TW202415952A - Device, biomarker group for predicting, assessing or diagnosing preeclampsia risk and use thereof in preparing device for assessing preeclampsia risk - Google Patents

Abstract

The invention provides a device, a biomarker group for predicting, assessing or diagnosing the risk of preeclampsia and their use in preparing a device for assessing the risk of preeclampsia. Specifically, the invention provides a biomarker group and related kits or device for predicting, evaluating or diagnosing the risk of preeclampsia, and also provides a method of predicting, assessing whether a subject is at risk of developing preeclampsia, or diagnosing whether a subject has preeclampsia, and screening for preeclampsia risk prediction, assessment, or preeclampsia. The biomarker group includes Endoglin, sVEGFR2 and RBP4. The invention can more accurately assess the risk of PE before 16 weeks.

Description

Device for prediction, assessment or diagnosis of preeclampsia risk, biomarker group and use thereof in the preparation of a device for assessing preeclampsia risk

The present invention relates to the field of detection, and more particularly to a biomarker group and related reagent kits or devices for predicting, evaluating or diagnosing the risk of preeclampsia. The present invention also relates to a method for predicting or evaluating whether a subject has a risk of preeclampsia or diagnosing whether a subject has preeclampsia, and a method for screening a biomarker group for predicting or evaluating the risk of preeclampsia or diagnosing preeclampsia.

Preeclampsia (PE) is a systemic multisystem syndrome unique to pregnancy, with an incidence of about 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 level in different regions.

Early prediction and accurate identification of disease risk are crucial to optimizing pre-eclampsia management, effectively reducing disease morbidity and mortality, and improving PE outcomes.

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, 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: preterm delivery of preeclampsia (delivery at less than 37 ⁺⁰ weeks) and full-term delivery of preeclampsia (delivery at ≥37 ⁺⁰ weeks).

The description of preeclampsia in the "Guidelines for the Diagnosis and Treatment of Hypertension in Pregnancy (2020)" (hereinafter referred to as the "Guidelines") is as follows: after 20 weeks of pregnancy, pregnant women have systolic blood pressure ≥140mmHg and/or diastolic blood pressure ≥90mmHg, 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 not conditional); no proteinuria but accompanied by any of the following organ or system involvement: 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.

However, the Guidelines also clearly point out that not every pregnant woman with preeclampsia has risk factors. Most cases of preeclampsia occur in so-called "healthy" pregnant women without obvious risk factors. Therefore, the "gold standard" is not completely applicable to all patients, which may cause PE patients to miss early prevention.

Currently, there is no effective treatment for PE, and only childbirth can finally relieve the disease. However, studies have shown that taking aspirin before 16 weeks of pregnancy can reduce the incidence of preeclampsia, fetal growth restriction and perinatal death, while taking aspirin after 16 weeks of pregnancy has no significant benefit.

From the perspective of clinical needs, early prediction of PE (ie, ≤16 weeks of gestation) is crucial so that high-risk groups can use low-dose aspirin intervention at an early stage. Although clinical symptoms appear later, abnormal interactions between fetal and maternal tissues already exist at 8 to 18 weeks of gestation.

The "Guidelines" point out that the sFlt-1/PlGF ratio has clinical value for the short-term prediction of preeclampsia. When the sFlt-1/PlGF ratio is ≤38, the negative predictive value (excluding preeclampsia within 1 week) is 99.3%; when the sFlt-1/PlGF ratio is >38, the positive predictive value (predicting preeclampsia within 4 weeks) is 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 sFlt-1/PlGF ratio is too late in gestation, far behind the aspirin taking time recommended by the Guidelines (16 weeks of gestation), which is not conducive to the early prevention of PE. In addition, 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 is costly for pregnant women.

Although the existing methods for predicting preeclampsia have certain clinical value, none of them is an effective and highly specific method for predicting preeclampsia. If preeclampsia can only be predicted after 20 weeks of pregnancy, the best time to take aspirin for prevention will be missed.

Currently, there is no effective test method that can assess the risk of PE in early pregnancy. One of the reasons is the lack of reliable biomarkers to predict the risk of ejaculation in early pregnancy. Therefore, an effective test method is needed to more accurately assess the risk of PE before 16 weeks and provide corresponding treatment, and to develop a predictive product for premature ejaculation to meet the huge clinical needs.

The purpose of the present invention is to find biomarkers for predicting the risk of ejaculation in early pregnancy, and thereby establish products and methods for assessing the risk of PE in early pregnancy.

In one aspect, the present invention provides a biomarker panel comprising Endoglin, sVEGFR2 and RBP4.

In some embodiments, the biomarker group is used for disease risk prediction or assessment or disease diagnosis, preferably for preeclampsia-related condition assessment, and more preferably for preeclampsia risk prediction or assessment or preeclampsia diagnosis.

In another aspect, the present invention provides a kit or apparatus comprising a detection reagent for detecting the expression amount of a biomarker in a biomarker group in a sample from a subject, wherein the biomarker group includes Endoglin, sVEGFR2 and RBP4.

In some embodiments, the biomarker group is used for disease risk prediction or assessment or disease diagnosis, preferably for preeclampsia-related condition assessment, and more preferably for preeclampsia risk prediction or assessment or preeclampsia diagnosis.

On the other hand, the present invention provides a method for screening a biomarker group for the prediction or assessment of pre-eclampsia risk or the diagnosis of pre-eclampsia, comprising the following steps: 1) Retrieving potential candidate biomarkers associated with pre-eclampsia; 2) Further confirming the candidate biomarkers whose expression levels have changed in the samples of the subjects; 3) Comparing with the clinical information of the subjects, and calculating the pre-eclampsia risk score by constructing a formula; 4) Selecting the best cutoff value of the pre-eclampsia risk model as the threshold; 5) When the pre-eclampsia risk score is higher than the threshold, the combination of candidate biomarkers with good clinical performance is determined as the biomarker group.

