WO2024077957A1 - 用于先兆子痫风险预测、评估或诊断的生物标志物、试剂盒及方法 - Google Patents

用于先兆子痫风险预测、评估或诊断的生物标志物、试剂盒及方法 Download PDF

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WO2024077957A1
WO2024077957A1 PCT/CN2023/095230 CN2023095230W WO2024077957A1 WO 2024077957 A1 WO2024077957 A1 WO 2024077957A1 CN 2023095230 W CN2023095230 W CN 2023095230W WO 2024077957 A1 WO2024077957 A1 WO 2024077957A1
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eclampsia
subject
risk
biomarker
preeclampsia
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PCT/CN2023/095230
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French (fr)
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WO2024077957A9 (zh
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陈利民
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天津云检医疗器械有限公司
天津云检医学检验所有限公司
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Publication of WO2024077957A1 publication Critical patent/WO2024077957A1/zh
Publication of WO2024077957A9 publication Critical patent/WO2024077957A9/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/74—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving hormones or other non-cytokine intercellular protein regulatory factors such as growth factors, including receptors to hormones and growth factors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/46—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans from vertebrates
    • G01N2333/47—Assays involving proteins of known structure or function as defined in the subgroups
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/705—Assays involving receptors, cell surface antigens or cell surface determinants
    • G01N2333/71—Assays involving receptors, cell surface antigens or cell surface determinants for growth factors; for growth regulators
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/36—Gynecology or obstetrics
    • G01N2800/368—Pregnancy complicated by disease or abnormalities of pregnancy, e.g. preeclampsia, preterm labour
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/50—Determining the risk of developing a disease

Definitions

  • the present invention relates to the field of detection, and specifically to a biomarker group and related kits or devices for predicting, evaluating or diagnosing the risk of pre-eclampsia.
  • the present invention also relates to a method for predicting and evaluating whether a subject has a risk of developing pre-eclampsia or diagnosing whether a subject has pre-eclampsia, and a method for screening a biomarker group for predicting, evaluating or diagnosing the risk of pre-eclampsia.
  • PE Preeclampsia
  • PE is a systemic multisystem syndrome unique to pregnancy, with an incidence of approximately 2% to 8%, accounting for 10% to 15% of maternal deaths.
  • PE is also one of the main causes of premature birth, neonatal illness or death. Due to climate, dietary habits, and different levels of diagnosis and treatment, there are large differences in the incidence levels in different regions. Early prediction and accurate identification of the risk of disease are crucial to optimizing the management of preeclampsia, effectively reducing the morbidity and mortality of the disease, and improving the outcomes of PE.
  • preeclampsia According to the time of occurrence of preeclampsia, it is divided into early-onset preeclampsia (20 +0 -33 +6 weeks) and late-onset preeclampsia (34 +0 weeks-delivery). Early-onset preeclampsia has a typical placental pathological basis, is often accompanied by fetal growth restriction, and is associated with severe adverse maternal and fetal outcomes. Late-onset preeclampsia is closely related to maternal factors (obesity, diabetes), maternal complications are relatively mild and fetal prognosis is relatively good. According to the time of delivery of preeclampsia, it is divided into: premature delivery of preeclampsia (delivery ⁇ 37 +0 weeks) and full-term delivery of preeclampsia (delivery ⁇ 37 +0 weeks).
  • preeclampsia in the Guidelines for the Diagnosis and Treatment of Hypertensive Disorders Complicating Pregnancy (2020) (hereinafter referred to as the Guidelines) is as follows: after 20 weeks of pregnancy, pregnant women have systolic blood pressure ⁇ 140 mmHg and/or diastolic blood pressure ⁇ 90 mmHg, accompanied by any of the following: urine protein quantification ⁇ 0.3g/24h, or urine protein/creatinine ratio ⁇ 0.3, or random urine protein ⁇ (+) (examination method when protein quantification is unconditional); no proteinuria but accompanied by any of the following organs or systems involved: important organs such as the heart, lungs, liver, and kidneys, or abnormal changes in the blood system, digestive system, and nervous system, placenta-fetus involvement, etc. This is considered the "gold standard" for the diagnosis of preeclampsia.
  • the sFlt-1/PlGF ratio has clinical value for short-term prediction of preeclampsia.
  • the negative predictive value excluding preeclampsia within 1 week
  • the positive predictive value predicting preeclampsia within 4 weeks
  • the method of predicting preeclampsia by the sFlt-1/PlGF ratio is too late in gestation, which is much later than the aspirin taking time recommended by the Guidelines (16 weeks of gestation), which is not conducive to the early prevention of PE.
  • the sFlt-1/PlGF ratio is mostly used to exclude the risk of PE within 1 week, and multiple tests are required throughout the pregnancy, which results in high costs for pregnant women.
  • preeclampsia Although the existing methods for predicting preeclampsia have certain clinical value, none of them is effective and highly specific. If preeclampsia can only be predicted after 20 weeks of pregnancy, the best time to take aspirin for prevention will be missed.
  • the purpose of the present invention is to find biomarkers for predicting the risk of eclampsia in early pregnancy, and thereby establish products and methods for assessing the risk of PE in early pregnancy.
  • the invention provides a panel of biomarkers comprising Endoglin, sVEGFR2 and RBP4.
  • the biomarker group is used for disease risk prediction or assessment or disease diagnosis, preferably for pre-eclampsia related condition assessment, more preferably for pre-eclampsia risk prediction or Evaluation or diagnosis of preeclampsia.
  • the present invention provides a kit or device comprising a detection reagent for detecting the expression level of a biomarker in a biomarker group in a sample of a subject, wherein the biomarker group includes Endoglin, sVEGFR2 and RBP4.
  • the biomarker group is used for disease risk prediction or assessment or disease diagnosis, preferably for assessment of pre-eclampsia related conditions, more preferably for pre-eclampsia risk prediction or assessment or pre-eclampsia diagnosis.
  • the present invention provides a method for screening a biomarker panel for the prediction or assessment of the risk of pre-eclampsia or the diagnosis of pre-eclampsia, comprising the following steps:
  • the present invention provides a method for predicting whether a subject is at risk of developing pre-eclampsia, comprising:
  • the present invention provides a method for assessing a subject's risk of developing pre-eclampsia, comprising:
  • the present invention provides a method for diagnosing whether a subject has pre-eclampsia, comprising:
  • the present invention provides a use of a biomarker group comprising Endoglin, sVEGFR2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker group, in the preparation of a kit or device for predicting whether a subject has a risk of developing pre-eclampsia, wherein the prediction comprises:
  • the present invention provides a biomarker group including Endoglin, sVEGFR2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker group, for use in preparing a kit or device for assessing a subject's risk of developing pre-eclampsia, wherein the assessment comprises:
  • the present invention provides a biomarker group including Endoglin, sVEGFR2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker group, for use in preparing a kit or device for diagnosing whether a subject has pre-eclampsia, wherein the diagnosis comprises:
  • the sample is a body fluid sample, preferably a blood, serum or plasma sample.
  • the expression level of the biomarker is the expression level at the protein level or the nucleic acid level.
  • the subject is a pregnant subject with a gestational age of 6 to 40 weeks, such as 6 to 13 weeks, such as 11 to 13 weeks, such as 20 to 40 weeks, such as 23 to 33 weeks, such as 34 to 40 weeks.
  • the pregnant subject has a gestational age of 6 to 13 weeks, such as 11 to 13 weeks.
  • the pre-eclampsia is pre-term pre-eclampsia.
  • the formula is
  • ⁇ is between -5.487 and -1.261
  • ⁇ 1 is between 0.041 and 0.304
  • ⁇ 2 is between 0.001 and 0.086
  • ⁇ 3 is between 0.025 and 0.172.
  • the threshold is between 0.350 and 0.394, or any simple adjustment resulting from a simple adjustment of the formula.
  • the formula is
  • the threshold is 0.379, or any simple adjustment resulting from a simple adjustment of the formula.
  • the pregnant subject has a gestational age of 20 to 40 weeks.
  • the formula is
  • ⁇ is between -1.537 and -1.399
  • ⁇ 1 is between 0.129 and 0.403
  • ⁇ 2 is between -0.163 and -0.004
  • ⁇ 3 is between -0.029 and 0.000.
  • the pregnant subject has a gestational age of between 23 and 33 weeks.
  • the pre-eclampsia is early-onset eclampsia.
  • the threshold is between 0.550 and 0.781, or any simple adjustment resulting from a simple adjustment of the formula.
  • the formula is
  • the threshold is 0.761, or any simple adjustment resulting from a simple adjustment of the formula.
  • the pregnant subject has a gestational age of between 34 and 40 weeks.
  • the pre-eclampsia is late-onset pre-eclampsia.
  • the threshold is between 0.556 and 0.773, or any simple adjustment resulting from a simple adjustment of the formula.
  • the formula is
  • the threshold is 0.723, or any other simple adjustment thereof resulting from a simple adjustment of the formula.
  • the present invention has successfully screened biomarkers related to preeclampsia, which can more accurately predict the risk of preeclampsia at 5-25 weeks of pregnancy, especially 11-13+6 weeks, filling the gap in preeclampsia risk prediction reagents at home and abroad.
  • the prediction does not require the combination of other indicators including maternal risk factors, mean arterial pressure (MAP), pregnancy-associated protein A (PAPPA) and uterine artery pulsatility index (UTPI), and has a high PE prediction accuracy in early pregnancy.
  • MAP mean arterial pressure
  • PAPPA pregnancy-associated protein A
  • UTPI uterine artery pulsatility index
  • sFlt-1/PlGF can only be used for short-term prediction and auxiliary prediction of preeclampsia after 20 weeks of pregnancy, and multiple tests are required.
  • the method of the present invention can predict the risk of preeclampsia during the entire pregnancy period and is suitable for all pregnant women undergoing prenatal examinations.
  • each intermediate value between the upper and lower limits of the range is also specifically disclosed.
  • Each smaller range between any of the values or intermediate values in the range and any other values or intermediate values in the range is included in the present invention.
  • the upper and lower limits of these smaller ranges may be independently included in the range or excluded from the range, and each range in which any limit, no limit, or both limits are included in the smaller range is also included in the present invention, subject to any explicitly excluded limits in the range.
  • the range includes one or two limits, the range excluding any one or both of those included limits is also included in the present invention.
  • Pre-eclampsia is also known as “pre-eclampsia”, which is a precursor to eclampsia.
  • Pre-eclampsia risk refers to the statistically significantly increased probability that a pregnant subject with a risk of pre-eclampsia will develop pre-eclampsia within a future prognostic window compared to a pregnant subject without a risk of pre-eclampsia.
  • the probability is at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, at least 99% or up to 100%.
  • the biomarker group for the prediction, assessment or diagnosis of the risk of pre-eclampsia of the present invention includes Endoglin, sVEGFR2 and RBP4.
  • Endoglin is a soluble receptor of the transforming growth factor ⁇ (TGF- ⁇ ) subtype.
  • TGF- ⁇ transforming growth factor ⁇
  • Preeclampsia patients overexpress Endoglin protein in the placenta, which causes an increase in the level of Endoglin protein in the circulation, blocking the angiogenesis and vasodilation effects of TGF- ⁇ , causing angiogenesis disorders and endothelial damage.
  • TGF- ⁇ transforming growth factor ⁇
  • vascular endothelial growth factor receptor plays a key role in promoting angiogenesis and regulation.
  • the expression level of sVEGFR2 is reduced in patients with preeclampsia.
  • the risk of preeclampsia in pregnant women can be predicted, which can provide guidance for intervention throughout the pregnancy.
  • retinol binding protein As a new adipokine, retinol binding protein (RBP4) is closely related to the regulation of glucose and lipid metabolism and insulin resistance. Preeclampsia may be related to insulin resistance and hyperinsulinemia. Elevated serum RBP4 levels may lead to endothelial dysfunction, weaken nitrous oxide-dependent vasodilation, aggravate vascular lesions, and thus lead to the occurrence of preeclampsia.
  • the present invention found that the change of Endoglin/sVEGFR2/RBP4 concentration is significantly earlier than the onset of preeclampsia, and the calculated value of Endoglin/sVEGFR2/RBP4 can better reflect the growth of blood vessels.
  • the expression of Endoglin/sVEGFR2/RBP4 is jointly detected and the calculated value is obtained, which has good risk prediction, evaluation or diagnosis value and guiding significance for preeclampsia.
  • the present invention may also include other biomarkers for risk prediction, assessment or diagnosis of preeclampsia, provided that the calculated values of these biomarkers and Endoglin/sVEGFR2/RBP4 can have good risk prediction, assessment or diagnostic value and guiding significance for preeclampsia.
  • the present invention accordingly develops a kit or device for disease diagnosis or disease risk prediction or assessment, preferably for pre-eclampsia related condition assessment, more preferably for pre-eclampsia diagnosis or risk prediction or assessment.
  • the kit or device includes a detection reagent for detecting the expression amount of a biomarker in a biomarker group in a subject sample, and the biomarker group includes Endoglin, sVEGFR2 and RBP4.
  • the term "subject” refers to an animal, preferably a mammal, more preferably a human.
  • the subject in the present invention needs to be a pregnant subject.
  • the subject of the present invention should not show symptoms of pre-eclampsia.
  • Such pre-eclampsia symptoms are preferably clinical symptoms specifically described in other parts of the present invention. More preferably, the symptoms include at least one symptom selected from the following: upper abdominal pain, headache, visual disturbances, edema.
