A method of diagnosing or predicting preeclampsia

Abstract

A method for diagnosing or predicting preeclampsia in a pregnant woman or determining whether a pregnant woman is at risk of developing preeclampsia is provided, comprising: a) obtaining at least one (such as two or more) maternal or fetal parameter or factor of the pregnant woman selected from the group comprising: (i) one or more of mean arterial pressure, BMI at sampling, ΔBMI, gestational age, and ΔBMI / GA; (ii) maternal age; (iii) one or more of urinary protein level and ratio of urinary protein level (such as total urinary protein) to urinary creatinine level; (iv) one or more of history of preeclampsia in previous pregnancies and having received antihypertensive medication; and (v) any combination of (i)-(iv), b) determining whether the pregnant woman has misfolded proteins or misfolded protein aggregates in her urine, and c) combining the at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded proteins or misfolded protein aggregates in the pregnant woman's urine to diagnose or predict preeclampsia or determine the risk of preeclampsia.

Description

Technical Field

[0001] This invention generally relates to a method for diagnosing or predicting diseases, and more particularly to a method for diagnosing or predicting preeclampsia. Background Technology

[0002] Preeclampsia is a pregnancy disorder associated with new-onset hypertension, most commonly occurring after 20 weeks of gestation. Globally, 2-5% of pregnant women have preeclampsia, with a higher rate in developing countries. Clinical manifestations of preeclampsia include hypertension and proteinuria, or critical organ dysfunction such as visual disturbances, headache, upper abdominal pain, and rapidly developing edema. It is a leading cause of maternal and perinatal morbidity and mortality. Serious consequences of preeclampsia can include cerebral hemorrhage, disseminated intravascular coagulation (DIC), maternal kidney failure, and stillbirth or premature birth, all of which endanger maternal and infant health. Termination of pregnancy is often necessary to avoid serious complications. According to guidelines from the American College of Obstetricians and Gynecologists (ACOG) and the Society of Obstetricians and Gynecologists of Canada (SOGC), or *Williams Obstetrics*, 24th edition, the diagnosis of preeclampsia is based on subjective judgment of hypertension and proteinuria, or hypertension and other symptoms. However, diagnosing proteinuria with a single test strip reading is unreliable, and nonspecific clinical symptoms and signs are often cumbersome; currently, there is no accurate, objective, and widely available diagnostic method to confirm the definitive diagnosis. Therefore, there is an unmet need for developing a simple and objective test method for the diagnosis and screening of preeclampsia. Summary of the Invention

[0003] On one hand, this disclosure provides a method for diagnosing or predicting preeclampsia in pregnant women, comprising: a) obtaining at least one maternal or fetal parameter or factor, b) determining whether the urine of the pregnant woman contains misfolded proteins or misfolded protein aggregates, and c) using a combination of the at least one maternal or fetal parameter or factor obtained in step a) and the presence or absence of misfolded proteins or misfolded protein aggregates in the urine of the pregnant woman to diagnose or predict preeclampsia.

[0004] On the other hand, this article provides a method for diagnosing or predicting preeclampsia in pregnant women or determining whether a pregnant woman is at risk of developing preeclampsia, which includes: a) Obtain at least one (e.g., two or more) maternal or fetal parameters or factors for the pregnant woman, said parameters or factors being selected from the group consisting of: (i) mean arterial pressure (MAP), BMI 采样时 (i.e., the pregnant woman's BMI at the time of urine sampling), ΔBMI (i.e., BMI) 采样时 - BMI 初始(i) The pregnant woman’s BMI before pregnancy, in early pregnancy, or in the first month of her first pregnancy, gestational age (GA, optionally in weeks), and ΔBMI / GA, or more of the following: (ii) Maternal age (optionally in years); (iii) The level of urine protein and the ratio of urine protein level (e.g., total urine protein (UTP)) to urine creatinine level (Cre) (UTP / Cre); (iv) A history of preeclampsia (PEhis) and treatment with antihypertensive drugs (Antihyper); and (v) Any combination of (i)-(iv). b) Determine whether the pregnant woman's urine contains misfolded proteins or misfolded protein aggregates, and c) Combine at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded protein or misfolded protein aggregates in the pregnant woman's urine to diagnose or predict preeclampsia or determine the risk of preeclampsia.

[0005] The methods disclosed herein can significantly improve the sensitivity and / or specificity of preeclampsia diagnosis. The above and other advantages and features of the invention, and their implementations, will become more apparent upon taking into account the following detailed description, including the accompanying drawings illustrating preferred and exemplary embodiments. Detailed Implementation

[0006] The inventors have surprisingly discovered that combining the urine misfolded protein test with at least one maternal or fetal parameter or factor produces a synergistic effect and significantly improves the accuracy of diagnosis or prediction of preeclampsia.

[0007] As used herein, the term "misfolded protein" is used in contrast to a correctly folded protein. The most common outcome for misfolded proteins is self-aggregation due to the exposure of hydrophobic protein surfaces that are normally embedded within the protein, resulting in high viscosity. β-sheet structural motifs provide the most favorable organizational form for these intermolecular aggregates; therefore, misfolded proteins typically exist as aggregates (oligomers and fibrous aggregates). In this invention, "misfolded protein" includes such aggregates formed by misfolded proteins.

[0008] For example, one study found that the urine and placental tissue of patients with preeclampsia contained large amounts of misfolded proteins and their aggregates. These aggregates specifically bind to Congo red, and under an electron microscope, these Congo red-loving misfolded protein aggregates exhibit a fibrous structure very similar to amyloid structures, while such aggregates are not present in normal pregnant women (Buhimschi et al., Translational Medicine, 2014, 6(245):245ra92).

[0009] Once a conformational change occurs and misfolding cannot be repaired or eliminated, even structurally and sequence-dissimilar proteins can form β-sheet structures. The β-sheet structures of different proteins can interact to form aggregates containing different misfolded proteins, and these aggregates can specifically bind to Congo red.

[0010] A capillary-based method can be used to detect misfolded proteins or misfolded protein aggregates in pregnant women via immediate detection of urine samples. In the capillary-based method (such as the so-called "CapCord" method used in Example 3), the diffusion area of ​​dye on filter paper is used to determine the presence or absence of misfolded proteins. If the biological sample does not contain misfolded proteins or misfolded protein aggregates, the test result obtained by this method is negative. That is, when a mixture of a negative sample and dye is released onto the filter paper through a capillary, no obvious diffusion spot forms on the filter paper, or in some embodiments, a small diffusion spot is formed with a radius smaller than a reference value, which can be determined by the maximum radius of diffusion spots formed by a specific number (e.g., 50-100) of clinically determined negative patient samples. If the biological sample contains misfolded proteins or misfolded protein aggregates, the test result obtained by this method is positive. That is, when a mixture of a positive sample and dye is released onto the filter paper through a capillary, a large diffusion spot forms on the filter paper. In some embodiments of the invention, positive samples produce large diffusion patches with radii greater than or equal to a specific reference value, which can be determined by the minimum radii of diffusion patches formed from a specific number (e.g., 50-100) of clinically confirmed positive patient samples. It is noteworthy that in some cases, diffusion patches with one or more pseudopod-like features may be produced, with radii larger than those formed from negative samples, or larger than the aforementioned reference value. Even so, this type of diffusion result is still classified as negative.