On the other hand, the present invention provides a method for predicting whether a subject has a risk of developing preeclampsia, comprising: 1) determining the expression of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) calculating a preeclampsia risk score using a formula based on the expression of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if it is higher than the threshold value, predicting that the subject has a risk of developing preeclampsia.

On the other hand, the present invention provides a method for assessing the risk of a subject suffering from preeclampsia, comprising: 1) determining the expression of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) calculating the preeclampsia risk score using a formula based on the expression of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if the score is higher than the threshold value, the higher the score, the higher the risk of the subject suffering from preeclampsia.

On the other hand, the present invention provides a method for diagnosing whether a subject suffers from preeclampsia, comprising: 1) determining the expression of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) calculating the preeclampsia risk score using a formula based on the expression of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if it is higher than the threshold value, diagnosing that the subject suffers from preeclampsia.

On the other hand, the present invention provides a biomarker group including Endoglin, sVEGFR2 and RBP4, or a detection reagent specifically binding to a biomarker in the biomarker group for use in preparing a reagent kit or device for predicting whether a subject has a risk of developing preeclampsia, wherein the prediction comprises: 1) determining the expression amount of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) calculating a preeclampsia risk score using a formula based on the expression amount of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if it is higher than the threshold value, predicting that the subject has a risk of developing preeclampsia.

On the other hand, the present invention provides a biomarker group including Endoglin, sVEGFR2 and RBP4, or a detection reagent specifically binding to a biomarker in the biomarker group for preparing a reagent kit or device for assessing the risk of a subject suffering from preeclampsia, wherein the assessment comprises: 1) determining the expression amount of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) calculating the preeclampsia risk score using a formula based on the expression amount of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if it is higher than the threshold value, the higher the score, the higher the risk of the subject suffering from preeclampsia.

On the other hand, the present invention provides a biomarker group including Endoglin, sVEGFR2 and RBP4, or a detection reagent that specifically binds to the biomarkers in the biomarker group for use in preparing a reagent kit or device for diagnosing whether a subject suffers from preeclampsia, wherein the diagnosis comprises: 1) determining the expression amount of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) calculating the preeclampsia risk score using a formula based on the expression amount of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if it is higher than the threshold value, diagnosing that the subject suffers from preeclampsia.

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 the expression level at the protein level or the nucleic acid level.

In some embodiments, 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.

In some embodiments, the pregnant subject has a gestational age of 6 to 13 weeks, such as 11 to 13 weeks.

In some embodiments, the preeclampsia is preeclampsia of premature type.

In some embodiments, the formula is or any simple adjustment of their results, 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 arbitrary simple adjustment resulting from a simple adjustment of the formula.

In some embodiments, the formula is Or any simple adjustment of its results.

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

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

In some embodiments, the formula is or any simple adjustment of their results, where 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 age of 23 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 arbitrary simple adjustment resulting from a simple adjustment of the formula.

In some embodiments, the formula is Or any simple adjustment of its results.

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

In some embodiments, the pregnant subject has a gestational age of 34 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 other simple adjustment resulting from a simple adjustment of the formula.

In some embodiments, the formula is Or any simple adjustment of its results.

In some embodiments, 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, and can more accurately predict the risk of preeclampsia at 5-25 weeks of pregnancy, especially 11-13+6 weeks, filling the gap in the risk prediction test for preeclampsia at home and abroad. The prediction does not need to be combined with 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 accuracy in predicting PE in early pregnancy.

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. In contrast, 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.

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

Where a numerical range is provided, it is understood that each intervening value between the upper and lower limits of the range is also specifically disclosed unless the context clearly indicates otherwise. Each smaller range between any stated value or intervening value in the stated range and any other stated value or intervening value in the stated range is encompassed within the 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 either, none, or both limits are included in the smaller range is also encompassed within the invention, subject to any explicitly excluded limits in the stated range. Where the stated range includes one or both limits, ranges excluding either or both of those included limits are also encompassed within the invention.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by a person of ordinary skill in the art to which the present invention belongs. Although any methods and materials similar or equivalent to those described herein can be used to implement or test 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 methods and/or materials in conjunction with the cited publications. It should be understood that to the extent of a conflict, the present disclosure supersedes any disclosure of the incorporated publications.

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

"Preseizure" is also called "pre-seizure period", which is a precursor to the occurrence of seizure. "Risk of preseizure" means that compared with pregnant subjects without risk of preseizure, pregnant subjects with risk of preseizure have a statistically significantly increased probability of developing preseizure in the future prognostic time window. Preferably, 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 predicting, evaluating or diagnosing the risk of preeclampsia of the present invention includes Endoglin, sVEGFR2 and RBP4.

Endoglin is a soluble receptor of the transforming growth factor β (TGF-β) subtype. In patients with preeclampsia, Endoglin protein is overexpressed in the placenta, causing an increase in the level of Endoglin protein in the cycle, blocking the angiogenesis and vasodilation effects of TGF-β, causing angiogenesis disorders and endothelial damage. By detecting the level of Endoglin protein expression in pregnant women, the risk of pregnant women suffering from preeclampsia can be predicted, which can provide intervention guidance for the entire pregnancy.

Vascular endothelial growth factor receptor (sVEGFR2) plays a key role in promoting and regulating angiogenesis. The expression level of sVEGFR2 is reduced in patients with preeclampsia. By detecting the level of sVEGFR2 expression in pregnant women, the risk of pregnant women suffering from preeclampsia can be predicted, which can provide intervention guidance for the entire pregnancy.

As a new fat factor, retinol binding protein (RBP4) is closely related to the regulation of glycolipid metabolism and insulin resistance. Preeclampsia may be related to insulin resistance and hyperinsulinemia. Elevated serum RBP4 levels may lead to endothelial function damage, 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. On the basis of evaluating proteinuria and blood pressure, the combined detection of Endoglin/sVEGFR2/RBP4 expression and the calculated value obtained have good risk prediction, evaluation or diagnostic value and guiding significance for preeclampsia.

In addition to the above-mentioned biomarkers, 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, wherein the biomarker group includes Endoglin, sVEGFR2 and RBP4.

