CN117867094A

CN117867094A - Biomarkers, kits, and methods for preeclampsia risk prediction, assessment, or diagnosis

Info

Publication number: CN117867094A
Application number: CN202310102479.9A
Authority: CN
Inventors: 陈利民
Original assignee: Tianjin Yunjian Medical Lab Co ltd; Tianjin Yunjian Medical Instrument Co ltd
Current assignee: Tianjin Yunjian Medical Lab Co ltd; Tianjin Yunjian Medical Instrument Co ltd
Priority date: 2022-10-10
Filing date: 2022-10-10
Publication date: 2024-04-12
Also published as: TW202415952A; CN115308423A; WO2024077957A1; CN115308423B; WO2024077957A9

Abstract

Biomarkers, kits, and methods for preeclampsia risk prediction, assessment, or diagnosis are disclosed. In particular to a biomarker group and related kit or device for pre-eclampsia risk prediction, assessment or diagnosis, as well as to a method of predicting, assessing whether a subject is at risk of developing pre-eclampsia or diagnosing whether a subject is suffering from pre-eclampsia, and a method of screening a biomarker group for pre-eclampsia risk prediction, assessment or diagnosis. The invention can more accurately evaluate the risk of PE before 16 weeks and give corresponding treatment, and thus develop a predictive product for preeclampsia in premature labor to meet huge clinical demands.

Description

Biomarkers, kits, and methods for preeclampsia risk prediction, assessment, or diagnosis

The present application is a divisional application of chinese patent application 202211232221.2 entitled "biomarker, kit and method for preeclampsia risk prediction, assessment or diagnosis" filed 10/2022.

Technical Field

The present invention relates to the field of assays, and in particular to biomarker panels and associated kits or devices for use in the prediction, assessment or diagnosis of risk of preeclampsia, methods of predicting, assessing whether a subject is at risk of developing preeclampsia or diagnosing whether a subject is suffering from preeclampsia, and methods of screening biomarker panels for use in the prediction, assessment or diagnosis of preeclampsia.

Background

Preeclampsia (PE) is a characteristic systemic multisystem syndrome of gestation with a incidence of about 2% to 8% accounting for 10% to 15% of the causes of maternal mortality. PE is also one of the leading causes of premature birth, neonatal illness or death. The incidence rate level in different areas is greatly different due to different climates, eating habits, diagnosis and treatment levels and the like. Early prediction and accurate identification of the risk of onset are important for optimizing preeclampsia management, effectively reducing disease morbidity and mortality and improving PE outcome.

The time period according to preeclampsia is divided into: early onset preeclampsia (20) ⁺⁰ -33 ⁺⁶ Week) and late onset preeclampsia (34) ⁺⁰ Week-labor). Preeclampsia has a typical placental pathological basis, often combined with fetal growth limitation, and is associated with severe maternal-fetal poor outcomes. Late onset preeclampsia is closely related to maternal own factors (obesity, diabetes), maternal complications are relatively light and fetal prognosis is relatively good. The time period for labor in preeclampsia is divided into: preeclampsia premature delivery @<37 ⁺⁰ Zhou Fenmian), preeclampsia term production (. Gtoreq.37) ⁺⁰ Zhou Fenmian).

Description of preeclampsia in guidelines for diagnosis and treatment of gestational hypertension (2020) (hereinafter abbreviated as guidelines) for preeclampsia: after 20 weeks of gestation, the pregnant woman has a systolic pressure of greater than or equal to 140mmHg and/or a diastolic pressure of greater than or equal to 90mmHg, with any one of the following: urine protein ration is more than or equal to 0.3g/24h, or urine protein/creatinine ratio is more than or equal to 0.3, or random urine protein is more than or equal to (+) (checking method when protein ration is performed unconditionally); proteinuria-free but with any of the following organ or system involvement: important organs such as heart, lung, liver and kidney, abnormal changes in blood system, digestive system and nervous system, and placenta-fetus involvement. This is considered the "gold standard" for preeclampsia diagnosis.

However, it is also explicitly stated in the guidelines that not every preeclampsia pregnant woman has a risk factor, most preeclampsia is found in so-called "healthy" pregnant women without significant risk factors. Thus, the "gold standard" is not fully applicable to all patients, and is prone to missed early prevention in PE patients.

At present, PE is temporarily not available as an effective treatment, and only delivery can lead to final alleviation of the disease. However, studies have shown that administration of aspirin 16 weeks prior to gestation can reduce the incidence of preeclampsia, fetal growth restriction, and perinatal mortality, whereas administration of aspirin 16 weeks after gestation has no significant benefit.

From the clinical demand point of view, early prediction of PE (i.e., pregnancy < 16 weeks) is critical, so that high risk populations can intervene early with low dose aspirin. Although clinical symptoms appear later, there is already an abnormal interaction between fetal and maternal tissues between 8-18 weeks of gestation.

The guidelines indicate that the sFlt-1/PlGF ratio has clinical value for short-term pre-eclampsia prediction, and that the negative predictive value (excluding pre-eclampsia within 1 week) is 99.3% when the sFlt-1/PlGF ratio is less than or equal to 38; the positive predictive value (prediction of preeclampsia within 4 weeks) for sFlt-1/PlGF ratio >38 was 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). However, the method of predicting preeclampsia by the sFlt-1/PlGF ratio has a late gestation period, which is far later than the recommended aspirin administration time (16 weeks gestation) of the guidelines, and is unfavorable for PE early prevention. In addition, the sFlt-1/PlGF ratio is used for eliminating the risk of PE occurrence within 1 week, multiple detection is needed during the whole pregnancy period, and the expenditure cost of pregnant women is high.

Although the existing preeclampsia prediction modes have a certain clinical value, none of the existing preeclampsia prediction modes has an effective preeclampsia prediction method with high specificity. And preeclampsia is predicted after 20 weeks of gestation, the best time for aspirin prophylaxis is missed.

There is currently no effective detection method that can assess PE risk early in pregnancy. The lack of reliable biomarkers for predicting the risk of eclampsia in early gestation is one of the reasons. There is therefore a need for an effective test method that can more accurately assess the risk of PE and administer a corresponding treatment 16 weeks ago, and thereby develop a predictive product for preeclampsia, to meet the tremendous clinical demands.

Disclosure of Invention

The object of the present invention is to find biomarkers for predicting the risk of eclampsia in early gestation and thereby to establish products and methods for assessing the risk of PE in early gestation.

In one aspect, the invention provides biomarker panels comprising Endoglin, svgfr 2 and RBP4.

In some embodiments, the biomarker panel 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 assessment or pre-eclampsia diagnosis.

In another aspect, the invention provides a kit or device comprising a detection reagent for detecting the expression level of a biomarker in a biomarker panel comprising Endoglin, svgfr 2 and RBP4 in a sample from a subject.

In another aspect, the invention provides a method of screening a biomarker panel for preeclampsia risk prediction or assessment or diagnosis, comprising the steps of:

1) Retrieving potential candidate biomarkers associated with preeclampsia;

2) Further identifying the candidate biomarker whose expression level is altered in a sample from the subject;

3) Comparing with clinical information of the subject, and calculating a preeclampsia risk score by constructing a formula;

4) Selecting a demarcation value with the best performance of the preeclampsia risk model as a threshold value;

5) When the preeclampsia risk score is above the threshold, the combination of candidate biomarkers that are validated for good clinical performance is determined to be a biomarker panel.

