-induced pruritus is independent of the histaminergic system. To further examine in detail the role of the hX4 pathway in sulfated progesterone-induced pruritus, we tested two metabolites with weak (PregS) or no (3β5α-diolS) hX4 activation effects. Neither of these two metabolites caused significant pruritus in human subjects, similar to the results we obtained in hX4 humanized rats, and these findings suggest that sulfated progesterone most likely induces rapid pruritus sensation in humans by activating hX4.

[0117] 6) Sulfated progesterone levels are correlated with the intensity of ICP pruritus

[0118] Clinically, we examined whether sulfated progesterone levels were associated with the presence and / or intensity of ICP-related pruritus. We collected plasma samples from ICP patients and healthy pregnant women at each trimester and postpartum, and measured the intensity of pruritus in the third trimester of pregnancy ( Figure 4 A in Figure 4 ). We separated and quantified various sulfated progesterones in plasma samples using high-pressure liquid chromatography-tandem mass spectrometry (HPLC-MS / MS) ( Figure 4 B in Figure 6 A). Since it was difficult to completely separate chiral isomers, such as PM4S, PM5S, and PM7S isomers, we calculated the total plasma concentration of PM4S, PM5S, and PM7S isomers, hereinafter referred to as "PMS" ( Figure 4 B in Figure 4 B). Similarly, "PM3S+" represents the sum of two chiral isomers, SPM3s and 3β5α-diolS. We also measured the plasma concentrations of PregS and Pregl7olS; we were unable to measure PM3DiS because there was no standard available on the market. We found that in ICP patients, except for PregS, almost all measured endogenous sulfated progesterone levels gradually increased during pregnancy, peaked in the third trimester, and then decreased after delivery ( Figure 4 B in Figure 4 C), which was consistent with the temporal course of ICP-related pruritus intensity. Importantly, in women without ICP-related pruritus (

[0119] 7) Sulfated progesterone levels can detect pre-symptomatic cases of ICP

[0120] Figure 4 B), the levels of sulfated progesterone showed minimal or slight changes throughout pregnancy. Next, we examined whether a combination of sulfated progesterone at pathological relevant levels was sufficient to activate hX4. We prepared mixtures of sulfated progesterone similar to the plasma sulfated progesterone levels of healthy pregnant women in the third trimester ("healthy mixture") or ICP patients ("ICP mixture"), estimated according to our quantification results ( Figure 4 B). We found that the ICP mixture, but not the healthy mixture, induced a significant Ca2+ signal in HEK293T cells expressing hX4. Notably, in the third trimester of pregnancy, we found a strong correlation between sulfated progesterone levels and the intensity of pruritus reported by ICP patients, with PM3S+ showing the highest positive correlation among the five sulfated progesterones ( Figure 4 C). These results support the view that endogenous sulfated progesterone is the pruritogen that induces ICP-related pruritus.

[0119] Next, we investigated whether sulfonated progesterone levels could be used to predict the risk of ICP before the onset of symptoms in early pregnancy. Therefore, we collected plasma samples in early pregnancy (233 ICP patients and 207 healthy controls) and mid-pregnancy (222 ICP patients and 209 healthy controls) from a retrospective multicenter cohort and measured sulfonated progesterone levels, TBA, AST, and ALT levels ( Figure 5 We found that patients with ICP had higher levels of sulfonated progesterone in early and mid-pregnancy compared with healthy controls. In addition, some single biomarkers were able to predict ICP in early pregnancy, with the highest AUC of 0.74 ( Figure 5 The prediction model using GLM combining all five biomarkers produced a higher AUC of 0.77 ( Figure 5 Using this “PS-1st” model (i.e., the PS model for the first trimester), we obtained a score reflecting the predicted risk of ICP for each participant, with a prediction accuracy of 0.7l if we set 0.49 as the cutoff. In the second trimester, sulfonated progesterone was generally better than in the first trimester in predicting ICP. The AUC for Preg17olS alone in predicting ICP was 0.76 ( Figure 5 The combined prediction model “PS-2nd” has an AUC of 0.84 and an accuracy of 0.78 ( Figure 5 Importantly, the average predicted risk of ICP was higher in patients who developed ICP at different time points during pregnancy than in women with healthy term pregnancies; in contrast, TBA, ALT, and early pregnancy AST were only slightly different between patients who ultimately developed ICP and women with healthy pregnancies and were therefore insufficient to predict ICP, especially in early pregnancy ( Figure 5 If we incorporate these classical biomarkers into the progesterone sulfonate-based model (i.e., PS-1st and PS-2nd for predicting ICP using first- and second-trimester samples, respectively), the new models, called ICP-PS-1st and ICP-PS-2nd, have even higher AUC values ​​of 0.80 and 0.88, respectively ( Figure 5 B3, B4, C3 and C4 in the above data) and has better performance in early prediction of ICP ( Figure 5 Using these improved models, the mean predicted risk of ICP among participants who developed ICP in late pregnancy remained above 50% throughout pregnancy; in contrast, the predicted risk among women who ultimately had a healthy term pregnancy remained below 50% ( Figure 5 It is worth noting that the median time of successful prediction is about 14 weeks, which is significantly earlier than the median time of clinical diagnosis, which is about 163 days, and the latter is about 37 weeks (Figure 5 in F). Collectively, these results suggest that measuring sulfated progesterone levels in early pregnancy can accurately predict the risk of ICP before the onset of symptoms.

