US20200264188A1 - Preeclampsia biomarkers and related systems and methods - Google Patents

Preeclampsia biomarkers and related systems and methods Download PDF

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US20200264188A1
US20200264188A1 US16/646,552 US201816646552A US2020264188A1 US 20200264188 A1 US20200264188 A1 US 20200264188A1 US 201816646552 A US201816646552 A US 201816646552A US 2020264188 A1 US2020264188 A1 US 2020264188A1
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proteins
preeclampsia
probes
clec4a
kim1
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Matthew Cooper
Sharat Singh
Karen A.F. COPELAND
Lyndal HESTERBERG
Amin R. MAZLOOM
Mohammad ABBASI
Richard GIULIO DEL MASTRO
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Boulder Statistics LLC
Hesterberg Consulting Services LLC
Biora Therapeutics Inc
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Progenity Inc
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Definitions

  • Preeclampsia is a serious multisystem complication of pregnancy.
  • the incidence of the disorder is generally considered to between 2% to 8% of all pregnancies, and the disorder carries significant morbidity and mortality risks for both mothers and infants.
  • Preeclampsia is the second largest cause of maternal/fetal deaths, and is responsible for approximately twenty billion dollars in healthcare costs annually.
  • preeclampsia The cause(s) and pathogenesis of preeclampsia remain uncertain, and the identification (or ruling out) of preeclampsia using the classical clinical symptoms of the disease is non-ideal.
  • the presentation of classical clinical symptoms can be highly variable, and the symptoms can be indicative of other distinct disorders, such as chronic hypertension, gestational hypertension, temporary high blood pressure, and gestational diabetes.
  • Current laboratories tests e.g., tests for proteinuria
  • Methods for more reliably determining whether a pregnant woman does or does not have preeclampsia may, among other things, (1) lead to a more timely diagnosis, (2) improve the accuracy of a diagnosis, and/or (3) prevent the unnecessary treatment of women with preeclampsia treatments.
  • FIG. 1A shows a plot of LogWorth vs. effect size from the initial ANOVA-based screening for markers of interest presented in Example 3.
  • FIG. 1B provides illustrations of data spread for markers for distinguishing between preeclampsia and non-preeclampsia.
  • FIG. 3 shows a four-subcohort analysis that is similar to the four-subcohort analysis of FIG. 2 for the literature-identified candidate analytes Fibronectin, sFlt1, PlGF, and PAPP-A.
  • FIG. 4 shows a ROC curve and summary statistics for logistic regression model for non-preeclampsia vs preeclampsia built from the top nine predictors identified in a multivariate graded-response-based analysis in Example 7.
  • FIG. 5 shows a ROC curve and summary statistics for logistic regression model built in Example 7 for non-preeclampsia (nonPreE) vs preeclampsia (PreE) built from sFlt1 and PlGF plus the top 2 predictors identified in Example 3.
  • FIG. 6 shows a system for implementing the methods of the disclosure.
  • FIG. 13A , FIG. 13B , FIG. 13C , FIG. 13D , FIG. 13E , FIG. 13F , FIG. 13G , FIG. 13H , FIG. 13I , FIG. 13J , and FIG. 13K depict log-transforms of expression levels of the top 11 markers identified for detection of preeclampsia in both preeclampsia and non-preeclampsia samples.
  • FIG. 14 depicts an exemplary procedure wherein a Loess model is used to perform gestational-age correction of biomarker (PlGF) expression levels.
  • PlGF biomarker
  • FIG. 15 depicts a graph of a principal component analysis of non-preeclampsia ( ⁇ ), preeclampsia (+), false positive (O) and false negative (X) samples, showing that false negative samples cluster with non-preeclampsia samples.
  • FIG. 16 is a diagram showing exemplary functional roles for various markers in the pathophysiology of preeclampsia.
  • FIG. 17 provides a flow diagram of a method for building biomarker models suitable for identification of preeclampsia.
  • FIG. 18 lists various antibodies or other antigen-binding agents for use in some embodiments disclosed herein.
  • FIG. 19 provides a flow diagram for a “stacked” decision structure for ruling out preeclampsia.
  • FIG. 20 is a figure showing, via color, the extent to which various markers reveal orthogonal information for ruling out preeclampsia.
  • FIG. 21 is a diagram that shows the relative predictive weights of various individual biomarkers for ruling out preeclampsia.
  • preeclampsia Detection of preeclampsia using the classic clinical symptoms of the disease is error prone, risking significant adverse outcome for patients and added burden to the healthcare system through misdiagnosis.
  • Measurement of proteinuria is prone to inaccuracies (e.g., false negatives and false positives), preeclampsia complications may occur before proteinuria becomes significant, the clinical presentation of preeclampsia can be highly variable (from severe, rapidly progressing, and early-onset to late-onset and less severe), and the symptoms (hypertension, proteinuria, or both) can be indicative of other distinct disorders that could utilize a less aggressive treatment course (chronic hypertension, gestational hypertension, temporary high blood pressure, and gestational diabetes).
  • preeclampsia hypertension and proteinuria merely reflect downstream consequences of the actual preeclampsia disease process.
  • Traditional diagnoses of preeclampsia lead to masking of the disease because the only reliable treatment is delivery.
  • preeclampsia research into the dysfunctional angiogenic processes associated with preeclampsia has been undertaken to find better and/or more direct indicators of preeclampsia.
  • One avenue of this research has led to the use of the sFlt1/PlGF ratio in patient serum as a method for identifying preeclampsia (see e.g., Zeisler et al. NEJM 274(2017):13-22 or Verlohren et al. Hypertension.
  • the disclosure provides for one or more tests with improved characteristics for assessing the risk of preeclampsia in a subject, wherein the test can be used to identify subjects that should not be treated for preeclampsia (e.g., in some instances identifying a subject that shows one or more signs, symptoms, or risk factors of preeclampsia, but should not be treated for preeclampsia).
  • this test is a multi-marker serum or plasma protein assay.
  • the test is a multiplexed serum/plasma protein assay.
  • the one or more symptoms associated with preeclampsia can be diabetes (e.g.
  • gestational type I or type II
  • hypertension e.g., chronic or non-chronic
  • excessive or sudden weight gain higher than normal weight
  • obesity higher than normal body mass index (BMI)
  • abnormal weight gain abnormal blood pressure, water retention, hereditary factors, abnormal proteinuria, headache, edema, abnormal protein/creatinine ratio, abnormal platelet count, stress, nulliparity, abnormal Papanicolaou test results (Pap smear), prior preeclampsia episodes (e.g., personal history of PreE), familial history of preeclampsia, preeclampsia in prior pregnancies, renal disease, thrombophilia, or any combination thereof.
  • the disclosure provides for tests, systems, and methods for assessing a risk of preeclampsia in a subject, such as ruling out a patient as having or needing treatment for preeclampsia.
  • a test is used to discern whether a patient having one or symptoms or risk factors associated with preeclampsia should be treated for preeclampsia.
  • the one or more symptoms or risk factors associated with preeclampsia can be diabetes (e.g.
  • gestational type I or type II
  • hypertension e.g., chronic or non-chronic
  • excessive or sudden weight gain higher than normal weight
  • obesity higher than normal body mass index (BMI)
  • abnormal weight gain abnormal blood pressure, water retention, hereditary factors, abnormal proteinuria, headache, edema, abnormal protein/creatinine ratio, abnormal platelet count, stress, nulliparity, abnormal Papanicolaou test results (Pap smear), prior preeclampsia episodes (e.g., personal history of PreE), familial history of preeclampsia, preeclampsia in prior pregnancies, renal disease, thrombophilia, or any combination thereof.
  • the tests disclosed herein can be used to identify pregnant women who are symptomatic (and/or have one or more risk factors for preeclampsia), but do not have preeclampsia that is likely to require preterm birth.
  • the test may be used on asymptomatic patients or patients with little or no risk identified (or identifiable) risk factors.