[0011] Urinary misfolded proteins can also be detected by chromatography. When a mixture of Congo red and pregnant woman's urine is loaded onto a chromatographic column mounted in a detection cuvette, the presence (positive) or absence (negative) of misfolded proteins can be determined by the color of the eluent in the cuvette. The eluent can also be quantitatively measured using a spectrophotometer (e.g., a HACH DR1900 instrument) by detecting the absorbance at wavelengths of 520–600 nm.

[0012] Exemplary apparatus and methods for detecting misfolded proteins are described in WO2010 / 062377, WO2019 / 128506 and WO2022105923.

[0013] This article provides a method for diagnosing or predicting preeclampsia in pregnant women or determining whether a pregnant woman is at risk of developing preeclampsia, which includes: a) Obtain at least one (e.g., two or more) maternal or fetal parameters or factors for the pregnant woman, said parameters or factors being selected from the group consisting of: (i) mean arterial pressure (MAP), BMI 采样时 (i.e., the pregnant woman's BMI at the time of urine sampling), ΔBMI (i.e., BMI) 采样时 – BMI 初始 (i) One or more of the following: (i) the pregnant woman's BMI before pregnancy, in early pregnancy, or in the first month of her first pregnancy; (ii) gestational age (GA, optionally in weeks); and ΔBMI / GA; (iii) maternal age (optionally in years); (iii) one or more of the following: urine protein level and the ratio of urine protein level (e.g., total urine protein (UTP)) to urine creatinine level (Cre) (UTP / Cre); (iv) one or more of the following: a history of preeclampsia (PEhis) and treatment with antihypertensive drugs (Antihyper); and (v) any combination of (i)-(iv). b) Determine whether the pregnant woman's urine contains misfolded proteins or misfolded protein aggregates, and c) Combine at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded protein or misfolded protein aggregates in the pregnant woman's urine to diagnose or predict preeclampsia or determine the risk of preeclampsia.

[0014] In some embodiments, the content level of the misfolded protein or misfolded protein aggregate is determined as a qualitative measurement (presence or absence), a quantitative measurement, or a semi-quantitative measurement.

[0015] In some embodiments, the content level of the misfolded protein or misfolded protein aggregate is determined as a qualitative measurement (i.e., presence or absence), a quantitative measurement, or a semi-quantitative measurement.

[0016] In some implementations, step c) is performed by an algorithm (e.g., a regression algorithm, such as a linear regression algorithm, a binary regression algorithm, or a multiple linear regression algorithm) to obtain a score p, and optionally, the score p being higher or lower than a predetermined threshold indicates that the pregnant woman is having preeclampsia, is predicted to develop preeclampsia, or is at an increased or decreased risk of having preeclampsia.

[0017] In some embodiments, the at least one maternal or fetal parameter or factor includes (i) MAP and BMI. 采样时 (ii) MAP and maternal age; or (iii) MAP and BMI 采样时And maternal age. In some further embodiments, the prediction includes long-term prediction, such as predicting that preeclampsia will occur more than 4 weeks after the determining step (e.g., 6, 8, 10, 12, 14, 16, or 20 weeks), and optionally, the determining step is not earlier than 20 weeks of gestation, e.g., not earlier than 17 weeks of gestation, e.g., between 10 and 16 weeks of gestation, e.g., 11, 12, 13, 14, 15, or 16 weeks.

[0018] In some embodiments, the at least one maternal or fetal parameter or factor includes: at least one (such as two, three, four, five, six, seven or more) of the maternal or fetal parameters or factors of the pregnant woman selected from the group comprising MAP, PEhis, Antihyper, ΔBMI, ΔBMI / GA, UTP, UTP / Cre and maternal age, for example selected from the group comprising MAP, PEhis, Antihyper, ΔBMI, ΔBMI / GA, UTP and UTP / Cre. In some further embodiments, the at least one maternal or fetal parameter or factor includes: (1) MAP, PEhis, Antihyper, ΔBMI / GA, and UTP / Cre; (2) MAP, PEhis, Antihyper, ΔBMI, and UTP / Cre; (3) MAP, PEhis, Antihyper, ΔBMI / GA, and UTP; (4) MAP, PEhis, Antihyper, ΔBMI / GA, and UTP; (5) MAP, PEhis, Antihyper, ΔBMI, and UTP; (6) MAP, PEhis, Antihyper, and UTP / Cre; (7) MAP, PEhis, Antihyper, and ΔBMI / GA; (8) MAP, PEhis, ΔBMI / GA, and UTP / Cre; (9) MAP, PEhis, Antihyper, and ΔBMI; (10) MAP, Antihyper, ΔBMI and UTP / Cre; (11) MAP, PEhis, ΔBMI and UTP / Cre; (12) MAP, PEhis, Antihyper and UTP; (13) MAP, Antihyper, ΔBMI / GA and UTP; (14) MAP, PEhis, ΔBMI / GA and UTP; (15) MAP, Antihyper, ΔBMI and UTP; (16) MAP, PEhis, ΔBMI and UTP; (17) MAP, PEhis and Antihyper; (18) MAP, Antihyper and ΔBMI / GA; (19) MAP, PEhis and ΔBMI / GA; (20) MAP, Antihyper and UTP / Cre; (21) MAP, PEhis and UTP / Cre; (22) MAP, ΔBMI / GA and UTP / Cre; (23) PEhis, ΔBMI / GA and UTP / Cre; (24) MAP, Antihyper and ΔBMI; (25) MAP, PEhis and ΔBMI; (26) MAP, ΔBMI and UTP / Cre; (27) PEhis, ΔBMI and UTP / Cre;(28) MAP, Antihyper, and UTP; (29) MAP, PEhis, and UTP; (30) MAP, ΔBMI / GA, and UTP; (31) PEhis, ΔBMI / GA, and UTP; (32) MAP, ΔBMI, and UTP; (33) PEhis, ΔBMI, and UTP; (34) MAP and Antihyper; (35) MAP and PEhis; (36) MAP and ΔBMI / GA; (37) MAP and UTP / Cre; (38) PEhis and ΔBMI / GA; (39) PEhis and UTP / Cre; (40) ΔBMI / GA and UTP / Cre; (41) MAP and ΔBMI; (42) PEhis and ΔBMI; (43) ΔBMI and UTP / Cre; (44) MAP and UTP; (45) PEhis and UTP; (46) ΔBMI / GA and UTP; (47) ΔBMI and UTP; (48) ΔBMI / GA and maternal age; or (49) MAP, ΔBMI / GA and maternal age; (50) ΔBMI / GA. In some further embodiments, the prediction includes short-term prediction, such as predicting the occurrence of preeclampsia within 4 weeks after the determination step (e.g., within 1, 2, 3, or 4 weeks), and optionally, the urine measurement is later than 20 weeks of gestation, for example, between 20 and 37 weeks of gestation, such as 22, 24, 26, 28, 30, 32, 34, or 36 weeks.

[0019] In some implementations, the pregnant woman is a woman suspected of having preeclampsia or at high risk of having preeclampsia.