The term "subject" refers to animals, preferably mammals, and more preferably humans. The subjects in the present invention must be pregnant subjects. Preferably, the subjects of the present invention should not show symptoms of preeclampsia. Such preeclampsia 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 impairment, edema. However, the subjects of the present invention may also show at least one of the above symptoms and are therefore suspected of having preeclampsia.

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

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

According to the present invention, 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 molecular amount 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.

According to the present invention, determining the amount of the protein can be achieved by all known means for determining the amount of protein in a sample. The means include immunoassay devices and methods using labeled molecules in various sandwich methods, competitive methods, or other assay forms. 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 to (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 includes 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.

Specifically, the protein detection of the present invention is based on acridinium ester chemiluminescent immunology of magnetic particles. Acridinium ester markers have special luminescent groups in their chemical structure. After adding the excitation solution in the luminescent immunoassay process, they can directly participate in the luminescent reaction without the need for a substrate solution. Usually, such substances have no background luminescence and are a type of luminescent agent with high luminescent efficiency. Acridinium esters or acridinium sulfonamides can be combined with antibodies (or antigens) to produce markers with strong chemiluminescent activity and high immunoreaction specificity. Acridinium esters are usually labeled on the amino groups of antibodies or antigens. When labeling antibodies, it is best to direct the coupling to the fixed region of the antibody so that the antibody can be labeled with relatively high efficiency without damaging the antibody activity. Magnetic particles are microspheres or particles formed by polymerizing macromolecular monomers, with diameters of micrometers or millimeters. Their surfaces carry functional groups that can bind to antibodies or antigens, such as amino, carboxyl, and hydroxyl groups. Therefore, they can be chemically coupled through specific coupling methods, and have the advantages of strong binding force and large capacity. During the immune reaction, magnetic particles can be evenly dispersed in the reaction solution, with a large specific surface area, which is conducive to accelerating the reaction and increasing 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 acridinium ester. It does not require the introduction of a biotin-streptavidin system, is simple to operate, has good reproducibility, and has high coupling efficiency and strong luminescent signal, which is convenient for large-scale application. This method can obtain higher sensitivity and a wide linear range. The joint test item kit under the magnetic particle-acridinium ester system platform can simultaneously detect three projects of Endoglin/sVEGFR2/RBP4, and make judgments through formula calculation results. It can assist clinical practice in earlier and faster prediction of preeclampsia and 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 sample of a subject to be studied by the method of the present invention, such as an antibody that specifically binds to Endoglin, sVEGFR2 or RBP4. On the other hand, the sample is separated from the complex before measuring the amount of the complex formed between the detection reagent and the at least one marker. Therefore, on the one hand, the detection reagent may be immobilized on a solid support. On the other hand, the sample may be separated from the complex formed on the solid support by applying a washing 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 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, the identification and production of suitable antibodies and aptamers can be provided by suppliers. Ordinary technicians in this field are 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 resulting derivatives are then tested for binding 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 their fragments, 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 antigenic 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 contain other structural and/or functionally related amino acid residues of the donor antibody. The hybrid can be prepared by several methods known in the art. Preferably, the ligand or reagent specifically binds to the protein. According to the present invention, specific binding 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-reacts occur. Preferably, 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 a Western Blot, or by its relatively higher abundance in a sample, then the non-specific binding is likely to 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 the apparatus of the present invention include clinical chemistry analyzers, coagulation chemistry analyzers, immunochemistry analyzers, urine analyzers, nucleic acid analyzers, reagent kits, and the like for detecting chemical or biological reaction results or monitoring chemical or biological reaction processes.

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 in the present invention may be in operative communication with the computing unit disclosed in the present invention by any known connection means. In addition, according to the present invention, the analyzer unit may include an independent device or element in a larger apparatus for sample detection for predictive purposes, such as one or both of qualitative and/or quantitative evaluation. For example, the analyzer unit may perform or assist in the pipetting, metering, mixing of samples and/or reagents. The analyzer unit may include a reagent holding unit for holding a reagent for determination. The reagents may be arranged, for example, in containers or boxes containing individual reagents or a group of reagents, placed in suitable holders or locations in a storage chamber or conveyor. The test reagents may also be fixed on a solid support in contact with the sample. The analyzer unit may also include processing and/or detection elements optimized for a particular analysis.

According to some embodiments, the analyzer unit can be configured to perform optical detection of an analyte, such as a marker, in a 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. According to the present disclosure, 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 equipment can also include, for example, photodiodes, including avalanche photodiodes, phototransistors, photoconductivity 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 a photomultiplier tube, may include additional signal conditioning or processing electrical components. For example, the optical detector may include at least one pre-amplifier, electronic filter, or integrated circuit. Suitable pre-amplifiers include, for example, integrated, transimpedance, and current gain (current mirror) pre-amplifiers.

In addition, 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 be composed of at least one light emitting element (e.g., a light emitting diode, an electric power 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).

In addition, the analyzer unit of the apparatus may include one or more incubation units (e.g., for maintaining a sample or reagent at a specific temperature or temperature range). In some embodiments, the analyzer unit may include a thermocycler, including a real-time thermocycler, for subjecting a sample to repeated temperature cycles and monitoring changes in the amount of an expanded product in the sample.

The analyzer unit of the apparatus disclosed herein may also include or be operably connected to a reaction vessel or cuvette transport unit. Examples of transport 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 tip. The analyzer unit may also include one or more mixing units, such as an oscillator for vibrating a cuvette containing a liquid, or a stirring paddle for mixing the liquid in a cuvette or reagent container.