  • hypertension refers to abnormally high blood pressure. Hypertension can be identified in any suitable manner, such as by reference to a sitting systolic blood pressure (sSBP) of greater than 140 mmHg or a sitting diastolic blood pressure (sDBP) of greater than 90 mmHg. Hypertension can be further classified into class 1 or class 2 hypertension, with class 1 exhibiting sSBP of 140-149 mmHg or 90-99 mmHg sDBP, and class 2 exhibiting greater than 160 mmHg sSBP or 100 mmHg sDBP. (See, e.g., The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.
  • Hypertension can also be determined according to the 2017 AHA guidelines (see Whelton et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults, Journal of the American College of Cardiology (2017), doi: 10.1016/j.jacc.2017.11.006).
  • Such guidelines identify “normal” blood pressure as less than 120/80 mmHg, “elevated” as systolic between 120-129 mmHg and diastolic less than 80 mmHg, “stage 1” as systolic between 120-139 mmHg or diastolic between 80-89 mmHg, “stage 2” as systolic at least 140 mmHg or diastolic at least 90 mmHg, and “hypertensive crisis” as systolic over 180 and/or diastolic over 120.
  • proteinuria is defined as the presence of abnormal protein in the urine.
  • a number of indicator dyes and reagents can used to measure proteinuria semi-quantitatively (e.g., bromophenol blue).
  • concentrations of protein in urine can be determined by a semi quantitative “dipstick” analysis and graded as negative, trace (10-20 mg/dL), 1+( ⁇ 30 mg/dL), 2+( ⁇ 100 mg/dL), 3+( ⁇ 300 mg/dL), or 4+(1,000 mg/dL), with 2+ commonly being used as the threshold for problematic proteinuria.
  • concentrations of protein in urine can also be measured per 24 hour urine collection, in which greater than or equal to 300 mg protein indicates proteinuria.
  • concentrations of protein in urine can be measured in a spot urine sample, in which 30 mg of protein per deciliter or greater indicates proteinuria.
  • proteinuria can also be expressed as the protein/creatinine ratio (Pr/Cr) in urine, in which a Pr/Cr ratio of >0.3 is indicative of problematic proteinuria.
  • antibody or fragments thereof is used in the broadest sense and specifically encompasses intact monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g. bispecific antibodies) formed from at least two intact antibodies, and antibody fragments.
  • Antibody fragments comprise a portion of an intact antibody that retains antigen-binding activity; examples include Fab, Fab′, F(ab) 2 , F(abc) 2 , and FIT fragments as well as diabodies, linear antibodies, scFvs, and multispecific antibodies formed from antibody fragments.
  • aptamer refers to an oligonucleotide that is capable of forming a complex with an intended target substance. Such complex formation is target-specific in the sense that other materials which may accompany the target do not complex to the aptamer. It is recognized that complex formation and affinity are a matter of degree; however, in this context, “target-specific” means that the aptamer binds to target with a much higher degree of affinity than it binds to contaminating or “off-target” materials.
  • preeclampsia refers to a pregnancy-specific disorder involving multiple organ systems thought to originate from abnormal placentation, dysfunctional trophoblast development, defective placental angiogenesis, and a heightened systemic inflammatory response in the mother.
  • Preeclampsia when untreated, can progress to ecclampsia, HELLP syndrome, hemorrhagic or ischemic stroke, liver damage, acute kidney injury, and acute respiratory distress syndrome (ARDS).
  • preeclampsia frequently presents with symptoms such as hypertension and proteinuria
  • symptoms such as hypertension and proteinuria
  • symptoms reflective of vascular tension disorders and kidney dysfunction on their own therefore have less than ideal predictive/diagnostic value for preeclampsia.
  • Further information on the pathophysiology of preeclampsia can be found, e.g., in Phipps et al. Clin J Am Soc Nephrol 11(2016):1102-1113.
  • a traditional diagnosis of preeclampsia is made when hypertension and proteinuria as defined above are detected at the same time.
  • a traditional diagnosis of preeclampsia is made upon simultaneous detection of hypertension (blood pressure greater than or equal to 140 mmHg systolic or greater than or equal to 90 mmHg diastolic on two occasions at least 4 hours apart after 20 weeks gestation in a woman with a previously normal blood pressure, or blood pressure with greater than equal to 160 mmHg systolic or greater than or equal to 110 mmHg diastolic within a short interval of minutes) and proteinuria (greater than or equal to 300 mg per 24-hour urine collection either measured or extrapolated, or protein creatinine ratio greater than or equal to 0.3, or a dipstick reading of 1+) or upon new onset hypertension in the absence of proteinurea when (a) blood platelet count is less
  • subject can include human or non-human animals.
  • methods and described herein are applicable to both human and veterinary disease and animal models.
  • Preferred subjects are “patients,” i.e., living humans that are receiving medical care for a disease or condition. This includes persons with no defined illness who are being investigated for signs of pathology.
  • FRET pair refers to a pair of dye molecules where one of the dye molecules absorbs light (the acceptor or quencher dye) at a wavelength at which the other emits light (the donor dye) and the two dyes are spatially separated by a distance that permits energy transfer, with the disclosed embodiments generally being within about 100 angstroms, such as within about 50, 20 or 10 angstroms of each other (for example, because they are bonded to the same substrate moiety). Excitation of the donor dye leads to excitation of the acceptor dye through the FRET mechanism, and a lower level of fluorescence is observed from the donor dye.
  • the efficiency of FRET depends on the distance between the dyes, the quantum yield of the donor dye, the fluorescence lifetime of the donor dye, and the overlap of the donor's emission spectrum and the acceptor's absorption spectrum.
  • photosensitizer refers to a photoactivatable compound, or a biological precursor of a photoactivatable compound, that produces a reactive species (for e.g., oxygen) having a photochemical effect on a biomolecule.
  • oxygen-sensitive dye refers a fluorescent dye that changes its fluorescence intensity or emission maximum after binding to molecular oxygen (such dyes are used for FOCI assays), or a chemiluminescent dye that emits light after binding to molecular oxygen (such dyes are used for LOCI assays).
  • affimer refers to small, highly stable proteins that bind to a target molecule with similar specificity and affinity to that of antibodies.
  • preeclampsia can refer to treatments for preeclampsia that would be, statistically speaking, unjustified when a practitioner takes into account the results of a test or procedure described herein, such as a test based on the determination of an amount of concentration of various protein biomarkers.
  • unnecessary treatment can include preterm induction based on one or more symptoms or risk factors for preeclampsia.
  • the methods, compositions, systems and kits provided herein are used for detecting or predicting a condition in a pregnant human subject at any stage in pregnancy.
  • the pregnant human subject is post-the 20 th week of gestation.
  • the pregnant human subject is post-first or -second trimester of pregnancy.
  • the pregnant human subject is post-21st, 22nd, 23rd, 24th, 25th, 26th, 27th, 28th, 29th, 30th, 31st, 32nd, 33rd, 34th, 35th, 36th, 37th, 38th, 39th, 40th, 41st, or 42nd week of gestation.
  • preeclampsia preeclampsia
  • NonPreE non-preeclampsia
  • preeclampsia is further divided into very early-onset (before 25 weeks gestation), early-onset (before 34 weeks gestation) and late-onset (after 34 weeks gestation) preeclampsia.
  • the patient is considered NonPreE.
  • the patient is generally only suspected to have preeclampsia.
  • preeclampsia complications may occur before proteinuria becomes significant
  • the clinical presentation of preeclampsia can be highly variable (from severe, rapidly progressing, and early-onset to late-onset and less severe), and the symptoms (hypertension, proteinuria, or both) can be indicative of other distinct disorders that could utilize a different treatment course.
  • a subject therefore can be a pregnant female that has no known risk factors, or has one or more at-risk factors for a condition such as PreE.
  • hypertension and/or proteinuria can indicate a subject at risk of preeclampsia.
  • a subject at risk of preeclampsia can have a urine protein content measured as 2+ (100 mg/dL) or greater by dipstick assay, greater than or equal to 300 mg per 24 hour urine collection, 30 mg of protein per deciliter or greater in a spot urine sample, or a Pr/Cr ratio in urine of >30 mg per millimole.