  • the subject of the present invention may also show at least one of the above symptoms, and thus has been suspected of suffering from pre-eclampsia.
  • sample refers to a body fluid sample, an isolated cell sample, or a sample from a tissue or organ.
  • Body fluid samples can be obtained by known techniques, and preferably include blood, plasma, serum or urine samples. More preferably, blood, plasma or serum samples are used.
  • Tissue or organ samples can be obtained from any tissue or organ, for example, by biopsy. Separated cells can be obtained from body fluids, tissues or organs by separation techniques, such as centrifugation or cell sorting.
  • the cell, tissue or organ sample is obtained from those cells, tissues or organs that express or produce the peptides described in the present invention.
  • expression amount refers to the protein or nucleic acid expression level of a biomarker, and the nucleic acid includes, for example, DNA or RNA.
  • expression amount preferably refers to the protein expression level of a biomarker.
  • determining the expression of the biomarker of the present invention can be achieved by all known means. Taking protein expression as an example, it can be measured directly or indirectly. Direct measurement involves measuring the amount or concentration of the protein based on the signal obtained by the protein and the signal intensity directly related to the number of molecules of the protein in the sample. Indirect measurement includes measuring the signal obtained by, for example, a ligand, a label or an enzyme reaction product.
  • the amount of the protein can be determined by all known means for determining the amount of the protein in the sample.
  • the means include the use of labeled molecules, in various sandwich methods, competitive methods, or other assay forms of immunoassay equipment and methods.
  • the assay will produce a signal indicating whether the protein is present.
  • the intensity of the signal is preferably directly or indirectly related to the amount of the protein in the sample (e.g., inversely proportional).
  • Other suitable methods include measuring physical or chemical properties specific to the protein, such as its accurate molecular weight or NMR spectrum.
  • the method includes, preferably biosensors, optical devices coupled to immunoassays, biochips, analytical equipment, such as mass spectrometers, NMR-analyzers, or chromatographic equipment, etc.
  • the protein detection of the present invention is based on the acridinium ester chemiluminescence immunology of magnetic particles.
  • Acridinium ester markers have a special group that produces luminescence in the chemical structure. After adding the excitation liquid in the luminescent immunoassay process, they can directly participate in the luminescence reaction without the need for substrate liquid. Usually, such substances have no background luminescence and are a type of luminescent agent with high luminescence efficiency.
  • Acridinium esters or acridinium sulfonamides can be combined with antibodies (or antigens) to produce markers with strong chemiluminescence activity and high immunoreaction specificity. Acridinium esters are usually labeled on the amino groups of antibodies or antigens.
  • Magnetic particles are microspheres or particles polymerized from high molecular monomers, with diameters mostly in the micrometer or millimeter range, and their surfaces carry functional groups that can bind to antibodies or antigens, such as amino groups, carboxyl groups, hydroxyl groups, etc., so they can be chemically coupled by a specific coupling method, and have the advantages of strong binding force and large capacity.
  • the magnetic particles can be evenly dispersed in the reaction solution, with a large specific surface area, which is conducive to accelerating the reaction and improving the reaction rate.
  • the present invention adopts a coupling method in which the antibody is directly coated on magnetic particles and the antibody is directly labeled with an acridinium ester.
  • a biotin-streptavidin system the operation is simple, the repeatability is good, the coupling efficiency is high, the luminescent signal is strong, and it is convenient for large-scale application.
  • This method can obtain higher sensitivity and a wide linear range.
  • the joint inspection project kit under the magnetic particle-acridinium ester system platform can simultaneously detect the three projects of Endoglin/sVEGFR2/RBP4, and the results are determined by formula calculation. It can assist the clinic to predict preeclampsia earlier and faster, predict adverse pregnancy outcomes, and help doctors identify and treat high-risk groups, thereby ensuring the safety of mothers and infants during pregnancy.
  • a suitable "detection reagent” may be a ligand that specifically binds to at least one marker in a subject sample to be studied by the method of the present invention, such as an antibody that specifically binds to Endoglin, sVEGFR2 or RBP4.
  • the detection reagent before measuring the amount of the complex formed between the detection reagent and the at least one marker, the sample is separated from the complex. Therefore, on the one hand, the detection reagent can be immobilized on a solid support. On the other hand, the sample can be separated from the complex formed on the solid support by applying a cleaning solution. The complex formed is proportional to the amount of at least one marker present in the sample. It can be understood that the specificity and/or sensitivity of the detection reagent to be used determines the degree of proportion of at least one marker that can be specifically bound contained in the sample.
  • Determining the amount of a protein may preferably include the following steps: (a) contacting the protein with a specific ligand, (b) preferably removing unbound ligand, and (c) measuring the amount of bound ligand.
  • the bound ligand will generate an intensity signal.
  • Binding in the present invention includes covalent and non-covalent binding.
  • the ligand in the present invention can be any compound that binds to the protein in the present invention, such as a peptide, polypeptide, nucleic acid or small molecule.
  • Preferred ligands include antibodies, nucleic acids, peptides or polypeptides, such as receptors or binding partners of the protein and fragments thereof containing the binding domain of the protein, and aptamers, such as nucleic acid or peptide aptamers.
  • Methods for preparing such ligands are well known in the art. For example, identification and production of suitable antibodies and aptamers can be provided by suppliers.
  • a person of ordinary skill in the art is familiar with methods for developing derivatives of the above-mentioned ligands with higher affinity and specificity. For example, random mutations can be introduced into the nucleic acid, peptide or polypeptide. The binding force of the resulting derivatives is then tested by screening procedures known in the art, such as phage display.
  • Antibodies referred to in the present invention include polyclonal antibodies and monoclonal antibodies and fragments thereof, such as Fv, Fab and F(ab)2 fragments that can bind to antigens or haptens.
  • the present invention also includes single chain antibodies, as well as humanized hybrid antibodies, in which the amino acid sequence of a non-human donor antibody showing the desired antigen specificity is combined with the amino acid sequence of a human acceptor antibody.
  • the donor sequence generally includes at least the antigen-binding amino acid residues of the donor, but may also include other structural and/or functional related residues of the donor antibody. Amino acid residues.
  • the hybrid can be prepared by several methods known in the art. Preferably, the ligand or reagent binds specifically to the protein.
  • Specific binding refers to that the ligand or reagent does not substantially bind to other proteins or substances present in the sample to be analyzed, i.e., cross-reaction occurs.
  • the specific binding protein has a binding affinity that is at least 3 times stronger, more preferably at least 10 times, and even more preferably at least 50 times stronger than any other related protein. If, for example, it can still be clearly distinguished and measured based on its size on Western Blot, or by its relatively higher abundance in the sample, then the non-specific binding may be tolerable.
  • the binding of the ligand can be measured by any method known in the art. Preferably, the method is semi-quantitative or quantitative.
  • Examples of devices of the present invention include clinical chemistry analyzers for detecting chemical or biological reaction results or monitoring the progress of chemical or biological reactions, coagulation chemistry analyzers, immunochemistry analyzers, urine analyzers, nucleic acid analyzers, test kits, and the like.
  • the embodiment of the device may include one or more analyzer units for practicing the subject matter of the present invention.
  • the analyzer unit of the device disclosed in the present invention may be operably communicated with the computing unit disclosed in the present invention by any known connection mode.
  • the analyzer unit may include a sample detection for predictive purposes in a larger device, such as an independent device or element of one or both of qualitative and/or quantitative evaluation.
  • the analyzer unit can perform or assist the pipetting, metering, and mixing of samples and/or reagents.
  • the analyzer unit may include a reagent clamping unit for clamping reagents for determination.
  • the arrangement of reagents may be, for example, in a container or box containing a single reagent or a group of reagents, placed in a suitable holder or position in a storage room or conveyor.
  • the detection reagent may also be fixed on a solid support in contact with the sample.
  • the analyzer unit may also include a processing and/or detection component optimized for a specific analysis.
  • the analyzer unit may be configured to optically detect an analyte, such as a marker, in a sample.
  • analyzer units for optical detection include devices configured to convert electromagnetic energy into electrical signals, including single and multi-element or array optical detectors.
  • the optical detector can monitor the photoelectromagnetic signal and provide an electrical output signal representing the presence and/or concentration of the analyte in the sample placed in the optical path, or a response signal relative to a baseline signal.
  • the device may also include, for example, a photodiode, including an avalanche photodiode, a phototransistor, a photoconductivity detector, a linear sensor array, a CCD detector, a CMOS detector, including a CMOS array detector, a photomultiplier tube, and a photomultiplier tube array.
  • a photodiode including an avalanche photodiode, a phototransistor, a photoconductivity detector, a linear sensor array, a CCD detector, a CMOS detector, including a CMOS array detector, a photomultiplier tube, and a photomultiplier tube array.
  • an optical detector such as a photodiode or a photomultiplier tube may include additional signal conditioning or processing electrical components.
  • the optical detector may include at least one preamplifier, electronic filter, or integrated circuit. Suitable preamplifiers include, for example, integrating, transimpedance, and current gain (current mirror) preampli
  • one or more analyzer units of the present invention may include a light source for emitting light.
  • the light source of the analyzer unit may be composed of at least one light emitting element (e.g., a light emitting diode, a power emitting source such as an incandescent lamp, an electroluminescent lamp, a gas discharge lamp, a high intensity discharge lamp, a laser) for measuring the concentration of an analyte in a sample to be tested, or enabling energy conversion (e.g., by fluorescence resonance energy transfer or catalytic enzymes).
  • a light emitting element e.g., a light emitting diode, a power emitting source such as an incandescent lamp, an electroluminescent lamp, a gas discharge lamp, a high intensity discharge lamp, a laser
  • energy conversion e.g., by fluorescence resonance energy transfer or catalytic enzymes.
  • the analyzer unit of the device may include one or more incubation units (e.g., for maintaining samples or reagents at a specific temperature or temperature range).
  • the analyzer unit may include a thermal cycler for subjecting the sample to repeated temperature cycles and monitoring changes in the amount of amplification product in the sample, including a real-time thermal cycler.
  • the analyzer unit of the device disclosed herein may also include or be operably connected to a reaction vessel or cuvette delivery unit.
  • delivery units include liquid processing units, such as pipetting units, for delivering samples and/or reagents to reaction vessels.
  • the pipetting unit may include a reusable washable needle, such as a steel needle, or a disposable pipetting head.
  • the analyzer unit may also include one or more mixing units, such as an oscillator for oscillating a cuvette containing a liquid, or a stirring paddle for mixing liquids in a cuvette or reagent container.
  • the present invention also relates to a device suitable for predicting whether a pregnant subject is at risk of suffering from pre-eclampsia by implementing the above method, comprising:
  • an analyzer unit comprising detection reagents that specifically bind to Endoglin, sVEGFR2, and RBP4, said unit being suitable for determining the expression amount of Endoglin, sVEGFR2, and RBP4 in a sample of a pregnant subject;
  • An evaluation unit comprising a data processor having an execution algorithm for carrying out the following steps:
  • the term "device” as used in the present invention relates to a system comprising the above-mentioned units operatively linked to each other, which allows the prediction according to the method of the present invention to be performed.
  • Preferred detection reagents that can be used in the analysis unit are disclosed in other parts of the present invention.
  • the analysis unit (or analyzer unit) preferably comprises a detection reagent in an immobilized form on a solid support, which will react with a sample containing the biomarker whose amount is to be determined.
  • the analysis unit may also include a detector for determining the amount of the detection reagent specifically bound to the biomarker. The determined amount may be transferred to the evaluation unit.
  • the evaluation unit includes a data processing element with an execution algorithm, such as a computer, which implements the steps of the method of the present invention by executing a computer-based algorithm, thereby performing ratio calculations, comparing the calculated ratios, and evaluating the comparison results, wherein the steps of the method of the present invention have been described in detail in other parts of the present invention.
  • the results may be given as parameterized predicted raw data outputs. It is understood that these data usually require interpretation by a doctor. However, it is also possible to foresee an expert system device, in which the above-mentioned output contains processed predicted raw data that does not require interpretation by a professional doctor.
  • kit used in the present invention refers to a collection of the various detection reagents and component components mentioned above, preferably, they are provided separately or in a single container.
  • the container also includes an operating guide for implementing the method of the present invention.
  • These operating guides can be in the form of a user manual or provided by a computer program code, which, when run on a computer or data processing device, can perform the calculations and comparisons in the method of the present invention and establish predictions accordingly.
  • the computer program code can be on a data storage medium or device, such as an optical storage medium (e.g., a CD), or directly on a computer or data processing device.
  • the kit may preferably contain a standard amount of biomarkers for calibration purposes, which are described in other parts of the present invention.
  • prediction relates to determining whether a subject is at risk of developing pre-eclampsia and is used to determine the likelihood of a subject developing the disease before symptoms develop (ie, assess the risk of developing the disease in the future).
  • assessment refers to determining whether a subject is at high or low risk for pre-eclampsia. Preferably, it should be determined whether the subject is at an elevated risk or a reduced risk compared to the average risk of the subject population. For subjects at sufficient risk (determined based on the test results), preventive interventions may be taken.
  • diagnosis is used herein to refer to the identification or classification of a molecular or pathological state, disease or illness (e.g., pre-eclampsia).
  • diagnosis may refer to the identification of a specific type of pre-eclampsia.
  • the “diagnosis” of the present invention may be combined with other diagnostic criteria provided in the Guidelines to provide additional information to help determine or verify the clinical status of a subject.