The present invention also relates to a device suitable for predicting whether a pregnant subject has a risk of preeclampsia by implementing the above method, which comprises: a) an analyzer unit, which contains a detection reagent that specifically binds to Endoglin, sVEGFR2 and RBP4, and the unit is suitable for determining the expression of Endoglin, sVEGFR2 and RBP4 in a sample of a pregnant subject; and b) an evaluation unit containing a data processor, the data processor having an execution algorithm for implementing the following steps: i) calculating a preeclampsia risk score using a formula based on the expression of the biomarker; ii) comparing the preeclampsia risk score, and if it is higher than a threshold value, predicting that the subject has a risk of 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 allows predictions according to the method of the present invention. 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 includes a detection reagent in an immobilized form on a solid support, which will be in contact with the sample containing the biomarker whose amount is to be determined. In addition, the analysis unit can also include a detector for determining the amount of the detection reagent specifically bound to the biomarker. The determined amount can 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 can be given as parameterized prediction raw data output. It can be understood that these data usually need to be interpreted by a doctor. However, expert system equipment can also be expected, wherein the above-mentioned output includes processed prediction raw data that do not require interpretation by professional doctors.

The term "kit" used in the present invention refers to a collection of various test reagents and components, which are preferably provided separately or in a single container. The container also includes instructions for implementing the method of the present invention. These instructions can be in the form of a 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 (such as a disc), or provided directly on a computer or data processing device. In addition, the kit may preferably contain a standard amount of a biomarker for calibration purposes, and the biomarker is described in other parts of the present invention.

The term "prediction" involves determining whether a subject is at risk for developing preeclampsia and is used to determine the likelihood of a subject developing the disorder before symptoms develop (i.e., assess the risk of future onset of the disorder).

The term "assessment" refers to determining the risk of a subject developing preeclampsia. Preferably, it should be determined whether the subject is at increased risk or decreased risk compared to the average risk of the subject population. For subjects who are at sufficient risk (as determined by test results), preventive intervention measures 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 specific type of preeclampsia. 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.

As will be appreciated by those skilled in the art, such predictions, assessments, and diagnoses, while preferred, may not be correct for 100% of the subjects studied. However, the term requires that a statistically significant portion of subjects be correctly assessed to identify them as being at risk for, at a high or low risk for, and as having, pre-eclampsia.

In the present invention, clinical performance is divided into sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

"Sensitivity" can be used to measure the ability of a test to detect sick people. Sensitivity is the proportion of people who are actually sick who are correctly judged as true positive. Sensitivity = number of true positive people/(number of true positive people + number of false negative people)*100%.

"Specificity" is a measure of the ability of a test to correctly identify people without disease. Specificity is the proportion of people who are actually disease-free who are correctly identified as true negative. Specificity = number of true negative people/(number of true negative people + number of false positive people)*100%.

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 pre-eclampsia risk score based on the expression of the biomarker. The calculation formula can be based on different algorithms, such as elastic network regression algorithm. Specifically, the present invention uses the category of the sample, i.e., pre-eclampsia diseased or normal, as the dependent variable, the sample eigenvalue matrix as the argument, defines the target function, and performs modeling to form a pre-eclampsia risk score formula, such as Wherein e is a natural constant, α, β ₁ , β ₂ and β ₃ are characteristic coefficients, and Endoglin, sVEGFR2 and RBP4 are the expression amounts of the corresponding biomarkers. The pre-eclampsia 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 identified in the present invention, a person skilled in the art can construct an appropriate pre-eclampsia risk score formula according to the different subject 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 values of characteristic coefficients α, _β1 , _β2 and _β3 . For example, with an index of AUC greater than 0.85, α, _β1 , _β2 and _β3 can be adjusted within a certain range. With an index of AUC greater than 0.9, the ranges of α, _β1 , _β2 and _β3 may change accordingly. The numerical ranges of α, _β1 , _β2 and _β3 in the present invention are only examples and should not be understood as limitations on the technical solutions of the present invention.

Based on specific clinical use scenarios, the threshold value can be determined according to different clinical performance requirements. For example, the threshold value for this clinical use scenario can be selected based on the requirements of sensitivity reaching more than 90%, specificity reaching more than 90%, and NPV reaching more than 90%, so that the cutoff value that can make PPV reach the highest can be selected. When the clinical use scenario and clinical performance requirements change, the threshold value will also change accordingly. Since the values of the characteristic coefficients α, β ₁ , β ₂ , and β ₃ are all within a range, the threshold value can be fixed within a range based on the fixed values at both ends of each characteristic coefficient.

For the convenience of clinical use, the pre-eclampsia risk score formula of the present invention can be adjusted arbitrarily based on its result. 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.

Correspondingly, the threshold value of the present invention can also be arbitrarily adjusted by simply adjusting 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.

The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, so that those skilled in the art can better understand and use the present invention, rather than limiting the scope of protection of the present invention.

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. Technical personnel in this 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.

Example 1: Biomarker discovery and validation process

Although the early clinical symptoms of preeclampsia appear later, there is already an abnormal interaction between the fetus and maternal tissues. Therefore, in this study, we need to identify potential biomarkers related to preeclampsia through big data analysis, and then screen and obtain multiple groups of candidate biomarkers through omics analysis. The process is shown in Figure 1, which generally includes the following steps. 1. Collect global biological databases; 2. Obtain candidate biomarkers related to 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, IL1RAP, INHA, INHBA, ITIH3, KRT1, MFAP5, MTR, PLTP, PROCR, PVRL4, RBP4, SAA1, SDC1, SELL, SERPINA3, SERPING1, SH#BGRL3, SIGLEC6, SLC2A1VTN, WWF, etc.; 3. Further screening was performed from the candidate biomarkers by mass spectrometry proteomics data independent scanning mode (DIA) analysis 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.; 4. Analyzing and verifying samples by immunological technology (such as Luminex IVD); 5. Data model calculation; 6. Compare with clinical information to obtain a set of biomarkers for subsequent testing and conduct follow-up research.

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. Specifically, the three biomarkers are Endoglin, sVEGFR2 and RBP4.