[0020] In some short-term prediction implementations, a higher P-value indicates a higher risk of preeclampsia occurring over a period of time (e.g., within 4 weeks, such as 1, 2, 3, or 4 weeks), while a lower P-value indicates a lower risk of preeclampsia occurring over a period of time (e.g., within 4 weeks, such as 1, 2, 3, or 4 weeks). In some implementations, maternal or fetal parameters or factors positively associated with the risk of preeclampsia (PE) are selected from groups including MAP, PEhis, Antihyper, ΔBMI, ΔBMI / GA, UTP, UTP / Cre, and maternal age. In some implementations, higher MAP, ΔBMI / GA, UTP, or UTP / Cre values ​​and maternal age are associated with a higher risk of PE, while lower MAP, ΔBMI / GA, UTP, or UTP / Cre values ​​and maternal age are associated with a lower risk of PE. In some implementations, the presence of PEhis or Antihyper is associated with a higher risk of PE, and the absence of PEhis or Antihyper is associated with a lower risk of PE. In some implementations, depending on the model used, the P-value can be calculated using one of the following formulas: logit(P) = 2.733 * CercaTest + 0.102 * MAP + 1.629 * antiHyper + 3.743 * PEhis + 6.256 * ΔBMI / GA + 0.050 * UTP / cre - 14.816, with a cutoff value of -1.280384; logit(P) = 3.238 * CercaTest + 0.127 * MAP + 1.450 * antiHyper + 4.364 * PEhis + 5.749 * ΔBMI / GA - 16.450, cutoff value is -1.2945526; or logit(P) = 3.256 * CercaTest + 0.129 * MAP + 1.361 * AntiHyper + 4.297 * PEhis - 15.924, with a cutoff value of -1.333000.

[0021] In some implementations, maternal or fetal parameters can be assigned values ​​in formulas as follows: 1 for positive misfolded protein (i.e., CercaTest positive) and 0 for negative misfolded protein (i.e., CercaTest negative); MAP is calculated using the formula: diastolic blood pressure + (systolic blood pressure - diastolic blood pressure) / 3, and the resulting value is used; history of PE is coded as 1 if present, 0 if absent; treatment with antihypertensive medication is coded as 1 if present, 0 otherwise; BMI is calculated by dividing individual weight (kg) by the square of height (m). ΔBMI, ΔBMI / GA, UTP, UTP / Cre, and maternal age are all assigned their measured values.

[0022] In some long-term prediction implementations, a higher P-value indicates a higher risk of preeclampsia occurring more than 4 weeks after the urine sample determination step (e.g., 6, 8, 10, 12, 14, 16, or 20 weeks later), while a lower P-value indicates a lower risk of preeclampsia occurring more than 4 weeks after the urine sample determination step (e.g., 6, 8, 10, 12, 14, 16, or 20 weeks later).

[0023] This article also provides a method for treating, preventing, or managing preeclampsia in pregnant women, which includes: a) Obtain at least one (e.g., two or more) maternal or fetal parameters or factors of the pregnant woman as defined herein. b) Determine whether the pregnant woman's urine contains misfolded proteins or misfolded protein aggregates. c) Using an algorithm, such as a regression algorithm (e.g., a linear regression algorithm, such as a binary regression algorithm or a multiple linear regression algorithm), combine at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded protein or misfolded protein aggregates in the pregnant woman's urine to obtain a score p. The score p being higher or lower than a predetermined threshold indicates that the pregnant woman is currently suffering from preeclampsia, is predicted to develop preeclampsia, or is at an increased or decreased risk of developing preeclampsia; and d) If the pregnant woman is diagnosed with preeclampsia, or is predicted to develop preeclampsia, or is at increased risk of developing preeclampsia, administer medication to the pregnant woman and / or manage the pregnant woman's condition to prolong the pregnancy.

[0024] In some implementations, medication administration includes aspirin administration, and / or management of the pregnant woman's condition, including weight loss, blood pressure and / or blood sugar control, and / or maintaining a regular exercise program.

[0025] This article also provides a computer-based method for diagnosing or predicting preeclampsia in pregnant women or determining whether a pregnant woman is at risk of developing preeclampsia, comprising: a) Obtain at least one (e.g., two or more) maternal or fetal parameters or factors of the pregnant woman as defined herein. b) Obtain the level of misfolded proteins or misfolded protein aggregates in the urine of the pregnant woman, and c) Combine at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded protein or misfolded protein aggregates in the pregnant woman's urine to diagnose or predict preeclampsia or determine the risk of preeclampsia.

[0026] This document also provides a data processing apparatus, device, or system including means for performing the steps of the methods disclosed herein, or including a processor adapted or configured to perform the steps of the methods disclosed herein.

[0027] This document also provides a computer program product or computer-readable medium including instructions that, when executed by a computer, cause the computer to perform the steps of the methods disclosed herein.

[0028] This document also provides a kit comprising reagents for performing the methods disclosed herein. In one embodiment, the kit may comprise reagents for detecting the presence of misfolded proteins. In one embodiment, the kit may further comprise reagents for detecting UTP, Cre, and / or UTP / Cre.

[0029] As used herein, the term "kit" refers to an article comprising one or more containers and optionally a data carrier. The one or more containers may contain one or more of the aforementioned means or reagents. The kit may contain additional containers containing, for example, diluents, buffers, and other reagents. The data carrier may be a non-electronic data carrier, such as a graphic data carrier like an information page, information table, barcode, or access code, or an electronic / computer-readable data carrier, such as an optical disc, digital multifunction optical disc, microchip, or other semiconductor-based electronic data carrier. The access code may allow access to a database, such as an internet database, a centralized database, or a decentralized database. The data carrier may contain instructions for using the kit in the methods disclosed herein. The data carrier may contain a threshold or reference level, or a P-score calculated according to the method of the invention. If the data carrier contains an access code that allows access to a database, the threshold or reference level is stored in that database. Furthermore, the data carrier may contain information or instructions on how to implement the methods of the invention.

[0030] On the other hand, a method for diagnosing or predicting preeclampsia in pregnant women is provided, comprising: a) Obtain at least one maternal or fetal parameter or factor selected from the group comprising: (i) one or more of blood pressure (e.g., mean arterial pressure), BMI, maternal abdominal circumference, and gestational age; (ii) one or more of uterine artery pulsatility index, maternal age, and fetal crown-rump length; (iii) one or more of creatinine level, urine protein, plasma and / or urine PlGF, plasma sFlt, and plasma PAPPA-A level; (iv) one or more of platelet count, GGT (gamma-glutamyl transferase), fasting blood glucose, and alanine aminotransferase level; (v) one or more of a history of preeclampsia, maternal history of preeclampsia, chronic hypertension, family history of hypertension, autoimmune disease, thyroid disease, diabetes, pre-gestational diabetes, proteinuria, kidney disease, mental disorder, uterine malformation, multiple pregnancy, nulliparity, and assisted reproductive technology; and (vi) any combination of (i)-(v). b) Determine whether the pregnant woman's urine contains misfolded proteins or misfolded protein aggregates, and c) Use at least one maternal or fetal parameter or factor obtained in step a) in combination with the presence or absence of misfolded proteins or misfolded protein aggregates in the pregnant woman's urine to diagnose or predict preeclampsia.

[0031] In one implementation, the at least one maternal or fetal parameter or factor includes two, three, or four of the following: blood pressure (e.g., mean arterial pressure), BMI, maternal abdominal circumference, and gestational age.

[0032] In one implementation, the at least one maternal or fetal parameter or factor includes blood pressure (such as mean arterial pressure) and BMI.

[0033] In one implementation, the at least one maternal or fetal parameter or factor includes blood pressure (such as mean arterial pressure) and maternal abdominal circumference.

[0034] In one implementation, the at least one maternal or fetal parameter or factor includes blood pressure (such as mean arterial pressure) and gestational age.

[0035] In one implementation, the at least one maternal or fetal parameter or factor includes blood pressure (such as mean arterial pressure), BMI, and maternal abdominal circumference.