[0121] 8) Sulfated progesterone levels can serve as biomarkers for predicting adverse outcomes in ICP

[0122] ICP is associated with an increased risk of maternal and neonatal adverse outcomes, including preterm birth (PTB), neonatal jaundice, and neonatal respiratory distress syndrome (NRDS). Therefore, identifying prognostic biomarkers will facilitate early intervention. Thus, in addition to collecting plasma samples, we also retrieved maternal and neonatal prognoses from medical records to develop a prognostic model for this analysis ( Figure 5 in A), and in addition to ICP patients in the above retrospective multicenter cohort, more patients with poor prognoses were included. We found that sulfated progesterone levels measured in early and mid-pregnancy were negatively correlated with gestational age at delivery; thus, higher levels of sulfated progesterone were associated with earlier gestational periods. Next, we developed DGLM-derived PTB prognostic prediction models based on sulfated progesterone levels ( Figure 5 ). These models demonstrated predictive ability, even when applied in early pregnancy (the first "trimester" of gestation), with AUCs reaching 0.71 and 0.73 when sulfated progesterone levels were measured in early and mid-pregnancy, respectively ( Figure 5 G1-G4, H1-H4 in), far higher than the reported AUC (0.60) for predicting PTB based on peak TBA levels. When comparing the gestational ages at delivery of women predicted to be at high risk of PTB and those predicted to be at low risk of PTB, we found that women at high risk did indeed give birth prematurely ( Figure 5 D and H in). We also tested the ability of BA, AST, and ALT to predict PTB, but the correlations between these classical biomarkers and gestational age at delivery were weak, and they failed to predict PTB when measured in early pregnancy ( Figure 5 G1-G4, H1-H4 in). Incorporating these classical biomarkers into the prognostic prediction model constructed based on sulfated progesterone did not improve the predictive performance ( Figure 5 B1-B4, C1-C4 in). Finally, sulfated progesterone levels can also predict other adverse consequences. Specifically, the AUCs for predicting neonatal jaundice and NRDS using sulfated progesterone levels measured in early pregnancy were 0.59 and 0.64, respectively ( Figure 6 I and J in), while the AUCs using measurements in mid-pregnancy were 0.62 and 0.64, respectively ( Figure 6 K and L in). Therefore, our results indicate that measuring sulfated progesterone levels in early or mid-pregnancy can predict the risk of adverse outcomes, particularly in preterm ICP inpatients and their neonates.

[0123] It should be understood that the systems, devices and methods described in the present invention can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, the functional units in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0125] The description of the above embodiments is only for understanding the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. Application of metabolite markers in the prognosis prediction of intrahepatic cholestasis of pregnancy, characterized in that: The metabolite markers include one or more of Preg17olS, PM6S, PM3S+, PregS, PMS, ALT, AST, and TBA; Preferably, the metabolite marker is a combination of Preg17olS, PM6S, PM3S+, PregS, PMS, ALT, AST, and TBA; Preferably, the PMS is a combination of three isomers: PM4S, PM5S and PM7S; Preferably, the PM3S+ is a combination of two isomers of PM3S and 3β5α-diolS.