To achieve the above objectives, the following steps were implemented to screen biomarkers and build prediction models:

Step 1: 252 people were identified as the model training samples, including 84 samples of patients with preeclampsia 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 markers with significant differences from preeclampsia according to p value < 0.05, multiple difference > 1.2 or < 1/1.2, AUC > 0.60. The multiple difference is a measure of 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, which is the full name of the receiver operating characteristic curve. It is a curve drawn based on a series of different cutoff values, with the true positive rate (sensitivity) as the vertical coordinate and the false positive rate (1-specificity) as the horizontal coordinate. AUC is a performance indicator to measure the quality of the learner, and its value range is between 0.5 and 1. The larger the AUC value, the better the classifier effect. In the univariate analysis of the embodiment of the present invention, the measurement A and measurement B calculated by the difference fold are the average expression of each biomarker in the preeclampsia sample and the average expression of each biomarker in the normal sample, respectively; the two categories distinguished by the classifier are the preeclampsia sample and the normal pregnancy sample, respectively. The detailed analysis results of the three biomarkers involved in the present invention are shown in Table 1, and the ROC curve is shown in Figure 2. Table 1 Univariate analysis results of biomarkers Landmarks P-value Difference multiple AUC Endoglin 0.000 1.69 0.80 sVEGFR2 0.000 1.35 0.78 RBP4 0.000 2.00 0.84

Step 3: Use the three biomarkers with significant differences selected as features, supervise 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 preeclampsia risk assessment. When the sample mean square prediction error is the smallest during average cross-validation, 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.

Specifically, Glmnet is a package for fitting generalized linear and similar models by penalized maximum likelihood. The elastic network regression algorithm belongs to the conventional algorithm, which is a hybrid technique of lasso regression and ridge regression. Lasso regression selects features, ridge regression retains all features, and elastic network integrates lasso regression and ridge regression. It introduces L1 penalty and L2 penalty into the minimization process of the objective function at the same time, and maintains the regularization property of ridge regression while obtaining sparse coefficients. The cost function of the elastic network regression algorithm uses two parameters λ and ρ to control the size of the penalty term:

The Glmnet algorithm uses a cyclic coordinate descent method, which continuously optimizes the objective function while keeping each parameter fixed, and iterates 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. 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. Then, the corresponding eigenvalues can be extracted through the function coef in the R package glmnet based on the two stored lambda values. The two sets of eigenvalues obtained at this time are the range boundary values of the model parameters in this case.

Specifically, the present invention uses the category of the sample, i.e., preeclampsia or normal, as the dependent variable, the sample eigenvalue matrix as the argument, and defines the target function, which includes regularization. Among them, the main function of regularization is to prevent overfitting, and adding regularization terms to the model can limit the complexity of the model, so that the model can achieve a balance between complexity and performance. Then use the cv.glmnet function to select the elastic network algorithm for modeling. When setting the parameters, since the elastic network algorithm is used in this case, the range of the parameter ρ is between 0 and 1, and ρ is the penalty coefficient. Then generate multiple models with different ρ within the range, and then use AUC (area under the ROC curve) as the evaluation index of the model, and use three-fold cross validation to verify the constructed model.

The risk formula constructed based on the elastic network model is as follows:

Where α is the intercept, β ₁ , β ₂ and β ₃ are the coefficients of Endoglin, sVEGFR2 and RBP4 respectively. According to the index of AUC greater than 0.85, α, β ₁ , β ₂ and β ₃ are within a certain range, and the specific range is shown in Table 2. Table 2 Pre-eclampsia risk score coefficient Coefficient Scope α [-5.487, -1.261] β ₁ [0.041, 0.304] β ₂ [0.001, 0.086] β ₃ [0.025, 0.172]

Step 4: Determine the model score threshold according to the clinical use scenario. Specifically, the gestational age of the sample was fixed at 11 ^{+ 0} to 13 ^{+ 6} weeks (corresponding to 11 to 13 weeks of gestation), the sample of pre-eclampsia patients was defined as pre-eclampsia patients with a gestational age of 37 weeks or earlier, and the normal control sample was defined as normal control personnel with a gestational age of 37 weeks or more. After the clinical use scenario was determined, there were 10 samples of pre-eclampsia patients and 68 normal control samples. Then, the samples of pre-eclampsia patients were randomly sampled with replacement to generate 20 diseased samples. Then, based on the incidence of preterm birth of pre-eclampsia of 0.9%, the 68 normal control samples were randomly sampled with replacement to generate 2203 normal samples. Finally, based on the newly generated dataset consisting of 20 preeclampsia samples and 2203 normal samples, the risk score was calculated using the preeclampsia risk model, and based on the requirements of sensitivity above 90%, specificity above 90%, and NPV above 90%, the cutoff value that can make the PPV reach the highest was selected as the threshold for this clinical use scenario. Since each coefficient in the third step is within a range, the threshold range can be fixed at 0.350 to 0.394 based on the fixed values of the two 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. Table 3 Performance of the optimal preeclampsia risk model under fixed clinical use scenarios Gestational age Threshold interval Best performance of the preeclampsia risk model 11 ⁺⁰ to 13 ⁺⁶ weeks 0.350-0.394 Sensitivity 95% CI: 90% (75%-100%) Specificity 95% CI: 93.8% (92.8%-94.8%) PPV: 11.7% NPV: 99.9% Morbidity: 0.9%

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. Specifically, the three biomarkers are Endoglin, sVEGFR2 and RBP4.

To achieve the above objectives, the following steps were implemented to build a prediction model:

Step 1: 63 people were identified as the model training samples, including 32 samples of patients with preeclampsia and 31 samples of normal pregnancy. The gestational age of all samples was between 20 and 40 weeks.

Step 2: Using the three locked biomarkers as features, supervised learning was performed through the elastic network algorithm in the R language package Glmnet, and parameter optimization was performed based on three-fold cross-validation to construct a model for predicting preeclampsia risk assessment. During 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. The risk formula constructed based on the elastic network model is as follows: Among them, α is the intercept, and β1, β2 and β3 are the coefficients of Endoglin, sVEGFR2 and RBP4 respectively. According to the index of AUC greater than 0.85, α, β1, β2 and β3 can be adjusted within a certain range. The specific range is shown in Table 4. Table 4 Pre-eclampsia risk score coefficient Coefficient Scope α [-1.537, -1.399] β ₁ [0.129, 0.403] β ₂ [-0.163, -0.004] β ₃ [-0.029, 0.000]

Step 3: Determine the model score threshold based on two different clinical use scenarios.