  • a subject at risk of preeclampsia can have a sitting systolic blood pressure (sSBP) of greater than 140 mmHg or a sitting diastolic blood pressure (sDBP) of greater than 90 mmHg or both.
  • sSBP sitting systolic blood pressure
  • sDBP sitting diastolic blood pressure
  • sFlt1/PlGF ratio can be used to identify subject at risk of preeclampsia (see, e.g., Zeisler et al. NEJM 274(2017):13-22 or Verlohren et al. Hypertension. 63(2014):346-52).
  • both hypertension and proteinuria can be used to identify a subject at risk of preeclampsia.
  • new onset hypertension in combination with one or more symptoms selected from (a) blood platelet count is less than 100,000 per milliliter, (b) serum creatinine is greater than 1.1 mg/dL or double baseline for the patient, or (c) blood concentration of liver transaminases is twice normal or greater can be used to identify a subject at risk of preeclampsia.
  • the molecules are circulating molecules (e.g. unbound to cells and freely circulating in bodily fluids such as blood, blood plasma or blood serum).
  • the molecules are expressed in the cytoplasm of blood, endothelial, or organ cells. In some cases, the molecules are expressed on the surface of blood, endothelial, or organ cells.
  • a sample can be any material containing tissues, cells, nucleic acids, genes, gene fragments, expression products, proteins, polypeptides, exosomes, gene expression products, or gene expression product fragments of a subject to be tested.
  • a sample can include but is not limited to, tissue, cells, plasma, serum, or any other biological material from cells or derived from cells of an individual.
  • the sample can be a heterogeneous or homogeneous population of cells or tissues.
  • the sample can be a fluid that is acellular or depleted of cells (e.g., plasma or serum).
  • the sample is from a single patient.
  • the method comprises analyzing multiple samples at once, e.g., via massively parallel multiplex expression analysis on protein arrays or the like.
  • the sample is preferably a bodily fluid.
  • the bodily fluid can be saliva, urine, blood, and/or amniotic fluid.
  • the sample can be a fraction of any of these fluids, such as plasma, serum or exosomes (exemplary exosome isolation techniques can be found, e.g. in Li et al. Theranostics. 7(2017): 789-804).
  • the sample is a blood sample, plasma sample, or serum sample.
  • the sample may be obtained using any method that can provide a sample suitable for the analytical methods described herein.
  • the sample may be obtained by a non-invasive method such as a throat swab, buccal swab, bronchial lavage, urine collection, scraping of the cervix, cervicovaginal sample secretion collection (e.g. with an ophthalmic sponge such as a Weck-Cel sponge), saliva collection, or feces collection.
  • the sample may be obtained by a minimally-invasive method such as a blood draw.
  • the sample may be obtained by venipuncture.
  • obtaining a sample includes obtaining a sample directly or indirectly.
  • the sample is taken from the subject by the same party (e.g. a testing laboratory) that subsequently acquires biomarker data from the sample.
  • the sample is received (e.g. by a testing laboratory) from another entity that collected it from the subject (e.g. a physician, nurse, phlebotomist, or medical caregiver).
  • the sample is taken from the subject by a medical professional under direction of a separate entity (e.g. a testing laboratory) and subsequently provided to said entity (e.g. the testing laboratory).
  • the sample is taken by the subject or the subject's caregiver at home and subsequently provided to the party that acquires biomarker data from the sample (e.g. a testing laboratory).
  • a testing laboratory e.g. a testing laboratory.
  • kits suitable for self or home collection of biological samples have been described commercially and in the literature; see e.g., US20170023446A1 and U.S. Pat. No. 4,777,964A.
  • the methods, kits, and systems disclosed herein may comprise data pertaining to one or more samples or uses thereof.
  • the data can be representative of an amount or concentration of one or more biomarkers, such as various proteins described herein. Stated differently, the data can be expression level data of proteins or polypeptides.
  • the expression level data of biomarkers described herein can be determined by protein array, proteomics, expression proteomics, mass spectrometry (e.g., liquid chromatography-mass spectrometry (LC-MS), multiple reaction monitoring (MRM), selected reaction monitoring (SRM), scheduled MRM, scheduled SRM), 2D PAGE, 3D PAGE, electrophoresis, proteomic chip, proteomic microarray, Edman degradation, direct or indirect ELISA, immunosorbent assay, immuno-PCR (see, e.g., Sano et al. Science. 258(1992):120-2), proximity extension assay (see, e.g., Thorsen et al. Journal of Translational Medicine.
  • mass spectrometry e.g., liquid chromatography-mass spectrometry (LC-MS), multiple reaction monitoring (MRM), selected reaction monitoring (SRM), scheduled MRM, scheduled SRM
  • 2D PAGE, 3D PAGE electrophoresis
  • proteomic chip e.g.,
  • Luminex assay or homogenous assays such as ALPHAscreen (see, e.g., Application Note. Nature Methods 5, (2008), U.S. Pat. Nos. 5,898,005A, 5,861,319A), time-resolved fluorescence resonance energy transfer (TR-FRET see e.g., US20130203068A1 and WO1998015830A2), time-resolved fluorescence (TRF), fluorescent oxygen channeling immunoassay (FOCI), or luminescent oxygen channeling immunoassay (LOCITM; see e.g. Ullman et al. Proc Natl Acad Sci USA. 91(1994):5426-5430 or Ullman et al. Clin Chem. 1996 September; 42(9):1518-26 for exemplary methods and reagents).
  • ALPHAscreen see, e.g., Application Note. Nature Methods 5, (2008), U.S. Pat. Nos. 5,898,005A, 5,861,319
  • compositions, methods and devices described herein make use of labeled molecules in various sandwich, competitive, or non-competitive assay formats to determine expression levels of biomarkers described herein. Such methods generate a signal that is related to the presence or amount of one or more of the proteins described herein. Suitable assay formats also include chromatographic, mass spectrographic, and protein “blotting” methods. Additionally, certain methods and devices, such as biosensors, optical immunoassays, immunosorbent assays, and enzyme immunoassays, may be employed to determine the presence or amount of analytes without the need for a labeled molecule.
  • EIA enzyme immunoassays
  • ECIA enzyme immunoassays
  • ECIA enzyme immunoassays
  • ECIA chemiluminescent enzyme immunoassay
  • ELIA electrochemiluminescence immunoassay
  • ELISA enzyme-linked immunosorbent assay
  • Robotic instrumentation for performing these immunoassays are commercially available including, but not limited to Abbott AXSYM®, IMx®, or Commander® systems; Biolog 24i® or CLC480 systems; Beckman Coulter ACCESS®, ACCESS 2®, or Unicel Dxl 600/800 systems; bioMerieux VIDAS® or mini-VIDAS® systems; Chimera Biotec GmbH Imperacer® assay; Dade Behring STRATUS® system; DiaSorin LIAISON XL® or ETI-Max 300 systems; Dynex Agility® system; Gold Standard Diagnostics Thunderbolt® analyzer; Gyrolab xPlore/xPand® system; Hudson Robotics ELISA Workstation; Ortho Clinical Diagnostics Vitros® ECL or 3600 systems; Hamiltorn Robotics ELISA NIMBUS or STARlet systems; Luminex xMAP® system; PerkinElmer ALPHAscreen® or AlphaLISA®; Phadia Laboratory System 100E, 250, 1000, 2
  • exemplary analytical systems include assay systems, such as, for example, optical systems containing one or more sources of radiation and/or one more detectors. Such systems may use, for example, a light source that illuminates and a sample and a detector configured to detect light that is emitted by the sample (e.g., fluorescence spectroscopy), optical density (e.g., the portion of light that passes through the sample), and/or light that is diffracted by sample (e.g., diffraction optics).
  • An analytical system may use, for example, ELISA (enzyme-linked immunosorbent assay).
  • An analytical system may use, for example, LOCI (luminescent oxygen channeling), FOCI (fluorescent oxygen channeling), or ALPHAscreen.
  • An analytical technique may involve incubating and/or diluting a sample before or during the analysis/assaying of the sample.