  • clinical performance is divided into sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
  • Positive predictive value (PPV) number of true positives/(number of true positives + number of false positives)*100%.
  • Negative predictive value (NPV) number of true negatives/(number of true negatives + number of false negatives)*100%.
  • the present invention uses a formula to calculate the risk score of preeclampsia based on the expression of the biomarker.
  • the calculation formula can be based on different algorithms, such as an elastic network regression algorithm.
  • the present invention uses the category of the sample, i.e., preeclampsia or normal, as the dependent variable, and the sample eigenvalue matrix as the independent variable, defines the objective function, and performs modeling to form a preeclampsia risk score formula, such as
  • e is a natural constant
  • ⁇ , ⁇ 1, ⁇ 2 and ⁇ 3 are characteristic coefficients
  • Endoglin, sVEGFR2 and RBP4 are the expression levels of the corresponding biomarkers.
  • the preeclampsia risk score formula is only an example and should not be construed as a limitation on the technical solution of the present invention. Based on the biomarkers determined by the present invention, those skilled in the art can construct a suitable preeclampsia risk score formula according to the different target populations, sample conditions, clinical use scenarios, clinical performance requirements, etc.
  • the present invention uses AUC (area under the ROC curve) as the evaluation index of the model to verify the constructed model.
  • Factors such as the level of AUC and clinical use scenarios may affect the above-mentioned preeclampsia risk score formula, such as the numerical values of characteristic coefficients ⁇ , ⁇ 1, ⁇ 2 and ⁇ 3.
  • AUC area under the ROC curve
  • Factors such as the level of AUC and clinical use scenarios may affect the above-mentioned preeclampsia risk score formula, such as the numerical values of characteristic coefficients ⁇ , ⁇ 1, ⁇ 2 and ⁇ 3.
  • an index of AUC greater than 0.85 ⁇ , ⁇ 1, ⁇ 2 and ⁇ 3 can be adjusted within a certain range.
  • With an index of AUC greater than 0.9 the ranges of ⁇ , ⁇ 1, ⁇ 2 and ⁇ 3 may change accordingly.
  • the numerical ranges of ⁇ , ⁇ 1, ⁇ 2 and ⁇ 3 in the present invention are only examples and should not be construed
  • thresholds can be determined according to different clinical performance requirements. For example, the sensitivity can reach more than 90%, the specificity can reach more than 90%, and the NPV can reach more than 90%. Based on the requirements above, the threshold value that can make PPV reach the highest is selected as the threshold value of this clinical use scenario. When the clinical use scenario and clinical performance requirements change, the threshold value will also change accordingly. Since the values of characteristic coefficients ⁇ , ⁇ 1, ⁇ 2 and ⁇ 3 are all within a range, the threshold value can be fixed within a range according to the fixed values of the two ends of each characteristic coefficient.
  • the pre-eclampsia risk score formula of the present invention can be adjusted arbitrarily based on its results. Still taking the above pre-eclampsia risk score formula as an example, since its calculation result is within the range of 0-1, it can be adjusted arbitrarily, such as but not limited to, multiplying by a multiple such as 10, 100, adding a constant such as 1, 2, etc., to increase its readability and ease of operation.
  • the threshold value of the present invention can also be arbitrarily adjusted with simple adjustments to the above formula, such as but not limited to, multiplying by a multiple such as 10, 100, adding a constant such as 1, 2, etc., to increase its readability and ease of operation.
  • Figure 1 shows the biomarker discovery and validation process.
  • FIG. 2 shows ROC curves of each of the biomarkers Endoglin, sVEGFR2, and RBP4.
  • FIG3 shows the ROC curve of the optimal preeclampsia risk model in the clinical use scenario of 11 + 0 to 13 + 6 weeks.
  • FIG4 shows the ROC curve of the optimal preeclampsia risk model in the clinical use scenario of 20 + 0 to 33 + 6 weeks.
  • FIG5 shows the ROC curve of the optimal preeclampsia risk model in the clinical use scenario of 34 +0 to delivery.
  • the names and abbreviations of the experimental methods, production processes, instruments and equipment involved in the embodiments of the present invention are all conventional names in the field and are very clear and unambiguous in the relevant application fields. Technicians in the field can understand the conventional process steps and apply the corresponding equipment according to the names and implement them according to conventional conditions or conditions recommended by the manufacturer.
  • the candidate biomarkers were further screened to obtain candidate biomarkers whose expression levels changed in the samples of pregnant subjects, among which the up-regulated ones included PAPPA2, SERPING1, SDC1, ENG, C1QTNF3, INHBE, VSIG4, DENND10, LHX5, CASP8, PTX3, CGB3, BNC2, ANGPTL6, etc.; the down-regulated ones included HBA1, IGHG3, SH3BGRL3, IGLC3, CLCN6, FLNA, IGKV2D-40, IGKV3D-20, MRT, etc.;
  • the up-regulated ones included PAPPA2, SERPING1, SDC1, ENG, C1QTNF3, INHBE, VSIG4, DENND10, LHX5, CASP8, PTX3, CGB3, BNC2, ANGPTL6, etc.
  • the down-regulated ones included HBA1, IGHG3, SH3BGRL3, IGLC3, CLCN6, FLNA, IGKV2D-40, IGKV3D-20,
  • Example 2 Construction of a gestational age prediction model from 6 to 14 weeks
  • One of the main purposes of the present invention is to use a prediction model constructed based on three biomarkers to assess the risk of preeclampsia in early pregnancy.
  • the three biomarkers are Endoglin, sVEGFR2 and RBP4.
  • Step 1 Determine 252 samples for model training, including 84 samples of preeclampsia patients and 168 samples of normal pregnancy. The gestational age of all samples was between 6 and 14 weeks.
  • Step 2 Perform univariate analysis on each biomarker, and select the markers with significant differences from preeclampsia according to p value ⁇ 0.05, difference multiple > 1.2 or ⁇ 1/1.2, AUC > 0.60.
  • the difference multiple is a measure that describes the quantitative change between measurement A and measurement B, which is defined as the ratio between two quantities;
  • AUC is the area under the ROC curve, and the full name of the ROC curve is the receiver operating characteristic curve. It is based on a series of different cutoff values, with the true positive rate (sensitivity) as the ordinate and the false positive rate (1-specificity) as the abscissa.
  • AUC is a performance indicator for measuring the pros and cons of the learner, and its value range is between 0.5 and 1.
  • the measurement A and measurement B calculated by the difference multiple are the average expression of each biomarker of the pre-eclampsia sample and the average expression of each biomarker of the normal sample, respectively; the two categories distinguished by the classifier are pre-eclampsia samples and normal pregnancy samples, respectively.
  • Table 1 The detailed analysis results of the three biomarkers involved in the present invention are shown in Table 1, and the ROC curve diagram is shown in Figure 2.
  • Step 3 Use the three biomarkers with significant differences as features, perform supervised learning through the elastic network regression algorithm in the R language package Glmnet, optimize parameters based on three-fold cross validation, and build a model for predicting the risk of preeclampsia.
  • average cross validation when the sample mean square prediction error is the smallest, the model with the best performance can be obtained; when the average cross validation error is within a variance range, a model with excellent performance can be obtained.
  • Glmnet is a package for fitting generalized linear and similarity models by penalizing maximum likelihood.
  • the elastic net regression algorithm is a conventional algorithm, which is a hybrid of lasso regression and ridge regression. Lasso regression selects features, while ridge regression retains all features.
  • the elastic net combines lasso regression and ridge regression. It introduces L1 penalty and L2 penalty into the minimization process of the objective function at the same time. While obtaining sparse coefficients, it maintains the regularization property of ridge regression.
  • the cost function of the elastic net regression algorithm controls the size of the penalty term through two parameters ⁇ and ⁇ :
  • the Glmnet algorithm uses a cyclic coordinate descent method, which continuously optimizes the objective function while keeping each parameter fixed, and repeats the cycle until convergence.
  • the size of w when the cost function is minimized is:
  • the function cv.glmnet in the R package glmnet saves two lambda values: lambda.min and lambda.1se, where lambda.min is the lambda value that gives the minimum mean cross-validation error.
  • the other lambda value, lambda.1se gives a model that makes the error within one standard error of the minimum value.
  • the function coef in the R package glmnet can extract the corresponding characteristic coefficients based on the two stored lambda values.
  • the two sets of characteristic coefficients obtained at this time are the range boundary values of the model parameters in this case.
  • the present invention uses the category of the sample, i.e., pre-eclampsia, as a dependent variable, and the sample eigenvalue matrix as an independent variable to define an objective function, which includes regularization.
  • the main function of regularization is to prevent overfitting, and adding a regularization term to the model can limit the complexity of the model, so that the model reaches a balance between complexity and performance.
  • use the cv.glmnet function to select the elastic network algorithm for modeling.
  • the range of the parameter p is between 0 and 1, and p is a penalty coefficient.
  • AUC area under the ROC curve
  • the risk formula constructed based on the elastic network model is as follows:
  • ⁇ is the intercept
  • ⁇ 1, ⁇ 2 and ⁇ 3 are the coefficients of Endoglin, sVEGFR2 and RBP4 respectively.
  • ⁇ , ⁇ 1, ⁇ 2 and ⁇ 3 are within a certain range, and the specific range is shown in Table 2.
  • Step 4 Determine the model score threshold based on the clinical usage scenario.
  • the weeks are fixed at 11 + 0 to 13 + 6 weeks (corresponding to 11-13 gestational weeks), the preeclampsia patient samples are defined as preeclampsia patients with a gestational age of 37 weeks or earlier, and the normal control samples are defined as normal control personnel with a gestational age of 37 weeks or more.
  • the clinical use scenario is determined, there are 10 preeclampsia patient samples and 68 normal control samples.
  • the preeclampsia patient samples are randomly sampled with replacement to generate 20 diseased samples, and then, based on the incidence of preeclampsia premature delivery of 0.9%, the 68 normal control samples are randomly sampled with replacement to generate 2203 normal samples.
  • the risk score is calculated by the preeclampsia risk model, and according to the requirements of sensitivity of more than 90%, specificity of more than 90% and NPV of more than 90%, the cutoff value that can make PPV reach the highest is selected as the threshold of this clinical use scenario. Since all coefficients in the third step are within a range, the threshold range can be fixed at 0.350 to 0.394 according to the fixed values of both ends of each characteristic coefficient. The optimal specific performance of the risk score under this range of thresholds is shown in Table 3, and the optimal ROC curve is shown in Figure 3.
  • Example 3 Construction of a gestational age prediction model from 20 to 40 weeks
  • One of the main purposes of the present invention is to use a prediction model constructed based on three biomarkers to assess the risk of preeclampsia in early pregnancy.
  • the three biomarkers are Endoglin, sVEGFR2 and RBP4.
  • Step 1 Determine 63 samples for model training, including 32 samples of preeclampsia patients and 31 samples of normal pregnancy. The gestational age of all samples was between 20 and 40 weeks.
  • Step 2 Use the three locked biomarkers as features, perform supervised learning through the elastic network algorithm in the R language package Glmnet, optimize parameters based on three-fold cross validation, and build a model for predicting the risk of preeclampsia.
  • the model with the best performance is obtained when the sample mean square prediction error is the smallest; when the average cross validation error is within a variance range, a model with the best performance is obtained.
  • the risk formula constructed based on the elastic network model is as follows:
  • ⁇ is the intercept
  • ⁇ 1, ⁇ 2 and ⁇ 3 are the coefficients of Endoglin, sVEGFR2 and RBP4 respectively.
  • ⁇ , ⁇ 1, ⁇ 2 and ⁇ 3 can be adjusted within a certain range, and the specific range is shown in Table 4.
  • Step 3 Determine the model score threshold based on two different clinical usage scenarios.
  • the gestational age of the sample is fixed at 20 + 0 to 33 + 6 weeks (corresponding to 20-33 gestational weeks)
  • the preeclampsia patient sample is defined as the early-onset preeclampsia patient sample with a sample collection time of less than 34 weeks
  • the normal control sample is defined as the normal control personnel sample with a sample collection time of less than 34 weeks.
  • the risk score is calculated by the preeclampsia risk model, and according to the requirements of sensitivity reaching more than 90%, specificity reaching more than 90% and PPV reaching more than 90%, the cutoff value that can make NPV reach the highest is selected as the threshold of this clinical use scenario. Since each coefficient in the second step is within a range, according to the fixation of the two end values of each characteristic coefficient, the threshold range can be fixed at 0.550 to 0.781.
  • the optimal ROC curve is shown in Figure 4.
  • Clinical use scenario 2 fixes the gestational age at 34 + 0 (corresponding to 34 gestational weeks) to delivery.
  • Preeclampsia patient samples are defined as samples of late-onset preeclampsia patients whose sample collection time is after 34 weeks
  • normal control samples are defined as samples of normal control personnel whose sample collection time is after 34 weeks.
  • the risk score is calculated using the eclampsia risk model, and the specificity is more than 90% and The PPV reaches the requirement of more than 90%, and the cutoff value that can make NPV and sensitivity reach the highest is selected as the threshold of this clinical use scenario.
  • the threshold range can be fixed at 0.556 to 0.773.
  • the performance of the preeclampsia risk model under this range threshold is shown in Table 5, and the optimal ROC curve at this time is shown in Figure 5.
  • Magnetic separation remove the supernatant, dilute and resuspend the mixture to 0.5 mg/mL with magnetic particle buffer to complete the preparation of magnetic separation reagent.