In another aspect, the invention provides a method of predicting whether a subject is at risk of developing preeclampsia, comprising:

1) Determining the expression level of biomarkers comprising Endoglin, svgfr 2 and RBP4 in a sample from said subject;

2) Calculating a preeclampsia risk score by using a formula based on the expression level of the biomarker;

3) Comparing the preeclampsia risk score to a threshold value, and if the preeclampsia risk score is above the threshold value, predicting that the subject is at risk of developing preeclampsia.

In another aspect, the invention provides a method of assessing the risk of a subject for preeclampsia, comprising:

3) Comparing the preeclampsia risk score to a threshold, and if the score is higher than the threshold, the higher the score, the higher the risk that the subject will have preeclampsia.

In another aspect, the invention provides a method of diagnosing whether a subject has preeclampsia, comprising:

3) Comparing the preeclampsia risk score to a threshold value, and if the preeclampsia risk score is above the threshold value, diagnosing the subject as having preeclampsia.

In another aspect, the invention provides the use of a biomarker panel comprising Endoglin, svgfr 2 and RBP4, or a detection reagent that specifically binds to a biomarker in said biomarker panel, in the manufacture of a kit or device for predicting whether a subject is at risk of developing preeclampsia, the prediction comprising:

In another aspect, the invention provides the use of a biomarker panel comprising Endoglin, svgfr 2 and RBP4, or a detection reagent that specifically binds to a biomarker in said biomarker panel, in the manufacture of a kit or device for assessing the risk of a subject for developing preeclampsia, the assessment comprising:

In another aspect, the invention provides the use of a biomarker panel comprising Endoglin, svgfr 2 and RBP4, or a detection reagent that specifically binds to a biomarker in said biomarker panel, in the manufacture of a kit or device for diagnosing whether a subject has preeclampsia, the diagnosis comprising:

In some embodiments, the sample is a body fluid sample, preferably a blood, serum or plasma sample.

In some embodiments, the expression level of the biomarker is an expression level at the protein level or at the nucleic acid level.

In some embodiments, the subject is a pregnant subject, with a gestation period of 6 weeks to 40 weeks, such as 6 weeks to 13 weeks, such as 11 weeks to 13 weeks, such as 20 weeks to 40 weeks, such as 23 weeks to 33 weeks, such as 34 weeks to 40 weeks.

In some embodiments, the pregnant subject has a gestation week of 6 weeks to 13 weeks, e.g., 11 weeks to 13 weeks.

In some embodiments, the preeclampsia is preeclampsia in the premature form.

In some embodiments, the formula is

Or any simple adjustment of the results thereof, where α is between-5.487 and-1.261, β1 is between 0.041 and 0.304, β2 is between 0.001 and 0.086, and β3 is between 0.025 and 0.172.

In some embodiments, the threshold is between 0.350 and 0.394, or any simple adjustment resulting from a simple adjustment of a formula.

In some embodiments, the formula is

Or any simple adjustment of the result thereof.

In some embodiments, the threshold is 0.379, or any simple adjustment resulting from simple adjustment of the formula.

In some embodiments, the pregnant subject has a gestational week of 20 weeks to 40 weeks.

In some embodiments, the formula is

Or any simple adjustment of the results thereof, wherein α is between-1.537 and-1.399, β1 is between 0.129 and 0.403, β2 is between-0.163 and-0.004, and β3 is between-0.029 and 0.000.

In some embodiments, the pregnant subject has a gestational week of from 23 weeks to 33 weeks.

In some embodiments, the preeclampsia is early onset eclampsia.

In some embodiments, the threshold is between 0.550 and 0.781, or any simple adjustment resulting from a simple adjustment of a formula.

In some embodiments, the formula is

Or any simple adjustment of the result thereof.

In some embodiments, the threshold is 0.761, or any simple adjustment resulting from simple adjustment of the formula.

In some embodiments, the pregnant subject has a gestational week of 34 weeks to 40 weeks.

In some embodiments, the preeclampsia is late onset preeclampsia.

In some embodiments, the threshold is between 0.556 and 0.773, or any simple adjustment resulting from simple adjustment of the formula.

In some embodiments, the formula is

Or any simple adjustment of the result thereof.

In some embodiments, the threshold is 0.723, or a simple adjustment to a formula results in any simple adjustment thereof.

The invention successfully screens the biomarker related to preeclampsia, can more accurately predict preeclampsia risk in 5-25 weeks, especially 11-13+6 weeks of gestation, and fills up the blank of preeclampsia risk prediction reagent at home and abroad. The prediction does not need to combine other indexes including maternal risk factors, mean Arterial Pressure (MAP), pregnancy related protein A (PAPA), uterine artery pulsation index (UTPI) and the like, and has higher PE prediction accuracy in early pregnancy.

sFlt-1/PlGF was able to make short-term and auxiliary predictions of preeclampsia only 20 weeks after gestation and required multiple tests. In contrast, the method of the invention can predict the risk of preeclampsia during pregnancy, and is suitable for all pregnant women for pregnancy test.

Before describing the present products and methods, it is to be understood that this invention is not limited to particular products or methods described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

Where a range of values is provided, it is understood that each intervening value, between the upper and lower limit of that range, is also specifically disclosed unless the context clearly dictates otherwise. Each smaller range between any stated value or intermediate value in the range and any other stated value or intermediate value in the range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the ranges or excluded from the ranges, and each range where either, none, or both limits are included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. It should be understood that to the extent that conflict exists, the present disclosure replaces any of the disclosures of the incorporated publications.

It will be apparent to those skilled in the art from this disclosure that each of the individual embodiments described and illustrated herein has discrete components and features that can be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. Any of the methods may be performed in the order of the events or in any other order that is logically possible.

"preeclampsia," also known as "preeclampsia," is a precursor to the occurrence of eclampsia. "risk of preeclampsia" refers to a statistically significant increased likelihood that a pregnant subject at risk for preeclampsia will develop preeclampsia within a future prognostic window of time as compared to a pregnant subject not at risk for preeclampsia. Preferably, the likelihood 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 panel for preeclampsia risk prediction, assessment or diagnosis of the present invention includes Endoglin, svgfr 2 and RBP4.

Endoglin is a soluble receptor of transforming growth factor beta (TGF-beta) subtype, and preeclampsia patients over express placenta Endoglin protein to cause the increase of the level of Endoglin protein in circulation, block the angiogenesis promoting effect and the vasodilation effect of TGF-beta, and cause angiogenesis disorder and endothelial injury. By detecting the expression level of Endoglin protein in the pregnant woman, the risk of the pregnant woman suffering from preeclampsia can be predicted, and the method plays an intervention guiding role in the whole pregnancy.

The vascular endothelial growth factor receptor (sVEGFR 2) plays a key role in promoting angiogenesis and regulation, the expression level of sVEGFR2 in a preeclampsia patient is reduced, and the risk of a pregnant woman suffering from preeclampsia can be predicted by detecting the expression level of sVEGFR2 in the pregnant woman, so that the vascular endothelial growth factor receptor plays an intervention guiding role in the whole pregnancy.

Retinol binding protein (RBP 4) is a novel fat factor, which is important in regulating glycolipid metabolism and insulin resistance, and preeclampsia may be associated with insulin resistance and hyperinsulinemia. Elevated serum RBP4 levels may lead to impaired endothelial function, impair nitrous oxide dependent vasodilation, and exacerbate vascular disease, leading to preeclampsia.

The invention discovers that the change of the concentration of Endoglin/sVEGFR2/RBP4 is obviously earlier than the onset of preeclampsia, and the calculated value of Endoglin/sVEGFR2/RBP4 can better reflect the growth condition of blood vessels. On the basis of evaluation of proteinuria and blood pressure, the combined detection of the expression level of Endoglin/sVEGFR2/RBP4 and the obtained calculated value have good risk prediction, evaluation or diagnosis value and guiding significance for preeclampsia.