Specifically, the gestational age of the samples was fixed at 20 ^{+ 0} to 33 ^{+ 6} weeks (corresponding to 20-33 gestational weeks) in clinical use scenario 1. The samples of preeclampsia patients were defined as samples of patients with early-onset preeclampsia whose sample collection time was within 34 weeks, and the samples of normal controls were defined as samples of normal controls whose sample collection time was within 34 weeks. After the clinical use scenario was determined, there were 15 samples of preeclampsia patients and 15 normal controls. The risk score was calculated by the preeclampsia risk model, and based on the requirements of sensitivity of more than 90%, specificity of more than 90% and PPV of more than 90%, the cutoff value that could make NPV reach the highest was selected as the threshold value of this clinical use scenario. Since all coefficients in the second step are within a range, the threshold range can be fixed at 0.550 to 0.781 according to the fixed values of both ends of each characteristic coefficient. The performance of the pre-eclampsia risk model under this range of thresholds is shown in Table 5. When α=-1.463, _β1 =0.286, _β2 =-0.128, _β3 =-0.008 and the threshold is 0.761, the pre-eclampsia risk model can achieve the best performance in this scenario. The optimal ROC curve at this time is shown in Figure 4.

Clinical use scenario 2 fixed the gestational age at 34 ^{+ 0} (corresponding to 34 gestational weeks) to delivery, and the preeclampsia patient samples were defined as the samples of late-onset preeclampsia patients whose sample collection time was after 34 weeks, and the normal control samples were defined as the samples of normal control personnel whose sample collection time was after 34 weeks. After the clinical use scenario was determined, there were 17 preeclampsia patient samples and 16 normal control samples. Then, the risk score was calculated through the preeclampsia risk model, and according to the requirements of more than 90% specificity and more than 90% PPV, the cutoff value that can make NPV and sensitivity reach the highest was selected as the threshold value of this clinical use scenario. Similarly, since all coefficients in the second step are within a range, the threshold range can be fixed at 0.556 to 0.773 according to the fixed values of the two ends of each characteristic coefficient. The performance of the pre-epileptic risk model under this range of thresholds is shown in Table 5, and the optimal ROC curve at this time is shown in Figure 5. Table 5 Performance of the optimal pre-epileptic risk model under fixed clinical use scenarios Gestational age Threshold interval Best performance of the preeclampsia risk model 22 ⁺⁰ to 33 ⁺⁶ weeks 0.550-0.781 Sensitivity 93.3% Specificity 99.9% PPV: 99.9% NPV: 93.8% 34 ⁺⁰ to delivery 0.556-0.773 Sensitivity 58.8% Specificity 99.9% PPV: 99.9% NPV: 68.2%

Example 4: Preparation method of reagent kit

1. Preparation method of the first Endoglin antibody labeled with acridinium ester: 1) Measure the labeling buffer solution into a centrifuge tube; 2) Add the first Endoglin antibody and mix thoroughly; 3) Add the acridinium ester solution, mix thoroughly, and react at room temperature in the dark; the molar ratio of acridinium ester to the first Endoglin antibody is 1:13; the time for the first Endoglin antibody and acridinium ester to react at room temperature in the dark is 30-150 minutes; 4) Place the above mixture into an ultrafiltration tube, centrifuge at 2000-4000rpm for 20-40 minutes; 5) Add an appropriate amount of labeling buffer solution for quantitative measurement, and seal and store at -20℃.

2. Preparation method of the first sVEGFR2 antibody labeled with acridinium ester: 1) Measure the labeling buffer solution into a centrifuge tube; 2) Add the first sVEGFR2 antibody and mix thoroughly; 3) Add the acridinium ester solution, mix thoroughly, and react with shaking at room temperature in the dark; the molar ratio of acridinium ester to the first sVEGFR2 antibody is 1:10; the time for the first sVEGFR2 antibody and acridinium ester to react with shaking at room temperature in the dark is 30-150 minutes; 4) Place the above mixture into an ultrafiltration tube, centrifuge at 2000-4000rpm for 20-40 minutes; 5) Add an appropriate amount of labeling buffer solution for quantitative measurement, and seal and store at -20℃.

3. Preparation method of the first RBP4 antibody labeled with acridinium ester: 1) Measure the labeling buffer solution into a centrifuge tube; 2) Add the first RBP4 antibody and mix thoroughly; 3) Add the acridinium ester solution, mix thoroughly, and react at room temperature in the dark; the molar ratio of acridinium ester to the first RBP4 antibody is 1:10; the first RBP4 antibody and acridinium ester react at room temperature in the dark for 30-150 minutes; 4) Place the above mixture into an ultrafiltration tube, centrifuge at 2000-4000rpm for 20-40 minutes; 5) Add an appropriate amount of labeling buffer solution for quantitative measurement, and seal and store at -20℃.

4. Preparation method of magnetic microparticles coated with the second Endoglin antibody: 1) Take 200 mg of magnetic microparticles, magnetically separate and remove the supernatant, and resuspend with 400 μL of 0.05 mol/L, pH 4.5-5.5 MES buffer; 2) Add 0.5-1 mL of freshly prepared 10 mg/mL EDC aqueous solution and suspend at room temperature for 30-60 min; 3) Magnetic separation, remove the supernatant, and resuspend with 400 μL of 0.05 mol/L, pH 4.5-5.5 MES buffer; 4) Add 50 μg of the second Endoglin antibody and suspend at room temperature for 10-30 min; 5) Magnetic separation, remove the supernatant, dilute and resuspend to 0 with magnetic microparticle buffer .5mg/mL, complete the preparation of magnetic separation reagent.