  • Magnetic separation remove the supernatant, dilute and resuspend the mixture to 0.5 mg/mL with magnetic particle buffer to complete the preparation of magnetic separation reagent.
  • Magnetic separation remove the supernatant, dilute and resuspend the mixture to 0.5 mg/mL with magnetic particle buffer to complete the preparation of magnetic separation reagent.
  • the preparation method of the pre-excitation solution of this embodiment is as follows: 0.8L of purified water, 4.862mL of concentrated nitric acid and 5.46mL of 30% hydrogen peroxide are added to a 1L light-proof wide-mouth glass container in sequence, purified water is added to make the volume to 1L, stirred and mixed, and filtered to obtain the pre-excitation solution; the pH value is 1.10, and the concentrations of the components are as follows: nitric acid: 0.07M; hydrogen peroxide: 0.6%;
  • the method for preparing the excitation buffer in this embodiment is as follows: 0.8L purified water and 4.82g hexadecyltrimethylammonium bromide are added to a 1L wide-mouth glass container in sequence, stirred until the solid is completely dissolved, 28.056g potassium hydroxide is added, stirred until completely dissolved, purified water is added to make the volume to 1L, and the excitation buffer is obtained by filtering; the pH of the buffer B prepared by the above method is 13.5, wherein the concentration of each component is as follows: potassium hydroxide: 0.5M; hexadecyltrimethylammonium bromide: 0.478wt%.
  • the testing process is as follows:
  • the method of using the soluble Endoglin protein (Endoglin antibody) quantitative detection kit is as follows:
  • the three kits of Endoglin, sVEGFR2 and RBP4 of this patent were used to detect the levels of three serum markers, Endoglin, sVEGFR2 and RBP4, in the sera of the preeclampsia group and the normal pregnancy group, respectively, and data analysis and comparison were performed to obtain ratios to verify their specificity and sensitivity for predicting the incidence of preeclampsia.
  • the present invention includes, but is not limited to, the following technical solutions:
  • Biomarker panel including Endoglin, sVEGFR2 and RBP4.
  • the biomarker group of Item 1 is used for predicting or assessing disease risk or diagnosing a disease, preferably for assessing pre-eclampsia-related conditions, and more preferably for predicting or assessing pre-eclampsia risk or diagnosing pre-eclampsia.
  • Item 3 A kit or device comprising a detection reagent for detecting the expression level of a biomarker in a biomarker group in a subject sample, wherein the biomarker group includes Endoglin, sVEGFR2 and RBP4.
  • Item 4 The kit or device of Item 3, wherein the biomarker group is used to determine the risk of disease Prediction or assessment or disease diagnosis, preferably for assessment of pre-eclampsia related conditions, more preferably for prediction or assessment of pre-eclampsia risk or diagnosis of pre-eclampsia.
  • a method for screening a biomarker panel for the prediction or assessment of the risk of pre-eclampsia or the diagnosis of pre-eclampsia comprising the following steps:
  • Item 6 The method of Item 5, wherein the biomarker group includes Endoglin, sVEGFR2 and RBP4.
  • Item 7 A method for predicting whether a subject is at risk of developing pre-eclampsia, comprising:
  • Item 8 Methods for assessing a subject's risk of developing pre-eclampsia, including:
  • Item 9 A method for diagnosing whether a subject has preeclampsia, comprising:
  • Item 10 Use of a biomarker group comprising Endoglin, sVEGFR2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker group, in the preparation of a kit or device for predicting whether a subject has a risk of developing pre-eclampsia, wherein the prediction comprises:
  • Item 11 Use of a biomarker group comprising Endoglin, sVEGFR2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker group, in the preparation of a kit or device for assessing a subject's risk of developing pre-eclampsia, wherein the assessment comprises:
  • Item 12 Use of a biomarker group comprising Endoglin, sVEGFR2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker group, in the preparation of a kit or device for diagnosing whether a subject has pre-eclampsia, wherein the diagnosis comprises:
  • Item 13 The kit, device, method or use of any one of Items 3-12, wherein the sample is a body fluid sample, preferably a blood, serum or plasma sample.
  • Item 14 The kit, device, method or use of any one of Items 3-13, wherein the expression level of the biomarker is the expression level at the protein level or the nucleic acid level.
  • Item 15 The kit or device, method or use of any one of Items 3-14, wherein the subject is a pregnant subject with a gestational age of 6 to 40 weeks, for example, 6 to 13 weeks, for example, 11 to 13 weeks, for example, 20 to 40 weeks, for example, 23 to 33 weeks, for example, 34 to 40 weeks.
  • Item 16 The method or use of Item 15, wherein the gestational age of the pregnant subject is between 6 and 13 weeks, for example, between 11 and 13 weeks.
  • Item 17 The method or use of Item 15, wherein the pre-eclampsia is premature pre-eclampsia.
  • Item 18 The method or use of Item 16 or 17, wherein the formula is
  • ⁇ is between -5.487 and -1.261
  • ⁇ 1 is between 0.041 and 0.304
  • ⁇ 2 is between 0.001 and 0.086
  • ⁇ 3 is between 0.025 and 0.172.
  • Item 19 The method or use of Item 18, wherein the threshold value is between 0.350 and 0.394, or any simple adjustment resulting from a simple adjustment of the formula.
  • Item 20 The method or use of Item 16 or 17, wherein the formula is
  • Item 21 The method or use of Item 20, wherein the threshold value is 0.379, or any simple adjustment resulting from a simple adjustment of the formula.
  • Item 22 The method or use of Item 15, wherein the gestational age of the pregnant subject is between 20 and 40 weeks.
  • Item 23 The method or use of Item 22, wherein the formula is
  • ⁇ is between -1.537 and -1.399
  • ⁇ 1 is between 0.129 and 0.403
  • ⁇ 2 is between -0.163 and -0.004
  • ⁇ 3 is between -0.029 and 0.000.
  • Item 24 The method or use of Item 22 or 23, wherein the gestational age of the pregnant subject is between 23 and 33 weeks.
  • Item 25 The method or use of Item 22 or 23, wherein the pre-eclampsia is early-onset eclampsia.
  • Item 26 The method or use of Item 24 or 25, wherein the threshold value is between 0.550 and 0.781, or any simple adjustment resulting from a simple adjustment of the formula.