In addition to the above biomarkers, the invention may also include other biomarkers for risk prediction, assessment or diagnosis of preeclampsia, provided that these biomarkers can have good risk prediction, assessment or diagnostic value and instructive significance for preeclampsia with calculated values for Endoglin/sVEGFR2/RBP 4.

The invention accordingly provides kits or devices for disease diagnosis or risk prediction or assessment, preferably for preeclampsia related condition assessment, more preferably for preeclampsia diagnosis or risk prediction or assessment. The kit or device comprises detection reagents for detecting the expression level of a biomarker in a biomarker panel comprising Endoglin, svgfr 2 and RBP4 in a sample from a subject.

The term "subject" relates to an animal, preferably a mammal, more preferably a human. The subject of the present invention is desirably a pregnant subject. Preferably, the subject of the invention should not exhibit symptoms of preeclampsia. Such preeclampsia symptoms are preferably clinical symptoms as specifically described in the remainder of the invention. More preferably, the symptoms comprise at least one symptom selected from the group consisting of: pain in the upper abdomen, headache, vision disorder, oedema. However, a subject of the invention may also exhibit at least one of the symptoms described above and thus have been suspected of having preeclampsia.

The term "sample" refers to a body fluid sample, an isolated cell sample, or a sample from a tissue or organ. The body fluid sample may be obtained by known techniques and preferably includes a blood, plasma, serum or urine sample, more preferably a blood, plasma or serum sample. Tissue or organ samples may be obtained from any tissue or organ, such as by biopsy. The isolated cells may be obtained from body fluids, tissues or organs by separation techniques such as centrifugation or cell sorting. Preferably, a cellular, tissue, or organ sample is obtained from those cells, tissues, or organs that express or produce the peptides of the invention.

The term "expression level" refers to the protein or nucleic acid expression level of a biomarker, including, for example, DNA or RNA. The expression level preferably refers to the protein expression level of the biomarker.

According to the invention, determining the expression level of the biomarkers of the invention can be achieved by all known means. Taking the protein expression level as an example, measurement may be performed directly or indirectly. Direct measurement involves measuring the amount or concentration of a protein based on the signal obtained from the protein, and the intensity of the signal directly related to the amount of the molecule of the protein in the sample. Indirect measurement includes measuring, for example, the signal obtained from the ligand, tag or enzyme reaction product.

According to the invention, determining the amount of the protein may be accomplished by all known means for determining the amount of protein in a sample. Such means include immunoassay devices and methods employing a marker molecule in a variety of different sandwich, competition, or other assay formats. The assay will produce a signal indicating the presence or absence of the protein. Moreover, the intensity of the signal is preferably directly or indirectly related (e.g., inversely proportional) to the amount of protein in the sample. Other suitable methods include measuring physical or chemical properties specific to the protein, such as its precise molecular weight or NMR spectrum. The method comprises, preferably, a biosensor, an optical device coupled to an immunoassay, a biochip, an analytical device, such as a mass spectrometer, an NMR-analyzer, or a chromatographic device, etc.

In particular, the protein detection of the invention is based on magnetic particle acridine ester chemiluminescent immunology. The acridinium ester marker has a special group for generating luminescence in a chemical structure, and can directly participate in luminescence reaction after adding excitation liquid in the luminescence immunoassay process without substrate liquid, and the substance generally has no background luminescence and is a luminescent agent with high luminous efficiency. The acridinium ester or acridine sulfonamide can be combined with an antibody (or antigen) to produce a marker with strong chemiluminescent activity and high immunoreaction specificity, the acridinium ester is usually marked on an amino group of the antibody or the antigen, and the acridinium ester is preferably directionally coupled with a fixed region of the antibody when the antibody is marked, so that the antibody can be marked with high efficiency and the activity of the antibody is not damaged. The magnetic particles are microspheres or particles polymerized by high molecular monomers, the diameter is at most micron or millimeter, and the surface of the magnetic particles is provided with functional groups which can be combined with antibodies or antigens, such as amino, carboxyl, hydroxyl and the like, so that the magnetic particles can form chemical coupling through a specific coupling method, and have the advantages of strong binding force and large capacity. In the immune reaction, the magnetic particles can be uniformly dispersed into the reaction solution, so that the specific surface area is large, the reaction is facilitated to be accelerated, and the reaction rate is improved.

The invention adopts a coupling method of directly coating the antibody on the magnetic particles and directly labeling the antibody by acridinium ester, does not need to introduce a biotin-streptavidin system, has simple operation, better repeatability, high coupling efficiency and strong luminous signal, and is convenient for large-scale application. The method can obtain the high sensitivity and wide linear range. The joint inspection project kit under the magnetic particle-acridinium ester system platform can detect three projects of Endoglin/sVEGFR2/RBP4 simultaneously, and can assist clinic earlier stage and quick pre-eclampsia prediction and bad pregnancy ending prediction, and help doctors to identify and treat high risk groups, so that the safety of pregnant women and infants is ensured.

Suitable "detection reagents" may be ligands that specifically bind to at least one marker in a sample of a subject to be studied by the methods of the invention, e.g., antibodies that specifically bind to Endoglin, svgfr 2 or RBP 4. In another aspect, the sample is separated from the complex formed between the detection reagent and the at least one marker prior to measuring the amount of the complex. Thus, in one aspect the detection reagent may be immobilized on a solid support. Alternatively, the sample may be separated from the complex formed on the solid support by application of a wash solution. The complex formed is proportional to the amount of at least one marker present in the sample. It will be appreciated that the specificity and/or sensitivity of the detection reagent to be applied determines the extent to which the proportion of at least one marker contained in the sample that can be specifically bound.

Determining the amount of protein may preferably comprise the steps of: (a) contacting the protein with a specific ligand, (b) preferably removing unbound ligand, (c) measuring the amount of bound ligand. The binding partner will generate an intensity signal. Binding in the present invention includes both covalent and non-covalent binding. The ligand in the present invention may 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 for the proteins, and fragments thereof comprising the binding domain of the proteins, 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 may be provided by suppliers. Those of ordinary skill in the art are familiar with methods to develop derivatives of the above ligands with higher affinity and specificity. For example, random mutations can be introduced into the nucleic acids, peptides or polypeptides. The resulting derivatives are then tested for binding by screening procedures known in the art, such as phage display. Antibodies referred to herein include polyclonal and monoclonal antibodies and fragments thereof, such as Fv, fab and F (ab) 2 fragments which bind antigen or hapten. The invention also includes single chain antibodies, as well as humanized hybrid antibodies, in which the amino acid sequence of a non-human donor antibody exhibiting the desired antigen specificity is bound to the amino acid sequence of a human acceptor antibody. The donor sequence typically includes at least the antigen binding amino acid residues of the donor, but may also include other structurally and/or functionally related amino acid residues of the donor antibody. The hybrids can be prepared by several methods known in the art. Preferably, the ligand or reagent specifically binds to the protein. Specific binding according to the present invention means that the ligand or reagent does not substantially bind to other proteins or substances present in the sample to be analyzed, i.e.: cross-reactions occur. Preferably, the specific binding protein has a binding affinity that is at least 3-fold, more preferably at least 10-fold, and even more preferably at least 50-fold stronger than any other related protein. Such non-specific binding may be tolerable if it can still be clearly distinguished and measured, for example, by its size on a Western Blot, or by its relatively higher abundance in the sample. Binding of the ligand may be measured by any method known in the art. Preferably, the method is semi-quantitative or quantitative.