5. Preparation method of magnetic microparticles coated with a second sVEGFR2 antibody: 1) Take 200 mg of magnetic microparticles, magnetically separate and remove the supernatant, and resuspend with 400 μL of 0.05 mol/L, pH 4.5-5.5 MES buffer; 2) Add 0.5-1 mL of freshly prepared 10 mg/mL EDC aqueous solution and suspend at room temperature for 30-60 min; 3) Magnetic separation, remove the supernatant, and resuspend with 400 μL of 0.05 mol/L, pH 4.5-5.5 MES buffer; 4) Add 50 μg of the second sVEGFR2 antibody and suspend at room temperature for 10-30 min; 5) Magnetic separation, remove the supernatant, dilute and resuspend to 0 with magnetic microparticle buffer. .5mg/mL, complete the preparation of magnetic separation reagent.

6. Preparation method of magnetic microparticles coated with the second RBP4 antibody: 1) Take 200 mg of magnetic microparticles, magnetically separate and remove the supernatant, and resuspend with 400 μL of 0.05 mol/L, pH 4.5-5.5 MES buffer; 2) Add 0.5-1 mL of freshly prepared 10 mg/mL EDC aqueous solution and suspend at room temperature for 30-60 min; 3) Magnetic separation, remove the supernatant, and resuspend with 400 μL of 0.05 mol/L, pH 4.5-5.5 MES buffer; 4) Add 50 μg of the second RBP4 antibody and suspend at room temperature for 10-30 min; 5) Magnetic separation, remove the supernatant, dilute and resuspend to 0 with magnetic microparticle buffer. .5mg/mL, complete the preparation of magnetic separation reagent.

The preparation method of the pre-excitation solution of this embodiment is as follows: 0.8L purified water, 4.862mL concentrated nitric acid and 5.46mL 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 up 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%.

Example 5: Method of using the reagent kit

The testing process is as follows:

1. The method of using the soluble Endoglin protein (Endoglin antibody) quantitative detection kit is as follows: 1) Add 25μL of calibrator, quality control or sample to be tested to the test tube; 2) Add 50μL of the second Endoglin antibody-magnetic particles to the test tube; 3) Add 50μL of the first Endoglin antibody acridinium ester to the test tube; 4) After mixing, incubate at 37±0.5℃ for 30 minutes; 5) Add 450μL of cleaning solution to the test tube and mix; 6) Magnetic separation to remove the supernatant; 7) Repeat steps 5 and 6 four times; 8) Add 100μL of pre-excitation solution A and 100μL of excitation solution B to the test tube; 9) Detect the luminescence intensity after 9.2s.

2. The method of using the sVEGFR2 quantitative detection kit is as follows: 1) Add 25μL of calibrator, quality control or sample to be tested to the test tube; 2) Add 50μL of the second sVEGFR2 antibody-magnetic microparticles to the test tube; 3) Add 50μL of the first sVEGFR2 antibody-acridinium ester to the test tube; 4) After mixing, incubate at 37±0.5℃ for 30 minutes; 5) Add 450L of cleaning solution to the test tube and mix; 6) Magnetic separation to remove the supernatant; 7) Repeat steps 5 and 6 four times; 8) Add 100μL of pre-stimulation solution A and 100μL of stimulation solution B to the test tube; 9) Detect the luminescence intensity after 2s.

3. The method of using the RBP4 quantitative detection kit is as follows: 10) Add 25μL of calibrator, quality control or sample to be tested to the test tube; 11) Add 50μL of the second RBP4 antibody-magnetic microparticles to the test tube; 12) Add 50μL of the first RBP4 antibody-acridinium ester to the test tube; 13) After mixing, incubate at 37±0.5℃ for 30 minutes; 14) Add 450L of cleaning solution to the test tube and mix; 15) Magnetic separation to remove the supernatant; 16) Repeat steps 5 and 6 four times; 17) Add 100μL of pre-excitation solution A and 100μL of excitation solution B to the test tube; 18) Detect the luminescence intensity after 9, 2s.

The three reagent kits of Endoglin, sVEGFR2 and RBP4 of the present invention were used to detect the levels of the 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:

Project 1. Biomarker panel including Endoglin, sVEGFR2 and RBP4.

Item 2. The biomarker panel of Item 1 is used for prediction or assessment of disease risk or diagnosis of disease, preferably for assessment of pre-eclampsia-related conditions, more preferably for prediction or assessment of pre-eclampsia risk or diagnosis of pre-eclampsia.

Item 3. A reagent kit or device comprising a detection reagent for detecting the expression amount 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 for predicting or assessing disease risk or diagnosing a disease, preferably for assessing a condition related to preeclampsia, and more preferably for predicting or assessing preeclampsia risk or diagnosing preeclampsia.

Item 5. A method for screening a biomarker group for the prediction or assessment of pre-eclampsia risk or the diagnosis of pre-eclampsia, comprising the following steps: 1) Retrieving candidate biomarkers associated with pre-eclampsia; 2) Further confirming the candidate biomarkers whose expression levels have changed in the samples of the subjects; 3) Comparing with the clinical information of the subjects, and calculating the pre-eclampsia risk score by constructing a formula; 4) Selecting the best cutoff value of the pre-eclampsia risk model as the threshold value; 5) When the pre-eclampsia risk score is higher than the threshold value, the combination of candidate biomarkers with good clinical performance is determined as the biomarker group.

Item 6. The method of Project 5, wherein the biomarker group includes Endoglin, sVEGFR2 and RBP4.

Item 7. A method for predicting whether a subject has a risk of developing preeclampsia, comprising: 1) determining the expression of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) calculating a preeclampsia risk score using a formula based on the expression of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if the score is higher than the threshold value, predicting that the subject has a risk of developing preeclampsia.

Item 8. A method for assessing a subject's risk of developing preeclampsia, comprising: 1) determining the expression of biomarkers including Endoglin, sVEGFR2 and RBP4 in the subject's sample; 2) calculating a preeclampsia risk score using a formula based on the expression of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if the score is higher than the threshold value, the higher the score, the higher the risk of the subject developing preeclampsia.

Item 9. A method for diagnosing whether a subject suffers from preeclampsia, comprising: 1) determining the expression of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) calculating a preeclampsia risk score using a formula based on the expression of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if it is higher than the threshold value, diagnosing that the subject suffers from preeclampsia.