[0036] In one implementation, the at least one maternal or fetal parameter or factor includes blood pressure (such as mean arterial pressure), BMI, and gestational age.

[0037] In one implementation, the at least one maternal or fetal parameter or factor includes blood pressure (such as mean arterial pressure), BMI, maternal abdominal circumference, and gestational age.

[0038] In one implementation, the at least one maternal or fetal parameter or factor includes BMI, maternal abdominal circumference, and gestational age.

[0039] In one implementation, the at least one maternal or fetal parameter or factor includes BMI and maternal abdominal circumference.

[0040] In one implementation, the at least one maternal or fetal parameter or factor includes BMI and gestational age.

[0041] In one implementation, the at least one maternal or fetal parameter or factor includes maternal abdominal circumference and gestational age.

[0042] In one implementation, the pregnant woman is diagnosed with preeclampsia or is predicted to develop preeclampsia if at least one maternal or fetal parameter or factor is above or below a predetermined threshold and / or present or absent, and if misfolded proteins or misfolded protein aggregates are present in the urine.

[0043] In one implementation, if at least one maternal or fetal parameter is above a predetermined threshold, and if misfolded proteins or misfolded protein aggregates are present in the urine, the pregnant woman is diagnosed with preeclampsia or is predicted to develop preeclampsia.

[0044] In one implementation, if at least one maternal or fetal parameter is below a predetermined threshold, and if misfolded proteins or misfolded protein aggregates are present in the urine, the pregnant woman is diagnosed with preeclampsia or is predicted to develop preeclampsia.

[0045] In one implementation, if at least one maternal or fetal factor is present, and if misfolded proteins or misfolded protein aggregates are present in the urine, the pregnant woman is diagnosed with preeclampsia or is predicted to develop preeclampsia.

[0046] In one implementation, the pregnant woman is predicted to develop preeclampsia in early pregnancy.

[0047] In another aspect, a method for determining whether a pregnant woman is at risk of preeclampsia is provided, which includes: a) Obtain at least one maternal or fetal parameter or factor selected from the group comprising: (i) one or more of blood pressure (e.g., mean arterial pressure), BMI, maternal abdominal circumference, and gestational age; (ii) one or more of uterine artery pulsatility index, maternal age, and fetal crown-rump length; (iii) one or more of creatinine level, urine protein, plasma and / or urine PlGF, plasma sFlt, and plasma PAPPA-A level; (iv) one or more of platelet count, GGT (gamma-glutamyl transferase), fasting blood glucose, and alanine aminotransferase level; (v) one or more of a history of preeclampsia, maternal history of preeclampsia, chronic hypertension, family history of hypertension, autoimmune disease, thyroid disease, diabetes, pre-gestational diabetes, proteinuria, kidney disease, mental disorder, uterine malformation, multiple pregnancy, nulliparity, and assisted reproductive technology; and (vi) any combination of (i)-(v). b) Determine whether the pregnant woman's urine contains misfolded proteins or misfolded protein aggregates, and c) Use at least one maternal or fetal parameter or factor obtained in step a) in combination with the presence or absence of misfolded proteins or misfolded protein aggregates in the pregnant woman's urine to determine the risk of the pregnant woman developing preeclampsia, for example, in early pregnancy.

[0048] In one implementation, the pregnant woman is identified as having a high risk of developing preeclampsia if at least one maternal or fetal parameter or factor is above or below a predetermined threshold and / or present or absent, and if misfolded proteins or misfolded protein aggregates are present in the urine.

[0049] This document also includes the following implementation schemes.

[0050] 1. A method for diagnosing or predicting preeclampsia in pregnant women or determining whether a pregnant woman is at risk of developing preeclampsia, comprising: a) Obtain at least one maternal or fetal parameter or factor selected from the group consisting of: blood pressure (e.g., mean arterial pressure), BMI, gestational age, maternal age, and any combination thereof. b) Determine whether the pregnant woman's urine contains misfolded proteins or misfolded protein aggregates, and c) Combine at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded protein or misfolded protein aggregates in the pregnant woman's urine to diagnose or predict preeclampsia or determine the risk of preeclampsia.

[0051] 2. The method as described in embodiment 1, wherein the content level of the misfolded protein or misfolded protein aggregate is determined as a qualitative measurement (presence or absence), a quantitative measurement, or a semi-quantitative measurement.

[0052] 3. The method as described in any of the preceding embodiments, wherein the combination step c) is performed by an algorithm, such as a regression algorithm (e.g., a linear regression algorithm, such as a binary regression algorithm or a multiple linear regression algorithm), to obtain a score p, and optionally, the score p being higher or lower than a predetermined threshold indicates that the pregnant woman is suffering from preeclampsia, or is predicted to develop preeclampsia, or is at an increased or decreased risk of suffering from preeclampsia.

[0053] 4. The method as described in any of the foregoing embodiments, wherein BMI is a BMI 采样时 This refers to the pregnant woman's BMI at the time of urine sampling, or ΔBMI, which is BMI. 采样时 - BMI 初始 (The BMI of the pregnant woman before or during the first month of pregnancy, or during the first month of her first pregnancy).

[0054] 5. The method as described in any of the preceding embodiments, wherein the at least one maternal or fetal parameter or factor includes blood pressure (such as mean arterial pressure) and BMI.

[0055] 6. The method as described in any of the preceding embodiments, wherein the at least one maternal or fetal parameter or factor includes blood pressure (such as mean arterial pressure) and maternal age.

[0056] 7. The method as described in any of the preceding embodiments, wherein the at least one maternal or fetal parameter or factor includes blood pressure (such as mean arterial pressure), BMI, and maternal age.

[0057] 8. The method as described in any of the preceding embodiments, wherein the prediction includes long-term prediction, such as predicting that preeclampsia will occur more than 4 weeks after the determining step (e.g., 6, 8, 10, 12, 14, 16 or 20 weeks later), and optionally, the determining step is not earlier than 17 weeks of gestation, such as between 10 and 16 weeks of gestation, for example 11, 12, 13, 14, 15 or 16 weeks.

[0058] 9. The method as described in any of the preceding embodiments, wherein the at least one maternal or fetal parameter or factor includes BMI and gestational age.

[0059] 10. The method of embodiment 9, wherein the at least one maternal or fetal parameter or factor includes ΔBMI / gestational age, optionally gestational age in weeks.

[0060] 11. The method of any one of embodiments 9-10, wherein the at least one maternal or fetal parameter or factor includes ΔBMI / GA and maternal age, or includes ΔBMI / GA and blood pressure (e.g., mean arterial pressure), or includes ΔBMI / GA, maternal age and blood pressure (e.g., mean arterial pressure).

[0061] 12. The method as described in any one of embodiments 9-11, wherein the prediction includes short-term prediction, such as predicting the occurrence of preeclampsia within 4 weeks (e.g., 1, 2, 3 or 4 weeks) after the determining step, and optionally, the urine test is performed later than 20 weeks of gestation, for example, between 20 and 37 weeks of gestation, such as 22, 24, 26, 28, 30, 32, 34 or 36 weeks of gestation.

[0062] 13. The method described in any of the preceding embodiments, wherein the pregnant woman is a woman suspected of having preeclampsia or at high risk of preeclampsia.