Examples of the apparatus of the present invention include a clinical chemistry analyzer for detecting a result of a chemical or biological reaction or monitoring the progress of a chemical or biological reaction, a coagulation chemistry analyzer (coagulation chemistry analyzers), an immunochemistry analyzer, a urine analyzer, a nucleic acid analyzer, a kit, and the like.

Embodiments of the apparatus may include one or more analyzer units for practicing the subject matter of the present invention. The analyzer unit of the apparatus disclosed herein may be in operative communication with the computing unit disclosed herein by any known connection means. Furthermore, according to the invention, the analyzer unit may comprise a separate device or element for sample detection for predictive purposes, e.g. one or both of qualitative and/or quantitative evaluation, in a larger apparatus. For example, the analyzer unit may perform or assist in pipetting, metering, mixing of samples and/or reagents. The analyzer unit may include a reagent holding unit for holding a reagent for performing the measurement. The arrangement of reagents may be, for example, in a container or cassette containing individual reagents or a set of reagents, in a suitable receptacle or location in a storage compartment or conveyor. The detection reagent may also be immobilized on a solid support that is in contact with the sample. The analyzer unit may also include processing and/or detection components that are optimized for a particular analysis.

According to some embodiments, the analyzer unit may be configured to optically detect an analyte, such as a marker, in the sample. Examples of analyzer units for optical detection include devices configured to convert electromagnetic energy into electrical signals, including single and multi-element or array optical detectors. In accordance with the present disclosure, an optical detector is capable of monitoring a photoelectromagnetic signal and providing an electrical output signal representative of the presence and/or concentration of an analyte within a sample disposed in an optical path, or a response signal relative to a baseline signal. The device may also include, for example, photodiodes, including avalanche photodiodes, phototransistors, photoconductive detectors, linear sensor arrays, CCD detectors, CMOS detectors, including CMOS array detectors, photomultiplier tubes, and photomultiplier tube arrays. According to certain embodiments, an optical detector, such as a photodiode or photomultiplier tube, may include additional signal conditioning or processing electrical components. For example, the optical detector may comprise at least one pre-amplifier, electronic filter, or integrated circuit. Suitable preamplifiers include, for example, integrated, transimpedance, and current gain (current mirror) preamplifiers.

Furthermore, one or more analyzer units of the present invention may include a light source for emitting light. For example, the light source of the analyzer unit may consist of at least one light emitting element (e.g. a light emitting diode, an electrical emission 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 the analyte in the sample to be measured or enabling energy conversion (e.g. by fluorescence resonance energy transfer or catalytic enzymes).

Furthermore, the analyzer unit of the device may comprise one or more incubation units (e.g. for maintaining the sample or reagent at a specific temperature or temperature range). In some embodiments, the analyzer unit may include a thermal cycler, including a real-time thermal cycler, for subjecting the sample to repeated temperature cycles and monitoring changes in the amount of amplification products in the sample.

The analyzer unit of the apparatus disclosed herein may also include or be operably connected to a reaction vessel or cuvette delivery unit. Examples of a delivery unit include a liquid processing unit, such as a pipetting unit, for delivering samples and/or reagents to a reaction vessel. The pipetting unit may comprise a reusable wash resistant needle, such as a steel needle, or a disposable pipetting head. The analyzer unit may also include one or more mixing units, such as a shaker for shaking the liquid-containing cuvette, or a paddle for mixing the liquid in the cuvette or reagent container.

The present invention also relates to a device adapted to predict whether a pregnant subject is at risk of developing preeclampsia by carrying out the method as described above, comprising:

a) An analyzer unit comprising a detection reagent that specifically binds Endoglin, sVEGFR2 and RBP4, said unit being adapted to determine the amount of Endoglin, sVEGFR and RBP4 expressed in a sample of a pregnant subject; and

b) An evaluation unit comprising a data processor having an execution algorithm for performing the steps of:

i) Calculating a preeclampsia risk score by using a formula based on the expression level of the biomarker;

ii) comparing the preeclampsia risk score, and if the preeclampsia risk score is above a threshold value, predicting the risk of the subject for developing preeclampsia.

The term "device" as used in the present invention relates to a system comprising the above-mentioned units operatively connected to each other, which makes it possible to make predictions according to the method of the present invention. Preferred detection reagents useful in the assay unit are disclosed in other parts of the invention. The analysis unit (or analyzer unit) preferably comprises a detection reagent in immobilized form on a solid support, which will be in contact with a sample comprising the amount of biomarker to be determined. Furthermore, the analysis unit may further comprise a detector for determining the amount of detection reagent that specifically binds to the biomarker. The determined quantity may be transferred to the evaluation unit. The evaluation unit comprises a data processing element, for example a computer, with an execution algorithm, which performs the steps of the method of the invention, which is described in detail in the rest of the invention, by executing the algorithm based on the computer, thereby performing the ratio calculation, comparing the calculated ratios, and evaluating the comparison result. The result may be given as a parameterized predicted raw data output. It will be appreciated that such data typically requires interpretation by a physician. Expert system devices are contemplated wherein the output contains processed predicted raw data that need not be interpreted by a specialist.

The term "kit" as used in the present invention refers to a collection of the various detection reagents and component parts described above, preferably provided separately or in a single container. The container also includes instructions for carrying out the method of the invention. These instructions may be in the form of a manual or may be provided by means of computer program code which, when run on a computer or data processing apparatus, is able to perform the calculations and comparisons in the method of the invention and to establish predictions accordingly. The computer program code may be provided on a data storage medium or device, such as an optical storage medium (e.g. an optical disc), or directly on a computer or data processing device. Moreover, the kit may preferably comprise a standard amount of a biomarker for calibration purposes, which is set forth in the other parts of the invention.

The term "predicting" relates to determining whether a subject is at risk of developing preeclampsia, for determining the likelihood of the subject developing a disease before symptoms occur (i.e., assessing the risk of future disease).

The term "assessing" refers to determining the level of risk of a subject for developing preeclampsia. Preferably, it should be determined whether the subject risk is at an elevated risk or a reduced risk compared to the average risk of the subject population. For subjects at sufficient risk (determined from the test results), preventive interventions can be taken.

The term "diagnosis" is used herein to refer to the identification or classification of a molecular or pathological state, disease or condition (e.g., preeclampsia). For example, "diagnosis" may refer to the identification of a particular type of preeclampsia. The "diagnosis" of the present invention may be combined with other diagnostic criteria provided in guidelines for providing additional information to assist in determining or verifying the clinical status of a subject.

As will be appreciated by those skilled in the art, such predictions, evaluations, diagnostics, while preferred, may not be correct for 100% of the subjects studied. However, the term requires that a subject having a statistically significant portion be able to be properly assessed to identify as whether it is at risk of developing preeclampsia, whether it is at high or low risk of developing preeclampsia, and whether it is.

The clinical performance of the invention is classified into sensitivity, specificity, positive Predictive Value (PPV), negative Predictive Value (NPV).

"sensitivity" is a measure of the ability of a test to detect a patient and is the proportion of individuals with actual disease that are correctly judged to be truly positive. Sensitivity = true positive number/(true positive number + false negative number) ×100%.

"specificity" is the ability of a measurement test to accurately determine a disease-free person, and specificity is the ratio at which an actual disease-free person is accurately determined to be truly negative. Specificity = true negative population/(true negative population + false positive population) ×100%.

Positive Predictive Value (PPV) =true positive number/(true positive number+false positive number) ×100%.

Negative Predictive Value (NPV) =true negative population/(true negative population+false negative population) ×100%.