  • Item 27 The method or use of Item 24 or 25, wherein the formula is
  • Item 28 The method or use of Item 27, wherein the threshold value is 0.761, or any simple adjustment resulting from a simple adjustment of the formula.
  • Item 29 The method or use of Item 22 or 23, wherein the gestational age of the pregnant subject is between 34 and 40 weeks.
  • Item 30 The method or use of Item 22 or 23, wherein the pre-eclampsia is late-onset pre-eclampsia.
  • Item 31 The method or use of Item 29 or 30, wherein the threshold value is between 0.556 and 0.773, or any simple adjustment resulting from a simple adjustment of the formula.
  • Item 32 The method or use of Item 29 or 30, wherein the formula is
  • Item 33 The method or use of Item 32, wherein the threshold value is 0.723, or an arbitrary simple adjustment thereof resulting from a simple adjustment of the formula.

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Abstract

本发明公开了用于先兆子痫风险预测、评估或诊断的生物标志物、试剂盒及方法。具体涉及用于先兆子痫风险预测、评估或诊断的生物标志物群组及相关试剂盒或设备，还涉及预测、评估受试者是否有患先兆子痫的风险或诊断受试者是否患有先兆子痫的方法，以及筛选用于先兆子痫风险预测、评估或先兆子痫诊断的生物标志物群组的方法。本发明能够在16周之前较为准确地评估PE的风险并给与相应的治疗，并由此开发针对早产型子痫前期的预测产品，以满足巨大的临床需求。

Description

用于先兆子痫风险预测、评估或诊断的生物标志物、试剂盒及方法 技术领域
本发明涉及检测领域，具体涉及用于先兆子痫风险预测、评估或诊断的生物标志物群组及相关试剂盒或设备，本发明还涉及预测、评估受试者是否有患先兆子痫的风险或诊断受试者是否患有先兆子痫的方法，以及筛选用于先兆子痫风险预测、评估或先兆子痫诊断的生物标志物群组的方法。
背景技术
先兆子痫(preeclampsia，PE)是妊娠期特有的全身性多系统综合征，发病率约为2％～8％，占孕产妇死亡原因的10％～15％。PE也是导致早产、新生儿患病或死亡的主要原因之一。由于气候、饮食习惯及诊疗水平不同等原因，不同地区的发病率水平存在较大差异。尽早预测、准确识别发病风险对于优化子痫前期管理，有效降低疾病发病率与死亡率，改善PE结局至关重要。
根据子痫前期发生的时间分为：早发型子痫前期(20+0-33+6周)和晚发型子痫前期(34+0周-分娩)。早发型子痫前期具有典型的胎盘病理基础，常合并胎儿生长受限，且与严重的母胎不良结局相关。晚发型子痫前期与孕妇自身因素(肥胖、糖尿病)密切相关，母体合并症比较轻且胎儿预后相对好。根据子痫前期分娩的时间分为：子痫前期早产(<37+0周分娩)，子痫前期足月产(≥37+0周分娩)。
《妊娠期高血压疾病诊治指南(2020)》(以下简称《指南》)中关于子痫前期的描述：妊娠20周后孕妇出现收缩压≥140mmHg和(或)舒张压≥90mmHg，伴有下列任意一项：尿蛋白定量≥0.3g/24h，或尿蛋白/肌酐比值≥0.3，或随机尿蛋白≥(+)(无条件进行蛋白定量时的检查方法)；无蛋白尿但伴有以下任何一种器官或系统受累：心、肺、肝、肾等重要器官，或血液系统、消化系统、神经系统的异常改变，胎盘-胎儿受到累及等。这被认为是子痫前期诊断的“金标准”。
然而《指南》中也明确指出，不是每例子痫前期孕妇都存在风险因素， 多数子痫前期见于无明显风险因素的所谓“健康”孕妇。因此，“金标准”并不完全适用所有患者，易导致PE患者错过早期预防。
目前，PE暂时还没有有效的治疗方法，只有分娩才能使疾病最终缓解。不过研究表明，妊娠16周之前服用阿司匹林可降低子痫前期、胎儿生长受限和围产期死亡的发生率，而妊娠16周后服用阿司匹林没有显著益处。
从临床需求的角度看，PE的早期预测(即妊娠≤16周)至关重要，从而使高危人群可在早期使用低剂量阿司匹林干预。虽然临床症状出现较晚，但在妊娠8～18周，胎儿和母体组织之间已存在异常相互作用。
《指南》指出sFlt-1/PlGF比值对短期预测子痫前期具有临床价值，sFlt-1/PlGF比值≤38时阴性预测值(排除1周内的子痫前期)为99.3％；sFlt-1/PlGF比值>38时阳性预测值(预测4周内的子痫前期)为36.7％(Zeisler H,Llurba E,Chantraine F,et al.Predictive vlue of the sFlt-1:PlGF ratio in women with suspected preeclampsia[J].N Engl J Med,2016,374(1):13-22)。然而通过sFlt-1/PlGF比值预测子痫前期的方法孕周较晚，远迟于《指南》推荐的阿司匹林服用时间(孕16周)，不利于PE早期预防。此外，sFlt-1/PlGF比值多用于排除1周内PE的发生风险，全孕期需要多次检测，孕妇支出成本较高。
现有的几种子痫前期预测方式虽有一定的临床价值，但均非有效且特异性高的子痫前期预测方法。而妊娠20周后才可预测子痫前期，则会错过服用阿司匹林预防的最佳时间。
目前尚无有效的检测方法，可以在妊娠早期评估PE风险。缺少可靠的在妊娠早期预测子痫风险的生物标志物是其中的原因之一。因此需要一种有效的检测方法能够在16周之前较为准确地评估PE的风险并给与相应的治疗，并由此开发针对早产型子痫前期的预测产品，以满足巨大的临床需求。
发明内容
本发明的目的是寻找在妊娠早期预测子痫风险的生物标志物，并由此建立妊娠早期评估PE风险的产品与方法。
一方面，本发明提供生物标志物群组，包括Endoglin，sVEGFR2和RBP4。
在一些实施方案中，所述生物标志物群组用于患病风险预测或评估或疾病诊断，优选用于先兆子痫相关状况评估，更优选用于先兆子痫风险预测或 评估或先兆子痫诊断。
另一方面，本发明提供试剂盒或设备，包括用于检测受试者样品中生物标志物群组中的生物标志物表达量的检测试剂，所述生物标志物群组包括Endoglin，sVEGFR2和RBP4。
在一些实施方案中，所述生物标志物群组用于患病风险预测或评估或疾病诊断，优选用于先兆子痫相关状况评估，更优选用于先兆子痫风险预测或评估或先兆子痫诊断。
另一方面，本发明提供筛选用于先兆子痫风险预测或评估或先兆子痫诊断的生物标志物群组的方法，包括以下步骤：
1)检索获得与先兆子痫相关的潜在候选生物标志物；
2)在受试者的样品中进一步确认表达量发生变化的所述候选生物标志物；
3)与所述受试者的临床信息比对，通过构建公式，计算先兆子痫风险分数；
4)选取先兆子痫风险模型表现最好的分界值作为阈值；
5)当先兆子痫风险分数高于阈值时，经验证临床性能好的候选生物标志物的组合确定为生物标志物群组。
另一方面，本发明提供预测受试者是否有患先兆子痫的风险的方法，包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即预测受试者有患先兆子痫的风险。
另一方面，本发明提供评估受试者患先兆子痫的风险高低的方法，包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，则分数越高，受试者患先兆子痫的风险也越高。
另一方面，本发明提供诊断受试者是否患有先兆子痫的方法，包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的 生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即诊断受试者患有先兆子痫。
另一方面，本发明提供包括Endoglin，sVEGFR2和RBP4的生物标志物群组，或者与所述生物标志物群组中的生物标志物特异性结合的检测试剂在制备用于预测受试者是否有患先兆子痫的风险的试剂盒或设备中的用途，所述预测包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即预测受试者有患先兆子痫的风险。
另一方面，本发明提供包括Endoglin，sVEGFR2和RBP4的生物标志物群组，或者与所述生物标志物群组中的生物标志物特异性结合的检测试剂在制备用于评估受试者患先兆子痫的风险高低的试剂盒或设备中的用途，所述评估包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，则分数越高，受试者患先兆子痫的风险也越高。
另一方面，本发明提供包括Endoglin，sVEGFR2和RBP4的生物标志物群组，或者与所述生物标志物群组中的生物标志物特异性结合的检测试剂在制备用于诊断受试者是否患有先兆子痫的试剂盒或设备中的用途，所述诊断包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即诊断受试者患有先兆子痫。
在一些实施方案中，所述样品是体液样品，优选血液、血清或血浆样品。
在一些实施方案中，所述生物标志物的表达量是蛋白水平或核酸水平的表达量。
在一些实施方案中，所述受试者是怀孕受试者，孕周在6周到40周，例如6周到13周，例如11周到13周，例如20周到40周，例如23周到33周，例如34周到40周。
在一些实施方案中，所述怀孕受试者的孕周在6周到13周，例如11周到13周。
在一些实施方案中，所述先兆子痫是早产型子痫前期。
在一些实施方案中，所述公式为
或其结果的任意的简单调整，其中α介于-5.487与-1.261之间，β1介于0.041与0.304之间，β2介于0.001与0.086之间，β3介于0.025与0.172之间。
在一些实施方案中，所述阈值介于0.350与0.394之间，或因公式的简单调整使其产生的任意的简单调整。
在一些实施方案中，所述公式为
或其结果的任意的简单调整。
在一些实施方案中，所述阈值为0.379，或因公式的简单调整使其产生的任意的简单调整。
在一些实施方案中，所述怀孕受试者的孕周在20周到40周。
在一些实施方案中，所述公式为
或其结果的任意的简单调整，其中α介于-1.537与-1.399之间，β1介于0.129与0.403之间，β2介于-0.163与-0.004之间，β3介于-0.029与0.000之间。
在一些实施方案中，所述怀孕受试者的孕周在23周到33周。
在一些实施方案中，所述先兆子痫是早发型子痫。
在一些实施方案中，所述阈值介于0.550与0.781之间，或因公式的简单调整使其产生的任意的简单调整。
在一些实施方案中，所述公式为
或其结果的任意的简单调整。
在一些实施方案中，所述阈值为0.761，或因公式的简单调整使其产生的任意的简单调整。
在一些实施方案中，所述怀孕受试者的孕周在34周到40周。
在一些实施方案中，所述先兆子痫是晚发型子痫前期。
在一些实施方案中，所述阈值介于0.556与0.773之间，或因公式的简单调整使其产生的任意的简单调整。
在一些实施方案中，所述公式为
或其结果的任意的简单调整。
在一些实施方案中，所述阈值为0.723，或因公式的简单调整使其产生其任意的简单调整。
本发明成功地筛选到了与子痫前期相关的生物标志物，可以在妊娠5-25周，特别是11-13+6周更准确预测子痫前期风险，填补了子痫前期风险预测试剂国内外空白。该预测不需要联合包括母体危险因素、平均动脉压(MAP)、妊娠相关蛋白A(PAPPA)及子宫动脉搏动指数(UTPI)等其他指标，孕早期具有较高PE预测准确性。
sFlt-1/PlGF仅对于妊娠20周后可做子痫前期短期预测与辅助预测，且需多次检测。相比之下，本发明的方法可在妊娠全孕期预测子痫前期风险，适合于所有产检孕妇。
在描述本发明的产品和方法之前，应理解本发明不限于所述的特定产品或方法，因而当然可改变。还应理解，本文所用的术语仅用于描述特定实施 方案的目的，而不欲具限制性，因为本发明的范围将仅由所附权利要求书限制。
在提供数值范围时，应理解，除非上下文另有明确说明，否则还特定公开介于所述范围的上限和下限之间的每一中间值。介于所述范围中的任何所述值或中间值与所述范围中的任何其它所述值或中间值之间的每一较小范围涵盖在本发明内。这些较小范围的上限和下限可独立地包括在范围中或排除在范围外，并且其中任一界限、无一界限或两个界限包括在较小范围内的每一范围也涵盖在本发明内，受制于所述范围中任何明确排除的界限。在所述范围包括一个或两个界限时，排除那些所包括界限的任一个或两个的范围也包括在本发明中。
除非另外定义，否则本文使用的所有技术和科学术语具有与本发明所属领域的普通技术人员通常所理解相同的含义。虽然与本文所述类似或等效的任何方法和材料可以用于实施或测试本发明，但现在描述一些潜在和优选的方法和材料。本文所提及的所有公开案通过引用的方式并入本文中以结合所引用的公开案来公开和描述方法和/或材料。应理解，在存在冲突的程度上，本公开取代所并入的公开案的任何公开内容。
本领域技术人员在阅读本公开内容后将显而易见，本文所描述和说明的每一单独的实施方案具有分立成分和特征，在不偏离本发明的范围或精神的情况下，其可容易地与任何其它若干个实施方案的特征分离或组合。可以所述事件的顺序或以逻辑上可能的任何其它顺序进行任何所述方法。
“先兆子痫”又称为“子痫前期”，是子痫发生的前兆。“先兆子痫风险”指与没有患先兆子痫风险的怀孕受试者相比，有患先兆子痫风险怀孕受试者具有统计学意义上显著提高的在未来的预后时窗内会患先兆子痫的可能性。优选地，所述的可能性是至少80％，至少85％，至少90％，至少95％，至少97％，至少98％，至少99％或高达100％。
本发明用于先兆子痫风险预测、评估或诊断的生物标志物群组包括Endoglin，sVEGFR2和RBP4。
Endoglin是转化生长因子β(TGF-β)亚型的可溶性受体，子痫前期患者通过胎盘Endoglin蛋白过度表达，引起循环中Endoglin蛋白水平升高，阻断TGF-β的促血管生成作用及血管舒张作用，引起血管生成障碍和内皮损伤。通过检测孕妇体内Endoglin蛋白表达量的高低，可以预测孕妇患有子痫前期 的风险，对整个孕期起干预指导作用。
血管内皮生长因子受体(sVEGFR2)在促进血管生成及调控方面起关键作用，在子痫前期患者中sVEGFR2表达水平降低，通过检测孕妇体内sVEGFR2表达量的高低，可以预测孕妇患有子痫前期的风险，对整个孕期起干预指导作用。