  • compositions, methods and devices described herein make use of fluorescent oxygen channeling immunoassay (FOCI) compositions and methods.
  • FOCI fluorescent oxygen channeling immunoassay
  • a first analyte-binding agent that is capable of binding to an analyte and comprises a photosensitizer is used in combination with a second analyte-binding agent comprising a fluorogenic dye.
  • the photosensitizer of the first analyte-binding agent generates singlet oxygen in an excited state thereby causing the fluorogenic dye of the second analyte-binding agent to emit fluorescence upon reacting with the singlet oxygen.
  • the emitted fluorescence can be detected to, e.g., determine the presence and/or absence of the analyte and/or to quantitate and/or analyze the analyte in a sample.
  • the first and the second analyte-binding agents bind to the same region (e.g., epitope) of the analyte (e.g., a protein).
  • the first and the second analyte-binding agents comprise the same type of analyte-binding moiety or reagent (e.g., the same antibody).
  • the first and the second analyte-binding agents bind to separate regions (e.g., epitopes) of the analyte (e.g., a protein).
  • the first and the second analyte-binding agents bind to the separate regions of the analyte (e.g., a protein) that do not spatially overlap.
  • the first analyte-binding agent and the second analyte-binding agent are configured such that when both analyte-binding agents are bound to the analyte, the singlet oxygen generated by photosensitizer of the first analyte-binding agent is in close proximity to the fluorogenic dye of the second analyte-binding agent.
  • the first and/or second analyte binding agent(s) is an antigen-binding agent (e.g., an antibody).
  • the first and/or second analyte binding agent(s) is an affimer.
  • the first and/or second analyte binding agent(s) is an antigen-binding agent is an aptamer.
  • both the photosensitizer and fluorogenic dye are provided in the form of beads.
  • arrays can use different probes (e.g., antibodies, scFvs, Fab fragments) attached to different particles or beads.
  • probes e.g., antibodies, scFvs, Fab fragments
  • the identity of which probe is attached to which particle or beads may be determinable from an encoding system.
  • the probes can be antibodies or antigen-binding fragments or derivatives thereof.
  • control samples can be samples from the same patient at different times.
  • the one or more control samples can comprise one or more samples from healthy subjects, unhealthy subjects, or a combination thereof.
  • the one or more control samples can comprise one or more samples from healthy subjects, subjects suffering from pregnancy-associated conditions other than preeclampsia, subjects suffering chronic conditions along with pregnancy associated conditions, or subjects suffering from chronic conditions alone.
  • the expression level data for various samples is used to develop or train an algorithm or classifier provided herein.
  • the subject is a patient, such as a pregnant female; gene expression levels are measured in a sample from the patient and a classifier or algorithm (e.g., trained algorithm) is applied to the resulting data in order to detect, predict, monitor, rule out, or estimate the risk of a pregnancy-associated condition such as preeclampsia.
  • a classifier or algorithm e.g., trained algorithm
  • analysis of expression levels initially provides a measurement of the expression level of each of several individual biomarkers.
  • the expression level can be absolute in terms of a concentration of a biomarker, or relative in terms of a relative concentration of an expression product of interest to another biomarker in the sample.
  • relative expression levels of proteins can be expressed with respect to the expression level of a house-keeping or structural protein in the sample.
  • Relative expression levels can also be determined by simultaneously analyzing differentially labeled samples bound to the same array. Expression levels can also be expressed in arbitrary units, for example, related to signal intensity.
  • the classifier is a 2-way classifier.
  • a two-way classifier can classify a sample from a pregnant patient into one of two classes comprising preeclampsia (PreE) and non-preeclampsia (nonPreE).
  • PreE preeclampsia
  • nonPreE non-preeclampsia
  • the classifier may be used classify a subject as not needing treatment for preeclampsia.
  • a multi-way classifier may be used (e.g., preeclampsia, non-preeclampsia, and indeterminate).
  • Classifiers and/or classifier probe sets can be used to either rule-in or rule-out a sample as from a patient to be treated for preeclampsia.
  • a classifier can be used to classify a sample as being from a healthy subject.
  • a classifier can be used to classify a sample as being from an unhealthy subject.
  • classifiers can be used to either rule-in or rule-out a sample as being from a subject who should be treated for preeclampsia.
  • the methods, kits, and systems disclosed herein can comprise algorithms or uses thereof.
  • the one or more algorithms can be used to classify one or more samples from one or more subjects.
  • the one or more algorithms can be applied to data from one or more samples.
  • the data can comprise biomarker expression data.
  • the methods disclosed herein can comprise assigning a classification to one or more samples from one or more subjects. Assigning the classification to the sample can comprise applying an algorithm to the expression level.
  • the gene expression levels are inputted to a data analysis system comprising a trained algorithm for classifying the sample as one of the conditions comprising preeclampsia, eclampsia, non-preeclampsia, chronic hypertension, gestational hypertension, or HELLP (Hemolysis, Elevated Liver enzymes, and Low Platelet count—see e.g., Weinstein et al. Am J Obstet Gynecol. 142(1982):159-67) syndrome.
  • the algorithm can, as part of its execution, calculate an index for a sample and compare the sample index to a threshold value; the predefined relationship can be indicative of a likelihood of the sample belonging to a particular classification.
  • the algorithm can provide a record of its output including a classification of a sample and/or a confidence level.
  • the output of the algorithm can be the possibility of the subject of having a condition comprising preeclampsia, eclampsia, chronic hypertension, gestational hypertension, or HELLP syndrome.
  • a data analysis system can be a trained algorithm.
  • the algorithm can comprise a linear classifier.
  • the linear classifier comprises one or more of linear discriminant analysis, Fisher's linear discriminant, Na ⁇ ve Bayes classifier, Logistic regression, Perceptron, Support vector machine, or a combination thereof.
  • the linear classifier can be a support vector machine (SVM) algorithm.
  • the algorithm can comprise a two-way classifier.
  • the two-way classifier can comprise one or more decision tree, random forest, Bayesian network, support vector machine, neural network, or logistic regression algorithms.
  • the algorithm can comprise one or more linear discriminant analysis (LDA), Basic perceptron, Elastic Net, logistic regression, (Kernel) Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis (DLDA), Golub Classifier, Parzen-based, (kernel) Fisher Discriminant Classifier, k-nearest neighbor, Iterative RELIEF, Classification Tree, Maximum Likelihood Classifier, Random Forest, Nearest Centroid, Prediction Analysis of Microarrays (PAM), k-medians clustering, Fuzzy C-Means Clustering, Gaussian mixture models, graded response (GR), Gradient Boosting Method (GBM), Elastic-net logistic regression, logistic regression, or a combination thereof.
  • LDA linear discriminant analysis
  • SVM Support Vector Machines
  • DLDA Diagonal Linear Discriminant Analysis
  • Golub Classifier Parzen-based
  • (kernel) Fisher Discriminant Classifier k-nearest neighbor
  • Iterative RELIEF Classification Tree
  • the algorithm can comprise a Diagonal Linear Discriminant Analysis (DLDA) algorithm.
  • the algorithm can comprise a Nearest Centroid algorithm.
  • the algorithm can comprise a Random Forest algorithm.
  • GBM gradient boosting method for discrimination of preeclampsia and non-preeclampsia
  • LDA linear discriminant analysis
  • SVM support vector machine
  • biomarker refers to a measurable indicator of some biological state or condition.
  • a biomarker can be a substance found in a subject, a quantity of the substance, or some other indicator.
  • a biomarker can be the amount of a protein and/or other gene expression products in a sample.
  • a biomarker is a full-length, unmodified protein.
  • a biomarker is an alternatively spliced, post-translationally cleaved, or post-translationally chemically modified (e.g., methylated, phosphorylated, glycosylated, formylated, etc) protein.
  • the methods, compositions and systems as described here also relate to the use of biomarker panels and/or gene expression products for purposes of identification, diagnosis, classification, treatment or to otherwise characterize various conditions of pregnant patients comprising NonPreE, PreE, chronic hypertension, gestational hypertension, or HELLP syndrome.