The preeclampsia risk score is calculated by utilizing a formula based on the expression quantity of the biomarker. The calculation formula may be based on different algorithms, such as an elastic network regression algorithm. Specifically, the invention uses the category of the sample, i.e. preeclampsia illness or normal as a dependent variable, uses a sample characteristic value matrix as an independent variable, defines an objective function, and models the objective function to form a preeclampsia risk score formula, for example

Where e is a natural constant, α, β1, β2 and β3 are characteristic coefficients, and Endoglin, sVEGFR and RBP4 are expression levels of the corresponding biomarkers. The preeclampsia risk score formula is merely exemplary and should not be construed as limiting the scope of the present invention. Based on the biomarkers determined by the invention, a person skilled in the art can construct a proper preeclampsia risk score formula according to different configurations of the subject population, sample conditions, clinical use scenes, clinical performance requirements and the like.

The invention uses AUC (area under ROC curve) as the judgment index of the model to verify the model. Factors such as the level of AUC and the clinical use scenario may influence the preeclampsia risk score formula described above, e.g. the values of the characteristic coefficients α, β1, β2 and β3. For example, α, β1, β2 and β3 may be adjusted within a range, as indicated by AUC greater than 0.85. The ranges of α, β1, β2 and β3 may vary accordingly, with an AUC greater than 0.9. The numerical ranges of α, β1, β2 and β3 in the present invention are only examples and should not be construed as limiting the technical solution of the present invention.

Based on the particular clinical usage scenario, the threshold may be determined according to different clinical performance requirements. For example, the sensitivity can reach more than 90%, the specificity reaches more than 90% and the NPV reaches more than 90%, and the threshold value which can make the PPV reach the highest boundary value is selected as the clinical use scene. When the clinical usage scenario and clinical performance requirements change, the threshold will also change accordingly. Since the values of the characteristic coefficients α, β1, β2 and β3 are all within a range, the threshold value can be fixed within a range according to the fixation of both end values of each characteristic coefficient.

For ease of clinical use, the preeclampsia risk score formula of the present invention may be arbitrarily and simply adjusted based on the results thereof. Still taking the preeclampsia risk score formula as described above as an example, since the calculation result is in the range of 0-1, any simple adjustment can be made, such as, but not limited to, multiplying by a multiple, such as 10, 100, plus a constant, such as 1, 2, etc., to increase the readability and ease of operation thereof.

Accordingly, the threshold of the present invention may be arbitrarily and simply adjusted with simple adjustments of the above formula, such as, but not limited to, multiplying by a multiple, e.g., 10, 100, plus a constant, e.g., 1, 2, etc., to increase its readability and ease of operation.

Drawings

Fig. 1 shows a biomarker discovery and validation flow.

Fig. 2 shows ROC graphs for each of biomarkers Endoglin, sVEGFR2 and RBP 4.

FIG. 3 shows 11 ⁺⁰ To 13 ⁺⁶ ROC plots for optimal preeclampsia risk models in weekly clinical use scenarios.

FIG. 4 shows 20 ⁺⁰ To 33 ⁺⁶ ROC plots for optimal preeclampsia risk models in weekly clinical use scenarios.

FIG. 5 shows 34 ⁺⁰ ROC graphs of optimal preeclampsia risk models to delivery clinical use scenarios.

Detailed Description

The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are presented only to more clearly illustrate the technical aspects of the present invention so that those skilled in the art can better understand and utilize the present invention without limiting the scope of the present invention.

The experimental methods, production processes, apparatuses and devices involved in the embodiments of the present invention, the names and abbreviations thereof all belong to the names conventional in the art, and are well-known and clear in the relevant fields of use, and those skilled in the art can understand the conventional process steps according to the names and apply the corresponding devices, and implement the methods according to the conventional conditions or the conditions suggested by the manufacturer.

Example 1: biomarker discovery and validation process

Although early clinical symptoms of preeclampsia appear later, abnormal interactions between fetal and maternal tissues have occurred, so in this study we need to identify potential biomarkers associated with preeclampsia by big data analysis, and then screen and obtain sets of candidate biomarkers from them by histologic analysis, flow chart 1, which generally includes the following steps.

1. Collecting a global biological related database;

2. obtaining candidate biomarkers associated with PE, including but not limited to 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, IL1 AP, INHA, INHBA, ITIH3, KRT1, MFAP5, MTR, PLTP, PROCR, PVRL4, RBP4, SAA1, SDC1, SELL, SERPINA3, SERPING1, SH#BGRL3, SIGLEC6, SLC2A1VTN, WWF, etc.;

3. further screening from said candidate biomarkers by mass spectrometry proteomic data independent scanning pattern (DIA) analysis, resulting in candidate biomarkers with altered expression in a sample of a pregnant subject, wherein upregulated include PAPPA2, SERPING1, SDC1, ENG, C1QTNF3, INHBE, VSIG4, DENND10, LHX5, CASP8, PTX3, CGB3, BNC2, ANGPTL6, etc.; downregulation includes HBA1, IGHG3, SH3BGRL3, IGLC3, CLCN6, FLNA, IGKV2D-40, IGKV3D-20, MRT, etc.;

4. validating the sample by immunological techniques (e.g., luminex IVD) analysis;

5. calculating a data model;

6. and comparing with clinical information to obtain a group of biomarkers for subsequent detection, and performing subsequent research.

Example 2: construction of a 6 to 14 week gestational week prediction model

One of the main objects of the present invention is the use of a predictive model constructed from three biomarkers to assess the risk of developing preeclampsia early in pregnancy. Specifically, the three biomarkers are Endoglin, svgfr 2 and RBP4.

In order to achieve the above object, the following steps of screening biomarkers and constructing a predictive model are performed:

the first step: model training samples 252 were determined, with 84 samples of preeclampsia patients, 168 samples of normal pregnancy, and week of pregnancy for all samples ranging from 6 to 14 weeks.

And a second step of: univariate analysis was performed on each biomarker, screening for markers with significant differences from preeclampsia according to p-values < 0.05, fold differences >1.2 or < 1/1.2, auc > 0.60. Where the fold difference, which is a measure describing the change in quantity between measurement A and measurement B, is defined as the ratio between the two quantities; the AUC is the area under the ROC curve, the ROC curve is totally called as the working characteristic curve of a subject, the curve is drawn by taking the true positive rate (sensitivity) as the ordinate and the false positive rate (1-specificity) as the abscissa according to a series of different demarcation values, the AUC is used as a performance index for measuring the quality of a learner, the range of the AUC value is between 0.5 and 1, and the classifier effect is better when the AUC value is larger. In the univariate analysis of the embodiment of the invention, the measurement A and the measurement B calculated by the difference times are respectively the average expression level of each biomarker of the preeclampsia sample and the average expression level of each biomarker of the normal sample; the two classes that the classifier distinguishes are pre-eclampsia samples and normal pregnancy samples, respectively. The detailed analysis results of three biomarkers related to the invention are shown in table 1, and the roc graph is shown in fig. 2.

TABLE 1 Single variable analysis results of biomarkers

Marker(s)	P value	Multiple of difference	AUC
				Endoglin	0.000	1.69	0.80
sVEGFR2	0.000	1.35	0.78
				RBP4	0.000	2.00	0.84

And a third step of: and taking the three screened biomarkers with significant differences as characteristics, performing supervised learning through an elastic network regression algorithm in an R language package Glmcet, performing parameter optimization according to triple cross validation, and constructing a pre-eclampsia risk assessment model. When the mean square prediction error of the sample is minimum in the mean cross validation, a 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.