视黄醇结合蛋白(RBP4)作为一种新的脂肪因子，与糖脂代谢的调节及胰岛素抵抗有重要关系，子痫前期可能与胰岛素抵抗和高胰岛素血症有关。血清RBP4水平升高有可能导致内皮功能受损，削弱氧化亚氮依赖的血管扩张，加重血管病变，从而导致子痫前期的发生。
本发明发现，Endoglin/sVEGFR2/RBP4浓度的改变明显早于先兆子痫发病，且Endoglin/sVEGFR2/RBP4的计算数值可以更好地反映血管的生长情况。在评估蛋白尿和血压的基础上，联合检测Endoglin/sVEGFR2/RBP4的表达量并得出的计算数值对先兆子痫具有良好的风险预测、评估或诊断价值和指导意义。
除了上述生物标志物，本发明还可包括其他用于先兆子痫风险预测、评估或诊断的生物标志物，前提是这些生物标志物与Endoglin/sVEGFR2/RBP4的计算数值可以对先兆子痫具有良好的风险预测、评估或诊断价值和指导意义。
本发明相应开发了试剂盒或设备，用于疾病诊断或患病风险预测或评估，优选用于先兆子痫相关状况评估，更优选用于先兆子痫诊断或风险预测或评估。所述试剂盒或设备包括用于检测受试者样品中生物标志物群组中的生物标志物表达量的检测试剂，所述生物标志物群组包括Endoglin，sVEGFR2和RBP4。
术语“受试者”涉及动物，优选哺乳动物，更优选人。本发明中的受试者需为怀孕受试者。优选地，本发明的受试者应不表现先兆子痫症状。这种先兆子痫症状优选地是本发明其它部分中具体描述的临床症状。更优选地，所述症状包含选自下述的至少一种症状：上腹部疼痛，头痛，视觉障碍，水肿。但是，本发明的受试者也可以表现上述的至少一种症状，因而已经疑似患上先兆子痫。
术语“样品”指体液样品，分离的细胞样品，或来自组织或器官的样品。可由公知的技术获得体液样品，且优选地包括血液，血浆，血清或尿液样品， 更优选血液，血浆或血清样品。可由任意的组织或器官通过，例如活组织检查，获取组织或器官样品。可通过分离技术，例如离心或者细胞分选，由体液、组织或器官获取分离的细胞。优选地，细胞的、组织的、或器官的样品由表达或产生本发明所述的肽的那些细胞、组织或器官获取。
术语“表达量”指生物标志物的蛋白或核酸表达水平，所述核酸包括例如DNA或RNA。所述“表达量”优选指生物标志物的蛋白表达水平。
根据本发明，确定本发明生物标志物的表达量可通过所有已知的手段实现。以蛋白表达量为例，可直接或间接地进行测量。直接测量涉及基于由蛋白获得的信号、以及与样品中所述蛋白的分子数量直接相关的信号强度，测量蛋白的数量或浓度。间接测量包括测量例如配体，标签或酶反应产物获得的信号。
根据本发明，确定所述蛋白的量可通过所有已知的用于确定样品中蛋白的量的手段实现。所述手段包括应用标记分子的，以各种不同的夹层法、竞争法，或其它测定法形式进行的免疫测量设备和方法。所述的测定法将产生表明是否存在所述蛋白的信号。而且，所述信号的强度，优选地与样品中蛋白的量直接或间接相关(例如，成反比)。其它适合的方法包括测量特异于所述蛋白的物理或化学特性，例如其精确的分子量或NMR谱。所述方法包括，优选生物传感器，与免疫分析偶联的光学设备，生物芯片，分析设备，例如质谱仪，NMR-分析仪，或色谱设备等。
具体的，本发明的蛋白检测基于磁微粒的吖啶酯化学发光免疫学。吖啶酯标记物在化学结构上有产生发光的特殊基团，在发光免疫分析过程中添加激发液后即可直接参与发光反应，无需底物液，通常此类物质无本底发光，是一类发光效率很高的发光剂。吖啶酯或吖啶磺酰胺均可与抗体(或抗原)结合，生产具有化学发光活性强，免疫反应特异性高的标记物，吖啶酯通常标记在抗体或抗原的氨基上，标记抗体时最好定向偶联在抗体的固定区上，以便使得抗体既能比较高效率标记又不会损伤抗体活性。磁微粒是由高分子单体聚合而成的微球或颗粒，直径多为微米级或毫米级，其表面带有能与抗体或抗原结合的功能团，如氨基，羧基，羟基等，故可以通过特定的偶联方法形成化学偶联，有结合力强，容量大的优点。在免疫反应时，磁微粒可以均匀地分散到反应溶液中，比表面积较大，有利于反应加速进行，提高了反应速率。
本发明采用将抗体直接包被在磁微粒上，吖啶酯直接标记抗体的偶联方法，不需要引入生物素-链霉亲和素系统，操作简单，重复性较好，并且偶联效率高，发光信号强，便于大规模应用。以该方法可以得到灵敏度更高、线性范围广。磁微粒-吖啶酯系统平台下的联检项目试剂盒，可以同时检测Endoglin/sVEGFR2/RBP4三个项目，通过公式计算结果进行判定，能够辅助临床更早期、快速的进行先兆子痫预测、预测不良妊娠结局，帮助医生对高危人群进行识别与治疗，从而保障妊娠期母婴安全。
合适的“检测试剂”，可以是与有待通过本发明的方法研究的受试者样品中的至少一种标志物特异性结合的配体，例如与Endoglin，sVEGFR2或RBP4特异性结合的抗体。另一方面，在测量所述检测试剂和所述的至少一种标志物之间形成的复合物的量之前，使所述的样品与所述复合物分离。因此，一方面所述检测试剂可以固定化于固体支持物上。另一方面可通过施用清洗溶液使所述的样品与在固体支持物上形成的复合物分离。所形成的复合物与存在于样品中的至少一种标志物的量是成比例的。可以理解待应用的检测试剂的特异性和/或灵敏度决定了样品中包含的可被特异性结合的至少一种标志物的比例程度。
确定蛋白的量可优选地包括下述步骤：(a)使所述的蛋白与特定的配体接触，(b)优选地去除未结合的配体，(c)测量结合配体的量。所述的结合配体将产生强度信号。本发明中的结合包括共价和非共价结合。本发明中的配体可以是与本发明中的蛋白结合的任意化合物，例如肽，多肽，核酸或小分子。优选的配体包括抗体，核酸，肽或多肽，例如所述蛋白、及其包含所述蛋白的结合结构域的片段的受体或结合配偶体，以及适体，例如核酸或肽适体。制备此类配体的方法是本领域共知的。例如适合的抗体和适体的鉴定和生产可由供应商提供。本领域的普通技术人员通晓研发具有更高亲和力及特异性的上述配体的衍生物的方法。例如可以向所述核酸、肽或多肽中引入随机突变。然后通过本领域已知的筛选程序，例如噬菌体展示，测试所得衍生物的结合力。本发明中所指的抗体包括多克隆抗体和单克隆抗体以及它们的片段，例如能结合抗原或半抗原的Fv，Fab和F(ab)2片段。本发明还包括单链抗体，以及人源化杂合抗体，其中显示所需抗原特异性的非人供体抗体的氨基酸序列与人受体抗体的氨基酸序列结合。所述的供体序列通常至少包括所述供体的抗原结合氨基酸残基，但也可包含所述供体抗体的其它结构和/或功能相关 氨基酸残基。所述的杂合体可通过本领域已知的若干种方法制备。优选地，所述配体或试剂与所述蛋白特异性地结合。根据本发明的特异性结合指，所述配体或试剂基本上不与存在于待分析样品中的其它蛋白或物质结合，即：发生交叉反应。优选地，所述特异性结合蛋白具有比任何其他相关的蛋白强达至少3倍，更优选至少10倍，和甚至更优选至少50倍的结合亲和力。如果例如根据其在Western Blot上的大小，或通过其在样品中相对更高的丰度，仍然可以明确地区分和测量，那么所述非特异性结合有可能是可容忍的。所述配体的结合可通过本领域任何已知的方法进行测量。优选地，所述方法是半定量或定量的。
本发明的设备的实例包括用于检测化学或生物反应结果或者监控化学或生物反应进程的临床化学分析仪，凝聚化学分析仪(coagulation chemistry analyzers)，免疫化学分析仪，尿分析仪，核酸分析仪，试剂盒，等等。
所述设备的实施方案可包括一个或以上的用于实践本发明的主题的分析仪单元。本发明中所公开的设备的分析仪单元可通过已知的任何连接方式与本发明所公开的计算单元可操作地通讯。此外，根据本发明，分析仪单元可以包括较大设备中用于预测目的的样品检测，例如定性和/或定量评估之一或两者的独立的装置或元件。例如，分析仪单元可以执行或辅助样品和/或试剂的移液，计量，混合。分析仪单元可包括用来夹持试剂以进行测定的试剂夹持单元。试剂的安排可以是，例如在盛有单独的试剂或一组试剂的容器或匣子里，置于储藏室或输送器中合适的托座或位置之中。检测试剂还可以固定在与样品相接触的固体支持物上。分析仪单元还可以包括对于特定的分析最优化的处理和/或检测组件。
根据一些实施方案，分析仪单元可配置为对样品中的分析物，例如标志物，进行光学检测。用于光学检测的分析仪单元的示例包括配置为将电磁能转化成电信号的设备，其包括单一的和多元件或阵列光学探测器。根据本公开，光学探测器能监控光电磁信号并提供代表置于光路中的样品内分析物的存在和/或浓度的电输出信号，或与相对于基线信号的应答信号。所述设备还可以包括，例如光电二极管，包括雪崩光电二极管，光电晶体管，光电导检测器，线性传感器阵列，CCD检测器，CMOS检测器，包括CMOS阵列检测器，光电倍增管，以及光电倍增管阵列。根据某些实施方案，光学检测器，例如光电二极管或光电倍增管可包括附加的信号调节或处理电器元件。例如 光学检测器可包括至少一个预放大器，电子过滤器，或集成电路。合适的预放大器包括，例如集成、跨阻抗，和电流增益(电流反射镜)预放大器。
此外，本发明的一个或以上的分析仪单元可包含用于发射光的光源。例如，分析仪单元的光源可以由至少一个光发射元件(例如，发光二极管，电力发射源如白炽灯，电致发光灯，气体放电灯，高强度放电灯，激光)组成，用于测量待测样品中分析物的浓度，或使得能够能量转换(例如，通过荧光共振能量转移或催化酶)。
此外，所述设备的分析仪单元可包括一个或以上的温育单元(例如用于将样品或试剂保持在特定的温度或温度范围)。在一些实施方案中，分析仪单元可包括用于使样品处于重复的温度循环中并监测样品中扩增产物量的变化的热循环仪，包括实时热循环仪。
本文中公开的设备的分析仪单元还可包括或可操作地连接于反应容器或小杯输送单元。输送单元的示例包括液体加工单元，例如移液单元，用来将样品和/或试剂递送到反应容器。所述移液单元可包含可重复使用的耐洗针，例如钢针，或者一次性的移液头。所述的分析仪单元还可包括一个或以上的混合单元，例如用于振荡含液体的小杯的振荡器，或用来混合小杯或试剂容器中的液体的搅拌桨。
本发明还涉及适于通过实施上述的方法预测怀孕受试者是否有患先兆子痫的风险的设备，其包括：
a)分析仪单元，其包含特异性地结合Endoglin、sVEGFR2和RBP4的检测试剂，所述单元适合于确定怀孕受试者样品中Endoglin、sVEGFR2和RBP4的表达量；和
b)含数据处理器的评估单元，所述数据处理器具有用于实施下述步骤的执行算法：
i)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
ii)比较所述先兆子痫风险分数，如果高于阈值，即预测受试者有患先兆子痫的风险。
本发明中使用的术语“设备”涉及包含彼此可操作地连接的上述单元的系统，其使得可根据本发明的方法进行预测。可用于所述分析单元的优选检测试剂在本发明的其它部分公开。分析单元(或分析仪单元)优选地包括处于固体支持物上的固定形式的检测试剂，其将与包含待确定其数量的生物标志 物的样品相接触。此外，所述分析单元还可以包含检测器，其用于确定与所述生物标志物特异性结合的检测试剂的量。可将经确定的量转移到所述评估单元。所述评估单元包括带有执行算法的数据处理元件，例如计算机，所述数据处理元件通过执行基于计算机的算法实施本发明的方法的步骤，由此实施比例计算，比较计算出的比例，和评估比较结果，其中所述本发明的方法的步骤已在本发明的其他部分详细阐述。所述结果可作为参数化的预测原始数据输出而给出。可以理解这些数据通常需要经过医生的解读。但是也可以预计专家系统设备，其中上述的输出包含无需专业医生进行解读的、经处理的预测原始数据。
本发明中使用的术语“试剂盒”指上述各种检测试剂和部件组分的集合，优选地，其单独地或在单一的容器内提供。所述的容器内还包括实施本发明的方法的操作指南。这些操作指南可以是使用手册的形式，也可以通过计算机程序代码提供，当在计算机或数据处理设备上运行所述计算机程序代码时，其能够执行本发明的方法中的计算和比较，并相应地建立预测。所述的计算机程序代码可以是在数据存储介质或设备上，例如光学存储介质(例如光盘)，或者直接在计算机或数据处理设备上提供。而且，所述的试剂盒可优选地包含用于校准目的的标准量的生物标志物，所述生物标志物在本发明的其他部分阐述。
术语“预测”涉及判断受试者是否有患先兆子痫的风险，用于在症状出现前确定受试者发病的可能性(即评估未来发病的风险)。
术语“评估”是指确定受试者患先兆子痫的风险的高低。优选的，应该确定较之对象人群的平均风险，受试者风险是否处于升高的风险或降低的风险。对于有足够风险的受试者(根据检测结果确定)，可采取预防性干预措施。
术语“诊断”在本文中用于指对分子或病理学状态、疾病或疾患(例如先兆子痫)的鉴定或分类。例如，“诊断”可以指特定先兆子痫类型的鉴定。本发明的“诊断”可以与《指南》中提供的其他诊断标准结合，用于提供附加信息，以帮助确定或验证受试者的临床状态。
如本领域技术人员将理解的，这种预测、评估、诊断虽然是优选的，但可能不会对100％的被研究的受试者都是正确的。然而，该术语要求能够正确地评估具有统计学意义的部分的受试者，从而将其识别为是否有患先兆子 痫的风险，患先兆子痫的风险高低以及是否患有先兆子痫。
本发明中临床性能分为灵敏度，特异性，阳性预测值(PPV)，阴性预测值(NPV)。
“灵敏度”可用来衡量某种试验检测出有病者的能力，灵敏度是将实际有病的人正确地判定为真阳性的比例。灵敏度＝真阳性人数/(真阳性人数+假阴性人数)*100％。
“特异性”是衡量试验正确地判定无病者的能力，特异度是将实际无病的人正确地判定为真阴性的比例。特异性＝真阴性人数/(真阴性人数+假阳性人数)*100％。
阳性预测值(PPV)＝真阳性人数/(真阳性人数+假阳性人数)*100％。
阴性预测值(NPV)＝真阴性人数/(真阴性人数+假阴性人数)*100％。
本发明基于所述生物标志物的表达量，利用公式计算出先兆子痫风险分数。计算公式可以基于不同的算法，例如弹性网络回归算法。具体地，本发明将样本的类别即先兆子痫患病或正常作为因变量，样本特征值矩阵作为自变量，定义目标函数，并进行建模，形成先兆子痫风险分数公式，例如
其中e为自然常数，α，β1，β2和β3为特征系数，Endoglin、sVEGFR2和RBP4为对应生物标志物的表达量。该先兆子痫风险分数公式仅为示例，不应理解为对本发明技术方案的限制。基于本发明确定的生物标志物，本领域技术人员可以根据对象人群、样本情况、临床使用场景、临床性能需求等的不同构建合适的先兆子痫风险分数公式。
本发明以AUC(ROC曲线下方面积)作为模型的评判指标，对所建模型进行验证。AUC的高低和临床使用场景等因素都可能影响上述先兆子痫风险分数公式，例如特征系数α，β1，β2和β3的数值。例如以AUC大于0.85的指标，α，β1，β2和β3可在一定范围内调整。以AUC大于0.9的指标，α，β1，β2和β3的范围又可能会相应变化。本发明中的α，β1，β2和β3的数值范围仅为示例，不应理解为对本发明技术方案的限制。
基于特定的临床使用场景，可以根据不同的临床性能需求来确定阈值。例如可以以敏感度达到90％以上，特异性达到90％以上和NPV达到90％以 上的要求，选取能使PPV达到最高的分界值为本临床使用场景的阈值。当临床使用场景和临床性能需求发生变化时，阈值也会相应发生变化。由于特征系数α，β1，β2和β3的数值均处于一个范围内，根据各特征系数两端值的固定，可将阈值固定在一个范围内。