[0063] 14. A method for treating, preventing, or managing preeclampsia in pregnant women, comprising: a) Obtain at least one maternal or fetal parameter or factor selected from the group consisting of: blood pressure (e.g., mean arterial pressure), BMI, gestational age, maternal age, and any combination thereof. b) Determine whether the pregnant woman's urine contains misfolded proteins or misfolded protein aggregates. c) Using an algorithm, such as a regression algorithm (e.g., a linear regression algorithm, such as a binary regression algorithm or a multiple linear regression algorithm), combine at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded protein or misfolded protein aggregates in the pregnant woman's urine to obtain a score p. The score p being higher or lower than a predetermined threshold indicates that the pregnant woman is currently suffering from preeclampsia, is predicted to develop preeclampsia, or is at an increased or decreased risk of developing preeclampsia; and d) If the pregnant woman is diagnosed with preeclampsia, or is predicted to develop preeclampsia, or is at increased risk of developing preeclampsia, administer medication to the pregnant woman and / or manage the pregnant woman's condition to prolong the pregnancy.

[0064] 15. The method as described in embodiment 14, wherein administering medication includes administering aspirin, and / or managing the pregnant woman's condition includes weight loss, controlling blood pressure and / or blood sugar, and / or maintaining a regular exercise program.

[0065] 16. The method as described in embodiment 14 or 15, wherein the level of the misfolded protein or misfolded protein aggregate is determined as a qualitative measurement (presence or absence), a quantitative measurement, or a semi-quantitative measurement.

[0066] 17. A method for diagnosing or predicting preeclampsia in pregnant women or determining whether a pregnant woman is at risk of developing preeclampsia, comprising: a) Obtain the ΔBMI / GA ratio of the pregnant woman. b) If the ratio is higher or lower than a predetermined threshold, the pregnant woman is diagnosed with preeclampsia, or is predicted to develop preeclampsia, or is at an increased or decreased risk of developing preeclampsia.

[0067] 18. The method of embodiment 17, wherein the ΔBMI / GA ratio is used alone to diagnose or predict preeclampsia in pregnant women or to determine whether a pregnant woman is at risk of developing preeclampsia.

[0068] 19. A computer-based method for diagnosing or predicting preeclampsia in pregnant women or determining whether a pregnant woman is at risk of developing preeclampsia, comprising: a) Obtain at least one maternal or fetal parameter or factor selected from the group consisting of: blood pressure (e.g., mean arterial pressure), BMI, gestational age, maternal age, and any combination thereof. b) Obtain the level of misfolded proteins or misfolded protein aggregates in the urine of the pregnant woman, and c) Combine at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded protein or misfolded protein aggregates in the pregnant woman's urine to diagnose or predict preeclampsia or determine the risk of preeclampsia.

[0069] 20. The method of embodiment 19, wherein the content level of the misfolded protein or misfolded protein aggregate is determined qualitatively (present or absent), quantitatively, or semi-quantitatively.

[0070] 21. The method as described in any one of embodiments 19-20, wherein the combination step is performed by an algorithm, such as a regression algorithm (e.g., a linear regression algorithm, such as a multiple linear regression algorithm), to obtain a score p, wherein a score p higher or lower than a predetermined threshold indicates that the pregnant woman is suffering from preeclampsia, or is predicted to develop preeclampsia, or is at an increased or decreased risk of suffering from preeclampsia.

[0071] 22. A data processing apparatus, device, or system comprising means for performing the steps of the method according to any one of embodiments 1-21, or comprising a processor adapted or configured to perform the steps of the method according to any one of embodiments 1-21.

[0072] 23. A computer program product or computer-readable medium comprising instructions that, when executed by a computer, cause the computer to perform the steps of the method described in any one of embodiments 1-21.

[0073] Example Example The following embodiments illustrate preferred embodiments of the present invention. Those skilled in the art should understand that the techniques disclosed in the following embodiments represent techniques that the inventors have found to be effective in the practice of the present invention, and therefore can be considered as preferred modes of implementation. However, based on this disclosure, those skilled in the art should understand that many modifications can be made to the specific embodiments disclosed without departing from the spirit and scope of the present invention, and the same or similar results can still be obtained.

[0074] Unless otherwise defined, 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 pertains. Publications cited herein, and the material cited therein, are specifically incorporated herein by reference.

[0075] Those skilled in the art will recognize or be able to determine many equivalents of the specific embodiments of the invention described herein using only conventional experiments. These equivalents are intended to be covered by the claims.

[0076] Example 1 1. Experimental Design Starting from early pregnancy, the inventors collected 5ml of urine from hundreds of pregnant women at high risk of PE who visited the clinic consecutively during each visit.

[0077] Inclusion criteria: • Age ≥ 18 years • ≥10 weeks of gestation • Singleton pregnancy • Initial assessment suggests risk of preeclampsia Exclusion criteria: • Unable to provide informed consent Multiple pregnancies • Pre-existing uncontrolled hypertension Information regarding maternal demographics, gestational age, medical history, and social history will be collected upon enrollment. Maternal and neonatal outcome data, along with relevant laboratory test results, will be retrieved postpartum. All pregnant women will be assigned to either the non-PE / MIPE group or the PE / MIPE group.

[0078] 2. Using CercaTest Red TM Detecting misfolded proteins in urine Urine samples will be collected according to CercaTest Red. TMFollow the instructions for use and test the cuvettes using a spectrophotometer to obtain readings. Calculate the results according to the formula: Abs520nm -- Abs600nm. For each collected urine sample, preserve at least one 2ml aliquot and store it at -80℃ for subsequent quantitative measurements.

[0079] 3. Analysis Plan 1. Use ROC plots to determine the optimal cutoff value for predicting PE / MIPE in our study population.

[0080] 2. Calculate the detection accuracy, sensitivity, specificity, positive and negative predictive values.

[0081] 3. CercaTest Red TM Test results are combined with other clinical features or laboratory results to differentiate between non-PE / MIPE and PE / MIPE, in combinations including but not limited to the following: A: Quantitative Maternal and Fetal Characteristics Group 1 Blood pressure (mean arterial pressure) BMI Maternal abdominal circumference gestational age B: Quantitative Maternal and Fetal Characteristics Group 2 Uterine artery pulsatility index Maternal age fetal crown-rump length C: Quantitative Laboratory Parameter Group 1 Creatinine Proteinuria Plasma / urine PlGF plasma sFlt Plasma PAPPA-A D: Quantitative Laboratory Parameter Group 2 Platelet count GGT (gamma-glutamyl transferase) fasting blood glucose alanine aminotransferase E: Qualitative maternal and fetal characteristics Previous history of preeclampsia The patient's mother has a history of preeclampsia. Chronic hypertension Family history of hypertension Autoimmune diseases Thyroid diseases diabetes Pregestational diabetes proteinuria Kidney disease mental disorders Uterine malformation Multiple pregnancies nulliparous women Assisted reproductive technology F: Any combination of the above factors Example 2 - Short-term prediction by combining one or more of ΔBMI / GA, maternal age, and mean arterial pressure (MAP) with misfolded proteins. This embodiment is based on the experimental design of Embodiment 1, and the data is analyzed according to the analysis plan of Embodiment 1.

[0082] According to the CercaTest RED preeclampsia test kit TM The instruction manual for CercaTest (Shuwen, China) is used to qualitatively detect misfolded proteins in urine samples. In data analysis, a positive result is assigned a value of "1", and a negative result is assigned a value of "0".

[0083] Queue composition: Recruit retrospectively from the database; 59 patients had preeclampsia (PE), 25 patients had impaired pregnancy (PIH), and 89 had normal pregnancies.