  • Sets of biomarkers and/or gene expression products useful for classifying biological samples are provided, as well as methods of obtaining such sets of biomarkers.
  • the pattern of levels of gene expression biomarkers in a panel (also known as a signature) is determined from one or more references samples and then used to evaluate the signature of the same panel of biomarkers in a test sample, such as by a measure of similarity between the test sample signature and the reference sample signature.
  • the methods, compositions, and systems described herein may involve the detection of one or more biomarker belonging to a particular functional class of biomarkers with a connection to one or more pathophysiological features of preeclampsia (see FIG. 16 , which shows various pathophysiological features or preeclampsia). While FIG. 16 shows various pathophysiological features, and associated biomarkers, that are thought to be associated with preeclampsia, numerous other methods for describing the pathophysiology and relationship between the markers is possible. For instance, KIM-1, CD274, and decorin can be considered as kidney damage associated proteins. Similarly, sFlt1, endoglin, pappalysin 2, and decorin can be considered as angiogenesis-associated proteins. A person of skill in the art will recognize that various other classification schemes could be similarly used.
  • preeclampsia is thought to originate in abnormal trophoblast invasion, which results in incomplete spiral artery remodeling and hypoperfusion of the placenta, and that this hypoperfusion of the placenta triggers dysfunction in multiple body systems causing the signs and symptoms of preeclampsia.
  • dysfunctional systems can include, as a result of placental hypoperfusion, angiogenesis and endothelial function, as evidenced e.g. by imbalances in pro-and-anti-angiogenic factors, many of which are released by the placenta in response to the abnormal physiology of preeclampsia, and which disrupt vascular homeostasis in the mother's body.
  • SFLT1 Soluble FMS-like tyrosine kinase 1, a tyrosine-protein kinase that acts as a cell-surface receptor for VEGFA, VEGFB and PlGF and decreases towards term, and plays an essential role in the development of embryonic vasculature
  • PlGF Placental growth factor, a proangiogenic protein peaking at 30 weeks of gestation that stimulates endothelial cell growth, proliferation, and migration
  • DCN Decorin, which is a functional component of the extracellular matrix and plays a role in tissue repair and regulation of cell adhesion and migration by binding to ECM molecules
  • ENG Endoglin, which in its soluble form, sENG is a powerful antiangiogenic molecule, and acts by inhibiting TGF- ⁇ 1 binding
  • FGF21 Fibroblast growth factor 21, which has been demonstrated to be expressed in placental syncytiotrophoblasts, and is both an adipokine and a regulator of glucose transport
  • KIM-1 Kidney Injury Molecule-1
  • Another such dysfunctional system is altered immune response, which may result from inflammation of placental tissues as a result of their hypoperfusion.
  • CLEC4A C-type Lectin domain family member A, which maintains the balance of polarization of na ⁇ ve Th cells into Th1 and Th2 effector cells
  • TFF2 Tufoil factor 2
  • CD274/PD-L1 cluster of differentiation 274 or programmed-death ligand 1
  • the methods herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers recited in the following table (Table A).
  • a condition e.g. preeclampsia
  • the methods herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers recited in the following table (Table B).
  • a condition e.g. preeclampsia
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from (e.g., based on analysis from) one or more biomarkers selected from Table A and Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from two or more biomarkers selected from Table A and Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from three or more biomarkers selected from Table A and Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g.
  • preeclampsia from one biomarker selected from Table A and Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from two biomarkers selected from Table A and Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from three biomarkers selected from Table A and Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from four biomarkers selected from Table A and Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from all the biomarkers identified in Table A and Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than 3 biomarkers selected from Table A and Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than 4 biomarkers selected from Table A and Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than 6 biomarkers selected from Table A and Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than 7 biomarkers selected from Table A and Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than 8 biomarkers selected from Table A and Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g.
  • preeclampsia from no more than 9 biomarkers selected from Table A and Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than 10 biomarkers selected from Table A and Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than 11 biomarkers selected from Table A and Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table A and one or more biomarkers selected from Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 2 or more biomarkers selected from Table A and one or more biomarkers selected from Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 or more biomarkers selected from Table A and one or more biomarkers selected from Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g.
  • preeclampsia from 4 biomarkers selected from Table A and one or more biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and one or more biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and two or more biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and three or more biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and four or more biomarkers selected from Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and five or more biomarkers selected from Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and six or more biomarkers selected from Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g.
  • preeclampsia from 3 biomarkers selected from Table A and one biomarker selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and two biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and three biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and four biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and five biomarkers selected from Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and six biomarkers selected from Table B. In some cases, the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from 3 biomarkers selected from Table A and seven biomarkers selected from Table B. The methods provided herein can comprise identifying or ruling out a condition (e.g.
  • preeclampsia from no more than one biomarker selected from Table A and no more than 2 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than two biomarkers selected from Table A and no more than 2 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than three biomarkers selected from Table A and no more than 2 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than one biomarker selected from Table A and no more than 2 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than one biomarker selected from Table A and no more than 3 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than two biomarkers selected from Table A and no more than 3 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than three biomarkers selected from Table A and no more than 3 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g.
  • preeclampsia from no more than one biomarker selected from Table A and no more than 3 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than one biomarker selected from Table A and no more than 4 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than two biomarkers selected from Table A and no more than 4 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than three biomarkers selected from Table A and no more than 4 biomarkers selected from Table B.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from no more than one biomarker selected from Table A and no more than 4 biomarkers selected from Table B.
  • the methods provided herein can comprises identifying or ruling out a condition (e.g. preeclampsia) from a panel of markers comprising sFLT-1, PlGF, FGF21, CLEC4a, endoglin, CD274, and decorin.
  • a condition e.g. preeclampsia
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from (e.g., based on analysis from) sFlt.1, PlGF, KIM1, and CLEC4A; sFlt.1, PlGF, KIM1, CLEC4A, and FGF21; sFlt.1, PlGF, KIM1, CLEC4A, and CD274; sFlt.1, PlGF, KIM1, CLEC4A, and ENDOGLIN; sFlt.1, PlGF, KIM1, CLEC4A, and DECORIN; sFlt.1, PlGF, KIM1, CLEC4A, FGF21, and ENDOGLIN; sFlt.1, PlGF, KIM1, CLEC4A, FGF21, and CD274; sFlt.1, PlGF, KIM1, CLEC4A, ENDOGLIN, and CD274; sFlt.1, PlGF, KIM1, CLEC4A
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from (e.g., based on analysis from) the following sets of four biomarkers optionally in combination with PlGF:SFLT1, KIM1, CLEC4A, FGF21; SFLT1, KIM1, CLEC4A, endoglin; SFLT1, KIM1, CLEC4A, decorin; SFLT1, KIM1, CLEC4A, CD274; SFLT1, KIM1, CLEC4A, HGF; SFLT1, KIM1, CLEC4A, TFF2; SFLT1, KIM1, CLEC4A, PAPP.A2; SFLT1, KIM1, FGF21, endoglin; SFLT1, KIM1, FGF21, decorin; SFLT1, KIM1, FGF21, CD274; SFLT1, KIM1, FGF21, HGF; SFLT1, KIM1, FGF21, TFF2; SFLT1, KIM1, FGF21,
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from (e.g., based on analysis from) the following sets of three biomarkers optionally in combination with PlGF: SFLT1, KIM1, CLEC4A; SFLT1, KIM1, FGF21; SFLT1, KIM1, endoglin; SFLT1, KIM1, decorin; SFLT1, KIM1, CD274; SFLT1, KIM1, HGF; SFLT1, KIM1, TFF2; SFLT1, KIM1, PAPP.A2; SFLT1, CLEC4A, FGF21; SFLT1, CLEC4A, endoglin; SFLT1, CLEC4A, decorin; SFLT1, CLEC4A, CD274; SFLT1, CLEC4A, HGF; SFLT1, CLEC4A, TFF2; SFLT1, CLEC4A, PAPP.A2; SFLT1, FGF21, endoglin; SF
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from (e.g., based on analysis from) the following sets of three biomarkers optionally in combination with PlGF: SFLT1, KIM1; SFLT1, CLEC4A; SFLT1, FGF21; SFLT1, endoglin; SFLT1, decorin; SFLT1, CD274; SFLT1, HGF; SFLT1, TFF2; SFLT1, PAPP.A2; KIM1, CLEC4A; KIM1, FGF21; KIM1, endoglin; KIM1, decorin; KIM1, CD274; KIM1, HGF; KIM1, TFF2; KIM1, PAPP.A2; CLEC4A, FGF21; CLEC4A, endoglin; CLEC4A, decorin; CLEC4A, CD274; CLEC4A, HGF; CLEC4A, TFF2; CLEC4A, PAPP.A2; F
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from (e.g., based on analysis from) the following sets of biomarkers: PlGF, sFLT1, KIM1; PlGF, sFLT1, CLEC4A; PlGF, sFLT1, FGF21; PlGF, sFLT1, Decorin; PlGF, sFLT1, CD274; PlGF, sFLT1, HGF; PlGF, sFLT1, TFF2; PlGF, sFLT1, PAPP-A2; PlGF, Endoglin, KIM1; PlGF, Endoglin, CLEC4A; PlGF, Endoglin, FGF21; PlGF, Endoglin, Decorin; PlGF, Endoglin, CD274; PlGF, Endoglin, HGF; PlGF, Endoglin, TFF2; PlGF, Endoglin, PAPP-A2; PlGF, KIM1, CLEC4A;
  • the methods provided herein can comprise identifying or ruling out a condition from one or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2 (chemokine C—C motif ligand 2), CD134 (cluster of differentiation 134), DCN, HGF (hepatocyte growth factor), NOS3 (nitric oxide synthase 3), PlGF, CD274, CDCP1 (cub domain containing protein 1), FGF-21, TGFa (transforming growth factor alpha), UPA (urokinase-type plasminogen activator), CLEC4A, CLEC4C (C-type lectin domain family 4 member C), ZBTB16 (Zinc Finger And BTB Domain Containing 16), APLP1 (Amyloid Beta Precursor Like Protein 1), DPP7 (Dipeptidyl Peptidase 7), GRAP2 (GRB2 Related Adaptor Protein 2), ITGB7 (Integrin Subunit Beta 7), PAG1 (Phosphoprotein Membrane Anchor With Glyrosphingolipid Micro
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from five or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from six or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from seven or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from eight or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from nine or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from ten or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from eleven or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from twelve or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from thirteen or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from fourteen or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from fifteen or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from sixteen or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from seventeen or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB
  • preeclampsia of a pregnant patient can be detected from eighteen or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from nineteen or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than twenty biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than nineteen biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than eighteen biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF,
  • preeclampsia of a pregnant patient can be detected from no more than seventeen biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than sixteen biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than fifteen biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than fourteen biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than thirteen biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than twelve biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than eleven biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than ten biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than nine biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than eight biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than seven biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than six biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ER
  • preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 biomarkers selected from Table 2, Table 3, Table 4, Table 5, CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof (“Group 2”).
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from five or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from six or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from seven or more biomarkers selected from Table 2, Table 3, Table 4, Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from eight or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from nine or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from ten or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from eleven or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from twelve or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from thirteen or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from fourteen or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from fifteen or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from sixteen or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from seventeen or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from eighteen or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from nineteen or more biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than twenty biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than nineteen biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than eighteen biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than seventeen biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than sixteen biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than fifteen biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than fourteen biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than thirteen biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than twelve biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than eleven biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than ten biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than nine biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than eight biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than seven biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than six biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 biomarkers selected from Table 2, Table 3, Table 4, or Table 5, and any combination thereof.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof (“Group 3”).
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from five or more biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from six or more biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from seven or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from eight or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from nine or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from ten or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from eleven or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from twelve or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from thirteen or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from fourteen or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from fifteen or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from sixteen or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from seventeen or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from eighteen or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from nineteen or more biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than twenty biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than nineteen biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than eighteen biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than seventeen biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than sixteen biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than fifteen biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than fourteen biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than thirteen biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than twelve biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than eleven biomarkers selected from Table 2, Table 3, or Table 4, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than ten biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than nine biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than eight biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than seven biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than six biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table 2 or Table 5, and any combination thereof (“Group 4”).
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from Table 2 or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from Table 2 or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from Table 2 or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from five or more biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from six or more biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from seven or more biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from eight or more biomarkers selected from Table 2 or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from nine or more biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from ten or more biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from 11, 12, 13, 14, 15, 16, 17, 18, or 19 or more biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than 20, 19, 18, 17, 16, 15, 14, 13, 12, or 11 biomarkers selected from Table 2 or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than ten biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than nine biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than eight biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than seven biomarkers selected from Table 2 or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from no more than six biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from Table 2 or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 biomarkers selected from Table 2 or Table 5, and any combination thereof.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof (“Group 5”).
  • a condition e.g. preeclampsia
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG,
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from five or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from six or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from seven or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from eight or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from nine or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from ten or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and
  • preeclampsia of a pregnant patient can be detected from 11, 12, 13, 14, 15, 16, 17, 18, or 19 or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PP
  • preeclampsia of a pregnant patient can be detected from no more than 20, 19, 18, 17, 16, 15, 14, 13, 12, or 11 biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB,
  • preeclampsia of a pregnant patient can be detected from no more than ten biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1,
  • preeclampsia of a pregnant patient can be detected from no more than nine biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and
  • preeclampsia of a pregnant patient can be detected from no more than eight biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and
  • preeclampsia of a pregnant patient can be detected from no more than seven biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and
  • preeclampsia of a pregnant patient can be detected from no more than six biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and
  • preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and
  • preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and
  • preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and
  • preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYND1, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PlGF, CD274, CDCP1, FGF-21, TGFa, UPA, CLEC4A, CLEC4C, ZBTB16, APLP1, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF,
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from five or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from six or more biomarkers selected from Table 5. In some cases, preeclampsia of a pregnant patient can be detected from nine biomarkers selected from Table 5. In some cases, preeclampsia of a pregnant patient can be detected from no more than eight biomarkers selected from Table 5. In some cases, preeclampsia of a pregnant patient can be detected from no more than seven biomarkers selected from Table 5. In some cases, preeclampsia of a pregnant patient can be detected from no more than six biomarkers selected from Table 5. In some cases, preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from Table 5. In some cases, preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from Table 5. In some cases, preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, or 9 biomarkers selected from Table 5.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from five or more biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from six or more biomarkers selected from Table 2. In some cases, preeclampsia of a pregnant patient can be detected from nine biomarkers selected from Table 2. In some cases, preeclampsia of a pregnant patient can be detected from no more than eight biomarkers selected from Table 2. In some cases, preeclampsia of a pregnant patient can be detected from no more than seven biomarkers selected from Table 2. In some cases, preeclampsia of a pregnant patient can be detected from no more than six biomarkers selected from Table 2. In some cases, preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from Table 2. In some cases, preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from Table 2. In some cases, preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, or 9 biomarkers selected from Table 2.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table 3.
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from 4.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from 4.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from 4.
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from Table 3.
  • preeclampsia of a pregnant patient can be detected from five or more biomarkers selected from Table 3.
  • preeclampsia of a pregnant patient can be detected from six or more biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from seven or more biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from eight or more biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from nine or more biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from ten or more biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from 11, 12, 13, 14, 15, 16, 17, 18, or 19 or more biomarkers selected from Table 3.
  • preeclampsia of a pregnant patient can be detected from no more than 20, 19, 18, 17, 16, 15, 14, 13, 12, or 11 biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from no more than ten biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from no more than nine biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from no more than eight biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from no more than seven biomarkers selected from Table 3.
  • preeclampsia of a pregnant patient can be detected from no more than six biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from Table 3. In some cases, preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 biomarkers selected from Table 3.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table 4.
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from 5.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from 5.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from 5.
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from Table 4.