为了便于临床使用，本发明的先兆子痫风险分数公式可以基于其结果做任意的简单调整。仍以上述先兆子痫风险分数公式为例，由于其计算结果在0-1的范围内，可以对其进行任意的简单调整，例如但不限于，乘以一个倍数例如10、100，加上一个常数例如1、2等，以增加其可读性和易操作性。
相应的，本发明的阈值也可随着上述公式的简单调整而进行任意的简单调整，例如但不限于，乘以一个倍数例如10、100，加上一个常数例如1、2等等，以增加其可读性和易操作性。
附图说明
图1显示生物标志物发现与验证流程。
图2显示生物标志物Endoglin、sVEGFR2和RBP4各自的ROC曲线图。
图3显示11+0到13+6周临床使用场景下最佳先兆子痫风险模型ROC曲线图。
图4显示20+0到33+6周临床使用场景下最佳先兆子痫风险模型ROC曲线图。
图5显示34+0到分娩临床使用场景下最佳先兆子痫风险模型ROC曲线图。
具体实施方式
下面结合附图和实施例，对本发明的具体实施方式作进一步详细描述。以下实施例仅用于更加清楚地说明本发明的技术方案，从而使本领域技术人员能很好地理解和利用本发明，而不是限制本发明的保护范围。
本发明实施例中涉及到的实验方法、生产工艺、仪器以及设备，其名称和简称均属于本领域内常规的名称，在相关用途领域内均非常清楚明确，本领域内技术人员能够根据该名称理解常规工艺步骤并应用相应的设备，按照常规条件或制造商建议的条件进行实施。
实施例1：生物标志物的发现与验证流程
虽然先兆子痫早期临床症状出现较晚，但胎儿和母体组织之间已经出现 异常的相互作用，因此这项研究中，我们需要通过大数据分析，识别潜在的与先兆子痫相关的生物标志物，再通过组学分析，从中筛选并得到多组候选的生物标志物，流程见图1，其大致包括以下步骤。
1、搜集全球生物相关数据库；
2、获得与PE相关的候选生物标志物，包括但不限于Leptin，sflt1，PIGF，ADAM12，AFM，APLN，APOA，APOD，APOE，C8B，CASP8，CLCN6，CP，CRH，EBI3，FGB，FN1，FSTL3，GPX3，HP，HEXB，HSD17B1，HTRA1，IGKV3D-20，IGLC3，IL1RAP，INHA，INHBA，ITIH3，KRT1，MFAP5，MTR，PLTP，PROCR，PVRL4，RBP4，SAA1，SDC1，SELL，SERPINA3，SERPING1，SH#BGRL3，SIGLEC6，SLC2A1VTN，WWF等；
3、通过质谱蛋白组学数据非依赖型扫描模式(DIA)分析，从所述候选生物标志物中进一步进行筛选，得到在怀孕受试者的样品中表达量发生变化的候选生物标志物，其中上调的包括PAPPA2，SERPING1，SDC1，ENG，C1QTNF3，INHBE，VSIG4，DENND10，LHX5，CASP8，PTX3，CGB3，BNC2，ANGPTL6等；下调的包括HBA1，IGHG3，SH3BGRL3，IGLC3，CLCN6，FLNA，IGKV2D-40，IGKV3D-20，MRT等；
4、通过免疫学技术(例如Luminex IVD)分析验证样本；
5、数据模型计算；
6、与临床信息比对，获得一组用于后续检测的生物标志物，进行后续研究。
实施例2：6到14周孕周预测模型的构建
本发明的主要目的之一在于根据三个生物标志物所构建的预测模型在怀孕早期来评估先兆子痫患病风险的应用。具体的，三个生物标志物为Endoglin，sVEGFR2和RBP4。
为了实现上述目的，实施了如下筛选生物标志物以及构建预测模型的步骤：
第一步：确定模型训练样本252人，其中先兆子痫患者样本84例，正常妊娠样本168例，所有样本的孕周均在6到14周。
第二步：对各生物标志物进行单变量分析，按照p值<0.05，差异倍数>1.2或者<1/1.2，AUC>0.60筛选出跟先兆子痫有显著差异的标志物。其中 差异倍数，是描述测量A和测量B间数量变化的量度，它被定义为两个量之间的比率；AUC为ROC曲线下的面积，ROC曲线全称为受试者工作特征曲线，它是根据一系列不同的分界值，以真阳性率(敏感性)为纵坐标，假阳性率(1-特异性)为横坐标绘制的曲线，AUC作为衡量学习器优劣的一种性能指标，其取值范围在0.5和1间，AUC值越大该分类器效果越好。在本发明实施例的单变量分析中，差异倍数所计算的测量A和测量B分别是先兆子痫样本的各生物标志物平均表达量和正常样本的各生物标志物平均表达量；分类器进行区分的两类分别是先兆子痫样本和正常妊娠样本。本发明涉及的三个生物标志物详细分析结果见表1，ROC曲线图见图2。
表1生物标记物单变量分析结果
第三步：用筛选出的有显著差异的三个生物标志物作为特征，通过R语言包Glmnet中的弹性网络回归算法进行监督学习，根据三倍交叉验证进行参数优化，构建预测先兆子痫风险评估模型。在平均交叉验证时，当样本均方预测误差最小时，可得到性能最佳的模型；当平均交叉验证误差在一个方差范围内，可得到具备优良性能得模型。
具体而言，Glmnet是一个通过惩罚最大似然拟合广义线性和相似模型的包。弹性网络回归算法属于常规算法，是套索回归和岭回归的混合技术，套索回归会进行特征的挑选，岭回归会保留所有的特征，而弹性网络综合了套索回归和岭回归，它将L1惩罚与L2惩罚同时引入到目标函数的最小化过程中，在获得稀疏系数的同时，维持了岭回归的正则属性。弹性网络回归算法的代价函数通过两个参数λ和ρ来控制惩罚项的大小：
Glmnet算法使用循环坐标下降法，该方法在每个参数固定不变的情况下连续优化目标函数，并反复循环直到收敛。代价函数最小时w的大小：
在R语言包glmnet中的函数cv.glmnet会保存两个λ值。lambda.min和lambda.1se，其中lambda.min是给出最小平均交叉验证误差的λ值。另一个λ值lambda.1se，它给出的模型使得误差在最小值的一个标准误差以内。然后根据所储存的两个λ值通过R语言包glmnet中的函数coef可提取出对应的特征系数，此时得到的两组特征系数则为本案例模型参数的范围边界值。
具体地，本发明将样本的类别即先兆子痫患病或正常作为因变量，样本特征值矩阵作为自变量，定义目标函数，目标函数包括正则化。其中，正则化的主要作用是防止过拟合，并对模型添加正则化项可以限制模型的复杂度，使得模型在复杂度和性能达到平衡。然后使用cv.glmnet函数，选择弹性网络算法进行建模。在参数设置时，由于本案例使用了弹性网络算法，所以给与参数ρ的范围在0到1之间，ρ为惩罚系数。然后在范围内生成多个不同ρ的模型，再以AUC(ROC曲线下方面积)作为模型的评判指标，并使用三折交叉验证，对所建模型进行验证。
上述根据弹性网络模型构建的风险公式如下：
其中α为截距，β1，β2和β3分别为Endoglin，sVEGFR2和RBP4的系数。根据AUC大于0.85的指标，α，β1，β2和β3处于一定的范围内，具体范围见表2。
表2先兆子痫风险分数系数
第四步：根据临床使用场景确定模型分数阈值。具体的，先将样本的孕 周固定在11+0到13+6周(对应为11-13孕周)，先兆子痫患者样本定义为生产孕周早于37周的先兆子痫患者，正常对照样本定义为生产孕周大于等于37周的正常对照人员，确定临床使用场景之后的先兆子痫患者样本有10例，正常对照样本有68例。然后将先兆子痫患者样本通过有放回的随机抽样自助生成20例的患病样本，再根据先兆子痫早产的发病率为0.9％，将正常对照样本的68例进行有放回的随机抽样自助生成2203例的正常样本。最后根据上述新生成的由20例先兆子痫患病样本和2203例正常样本的数据集，通过先兆子痫风险模型进行计算风险分数的计算，并根据敏感度达到90％以上，特异性达到90％以上和NPV达到90％以上的要求，选取能使PPV达到最高的分界值为本临床使用场景的阈值。由于第三步中各系数均处于一个范围内，根据各特征系数两端值的固定，可将阈值范围固定在0.350到0.394，在该范围阈值下风险分数的最优具体表现见表3，最优ROC曲线图见图3。
表3固定临床使用场景下最优先兆子痫风险模型的表现
实施例3：20到40周孕周预测模型的构建
本发明的主要目的之一在于根据三个生物标志物所构建的预测模型在怀孕早期来评估先兆子痫患病风险的应用。具体的，三个生物标志物为Endoglin，sVEGFR2和RBP4。
为了实现上述目的，实施了如下构建预测模型的步骤：
第一步：确定模型训练样本63人，其中先兆子痫患者样本32例，正常妊娠样本31例，其中所有样本的孕周均在20到40周。
第二步：用已锁定的三个生物标志物作为特征，通过R语言包Glmnet中的弹性网络算法进行监督学习，根据三倍交叉验证进行参数优化，构建预测先兆子痫风险评估模型。在平均交叉验证时，当样本均方预测误差最小时，可得到性能最佳的模型；当平均交叉验证误差在一个方差范围内，可得到具 备优良性能得模型。上述根据弹性网络模型构建的风险公式如下：
其中α为截距，β1，β2和β3分别为Endoglin，sVEGFR2和RBP4的系数。根据AUC大于0.85的指标，α，β1，β2和β3可在一定范围内调整，具体范围见表4。
表4先兆子痫风险分数系数
第三步：根据两种不同的临床使用场景确定模型分数阈值。
具体的，临床使用场景一将样本的孕周固定在20+0到33+6周(对应为20-33孕周)，先兆子痫患者样本定义为样本收集时间在34周以内的早发性先兆子痫患者样本，正常对照样本定义为样本收集时间在34周以内的正常对照人员样本，确定临床使用场景之后的先兆子痫患者样本有15例，正常对照样本有15例。通过先兆子痫风险模型进行计算风险分数的计算，并根据敏感度达到90％以上，特异性达到90％以上和PPV达到90％以上的要求，选取能使NPV达到最高的分界值为本临床使用场景的阈值。由于第二步中各系数均处于一个范围内，根据各特征系数两端值的固定，可将阈值范围固定在0.550到0.781，在该范围阈值下先兆子痫风险模型的表现见表5。其中当α＝-1.463，β1＝0.286，β2＝-0.128，β3＝-0.008且阈值为0.761时，先兆子痫风险模型在该场景下能得到最佳表现，此时最优ROC曲线图见图4。
临床使用场景二将孕周固定在34+0(对应为34孕周)到分娩，先兆子痫患者样本定义为样本收集时间在34周以后的晚发性先兆子痫患者样本，正常对照样本定义为样本收集时间在34周以后的正常对照人员样本，确定临床使用场景之后的先兆子痫患者样本有17例，正常对照样本有16例。然后通过兆子痫风险模型进行计算风险分数的计算，并根据特异性达到90％以上和 PPV达到90％以上的要求，选取能使NPV和敏感度达到最高的分界值为本临床使用场景的阈值。同样由于第二步中各系数均处于一个范围内，根据各特征系数两端值的固定，可将阈值范围固定在0.556到0.773，在该范围阈值下先兆子痫风险模型的表现见表5，此时最优ROC曲线图见图5。
表5固定临床使用场景下最优先兆子痫风险模型的表现
实施例4：试剂盒的制备方法
1、吖啶酯标记的第一Endoglin抗体制备方法：
1)量取标记缓冲溶液于离心管中；
2)加入第一Endoglin抗体，充分混匀；
3)加入吖啶酯溶液，充分混匀，室温避光震荡反应；吖啶酯与第一Endoglin抗体摩尔比为1：13；第一Endoglin抗体与吖啶酯室温避光震荡反应的时间为30-150min；
4)将以上混合物装入超滤管中，2000-4000rpm，离心20-40min；
5)加入适量的标记缓冲液定量，-20℃密封保存。
2、吖啶酯标记的第一sVEGFR2抗体制备方法：
1)量取标记缓冲溶液于离心管中；
2)加入第一sVEGFR2抗体，充分混匀；
3)加入吖啶酯溶液，充分混匀，室温避光震荡反应；吖啶酯与第一sVEGFR2抗体摩尔比为1：10；第一sVEGFR2抗体与吖啶酯室温避光震荡反应的时间为30-150min；
4)将以上混合物装入超滤管中，2000-4000rpm，离心20-40min；
5)加入适量的标记缓冲液定量，-20℃密封保存。
3、吖啶酯标记的第一RBP4抗体的制备方法：
1)量取标记缓冲溶液于离心管中；
2)加入第一RBP4抗体，充分混匀；
3)加入吖啶酯溶液，充分混匀，室温避光震荡反应；吖啶酯与第一RBP4抗体摩尔比为1：10；第一RBP4抗体与吖啶酯室温避光震荡反应的时间为30-150min；
4)将以上混合物装入超滤管，2000-4000rpm，离心20-40min；
5)加入适量的标记缓冲液定量，-20℃密封保存。
4、包被有第二Endoglin抗体的磁微粒的制备方法：
1)取200mg磁微粒，磁分离去上清，用0.05mol/L，pH4.5-5.5MES缓冲液400ul重悬；
2)加入0.5-1mL新鲜配制的浓度为10mg/mL的EDC水溶液，室温混悬30-60min；
3)磁分离，去上清，用0.05mol/L，pH4.5-5.5MES缓冲液400ul重悬；
4)加入50ug的第二Endoglin抗体，室温混悬10-30min；
5)磁分离，去上清，用磁微粒缓冲液稀释重悬到0.5mg/mL，完成磁分离试剂的制备。
5、包被有第二sVEGFR2抗体的磁微粒的制备方法：
1)取200mg磁微粒，磁分离去上清，用0.05mol/L，pH4.5-5.5MES缓冲液400ul重悬；
2)加入0.5-1mL新鲜配制的浓度为10mg/mL的EDC水溶液，室温混悬30-60min；
3)磁分离，去上清，用0.05mol/L，pH4.5-5.5MES缓冲液400ul重悬；
4)加入50ug的第二sVEGFR2抗体，室温混悬10-30min；
5)磁分离，去上清，用磁微粒缓冲液稀释重悬到0.5mg/mL，完成磁分离试剂的制备。
6、包被有第二RBP4抗体的磁微粒的制备方法：
1)取200mg磁微粒，磁分离去上清，用0.05mol/L，pH4.5-5.5MES缓冲液400ul重悬；
2)加入0.5-1mL新鲜配制的浓度为10mg/mL的EDC水溶液，室温混悬30-60min；
3)磁分离，去上清，用0.05mo1/L，pH4.5-5.5MES缓冲液400ul重悬；
4)加入50ug的第二RBP4抗体，室温混悬10-30min；
5)磁分离，去上清，用磁微粒缓冲液稀释重悬到0.5mg/mL，完成磁分离试剂的制备。
本实施例的预激发液的制备方法为：将0.8L纯化水、4.862mL浓硝酸和5.46mL30％双氧水依次加入1L避光广口玻璃容器中，加纯化水定容至1L，搅拌混匀后，过滤得预激发液；其pH为1.10，其中各组分的浓度为：硝酸：0.07M；过氧化氢：0.6％；
本实施例制备激发缓冲液的方法为：将0.8L纯化水、4.82g十六烷基三甲基溴化铵依次加入到1L广口玻璃容器中，搅拌至固体完全溶解，加入28.056g氢氧化钾，搅拌至完全溶解后，加纯化水定容至1L，过滤得激发缓冲液；以上方法制备缓冲液B的pH为13.5，其中各组分的浓度如下：氢氧化钾：0.5M；十六烷基三甲基溴化铵：0.478wt％。
实施例5：试剂盒的使用方法
检测流程如下：
1、可溶性Endoglin蛋白(Endoglin抗体)定量检测试剂盒的使用方法如下：
1)加25uL校准品、质控品或待测标本至检测管中；
2)加50uL第二Endoglin抗体-磁微粒至检测管中；
3)加50uL第一Endoglin抗体吖啶酯至检测管中；
4)混匀后，37±0.5℃温育30分钟；
5)加450uL清洗液至检测管中，混匀；
6)磁分离去上清；
7)重复步骤5、6，四遍；
8)加100ul预激发液A及100ul激发液B至检测管中；
9)9、2s后检测发光强度。
2、sVEGFR2定量检测试剂盒的使用方法如下：
1)加25uL校准品、质控品或待测标本至检测管中；