[0084] Enrollment was retrospectively conducted from the biobank, with urine samples collected from pregnant women in the first trimester (<20 weeks), second trimester (20-28 weeks), and third trimester (≥28 weeks). The sample collection process was rigorous, and all samples were stored at an optimal temperature of -80°C.

[0085] A key inclusion criterion was that each participant had at least one urine sample collected after 20 weeks of gestation. Other inclusion criteria included women over 18 years of age, singleton pregnancy, and a urine sample volume of at least 3 mL with satisfactory quality control. Exclusion criteria included visible hematuria and incomplete clinical information of participants, as this could hinder accurate assessment of potential confounding factors and interpretation of results. Ultimately, this study included 66 pregnant women with preeclampsia and 114 pregnant women without preeclampsia; the non-preeclampsia group included 23 patients with gestational hypertension (PIH).

[0086] result: The predictive performance of combining CercaTest values ​​with age and ΔBMI / GA using a joint model is as follows: Figure 1 As shown, logit(p) = 7.556 – 3.680*CercaTest - 0.176*age – 21.967*ΔBMI / GA.

[0087] The predictive performance of combining CercaTest values ​​with MAP, age, and ΔBMI / GA through a joint model is as follows: Figure 2 As shown, logit(p) = -16.077 – 4.805*CercaTest - 0.588*age – 76.985*ΔBMI / GA +0.409*MAP.

[0088] Example 3 – Long-term prediction by combining one or more of BMI, maternal age, and mean arterial pressure (MAP) with misfolded proteins. This embodiment is based on the experimental design of Embodiment 1, and the data is analyzed according to the analysis plan of Embodiment 1.

[0089] According to the instructions for use of the preeclampsia detection kit (spot diffusion method) (Shuwen Biotechnology Co., Ltd., China) (i.e., the "CapCord method"), misfolded proteins in urine samples were semi-quantitatively detected. In data analysis, based on... Figure 3 The diffusion pattern shown assigns integers 1-8 (TestValue) as the results of misfolded protein detection.

[0090] Queue composition: Recruit retrospectively from the database; The study included 13 patients with preeclampsia and 600 normal pregnancies.

[0091] result: The predictive performance of combining TestValue with maternal age, BMI, and MAP using a regression model is as follows: Figure 4 As shown, logit(p) = -19.048 + 0.850*TestValue + 0.059*Age + 0.039*BMI + 0.110*MAP.

[0092] Example 4 – Predicting preeclampsia by combining misfolded proteins with additional indicators Research Design This is a prospective, non-invasive clinical trial designed using the PICO (Patient, Problem or Population, Intervention, Control, Outcome) principle and based on evidence-based medicine principles.

[0093] recruit: Potential participants were identified by the research team from the prenatal clinic of the Women's Hospital affiliated with Zhejiang University School of Medicine. Eligible women who met all inclusion criteria and did not violate any exclusion criteria (text box 1) were provided with participant information and consent forms for signing. All participants were followed up until 42 days postpartum.

[0094] Text box 1. Determine the inclusion and exclusion criteria for trial eligibility.

[0095]

[0096] Research Group: Classification of Cases and Controls The standard diagnostic criteria for PE are new-onset hypertension after 20 weeks of gestation (measured twice at least 4 hours apart, with systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg) and new-onset proteinuria (≥300 mg / day or urine protein / creatinine ratio ≥0.3 mg / dL). The diagnostic criteria for the absence of proteinuria include any of the following: (1) Thrombocytopenia (platelet count <100×10⁻⁶). 9 (2) Liver dysfunction (blood liver transaminase concentration increased to twice the normal concentration); (3) Renal insufficiency (serum creatinine >1.1 mg / dL or serum creatinine concentration doubled, and no other kidney disease); (4) Pulmonary edema; and (5) Central nervous system abnormalities or visual impairment.

[0097] Cases are defined as patients who develop PE at any time from the first visit to 6 weeks (42 days) postpartum.

[0098] The control group was defined as patients who did not develop PE throughout the entire pregnancy up to 6 weeks (42 days) postpartum.

[0099] Outcome Indicators The primary outcome was the incidence of PE after the study was included. Secondary outcomes included the following adverse maternal and fetal outcomes: maternal death, pulmonary edema, acute renal failure, cerebral hemorrhage, cerebral thrombosis, disseminated intravascular coagulation, eclampsia, HELLP syndrome, uterine rupture, placental abruption, perinatal death, stillbirth, intrauterine growth restriction, small for gestational age, respiratory distress, necrotizing enterocolitis, intraventricular hemorrhage or subdural and cerebral hemorrhage, neonatal hypoxic-ischemic encephalopathy or periventricular leukomalacia. In addition, date and manner of delivery, fetal weight, and Apgar score were also important outcome indicators.

[0100] Data collection Participant data will be collected and entered into the electronic case report form (eCRF). This study includes six home visits and one postpartum record. The first home visit will begin at enrollment, followed by four weekly visits during participants' routine prenatal checkups. There will be one home visit at delivery and one postpartum record. Additional home visits may be required in case of pregnancy complications. The interval between two consecutive home visits may be 7 ± 2 days.

[0101] Urine sampling and storage All enrolled participants will be provided with a sterile cup at each visit for collecting at least 5 ml of midstream urine. The collected urine tubes will be frozen no later than 4 hours after collection and stored at -20°C. Samples will be transported frozen to the local laboratory for further analysis.

[0102] Laboratory testing Urine samples were thawed at room temperature and centrifuged at 3000 rpm for 10 minutes. The supernatant was then used for testing.

[0103] Using the CercaTest RED preeclampsia test kit TM (Data source: China) This method detects misfolded proteins in urine because misfolded proteins selectively bind to Congo red dye. When a mixture of Congo red and pregnant woman's urine is loaded onto a column mounted in a detection cuvette, the presence (positive) or absence (negative) of misfolded proteins can be determined by the color of the eluent in the cuvette. The eluent is also quantified using a HACH DR1900 instrument by detecting absorbance at wavelengths of 520–600 nm.

[0104] PlGF and sFLT-1 levels were measured using the Elecsys PlGF and Elecsys sFlt-1 assay kits (Roche, Germany). The urine total protein / creatinine ratio was measured using the urine / cerebrospinal fluid total protein (UTP) kit and the creatinine kit (Mindray, China), with all procedures performed according to the manufacturer's instructions.

[0105] Selection of predictor variables For the purposes of this study, the level of misfolded proteins in urine was considered a candidate predictor of PE morbidity or adverse outcomes. Other pre-specified variables included: (1) demographic factors; (2) urinary biomarkers; (3) drug treatment; and (4) routine clinical variables or laboratory tests in daily practice (text box 2). Valid variables were determined based on statistical analysis results with a significance level of P < 0.05.

[0106] Text box 2. Preset variable candidates for model development.

[0107]

[0108] Statistical analysis Compare all variables between the case group and the control group. Use parametric or nonparametric tests for continuous variables, and categorical variables... χ 2 Tests were conducted. Receiver operating characteristic analysis was performed where appropriate. Univariate or multivariate logistic regression was applied to develop predictive models. All p-values ​​were two-tailed. Statistical analyses were performed using SPSS version 27 (IBM) statistical software.

[0109] result A total of 365 pregnant women clinically suspected of having PE (80 patients with preeclampsia (PE), 76 patients with PIH, 25 patients with chronic hypertension, and 184 normal pregnancies) were enrolled in the trial. They were followed up and their PE status was monitored until 42 days postpartum. Statistical analysis was planned to begin after all participants reached the follow-up endpoint and complete clinical data were collected.