  • preeclampsia of a pregnant patient can be detected from five or more biomarkers selected from Table 4.
  • preeclampsia of a pregnant patient can be detected from six or more biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from seven or more biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from eight or more biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from nine or more biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from ten or more biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from 11, 12, 13, 14, 15, 16, 17, 18, or 19 or more biomarkers selected from Table 4.
  • preeclampsia of a pregnant patient can be detected from no more than ten biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from no more than nine biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from no more than eight biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from no more than seven biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from no more than six biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from Table 4.
  • preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from Table 4. In some cases, preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 biomarkers selected from Table 4.
  • the methods provided herein can comprise detecting a condition (such as preeclampsia) from one or more biomarkers selected from (a) known preeclampsia candidate biomarkers reported in the literature (such as PAPP-A, sFlt1, PlGF, or Fibronectin), (b) preeclampsia biomarkers specifically identified herein (such as those selected from Group 1, Group 2, Group 3, Group 4, Group 5, Table 2, Table 3, Table 4, or Table 5), or (c) any combination of (a) and (b).
  • a condition such as preeclampsia
  • the methods provided herein can comprise detecting a condition (such as preeclampsia) from two or more, three or more, four or more, five or more, six or more, or seven or more biomarkers selected from (a), (b), or (c). In other embodiments, the methods provided herein can comprise detecting a condition (such as preeclampsia) from no more than ten, no more than 9, no more than 8, no more than 7, no more than 6, no more than 5, no more than 4, or no more than 3 biomarkers selected from (a), (b), or (c).
  • a condition such as preeclampsia
  • the methods provided herein can comprise detecting a condition (such as preeclampsia) from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 biomarkers selected from (a), (b), or (c).
  • a condition such as preeclampsia
  • the methods, compositions, systems and kits provided herein can be used to detect, diagnose, predict or monitor a condition of a pregnant patient.
  • the methods, compositions, systems and kits described herein provide information to a medical practitioner that can be useful in making a clinical therapeutic decision.
  • Clinical and therapeutic decisions can include decisions to: continue with a particular therapy, modify a particular therapy, alter the dosage of a particular therapy, stop or terminate a particular therapy, altering the frequency of a therapy, introduce a new therapy, introduce a new therapy to be used in combination with a current therapy, or any combination of the above.
  • medical action taken may comprise watchful waiting or the administration of one or more additional diagnostic tests of the same or different nature.
  • a clinical decision may be made to not induce labor, or to proceed with ambulant monitoring of the subject.
  • the methods provided herein can be applied in an experimental setting, e.g., clinical trial.
  • the methods provided herein can be used to monitor a pregnant patient who is being treated with an experimental agent such as an angiogenic/antiangiogenic drug, compound, or therapeutic cell type.
  • the methods provided herein can be useful to determine whether a subject can be administered an experimental agent (e.g., an agonist, antagonist, peptidomimetic, protein, peptide, nucleic acid, therapeutic cell, small molecule, or other drug candidate) to reduce the risk of preeclampsia.
  • an experimental agent e.g., an agonist, antagonist, peptidomimetic, protein, peptide, nucleic acid, therapeutic cell, small molecule, or other drug candidate
  • the methods described herein can be useful in determining if a subject can be effectively treated with an experimental agent and for monitoring the subject for risk of preeclamp
  • the methods, compositions, systems and kits provided herein are particularly useful for detecting, diagnosing, or ruling out a condition of a pregnant patient such as a condition the pregnant patient has at the time of testing.
  • An exemplary condition that can be detected, diagnosed, or ruled out with the present method includes preeclampsia.
  • the methods, compositions, systems, and kits provided herein can also be useful, in combination with other standard clinical data collected for pregnant patients, for ruling in or ruling out a diagnosis of preeclampsia, hypertension, gestational hypertension, or HELLP syndrome.
  • the methods provided herein are particularly useful for pregnant patients who have exhibited one or more new-onset symptoms associated with preeclampsia prior to testing (e.g., hypertension, proteinuria, low platelet count, elevated serum creatinine levels, elevated liver enzymes, pulmonary edema, or cerebral/visual symptoms), such that the patients are suspected of having preeclampsia.
  • preeclampsia e.g., hypertension, proteinuria, low platelet count, elevated serum creatinine levels, elevated liver enzymes, pulmonary edema, or cerebral/visual symptoms
  • the methods, compositions, systems and kits provided herein can rule out a diagnosis of preeclampsia for a specified number of days in the future.
  • the specified number of days in the future is 1 to 30 days. In some instances the specified number of days in the future is at least 1 day. In some instances the specified number of days in the future is at most 30 days. In some instances the specified number of days in the future is 1 day to 5 days, 1 day to 10 days, 1 day to 30 days, 5 days to 10 days, 5 days to 30 days, or 10 days to 30 days. In some instances the specified number of days in the future is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 days.
  • the specified number of days in the future is 5 days to 10 days. In other preferred embodiments, the specified number of days in the future is 5, 6, 7, 8, 9, or 10 days.
  • the methods, compositions, systems and kits provided herein can rule-out mothers for hospital admission and preterm delivery. In some embodiments, the methods, compositions, systems and kits provided herein can rule out a diagnosis of preeclampsia for a specified number of weeks in the future. In some instances the specified number of weeks is at least 1 week. In some instances the specified number of weeks is at least 2 weeks. In some instances the specified number of weeks is at least 3 weeks. In some instances the specified number of weeks is at least 4 weeks. In some instances the specified number of weeks is at least 5 weeks.
  • the specified number of weeks is at least 6 weeks. In some instances the specified number of weeks is at most 1 week. In some instances the specified number of weeks is at most 2 weeks. In some instances the specified number of weeks is at most 3 weeks. In some instances the specified number of weeks is at most 4 weeks. In some instances the specified number of weeks is at most 5 weeks. In some instances the specified number of weeks is at most 6 weeks.
  • the methods, compositions, systems and kits provided herein can rule out a diagnosis of preeclampsia for a specified number of weeks in the future with a particular PPV.
  • the positive predictive value is at least about 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, or 57%, or any range in between these values.
  • the methods, compositions, systems and kits provided herein can rule out a diagnosis of preeclampsia for a specified number of weeks in the future with a particular NPV.
  • the NPV can be at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96.7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%, 99.7%, or 99.9%, or any range in between these values.
  • the methods, compositions, systems and kits provided herein can rule out a diagnosis of preeclampsia for a specified number of weeks in the future with a particular AUC.
  • the AUC of can be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any range in between these values.
  • the methods, compositions, systems and kits provided herein can rule out a diagnosis of preeclampsia for a specified number of weeks in the future with a particular AUP.
  • the AUP can be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any range in between these values.
  • the diagnosis, detection, or ruling out of a condition of the pregnant patient can be particularly useful in limiting the number of unnecessary invasive medical interventions that are administered to the patient, and/or indicating alternative less-invasive therapeutic interventions such as pharmacological therapies (anticonvulsants, antihypertensives, central alpha agonists, alpha-blockers, beta-blockers, calcium-channel blockers, vasodilators, cyclooxygenase inhibitors).
  • pharmacological therapies anticonvulsants, antihypertensives, central alpha agonists, alpha-blockers, beta-blockers, calcium-channel blockers, vasodilators, cyclooxygenase inhibitors.
  • the methods provided herein can limit, delay, or eliminate the use of preterm cesarean delivery or labor induction in patients suspected of having preeclampsia via high-confidence ruling out of a diagnosis of preeclampsia in the pregnant patient (e.g., via a high negative predictive value of the methods, compositions, systems, and kits provided herein).
  • the methods, compositions, systems and kits provided herein can be used alone or in combination with other standard diagnosis methods currently used to detect, diagnose, or rule out a condition of a pregnant patient, such as but not limited to blood pressure measurement, urine protein measurement, blood platelet counting, serum creatinine level measurement, creatinine clearance measurement, urine protein/creatinine ratio measurement, serum transaminase level measurement, serum LDH level measurement, serum bilirubin level measurement, or Doppler ultrasound indices (e.g., uterine artery indices).