2)加50uL第二sVEGFR2抗体-磁微粒至检测管中；
3)加50uL第一sVEGFR2抗体-吖啶酯至检测管中；
4)混匀后，37±0.5℃温育30分钟；
5)加450L清洗液至检测管中，混匀；
6)磁分离去上清；
7)重复步骤5、6，四遍；
8)加100uL预激发液A及100uL激发液B至检测管中；
9)2s后检测发光强度。
3、RBP4定量检测试剂盒的使用方法如下：
10)加25uL校准品、质控品或待测标本至检测管中；
11)加50uL第二RBP4抗体-磁微粒至检测管中；
12)加50uL第一RBP4抗体-吖啶酯至检测管中；
13)混匀后，37±0.5℃温育30分钟；
14)加450L清洗液至检测管中，混匀；
15)磁分离去上清；
16)重复步骤5、6，四遍；
17)加100uL预激发液A及100uL激发液B至检测管中；
18)9、2s后检测发光强度。
采用本专利的Endoglin、sVEGFR2、RBP4三种试剂盒分别检测先兆子痫组以及正常妊娠组血清中Endoglin、sVEGFR2、RBP4三种血清标志物的含量，并进行数据分析和比对，从而得出比值，以验证其对于预测先兆子痫发病率的特异性和敏感性。
本发明包括，但不限于以下技术方案：
项目1.生物标志物群组，包括Endoglin，sVEGFR2和RBP4。
项目2.项目1的生物标志物群组，用于患病风险预测或评估或疾病诊断，优选用于先兆子痫相关状况评估，更优选用于先兆子痫风险预测或评估或先兆子痫诊断。
项目3.试剂盒或设备，包括用于检测受试者样品中生物标志物群组中的生物标志物表达量的检测试剂，所述生物标志物群组包括Endoglin，sVEGFR2和RBP4。
项目4.项目3的试剂盒或设备，其中所述生物标志物群组用于患病风险 预测或评估或疾病诊断，优选用于先兆子痫相关状况评估，更优选用于先兆子痫风险预测或评估或先兆子痫诊断。
项目5.筛选用于先兆子痫风险预测或评估或先兆子痫诊断的生物标志物群组的方法，包括以下步骤：
1)检索获得与先兆子痫相关的候选生物标志物；
2)在受试者的样品中进一步确认表达量发生变化的所述候选生物标志物；
3)与所述受试者的临床信息比对，通过构建公式，计算先兆子痫风险分数；
4)选取先兆子痫风险模型表现最好的分界值作为阈值；
5)当先兆子痫风险分数高于阈值时，经验证临床性能好的候选生物标志物的组合确定为生物标志物群组。
项目6.项目5的方法，其中所述生物标志物群组包括Endoglin，sVEGFR2和RBP4。
项目7.预测受试者是否有患先兆子痫的风险的方法，包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即预测受试者有患先兆子痫的风险。
项目8.评估受试者患先兆子痫的风险高低的方法，包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，则分数越高，受试者患先兆子痫的风险也越高。
项目9.诊断受试者是否患有先兆子痫的方法，包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即诊断受试者患有先兆子痫。
项目10.包括Endoglin，sVEGFR2和RBP4的生物标志物群组，或者与所述生物标志物群组中的生物标志物特异性结合的检测试剂在制备用于预测受试者是否有患先兆子痫的风险的试剂盒或设备中的用途，所述预测包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即预测受试者有患先兆子痫的风险。
项目11.包括Endoglin，sVEGFR2和RBP4的生物标志物群组，或者与所述生物标志物群组中的生物标志物特异性结合的检测试剂在制备用于评估受试者患先兆子痫的风险高低的试剂盒或设备中的用途，所述评估包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，则分数越高，受试者患先兆子痫的风险也越高。
项目12.包括Endoglin，sVEGFR2和RBP4的生物标志物群组，或者与所述生物标志物群组中的生物标志物特异性结合的检测试剂在制备用于诊断受试者是否患有先兆子痫的试剂盒或设备中的用途，所述诊断包括：
1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即诊断受试者患有先兆子痫。
项目13.项目3-12任一项的试剂盒或设备、方法或用途，其中所述样品是体液样品，优选血液、血清或血浆样品。
项目14.项目3-13任一项的试剂盒或设备、方法或用途，其中所述生物标志物的表达量是蛋白水平或核酸水平的表达量。
项目15.项目3-14任一项的试剂盒或设备、方法或用途，其中所述受试者是怀孕受试者，孕周在6周到40周，例如6周到13周，例如11周到13周，例如20周到40周，例如23周到33周，例如34周到40周。
项目16.项目15的方法或用途，其中所述怀孕受试者的孕周在6周到13周，例如11周到13周。
项目17.项目15的方法或用途，其中所述先兆子痫是早产型子痫前期。
项目18.项目16或17的方法或用途，其中所述公式为
或其结果的任意的简单调整，其中α介于-5.487与-1.261之间，β1介于0.041与0.304之间，β2介于0.001与0.086之间，β3介于0.025与0.172之间。
项目19.项目18的方法或用途，其中所述阈值介于0.350与0.394之间，或因公式的简单调整使其产生的任意的简单调整。
项目20.项目16或17的方法或用途，其中所述公式为
或其结果的任意的简单调整。
项目21.项目20的方法或用途，其中所述阈值为0.379，或因公式的简单调整使其产生的任意的简单调整。
项目22.项目15的方法或用途，其中所述怀孕受试者的孕周在20周到40周。
项目23.项目22的方法或用途，其中所述公式为
或其结果的任意的简单调整，其中α介于-1.537与-1.399之间，β1介于0.129与0.403之间，β2介于-0.163与-0.004之间，β3介于-0.029与0.000之间。
项目24.项目22或23的方法或用途，其中所述怀孕受试者的孕周在23周到33周。
项目25.项目22或23的方法或用途，其中所述先兆子痫是早发型子痫。
项目26.项目24或25的方法或用途，其中所述阈值介于0.550与0.781之间，或因公式的简单调整使其产生的任意的简单调整。
项目27.项目24或25的方法或用途，其中所述公式为
或其结果的任意的简单调整。
项目28.项目27的方法或用途，其中所述阈值为0.761，或因公式的简单调整使其产生的任意的简单调整。
项目29.项目22或23的方法或用途，其中所述怀孕受试者的孕周在34周到40周。
项目30.项目22或23的方法或用途，其中所述先兆子痫是晚发型子痫前期。
项目31.项目29或30的方法或用途，其中所述阈值介于0.556与0.773之间，或因公式的简单调整使其产生的任意的简单调整。
项目32.项目29或30的方法或用途，其中所述公式为
或其结果的任意的简单调整。
项目33.项目32的方法或用途，其中所述阈值为0.723，或因公式的简单调整使其产生其任意的简单调整。

Claims (33)

  1. 生物标志物群组，包括Endoglin，sVEGFR2和RBP4。
  2. 权利要求1的生物标志物群组，用于患病风险预测或评估或疾病诊断，优选用于先兆子痫相关状况评估，更优选用于先兆子痫风险预测或评估或先兆子痫诊断。
  3. 试剂盒或设备，包括用于检测受试者样品中生物标志物群组中的生物标志物表达量的检测试剂，所述生物标志物群组包括Endoglin，sVEGFR2和RBP4。
  4. 权利要求3的试剂盒或设备，其中所述生物标志物群组用于患病风险预测或评估或疾病诊断，优选用于先兆子痫相关状况评估，更优选用于先兆子痫风险预测或评估或先兆子痫诊断。
  5. 筛选用于先兆子痫风险预测或评估或先兆子痫诊断的生物标志物群组的方法，包括以下步骤：
    1)检索获得与先兆子痫相关的候选生物标志物；
    2)在受试者的样品中进一步确认表达量发生变化的所述候选生物标志物；
    3)与所述受试者的临床信息比对，通过构建公式，计算先兆子痫风险分数；
    4)选取先兆子痫风险模型表现最好的分界值作为阈值；
    5)当先兆子痫风险分数高于阈值时，经验证临床性能好的候选生物标志物的组合确定为生物标志物群组。
  6. 权利要求5的方法，其中所述生物标志物群组包括Endoglin，sVEGFR2和RBP4。
  7. 预测受试者是否有患先兆子痫的风险的方法，包括：
    1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
    2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
    3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即预测受试者有患先兆子痫的风险。
  8. 评估受试者患先兆子痫的风险高低的方法，包括：
    1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的 生物标志物的表达量；
    2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
    3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，则分数越高，受试者患先兆子痫的风险也越高。
  9. 诊断受试者是否患有先兆子痫的方法，包括：
    1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
    2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
    3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即诊断受试者患有先兆子痫。
  10. 包括Endoglin，sVEGFR2和RBP4的生物标志物群组，或者与所述生物标志物群组中的生物标志物特异性结合的检测试剂在制备用于预测受试者是否有患先兆子痫的风险的试剂盒或设备中的用途，所述预测包括：
    1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
    2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
    3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即预测受试者有患先兆子痫的风险。
  11. 包括Endoglin，sVEGFR2和RBP4的生物标志物群组，或者与所述生物标志物群组中的生物标志物特异性结合的检测试剂在制备用于评估受试者患先兆子痫的风险高低的试剂盒或设备中的用途，所述评估包括：
    1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
    2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
    3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，则分数越高，受试者患先兆子痫的风险也越高。
  12. 包括Endoglin，sVEGFR2和RBP4的生物标志物群组，或者与所述生物标志物群组中的生物标志物特异性结合的检测试剂在制备用于诊断受试者是否患有先兆子痫的试剂盒或设备中的用途，所述诊断包括：
    1)在所述受试者的样品中，确定包括Endoglin，sVEGFR2和RBP4的生物标志物的表达量；
    2)基于所述生物标志物的表达量，利用公式计算先兆子痫风险分数；
    3)将所述先兆子痫风险分数与阈值比较，如果高于阈值，即诊断受试者患有先兆子痫。
  13. 权利要求3-12任一项的试剂盒或设备、方法或用途，其中所述样品是体液样品，优选血液、血清或血浆样品。
  14. 权利要求3-13任一项的试剂盒或设备、方法或用途，其中所述生物标志物的表达量是蛋白水平或核酸水平的表达量。
  15. 权利要求3-14任一项的试剂盒或设备、方法或用途，其中所述受试者是怀孕受试者，孕周在6周到40周，例如6周到13周，例如11周到13周，例如20周到40周，例如23周到33周，例如34周到40周。
  16. 权利要求15的方法或用途，其中所述怀孕受试者的孕周在6周到13周，例如11周到13周。
  17. 权利要求15的方法或用途，其中所述先兆子痫是早产型子痫前期。
  18. 权利要求16或17的方法或用途，其中所述公式为
    或其结果的任意的简单调整，其中α介于-5.487与-1.261之间，β1介于0.041与0.304之间，β2介于0.001与0.086之间，β3介于0.025与0.172之间。
  19. 权利要求18的方法或用途，其中所述阈值介于0.350与0.394之间，或因公式的简单调整使其产生的任意的简单调整。
  20. 权利要求16或17的方法或用途，其中所述公式为
    或其结果的任意的简单调整。
  21. 权利要求20的方法或用途，其中所述阈值为0.379，或因公式的简单调整使其产生的任意的简单调整。
  22. 权利要求15的方法或用途，其中所述怀孕受试者的孕周在20周到40周。
  23. 权利要求22的方法或用途，其中所述公式为
    或其结果的任意的简单调整，其中α介于-1.537与-1.399之间，β1介于0.129与0.403之间，β2介于-0.163与-0.004之间，β3介于-0.029与0.000之间。
  24. 权利要求22或23的方法或用途，其中所述怀孕受试者的孕周在23周到33周。
  25. 权利要求22或23的方法或用途，其中所述先兆子痫是早发型子痫。
  26. 权利要求24或25的方法或用途，其中所述阈值介于0.550与0.781之间，或因公式的简单调整使其产生的任意的简单调整。
  27. 权利要求24或25的方法或用途，其中所述公式为
    或其结果的任意的简单调整。
  28. 权利要求27的方法或用途，其中所述阈值为0.761，或因公式的简单调整使其产生的任意的简单调整。
  29. 权利要求22或23的方法或用途，其中所述怀孕受试者的孕周在34周到40周。
  30. 权利要求22或23的方法或用途，其中所述先兆子痫是晚发型子痫前期。
  31. 权利要求29或30的方法或用途，其中所述阈值介于0.556与0.773之间，或因公式的简单调整使其产生的任意的简单调整。
  32. 权利要求29或30的方法或用途，其中所述公式为
    或其结果的任意的简单调整。
  33. 权利要求32的方法或用途，其中所述阈值为0.723，或因公式的简单调整使其产生其任意的简单调整。
PCT/CN2023/095230 2022-10-10 2023-05-19 用于先兆子痫风险预测、评估或诊断的生物标志物、试剂盒及方法 WO2024077957A1 (zh)

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