Specifically, glmnt is a package that fits generalized linear and similar models by punishing maximum likelihood. The elastic network regression algorithm belongs to a conventional algorithm, is a mixed technology of lasso regression and ridge regression, the lasso regression can select features, the ridge regression can retain all features, and the elastic network integrates the lasso regression and the ridge regression, and introduces L1 penalty and L2 penalty into the minimizing process of the objective function at the same time, so that the regular attribute of the ridge regression is maintained while the sparse coefficient is obtained. The cost function of the elastic network regression algorithm controls the magnitude of the penalty term by two parameters λ and ρ:

The Glmnet algorithm uses a cyclic coordinate descent method that continuously optimizes the objective function with each parameter fixed, and loops back and forth until convergence. The magnitude of w when the cost function is minimum:

the function cv.glrnet in the R language packet glrnet will hold two lambda values. lambda.min and lambda.1se, where lambda.min is the lambda value giving the smallest average cross validation error. Another lambda value lambda.1se, which gives a model such that the error is within one standard error of the minimum value. Then, according to the two stored lambda values, the corresponding characteristic coefficients can be extracted through the function coef in the R language packet glmcet, and the two obtained characteristic coefficients are the range boundary values of the model parameters in the present case.

Specifically, the invention takes the category of a sample, namely preeclampsia, or preeclampsia, as a dependent variable, takes a sample eigenvalue matrix as an independent variable, and defines an objective function, wherein the objective function comprises regularization. The regularization is mainly used for preventing overfitting, and adding regularization terms to the model can limit the complexity of the model so that the model can reach a balance between complexity and performance. Then using the cv.glmnet function, an elastic network algorithm is selected for modeling. In parameter setting, since the elastic network algorithm is used in the present case, the given parameter ρ is a penalty coefficient ranging from 0 to 1. And then generating a plurality of models with different rho in the range, taking AUC (area under the ROC curve) as a judgment index of the model, and verifying the modeled model by using tri-fold cross verification.

The risk formula constructed according to the elastic network model is as follows:

where α is the intercept, β1, β2 and β3 are coefficients of Endoglin, svgfr 2 and RBP4, respectively. According to the index of AUC greater than 0.85, α, β1, β2 and β3 are within certain ranges, specific ranges are shown in table 2.

TABLE 2 preeclampsia risk score coefficient

Coefficients of	Range
		α	[-5.487，-1.261]
β ₁	[0.041，0.304]
		β ₂	[0.001，0.086]
β ₃	[0.025，0.172]

Fourth step: model score thresholds are determined based on clinical usage scenarios. Specifically, the gestational period of the sample is fixed at 11 ⁺⁰ To 13 ⁺⁶ Week (corresponding to 11-13 gestational weeks), pre-eclamptic patient samples were defined as producing pre-eclamptic patients with gestational weeks earlier than 37 weeks, normalControl samples were defined as 10 samples of preeclampsia patients after a clinical use was determined for normal control persons producing a week of pregnancy of 37 weeks or more, and 68 samples of normal control samples. Pre-eclamptic patient samples were then self-service to 20 diseased samples by the replaced random sampling, and then 68 normal control samples were self-service to 2203 normal samples by the replaced random sampling based on the pre-eclamptic premature delivery incidence of 0.9%. And finally, calculating the risk score through a pre-eclampsia risk model according to the newly generated data set of 20 pre-eclampsia diseased samples and 2203 normal samples, and selecting a demarcation value which can enable the PPV to reach the highest as a threshold value of the clinical use scene according to the requirements that the sensitivity reaches more than 90%, the specificity reaches more than 90% and the NPV reaches more than 90%. Since the coefficients in the third step are all in a range, the threshold range can be fixed between 0.350 and 0.394 according to the fixation of the two end values of each characteristic coefficient, and the optimal concrete expression of the risk score under the threshold of the range is shown in table 3, and the optimal ROC curve chart is shown in fig. 3.

TABLE 3 characterization of optimal preeclampsia risk models in fixed clinical usage scenarios

Example 3: construction of 20 to 40 week gestational week prediction model

In order to achieve the above object, the following steps of constructing a predictive model are implemented:

the first step: model training samples were determined for 63 persons, 32 of which pre-eclampsia patients, 31 of which normal pregnancy samples, and Zhou Jun of all samples were between 20 and 40 weeks.

And a second step of: and taking the locked three biomarkers as characteristics, performing supervised learning through an elastic network algorithm in an R language package Glmcet, performing parameter optimization according to triple cross validation, and constructing a predicted preeclampsia risk assessment model. When the mean square prediction error of the sample is minimum in the mean cross validation, a 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. The risk formula constructed according to the elastic network model is as follows:

Where α is the intercept, β1, β2 and β3 are coefficients of Endoglin, svgfr 2 and RBP4, respectively. The alpha, beta 1, beta 2 and beta 3 can be adjusted within a certain range according to the index of AUC greater than 0.85, and the specific ranges are shown in Table 4.

TABLE 4 preeclampsia risk score coefficient

Coefficients of	Range
		α	[-1.537，-1.399]
β ₁	[0.129，0.403]
		β ₂	[-0.163，-0.004]
β ₃	[-0.029，0.000]

And a third step of: model score thresholds are determined based on two different clinical usage scenarios.

Specifically, clinical use scenario one fixes the gestational period of the sample at 20 ⁺⁰ To 33 ⁺⁶ Week (corresponding to 20-33 gestational weeks), pre-eclampsia patient samples were defined as pre-eclampsia patient samples with sample collection times within 34 weeks, normal control samples were defined as normal control personnel samples with sample collection times within 34 weeks, 15 pre-eclampsia patient samples after determining clinical use scenario, and 15 normal control samples. Calculating a calculated risk score through a preeclampsia risk model, and selecting a demarcation value which can enable NPV to reach the highest as a threshold value of the clinical use scene according to the requirements that the sensitivity reaches more than 90%, the specificity reaches more than 90% and the PPV reaches more than 90%. Since the coefficients in the second step are all within a range, the threshold range, at which the preeclampsia risk model appears, may be fixed at 0.550 to 0.781 based on the fixation of the values at both ends of the characteristic coefficients, is shown in table 5. Wherein when α= -1.463, β1=0.286, β2= -0.128, β3= -0.008 and the threshold value is 0.761, the pre-eclampsia risk model can perform optimally in this scenario, and the optimal ROC graph is shown in fig. 4.

Clinical use scenario two fix the gestational period at 34 ⁺⁰ From (corresponding to 34 weeks of gestation) to delivery, the pre-eclampsia patient samples were defined as late onset pre-eclampsia patient samples with a sample collection time of 34 weeks later, the normal control samples were defined as normal control personnel samples with a sample collection time of 34 weeks later, and there were 17 pre-eclampsia patient samples after the clinical use scenario was determined, and there were 16 normal control samples. And then calculating a calculated risk score through a pre-eclampsia risk model, and selecting a demarcation value which can enable NPV and sensitivity to reach the highest as a threshold value of the clinical use scene according to the requirements that the specificity reaches more than 90% and the PPV reaches more than 90%. Also, since the coefficients in the second step are within a range, the threshold range can be fixed to 0.556 to 0.773 according to the fixation of the two end values of the characteristic coefficients, and the preeclampsia risk model is shown in Table 5 under the threshold range, and the optimal ROC curve is obtainedThe figure is shown in figure 5.

TABLE 5 manifestation of optimal preeclampsia risk model in fixed clinical usage scenarios

Example 4: preparation method of kit

1. Preparation method of acridinium ester-labeled first Endoglin antibody:

1) Measuring a marked buffer solution in a centrifuge tube;

2) Adding a first Endoglin antibody, and fully and uniformly mixing;

3) Adding an acridinium ester solution, fully and uniformly mixing, and carrying out light-shielding concussion reaction at room temperature; the molar ratio of acridinium ester to the first Endoglin antibody was 1:13; the first Endoglin antibody and the acridinium ester are subjected to light-shielding concussion reaction at room temperature for 30-150min;

4) Loading the above mixture into a ultrafilter tube, centrifuging at 2000-4000rpm for 20-40min;

5) Adding proper amount of marking buffer solution for quantification, and sealing and preserving at-20 ℃.