2. A prognostic risk model for intrahepatic cholestasis of pregnancy, characterized in that: The prognostic risk model evaluates the prognostic risk of the subject based on the metabolite markers described in claim 1 in the sample to be tested; Specifically, the risk refers to the risk of developing intrahepatic cholestasis of pregnancy and related adverse events; Preferably, the adverse events of intrahepatic cholestasis of pregnancy include premature birth, neonatal jaundice, and neonatal respiratory distress syndrome.

3. The prognostic risk model according to claim 2, characterized in that: The input variables of the prognostic risk model are the relative expression level of the metabolite marker described in claim 1 in the sample to be tested and the clinical characteristic data of the subject corresponding to the sample to be tested; Preferably, the input variables of the prognostic risk model are the absolute levels of Preg17olS, PM6S, PregS, PMS, PM3S+, ALT, AST, and TBA in human plasma, expressed as [Preg17olS], [PM6S], [PregS], [PMS], [PM3S+], [ALT], [AST], and [TBA], respectively, in μM; Preferably, the clinical characteristic data includes the subject's gestational stage; Preferably, the formulas of the prognostic risk models are: The formula for predicting the risk of intrahepatic cholestasis of pregnancy in early pregnancy is 8.55*[Preg17olS]+4.50*[PM6S]-1.72*[PregS]+1.92*[PMS]+0.37*[PM3S+]-0.01*[ALT]+0.01*[AST]-0.02*[TBA]-0.

09. Values ​​above the cutoff value are considered high risk, and values ​​below the cutoff value are considered low risk. The cutoff value is 0.

45. The formula for predicting the risk of premature birth related to intrahepatic cholestasis of pregnancy in early pregnancy is 8.59*[Preg17olS]+3.52*[PM6S]-1.96*[PregS]+2.07*[PMS]+0.36*[PM3S+]+0.

18. The value above the cutoff value is judged as high risk, and the value below the cutoff value is judged as low risk. The cutoff value is 0.

18. The formula for predicting the risk of neonatal jaundice related to intrahepatic cholestasis of pregnancy in early pregnancy is -33.28*[Preg17olS]+1.64*[PM6S]-2.23*[PregS]+1.44*[PMS]-0.09*[PM3S+]+2.

48. Values ​​above the cutoff value are considered high risk, and values ​​below the cutoff value are considered low risk. The cutoff value is 0.

35. The formula for predicting the risk of neonatal respiratory distress syndrome associated with intrahepatic cholestasis of pregnancy in early pregnancy is 33.56*[Preg17olS]-2.38*[PM6S]+2.11*[PregS]+0.16*[PMS]-0.56*[PM3S+]-4.

46. Values ​​above the cutoff value are considered high risk, and values ​​below the cutoff value are considered low risk. The cutoff value is 0.

14. The formula for predicting the risk of intrahepatic cholestasis of pregnancy in the second trimester is -12.98*[Preg17olS]-0.24*[PM6S]-1.14*[PregS]+1.69*[PMS]+0.54*[PM3S+]+0.01*[ALT]-0.001*[AST]+0.02*[TBA]-0.

98. Values ​​above the cutoff value are considered high risk, and values ​​below the cutoff value are considered low risk. The cutoff value is 0.

40. The formula for predicting the risk of premature birth related to intrahepatic cholestasis of pregnancy in the second trimester is 0.94*[Preg17olS]-0.29*[PM6S]-2.28*[PregS]+1.47*[PMS]+0.65*[PM3S+]+0.

42. Values ​​above the cutoff value are considered high risk, and values ​​below the cutoff value are considered low risk. The cutoff value is 0.

19. The formula for predicting the risk of neonatal jaundice related to intrahepatic cholestasis of pregnancy in the second trimester is -19.63*[Preg17olS]+2.67*[PM6S]-2.38*[PregS]+0.72*[PMS]-0.02*[PM3S+]+2.

43. Values ​​above the cutoff value are considered high risk, and values ​​below the cutoff value are considered low risk. The cutoff value is 0.

44. The formula for predicting the risk of neonatal respiratory distress syndrome associated with intrahepatic cholestasis of pregnancy in the second trimester is 72.42*[Preg17olS]+2.72*[PM6S]-1.53*[PregS]-1.11*[PMS]-0.45*[PM3S+]+0.