[0110] This study aimed to develop and evaluate predictive models to assess the risk of impending PE in patients clinically suspected of having preeclampsia. Among the collected clinical information, seven additional indicators, combined with misfolded protein indicators, were found to have significant predictive accuracy for PE onset. These seven additional indicators included: mean arterial pressure, PE history (PEhis), antihypertensive medication treatment (Antihyper), the difference between body mass index (BMI) at the time of measurement and pre-pregnancy BMI (ΔBMI), gestational age-adjusted ΔBMI (ΔBMI / GA), total urinary protein (UTP), and the ratio of total urinary protein to creatinine (UTP / cre). The primary goal of these models was to improve the predictive accuracy of PE onset within one week.

[0111] Table 1 describes each model, clarifies the combination of indices used, and presents the area under the curve (AUC) and p-values ​​derived from receiver operating characteristic (ROC) curve analysis. Significance levels were determined based on a Z-test comparing the AUC of each model with the AUC of the baseline model relying solely on misfolded proteins (AUC = 0.770). A p-value < 0.05 indicated a significant difference in AUC, suggesting that the additional indices significantly contributed to the model's predictive ability. ROC curves were calculated by assigning values ​​as follows: 1 for positive misfolded protein and 0 for negative; MAP was calculated using the formula: diastolic blood pressure + (systolic blood pressure - diastolic blood pressure) / 3, and the resulting value was used; PE history was coded as 1 if present and 0 if absent; antihypertensive medication treatment was coded as 1 if present and 0 otherwise; BMI was calculated by dividing individual weight (kg) by the square of height (m). ΔBMI, ΔBMI / GA, UTP, and UTP / Cre were assigned their measured values.

[0112] Of the models evaluated, model 1, which combined misfolded proteins with MAP, PE history, antihypertensive medications, ΔBMI / GA, and UTP / Cre, produced an AUC of 0.95, indicating strong predictive power. Models combining misfolded proteins, MAP, PE history, and antihypertensive medications with ΔBMI / GA (model 6) or ΔBMI (model 9) maintained a high AUC of 0.937, indicating that excluding UTP or UTP / Cre still provided significant predictive power. Furthermore, model 17, which excluded any form of UTP and BMI, achieved an AUC of 0.932, which, while slightly lower than models including more indicators, provided a simpler model with significant predictive power. Moreover, variations in the combination of indicators led to different AUC values, such as... Figure 5 As shown in the figure, this figure illustrates the difference in the area under the ROC curve between 47 models and a single misfolded protein index.

[0113] Table 2 shows the predictive performance of misfolded proteins, with a positive predictive accuracy of 60.0% and a negative predictive accuracy of 94.1%. The positive and negative predictive values ​​were 66.7% and 92.3%, respectively. These figures indicate that while a single indicator has a high ability to correctly predict the onset and absence of preeclampsia, its sensitivity is moderate. In contrast, Model 1 showed superior predictive performance, with a PPA of 85.0%, an NPA of 91.8%, a PPV of 67.1%, and an NPV of 96.9%. Models 6 and 17 showed comparable predictive performance, both with a PPA of 80.0%, with slight differences in NPA, PPV, and NPV. Notably, all models showed a significant improvement in sensitivity, which is crucial for identifying preeclampsia cases that might be missed by a single misfolded protein.

[0114] In summary, incorporating variables into predictive models (as shown in Models 1, 6, and 17) significantly improved the sensitivity and overall accuracy of preeclampsia prediction compared to using a single misfolded protein indicator. This study highlights the importance of integrating multiple indicators to develop robust predictive models for preeclampsia onset within one week, emphasizing their potential clinical utility in enhancing early detection and intervention strategies for preeclampsia, thereby improving maternal and infant outcomes.

[0115] Table 1: AUC values ​​and significance levels of preeclampsia prediction models based on misfolded proteins and additional indicators.

[0116]

[0117]

[0118] Table 2: Predictive performance of misfolded proteins on the onset of preeclampsia within 1 week

[0119] Kappa = 0.564 P<0.001.

[0120] Table 3: Predictive performance of Model 1 on the onset of preeclampsia within 1 week

[0121] Kappa = 0.694 P<0.001.

[0122] The model's predictive performance is as follows Figure 5 As shown, logit(P) = 2.733*CercaTest + 0.102*MAP + 1.629*antiHyper + 3.743*PEhis + 6.256*ΔBMI / GA + 0.050*UTP / cre - 14.816, with a cutoff value of -1.280384. A logit(P) value higher than the cutoff value indicates a high risk of PE onset within one week, while a logit(P) value lower than the cutoff value indicates a low risk of PE onset within one week.

[0123] Table 4: Predictive performance of Model 6 on the onset of preeclampsia within 1 week

[0124] Kappa = 0.661 P<0.001.

[0125] The model's predictive performance is as follows Figure 5 As shown, logit(P) = 3.238*CercaTest + 0.127*MAP + 1.450*antiHyper + 4.364*PEhis + 5.749*ΔBMI / GA - 16.450, with a cutoff value of -1.2945526. A logit(P) value higher than the cutoff value indicates a high risk of PE onset within one week, while a logit(P) value lower than the cutoff value indicates a low risk of PE onset within one week.

[0126] Table 5: Predictive performance of Model 17 on the onset of preeclampsia within 1 week

[0127] Kappa = 0.626, P<0.001.

[0128] The model's predictive performance is as follows Figure 5As shown, logit(P) = 3.256 * CercaTest + 0.129 * MAP + 1.361 * AntiHyper + 4.297 * PEhis - 15.924, with a cutoff value of -1.333000. A logit(P) value higher than the cutoff value indicates a high risk of PE onset within one week, while a logit(P) value lower than the cutoff value indicates a low risk of PE onset within one week.

[0129] All publications and patent applications mentioned in this specification represent the level of expertise in the field to which the invention pertains. All publications and patent applications are incorporated herein by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Mere mention of publications and patent applications does not necessarily constitute an admission that they are prior art to this application.

[0130] Although the invention has been described in detail by way of illustration and example for the purpose of clarity, it will be apparent that certain changes and modifications may be made within the scope of the appended claims.

Claims

1. A method for diagnosing or predicting preeclampsia in pregnant women or determining whether a pregnant woman is at risk of developing preeclampsia, comprising: a) Obtain at least one (e.g., two or more) maternal or fetal parameters or factors for the pregnant woman, said parameters or factors being selected from the group consisting of: (i) mean arterial pressure (MAP), BMI 采样时 (i.e., the pregnant woman's BMI at the time of urine sampling), ΔBMI (i.e., BMI) 采样时 – BMI 初始 (i) One or more of the following: (i) the pregnant woman's BMI before pregnancy, in early pregnancy, or in the first month of her first pregnancy; (ii) gestational age (GA, optionally in weeks); and ΔBMI / GA; (iii) maternal age (optionally in years); (iii) one or more of the following: urine protein level and the ratio of urine protein level (e.g., total urine protein (UTP)) to urine creatinine level (Cre) (UTP / Cre); (iv) one or more of the following: a history of preeclampsia (PEhis) and treatment with antihypertensive drugs (Antihyper); and (v) any combination of (i)-(iv). b) Determine whether the pregnant woman's urine contains misfolded proteins or misfolded protein aggregates, and c) Combine at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded protein or misfolded protein aggregates in the pregnant woman's urine to diagnose or predict preeclampsia or determine the risk of preeclampsia.