2. Preparation method of acridinium ester-labeled first sVEGFR2 antibody:

1) Measuring a marked buffer solution in a centrifuge tube;

2) Adding a first sVEGFR2 antibody, and fully and uniformly mixing;

3) Adding an acridinium ester solution, fully and uniformly mixing, and carrying out light-shielding concussion reaction at room temperature; the molar ratio of acridinium ester to first svgfr 2 antibody was 1:10; the time of the shake reaction of the first sVEGFR2 antibody and the acridinium ester at room temperature and in the dark is 30-150min;

3. Preparation method of acridinium ester-labeled first RBP4 antibody:

1) Measuring a marked buffer solution in a centrifuge tube;

2) Adding a first RBP4 antibody, and fully and uniformly mixing;

3) Adding an acridinium ester solution, fully and uniformly mixing, and carrying out light-shielding concussion reaction at room temperature; the molar ratio of acridinium ester to first RBP4 antibody is 1:10; the time of the light-shielding concussion reaction of the first RBP4 antibody and the acridinium ester at room temperature is 30-150min;

4. The preparation method of the magnetic particles coated with the second Endoglin antibody comprises the following steps:

1) Taking 200mg of magnetic particles, magnetically separating supernatant, and re-suspending with 0.05mol/L MES buffer solution with pH of 4.5-5.5 and 400 ul;

2) Adding 0.5-1mL of freshly prepared EDC water solution with the concentration of 10mg/mL, and suspending for 30-60min at room temperature;

3) Magnetically separating, removing supernatant, and re-suspending with 0.05mol/L MES buffer solution with pH of 4.5-5.5 and 400 ul;

4) Adding 50ug of a second Endoglin antibody, and suspending for 10-30min at room temperature;

5) And (3) magnetically separating, removing supernatant, diluting and re-suspending to 0.5mg/mL by using a magnetic particle buffer solution, and completing the preparation of the magnetic separation reagent.

5. The preparation method of the magnetic particles coated with the second sVEGFR2 antibody comprises the following steps:

4) Adding 50ug of a second sVEGFR2 antibody, and suspending at room temperature for 10-30min;

6. The preparation method of the magnetic particles coated with the second RBP4 antibody comprises the following steps:

3) Magnetically separating, removing supernatant, and re-suspending with 0.05mo1/L MES buffer solution with pH of 4.5-5.5 at 400 ul;

4) Adding 50ug of a second RBP4 antibody, and suspending for 10-30min at room temperature;

The preparation method of the pre-excitation liquid in the embodiment comprises the following steps: adding 0.8L of purified water, 4.862mL of concentrated nitric acid and 5.46mL of 30% hydrogen peroxide into a 1L light-resistant wide-mouth glass container in sequence, adding the purified water to a constant volume of 1L, stirring and mixing uniformly, and filtering to obtain a pre-excitation liquid; the pH is 1.10, wherein the concentration of each component is as follows: nitric acid: 0.07M; hydrogen peroxide: 0.6%;

the method for preparing the excitation buffer in this example is: adding 0.8L of purified water and 4.82g of cetyltrimethylammonium bromide into a 1L wide-mouth glass container in sequence, stirring until the solid is completely dissolved, adding 28.056g of potassium hydroxide, stirring until the solid is completely dissolved, adding purified water to a constant volume of 1L, and filtering to obtain an excitation buffer solution; the pH of buffer B prepared in the above manner was 13.5, wherein the concentrations of the components were as follows: potassium hydroxide: 0.5M; cetyl trimethylammonium bromide: 0.478wt%.

Example 5: method for using kit

The detection flow is as follows:

1. the use method of the soluble Endoglin protein (Endoglin antibody) quantitative detection kit is as follows:

1) Adding 25uL of calibrator, quality control product or sample to be detected into the detection tube;

2) Adding 50uL of a second Endoglin antibody-magnetic particles to the detection tube;

3) Adding 50uL of the acridinium ester of the first Endoglin antibody to the test tube;

4) After mixing well, incubating for 30 minutes at 37+ -0.5 ℃;

5) Adding 450uL of cleaning liquid into the detection tube, and uniformly mixing;

6) Magnetically separating the supernatant;

7) Repeating the steps 5 and 6 for four times;

8) Adding 100ul of pre-excitation liquid A and 100ul of excitation liquid B into a detection tube;

9) And 9, 2s later, detecting the luminous intensity.

2. The use method of the sVEGFR2 quantitative detection kit is as follows:

2) Adding 50uL of a second svgfr 2 antibody-magnetic particles to the detection tube;

3) Adding 50uL of the first svgfr 2 antibody-acridinium ester to the test tube;

4) After mixing well, incubating for 30 minutes at 37+ -0.5 ℃;

5) Adding 450L of cleaning liquid into the detection tube, and uniformly mixing;

6) Magnetically separating the supernatant;

7) Repeating the steps 5 and 6 for four times;

9) After 2s, the luminous intensity was measured.

3. The using method of the RBP4 quantitative detection kit is as follows:

10 Adding 25uL of calibrator, quality control product or sample to be detected into the detection tube;

11 Adding 50uL of the second RBP4 antibody-magnetic particles to the test tube;

12 Adding 50uL of the first RBP4 antibody-acridinium ester to the test tube;

13 After mixing well, incubating for 30 minutes at 37+/-0.5 ℃;

14 Adding 450L of cleaning liquid into the detection tube, and uniformly mixing;

15 Magnetically separating the supernatant;

16 Repeating the steps 5 and 6 for four times;

17 Adding 100uL of pre-excitation liquid A and 100uL of excitation liquid B into a detection tube;

18 9, 2s later, the luminous intensity is detected.

The contents of Endoglin, sVEGFR and RBP4 serum markers in the serum of the preeclampsia group and the serum of the normal pregnancy group are respectively detected by adopting the Endoglin, sVEGFR and RBP4 kit of the patent, and data analysis and comparison are carried out, so that a ratio is obtained, and the specificity and the sensitivity of the preeclampsia are verified.

The invention includes, but is not limited to, the following technical scheme:

item 1. Biomarker panel comprising Endoglin, svgfr 2 and RBP4.

Item 2. The biomarker panel of item 1 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 assessment or pre-eclampsia diagnosis.

Item 3. Kit or device comprising detection reagents for detecting the expression level of a biomarker in a biomarker panel comprising Endoglin, svgfr 2 and RBP4 in a sample from a subject.

Item 4. The kit or device of item 3, wherein the biomarker panel is for use in disease risk prediction or assessment or disease diagnosis, preferably for use in pre-eclampsia related condition assessment, more preferably for use in pre-eclampsia risk prediction or assessment or pre-eclampsia diagnosis.

Item 5. A method of screening a biomarker panel for preeclampsia risk prediction or assessment or diagnosis of preeclampsia, comprising the steps of:

1) Retrieving to obtain candidate biomarkers associated with preeclampsia;

The method of item 6, item 5, wherein the biomarker panel comprises Endoglin, svgfr 2 and RBP4.