Item 10. Use of a biomarker group including Endoglin, sVEGFR2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker group, in the preparation of a reagent kit or device for predicting whether a subject has a risk of developing preeclampsia, wherein the prediction comprises: 1) determining the expression level of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) calculating a preeclampsia risk score using a formula based on the expression level of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if it is higher than the threshold value, predicting that the subject has a risk of developing preeclampsia.

Item 11. Use of a biomarker group including Endoglin, sVEGFR2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker group, in the preparation of a reagent kit or device for assessing the risk of a subject suffering from preeclampsia, wherein the assessment comprises: 1) determining the expression level of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) calculating the preeclampsia risk score using a formula based on the expression level of the biomarkers; 3) comparing the preeclampsia risk score with a threshold value, and if the score is higher than the threshold value, the higher the score, the higher the risk of the subject suffering from preeclampsia.

Item 12. Use of a biomarker group including Endoglin, sVEGFR2 and RBP4, or a detection reagent that specifically binds to a biomarker in the biomarker group, in the preparation of a reagent kit or device for diagnosing whether a subject suffers from preeclampsia, wherein the diagnosis comprises: 1) Determining the expression level of biomarkers including Endoglin, sVEGFR2 and RBP4 in the sample of the subject; 2) Calculating a preeclampsia risk score using a formula based on the expression level of the biomarkers; 3) Comparing the preeclampsia risk score with a threshold value, and if it is higher than the threshold value, the subject is diagnosed with preeclampsia.

Item 13. The kit, apparatus, 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, apparatus, 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, apparatus, method or use of any one of Items 3-14, wherein the subject is a pregnant subject, and the gestational age is between 6 weeks and 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.

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

Item 17. The method or use of Item 15, wherein the preeclampsia is preterm preeclampsia.

Item 18. The method or use of Item 16 or 17, wherein the formula is or any simple adjustment of their results, 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.

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 the formula.

Item 20. The method or use of Item 16 or 17, wherein the formula is Or any simple adjustment of its results.

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.

Item 22. The method or use of Item 15, wherein the pregnant subject is at a gestational age of 20 to 40 weeks.

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

or any simple adjustment of their results, where 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.

Item 24. The method or use of Item 22 or 23, wherein the pregnant subject is at a gestational age of 23 to 33 weeks.

Item 25. The method or use of Item 22 or 23, wherein the 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 the formula.

Item 27. The method or use of Item 24 or 25, wherein the formula is Or any simple adjustment of its results.

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 the formula.

Item 29. The method or use of Item 22 or 23, wherein the pregnant subject is at a gestational age of 34 to 40 weeks.

Item 30. The method or use of Item 22 or 23, wherein the 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 a 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 its results.

Item 33. The method or use of Item 32, wherein the threshold is 0.723, or any simple adjustment thereof resulting from simple adjustment of the formula.

without

Figure 1 shows the biomarker discovery and validation process. Figure 2 shows the ROC curves of the biomarkers Endoglin, sVEGFR2, and RBP4. Figure 3 shows the ROC curve of the optimal preeclampsia risk model in the clinical use scenario of 11 ⁺⁰ to 13 ⁺⁶ weeks. Figure 4 shows the ROC curve of the optimal preeclampsia risk model in the clinical use scenario of 20 ⁺⁰ to 33 ⁺⁶ weeks. Figure 5 shows the ROC curve of the optimal preeclampsia risk model in the clinical use scenario of 34 ⁺⁰ to delivery.

Claims (21)

A biomarker panel including Endoglin, sVEGFR2 and RBP4. The biomarker group as described in claim 1 is used for predicting or evaluating disease risk or diagnosing a disease. The biomarker group as described in claim 1 is used for evaluating preeclampsia-related conditions. The biomarker group as described in claim 1 is used for predicting or assessing the risk of preeclampsia. The biomarker group as described in claim 1 is used for diagnosing preeclampsia. A device includes a detection reagent for detecting the expression amount of a biomarker in a biomarker group in a subject sample, wherein the biomarker group includes Endoglin, sVEGFR2 and RBP4. A device as described in claim 6, which is used for predicting or assessing disease risk or diagnosing a disease. The device as described in claim 6 is used for evaluating conditions related to preeclampsia. The device as described in claim 6 is used for predicting or assessing the risk of preeclampsia. The device as described in claim 6 is used for diagnosing preeclampsia. The device as described in any one of claims 6 to 10, which is a test kit. A use of a biomarker group or a detection reagent in the preparation of a device for predicting whether a subject has a risk of developing preeclampsia, wherein the biomarker group includes Endoglin, sVEGFR2 and RBP4, and the detection reagent specifically binds to the biomarkers in the biomarker group. A use of a biomarker group or a detection reagent in the preparation of a device for assessing a subject's risk of developing preeclampsia, wherein the biomarker group includes Endoglin, sVEGFR2 and RBP4, and the detection reagent specifically binds to the biomarkers in the biomarker group. A use of a biomarker group or a detection reagent in the preparation of a device for diagnosing whether a subject suffers from preeclampsia, wherein the biomarker group includes endoglin, sVEGFR2 and RBP4, and the detection reagent specifically binds to the biomarkers in the biomarker group. The use as described in any one of claims 12 to 14, wherein the subject is a pregnant subject with a gestational age of 6 to 40 weeks. The use as described in any one of claims 12 to 14, wherein the subject is a pregnant subject with a gestational age of 6 to 13 weeks. The use as described in any one of claims 12 to 14, wherein the subject is a pregnant subject with a gestational age of 11 to 13 weeks. The use as described in any one of claims 12 to 14, wherein the subject is a pregnant subject with a gestational age of 20 to 40 weeks. The use as described in any one of claims 12 to 14, wherein the subject is a pregnant subject with a gestational age of 23 to 33 weeks. The use as described in any one of claims 12 to 14, wherein the subject is a pregnant subject with a gestational age of 34 to 40 weeks. The use according to any one of claims 12 to 14, wherein the preeclampsia comprises premature preeclampsia, early-onset preeclampsia or late-onset preeclampsia.