2. The method of claim 1, wherein the content level of the misfolded protein or misfolded protein aggregate is determined by qualitative measurement (presence or absence), quantitative measurement, or semi-quantitative measurement.

3. The method of claim 1 or 2, wherein the content level of the misfolded protein or misfolded protein aggregate is determined as a qualitative measurement (i.e., presence or absence), a quantitative measurement, or a semi-quantitative measurement.

4. The method of any of the preceding claims, wherein the combining step c) is performed by an algorithm, such as a regression algorithm (e.g., a linear regression algorithm, such as a binary regression algorithm or a multiple linear regression algorithm), to obtain a score p, and optionally, the score p being higher or lower than a predetermined threshold indicates that the pregnant woman is having preeclampsia, or is predicted to develop preeclampsia, or is at an increased or decreased risk of having preeclampsia.

5. The method of any of the preceding claims, wherein the at least one maternal or fetal parameter or factor includes (i) MAP and BMI. 采样时 (ii) MAP and maternal age; or (iii) MAP and BMI 采样时 And maternal age.

6. The method of any of the preceding claims, wherein the prediction includes a long-term prediction, such as predicting that preeclampsia will occur more than 4 weeks after the determining step (e.g., 6, 8, 10, 12, 14, 16 or 20 weeks later), and optionally, the determining step is not earlier than 20 weeks of gestation, e.g., not earlier than 17 weeks of gestation, e.g., between 10 and 16 weeks of gestation, e.g., 11, 12, 13, 14, 15 or 16 weeks.

7. The method of any one of claims 1-4, wherein the at least one maternal or fetal parameter or factor comprises: The maternal or fetal parameters or factors of the pregnant woman selected from at least one (such as two, three, four, five, six, seven or more) of the group containing MAP, PEhis, Antihyper, ΔBMI, ΔBMI / GA, UTP, UTP / Cre and maternal age, for example selected from the group containing MAP, PEhis, Antihyper, ΔBMI, ΔBMI / GA, UTP and UTP / Cre; Optionally, the at least one maternal or fetal parameter or factor includes: (1) MAP, PEhis, Antihyper, ΔBMI / GA, and UTP / Cre; (2) MAP, PEhis, Antihyper, ΔBMI, and UTP / Cre; (3) MAP, PEhis, Antihyper, ΔBMI / GA, and UTP; (4) MAP, PEhis, Antihyper, ΔBMI / GA, and UTP; (5) MAP, PEhis, Antihyper, ΔBMI, and UTP; (6) MAP, PEhis, Antihyper, and UTP / Cre; (7) MAP, PEhis, Antihyper, and ΔBMI / GA; (8) MAP, PEhis, ΔBMI / GA, and UTP / Cre; (9) MAP, PEhis, Antihyper, and ΔBMI; (10) MAP, Antihyper, ΔBMI, and UTP / Cre; (11) MAP, PEhis, ΔBMI and UTP / Cre; (12) MAP, PEhis, Antihyper and UTP; (13) MAP, Antihyper, ΔBMI / GA and UTP; (14) MAP, PEhis, ΔBMI / GA and UTP; (15) MAP, Antihyper, ΔBMI and UTP; (16) MAP, PEhis, ΔBMI and UTP; (17) MAP, PEhis and Antihyper; (18) MAP, Antihyper and ΔBMI / GA; (19) MAP, PEhis and ΔBMI / GA; (20) MAP, Antihyper and UTP / Cre; (21) MAP, PEhis and UTP / Cre; (22) MAP, ΔBMI / GA and UTP / Cre; (23) PEhis, ΔBMI / GA and UTP / Cre; (24) MAP, Antihyper and ΔBMI; (25) MAP, PEhis and ΔBMI; (26) MAP, ΔBMI and UTP / Cre; (27) PEhis, ΔBMI and UTP / Cre; (28) MAP, Antihyper and UTP; (29) MAP, PEhis and UTP; (30) MAP, ΔBMI / GA and UTP; (31) PEhis, ΔBMI / GA and UTP; (32) MAP, ΔBMI and UTP; (33) PEhis, ΔBMI and UTP; (34) MAP and Antihyper; (35) MAP and PEhis; (36) MAP and ΔBMI / GA; (37) MAP and UTP / Cre;(38) PEhis and ΔBMI / GA; (39) PEhis and UTP / Cre; (40) ΔBMI / GA and UTP / Cre; (41) MAP and ΔBMI; (42) PEhis and ΔBMI; (43) ΔBMI and UTP / Cre; (44) MAP and UTP; (45) PEhis and UTP; (46) ΔBMI / GA and UTP; (47) ΔBMI and UTP; (48) ΔBMI / GA and maternal age; or (49) MAP, ΔBMI / GA and maternal age; (50) ΔBMI / GA.

8. The method of claim 7, wherein the prediction includes a short-term prediction, such as predicting the occurrence of preeclampsia within 4 weeks (e.g., 1, 2, 3, or 4 weeks) after the determining step, and optionally, the urine test is performed after 20 weeks of gestation, for example, between 20 and 37 weeks of gestation, such as 22, 24, 26, 28, 30, 32, 34, or 36 weeks.

9. The method of any of the preceding claims, wherein the pregnant woman is a woman suspected of having preeclampsia or at high risk of having preeclampsia.

10. A method for treating, preventing, or managing preeclampsia in pregnant women, comprising: a) Obtain at least one (e.g., two or more) maternal or fetal parameters or factors of the pregnant woman as defined in any of the preceding claims. b) Determine whether the pregnant woman's urine contains misfolded proteins or misfolded protein aggregates. c) Using an algorithm, such as a regression algorithm (e.g., a linear regression algorithm, such as a binary regression algorithm or a multiple linear regression algorithm), combine at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded protein or misfolded protein aggregates in the pregnant woman's urine to obtain a score p. The score p being higher or lower than a predetermined threshold indicates that the pregnant woman is currently suffering from preeclampsia, is predicted to develop preeclampsia, or is at an increased or decreased risk of developing preeclampsia; and d) If the pregnant woman is diagnosed with preeclampsia, or is predicted to develop preeclampsia, or is at increased risk of developing preeclampsia, administer medication to the pregnant woman and / or manage the pregnant woman's condition to prolong the pregnancy.

11. The method of claim 10, wherein administering the medication includes administering aspirin, and / or managing the pregnant woman's condition includes weight loss, controlling blood pressure and / or blood sugar, and / or maintaining a regular exercise program.

12. A computer-based method for diagnosing or predicting preeclampsia in pregnant women or determining whether a pregnant woman is at risk of developing preeclampsia, comprising: a) Obtain at least one (e.g., two or more) maternal or fetal parameters or factors as defined in any one of claims 1-10 for the pregnant woman. b) Obtain the levels of misfolded proteins or misfolded protein aggregates in the urine of the pregnant woman, and c) Combine at least one maternal or fetal parameter or factor obtained in step a) with the level of misfolded protein or misfolded protein aggregates in the pregnant woman's urine to diagnose or predict preeclampsia or determine the risk of preeclampsia.

13. A data processing apparatus, device, or system comprising means for performing the steps of the method according to any one of claims 1-12, or comprising a processor adapted or configured to perform the steps of the method according to any one of claims 1-12.

14. A computer program product or computer-readable medium comprising instructions that, when executed by a computer, cause the computer to perform the steps of the method according to any one of claims 1-12.

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