A method of predicting whether a subject is at risk for developing preeclampsia, comprising:

A method of assessing the risk of a subject for preeclampsia, comprising:

A method of diagnosing whether a subject has preeclampsia, comprising:

Use of a biomarker panel comprising Endoglin, svgfr 2 and RBP4, or a detection reagent that specifically binds to a biomarker in said biomarker panel, in the manufacture of a kit or device for predicting whether a subject is at risk of developing preeclampsia, said predicting comprising:

Use of a biomarker panel comprising Endoglin, svgfr 2 and RBP4, or a detection reagent that specifically binds to a biomarker in said biomarker panel, in the manufacture of a kit or device for assessing the risk of a subject for developing preeclampsia, said assessment comprising:

Use of a biomarker panel comprising Endoglin, svgfr 2 and RBP4, or a detection reagent that specifically binds to a biomarker in said biomarker panel, in the manufacture of a kit or device for diagnosing whether a subject has preeclampsia, said diagnosis comprising:

Item 13 the kit or the device, the method or the 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 or device, method or use of any one of items 3-13, wherein the expression level of the biomarker is an expression level at the protein level or at the nucleic acid level.

The kit or device, method or use of any of items 3-14, wherein the subject is a pregnant subject, and the week of pregnancy is from 6 weeks to 40 weeks, such as from 6 weeks to 13 weeks, such as from 11 weeks to 13 weeks, such as from 20 weeks to 40 weeks, such as from 23 weeks to 33 weeks, such as from 34 weeks to 40 weeks.

The method or use of item 15, wherein the pregnant subject has a gestational week of from 6 weeks to 13 weeks, for example from 11 weeks to 13 weeks.

The method or use of item 17, wherein said preeclampsia is pre-eclampsia of the pre-term type.

Item 18. The method or use of item 16 or 17, wherein the formula is

Item 19 the method or use of item 18, wherein the threshold is between 0.350 and 0.394, or any simple adjustment resulting from a simple adjustment of a formula.

Item 20. The method or use of item 16 or 17, wherein the formula is

Or any simple adjustment of the result thereof.

Item 21. The method or use of item 20, wherein the threshold is 0.379, or any simple adjustment resulting from a simple adjustment of the formula.

The method or use of item 15, wherein the pregnant subject has a gestational week of 20 weeks to 40 weeks.

Item 23 the method or use of item 22, wherein the formula is

The method or use of item 22 or 23, wherein the pregnant subject has a gestational week of 23 weeks to 33 weeks.

Item 25. The method or use of item 22 or 23, wherein said preeclampsia is early onset eclampsia.

Item 26. The method or use of item 24 or 25, wherein the threshold is between 0.550 and 0.781, or any simple adjustment resulting from a simple adjustment of a formula.

Item 27. The method or use of item 24 or 25, wherein the formula is

Or any simple adjustment of the result thereof.

Item 28. The method or use of item 27, wherein the threshold is 0.761, or any simple adjustment resulting from a simple adjustment of a formula.

The method or use of item 29, item 22 or 23, wherein the pregnant subject has a gestational week of 34 weeks to 40 weeks.

Item 30 the method or use of item 22 or 23, wherein said preeclampsia is late onset preeclampsia.

Item 31. The method or use of item 29 or 30, wherein the threshold is between 0.556 and 0.773, or any simple adjustment resulting from simple adjustment of the formula.

Item 32 the method or use of item 29 or 30, wherein the formula is

Or any simple adjustment of the result thereof.

Item 33. The method or use of item 32, wherein the threshold is 0.723, or a simple adjustment to a formula results in any simple adjustment thereof.

Claims

1. Biomarker panel comprising Endoglin, svgfr 2 and RBP4.

2. The biomarker panel of claim 1 for use in disease risk prediction or assessment or disease diagnosis, preferably for use in the assessment of a pre-eclampsia related condition, more preferably for use in pre-eclampsia risk prediction or assessment or pre-eclampsia diagnosis.

3. A kit or device comprising detection reagents for detecting the expression level of a biomarker in a biomarker panel comprising Endoglin, svgfr 2 and RBP4 in a sample from a subject.

4. A kit or device according to claim 3, wherein the biomarker panel 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 assessment or pre-eclampsia diagnosis.

5. A method of screening a biomarker panel for preeclampsia risk prediction or assessment or diagnosis of preeclampsia, comprising the steps of:

1) Retrieving to obtain candidate biomarkers associated with preeclampsia;

6. The method of claim 5, wherein the biomarker panel comprises Endoglin, svgfr 2 and RBP4.

7. A method of predicting whether a subject is at risk of developing preeclampsia, comprising:

8. A method of assessing the risk of a subject for preeclampsia, comprising:

9. A method of diagnosing whether a subject has preeclampsia, comprising:

10. Use of a biomarker panel comprising Endoglin, svgfr 2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker panel, in the manufacture of a kit or device for predicting whether a subject is at risk of developing preeclampsia, the prediction comprising:

11. Use of a biomarker panel comprising Endoglin, svgfr 2 and RBP4, or a detection reagent that specifically binds to a biomarker in said biomarker panel, in the manufacture of a kit or device for assessing the risk of a subject for developing preeclampsia, said assessment comprising:

12. Use of a biomarker panel comprising Endoglin, svgfr 2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker panel, in the manufacture of a kit or device for diagnosing whether a subject has preeclampsia, the diagnosis comprising:

13. The kit or device, method or use of any one of claims 3-12, wherein the sample is a body fluid sample, preferably a blood, serum or plasma sample.

14. The kit or device, method or use of any one of claims 3-13, wherein the expression level of the biomarker is an expression level at the protein level or at the nucleic acid level.

15. The kit or device, method or use of any one of claims 3-14, wherein the subject is a pregnant subject, with a gestation period of 6 weeks to 40 weeks, such as 6 weeks to 13 weeks, such as 11 weeks to 13 weeks, such as 20 weeks to 40 weeks, such as 23 weeks to 33 weeks, such as 34 weeks to 40 weeks.

16. The method or use of claim 15, wherein the pregnant subject has a gestational week of from 6 weeks to 13 weeks, such as from 11 weeks to 13 weeks.

17. The method or use of claim 15, wherein the preeclampsia is pre-eclampsia of the pre-term type.

18. The method or use of claim 16 or 17, wherein the formula is

19. The method or use of claim 18, wherein the threshold is between 0.350 and 0.394, or any simple adjustment resulting from a simple adjustment of a formula.

20. The method or use of claim 16 or 17, wherein the formula is

Or any simple adjustment of the result thereof.

21. The method or use of claim 20, wherein the threshold is 0.379, or any simple adjustment resulting from a simple adjustment of a formula.

22. The method or use of claim 15, wherein the pregnant subject has a gestational week of 20 weeks to 40 weeks.

23. The method or use of claim 22, wherein the formula is

24. The method or use of claim 22 or 23, wherein the pregnant subject has a gestational week of from 23 weeks to 33 weeks.

25. The method or use of claim 22 or 23, wherein the preeclampsia is pre-eclampsia.

26. The method or use of claim 24 or 25, wherein the threshold is between 0.550 and 0.781, or any simple adjustment resulting from simple adjustment of the formula.

27. The method or use of claim 24 or 25, wherein the formula is

Or any simple adjustment of the result thereof.

28. The method or use of claim 27, wherein the threshold is 0.761, or any simple adjustment resulting from a simple adjustment of a formula.

29. The method or use of claim 22 or 23, wherein the pregnant subject has a gestational week of 34 weeks to 40 weeks.

30. The method or use of claim 22 or 23, wherein the preeclampsia is late onset preeclampsia.

31. The method or use of claim 29 or 30, wherein the threshold is between 0.556 and 0.773, or any simple adjustment resulting from simple adjustment of the formula.

32. The method or use of claim 29 or 30, wherein the formula is

Or any simple adjustment of the result thereof.

33. The method or use of claim 32, wherein the threshold is 0.723, or a simple adjustment to a formula results in any simple adjustment thereof.