85. Values ​​above the cutoff value are judged as high risk, and values ​​below the cutoff value are judged as low risk. The cutoff value is 0.

27.

4. The prognostic risk model according to claim 2, characterized in that: The method for constructing the prognostic risk model comprises the following steps: Obtaining the plasma level data and clinical follow-up data of the metabolite marker according to claim 1, and constructing a prognostic risk model based on the level data and clinical follow-up data of the metabolite marker; Preferably, the prognostic risk model is determined using one or more algorithms selected from the following: generalized linear model, principal component analysis, logistic regression analysis, LASSO regression analysis, nearest neighbor analysis, support vector machine, neural network model, random forest model; Preferably, the prognostic risk model is determined using a generalized linear model.

5. Use of a reagent for detecting the level of the metabolite marker according to claim 1 in a sample in the preparation of a product for predicting the prognosis of intrahepatic cholestasis of pregnancy, characterized in that: The product includes a test kit, a chip, a test strip, or a device, an equipment, or a computer-readable storage medium.

6. The use according to claim 5, characterized in that: The reagents for detecting the level of the metabolite markers according to claim 1 in the sample include reagents used in one or more of the following methods: mass spectrometry, nuclear magnetic resonance spectroscopy, liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry, surface enhanced Raman spectroscopy, matrix-assisted laser desorption / ionization mass spectrometry, quantitative mass spectrometry imaging, surface-assisted laser desorption / ionization mass spectrometry; Preferably, the product further comprises a sample pretreatment reagent; Preferably, the sample includes serum, plasma, urine, saliva, cerebrospinal fluid, lymph fluid, amniotic fluid, follicular fluid, synovial fluid, milk, tears, semen, feces, intestinal extracts, and cell or tissue extracts; Preferably, the sample is plasma.

7. A prognostic assessment system for intrahepatic cholestasis of pregnancy, characterized in that: The prognosis assessment system comprises an analysis unit, which uses the prognosis risk model according to any one of claims 2 to 4 to perform analysis and scoring; Preferably, the prognosis assessment system further comprises an input unit and an output unit; Preferably, the input unit is used to input the absolute level data of the metabolite marker according to claim 1 in the sample to be tested; Preferably, the input unit is also used to input clinical characteristic data of the subject corresponding to the sample to be tested; Preferably, the clinical characteristic data includes the subject's gestational stage; Preferably, the output unit is used to output the risk value of intrahepatic cholestasis of pregnancy and related adverse events in the subject corresponding to the sample to be tested.

8. A prognosis assessment device for intrahepatic cholestasis of pregnancy, characterized in that: The prognosis assessment device includes a memory and a processor; The memory is used to store program instructions; The processor is used to execute program instructions. When the program instructions are executed, the processor is used to perform the following operations: obtaining the absolute level data of the metabolite marker described in claim 1 in the sample to be tested and the clinical characteristic data of the subject corresponding to the sample to be tested, inputting the relative expression level data of the metabolite marker described in claim 1 and the clinical characteristic data of the subject corresponding to the sample to be tested into the prognostic risk model described in any one of claims 2-4, and obtaining the prognostic evaluation result of the sample to be tested; Preferably, the prognostic evaluation result is a risk value of intrahepatic cholestasis of pregnancy and related adverse events in the subject corresponding to the sample to be tested.

9. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the following method when executed by a processor: obtaining the relative expression level data of the metabolite marker according to claim 1 in the sample to be tested and the clinical characteristic data of the subject corresponding to the sample to be tested, inputting the relative expression level data of the metabolite marker according to claim 1 and the clinical characteristic data of the subject corresponding to the sample to be tested into the prognostic risk model according to any one of claims 2 to 4, and obtaining the prognostic evaluation result of the sample to be tested; Preferably, the prognostic evaluation result is a risk value of intrahepatic cholestasis of pregnancy and related adverse events in the subject corresponding to the sample to be tested.

10. Use of the prognostic risk model according to any one of claims 2 to 4, the prognostic evaluation system according to claim 7, the prognostic evaluation device according to claim 8 and / or the computer-readable storage medium according to claim 9 in the preparation of a prognosis prediction product for intrahepatic cholestasis of pregnancy.

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