  • other standard diagnosis methods currently used to detect, diagnose, or rule out a condition of a pregnant patient such as but not limited to blood pressure measurement, urine protein measurement, blood platelet counting, serum creatinine level measurement, creatinine clearance measurement, urine protein/creatinine ratio measurement, serum transaminase level measurement, serum LDH level measurement, serum bilirubin level measurement, or Doppler ultrasound indices (e.g., uterine artery indices).
  • hypertension in a pregnant patient can be indicative of conditions such as chronic hypertension, gestational hypertension, or preeclampsia; ruling out the diagnosis of preeclampsia via the methods, compositions, systems and kits provided herein allows for the patient to be correctly diagnosed with chronic hypertension or gestational hypertension.
  • the methods provided herein can predict preeclampsia prior to actual onset of the condition or symptoms associated with the condition (e.g., hypertension or proteinuria). In some instances, the methods provided herein can predict preeclampsia or other disorders in a pregnant patient at least 1 day, 1 week, 2 weeks, 3 weeks, 1 month, 2 months prior to onset of the condition or symptoms associated with the condition. In other instances, the methods provided herein can predict preeclampsia or other disorders in a pregnant patient at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31 days prior to onset. In other instances, the methods provided herein can predict preeclampsia or other disorders in a pregnant patient at least 1, 2, 3, or 4 months prior to onset.
  • the monitoring is conducted by serial testing, such as serial non-invasive tests, serial minimally-invasive tests (e.g., blood draws), or some combination thereof.
  • serial non-invasive tests e.g., blood draws
  • serial minimally-invasive tests e.g., blood draws
  • the monitoring is conducted by administering serial non-invasive tests or serial minimally-invasive tests (e.g., blood draws).
  • the pregnant patient is monitored as needed (e.g., on an as-needed basis) using the methods described herein. Additionally or alternatively the pregnant patient can be monitored weekly, monthly, or at any pre-specified intervals. In some instances, the pregnant patient is monitored at least once every 24 hours. In some instances the pregnant patient is monitored at least once every 1 day to 30 days. In some instances the pregnant patient is monitored at least once every at least 1 day. In some instances the pregnant patient is monitored at least once every at most 30 days.
  • the pregnant patient is monitored at least (optionally on average) once every 1 day to 5 days, 1 day to 10 days, 1 day to 15 days, 1 day to 20 days, 1 day to 25 days, 1 day to 30 days, 5 days to 10 days, 5 days to 15 days, 5 days to 20 days, 5 days to 25 days, 5 days to 30 days, 10 days to 15 days, 10 days to 20 days, 10 days to 25 days, 10 days to 30 days, 15 days to 20 days, 15 days to 25 days, 15 days to 30 days, 20 days to 25 days, 20 days to 30 days, or 25 days to 30 days.
  • the pregnant patient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 28, 29, 30 or 31 days.
  • the pregnant patient is monitored at least once every 1, 2, or 3 months. In some instances, the pregnant patient is monitored via the methods described herein no more frequently than one week, 10 days, two weeks, three weeks, or one month. In other words, the predictive value of the some of the methods described herein can be of clinical use for at least one week, at least 10 days, at least two week, at least three weeks, or at least one month.
  • biomarker expression levels in the patients can be measured, for example, within, one week, two weeks, three weeks, or four weeks after detection of one or more symptoms associated with preeclampsia (e.g., hypertension or proteinuria).
  • biomarker expression levels are determined at regular intervals, e.g., every 1 week, 2 weeks, 3 weeks, 1 month, 2 months or 3 months post-conception, after the beginning of the 2 nd trimester, after the beginning of the 3 rd trimester, or after week 20 of the pregnancy, either indefinitely, or until evidence of a condition is observed.
  • biomarker expression levels are determined at regular intervals after week 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or 42 weeks. Where evidence of a condition is observed, the frequency of monitoring is sometimes increased.
  • baseline values of expression levels are determined in a subject before detection of one or more symptoms associated with preeclampsia (e.g., hypertension or proteinuria) in combination with determining expression levels after onset of symptoms.
  • the results of diagnosing, predicting, ruling out, or monitoring a condition of the pregnant patient can be useful for informing a clinical or therapeutic decision such as determining or monitoring a therapeutic regimen.
  • an entity that acquires sample data and/or classifies a sample from a patient as having preeclampsia is other than the physician, caregiver, or medical institution performing the treatment.
  • the entity acquiring sample data e.g. levels of levels of two or more proteins from Tables A, B, 2, 3, 4, and 5
  • calculating an index based (at least in part) on the levels of the plurality of the protein biomarkers, and/or determining risk of preeclampsia is a third-party testing service.
  • determining or monitoring a therapeutic regimen first comprises receiving information from a third-party testing service, which can comprise, for example (but not limited to), classification of a sample as being at risk or not of preeclampsia, risk of a pregnant patient having preeclampsia, levels of a plurality of protein biomarkers from the sample associated with preeclampsia (e.g. levels of two or more proteins from Tables A, B, 2, 3, 4, and 5), or the likelihood a pregnant patient will deliver preterm.
  • a third-party testing service can comprise, for example (but not limited to), classification of a sample as being at risk or not of preeclampsia, risk of a pregnant patient having preeclampsia, levels of a plurality of protein biomarkers from the sample associated with preeclampsia (e.g. levels of two or more proteins from Tables A, B, 2, 3, 4, and 5), or the likelihood a pregnant patient will deliver preterm.
  • an entity that acquires sample data, determines the risk of preterm birth of the patient from the sample, and/or classifies a sample from a patient as having a significant risk of preterm birth is the same entity that performing the treatment.
  • determining a therapeutic regimen can comprise administering a therapeutic drug. In some instances, determining a therapeutic regimen comprises modifying, continuing, initiating or stopping a therapeutic regimen. In some instances, determining a therapeutic regimen comprises treating the disease or condition (e.g., preeclampsia, eclampsia, gestational hypertension, hypertension, or HELLP syndrome). In some instances, the therapy is an anti-hypertensive therapy. In some instances, the therapy is an anti-cyclooxygenase (COX) therapy. In some instances, the therapy is an anti-convulsant therapy.
  • COX anti-cyclooxygenase
  • Modifying the therapeutic regimen can comprise terminating a therapy. Modifying the therapeutic regimen can comprise altering a dosage of a therapy. Modifying the therapeutic regimen can comprise altering a frequency of a therapy. Modifying the therapeutic regimen can comprise administering a different therapy.
  • the results of diagnosing, predicting, or monitoring a condition of the pregnant patient can be useful for informing a therapeutic decision such as caesarean delivery.
  • Other examples of therapeutic decisions can be cervical ripening and/or labor induction. Examples of agents that can be used for cervical ripening and/or labor induction include prostaglandins, misoprostol, mifepristone, relaxin, and oxytocin. Other examples of therapeutic decisions can be cesarean delivery.
  • Examples of a therapeutic regimen can include administering compounds or agents having anti-hypertensive properties (e.g., central alpha agonists such as methyldopa, vasodilators such as clonidine, diazoxide, hydralazine and prazosin, calcium-channel blockers such as nifedipine and verapamil, alpha-blockers such as labetalol, or beta-blockers such as oxprenolol), compounds or agents having anti-cyclooxygenase activity (e.g., acetylsalicylic acid), or compounds having anti-convulsant activity (e.g., phenytoin or magnesium sulfate). These compounds can be used alone or in combination.
  • compounds or agents having anti-hypertensive properties e.g., central alpha agonists such as methyldopa, vasodilators such as clonidine, diazoxide, hydralazine and
  • modifying the therapeutic regimen can comprise proceeding with treatment of said pregnant human in a manner that avoids unnecessary treatment of preeclampsia.
  • managing the pregnant human subject identified not as at risk for preeclampsia comprises ambulant monitoring, or refraining from the administration of any drug for treating preeclampsia.
  • antihypertensive drugs may be prescribed and/or administered to the patient.
  • the methods, kits, and systems disclosed herein for use in identifying, classifying (or ruling out a classification) or characterizing a sample can be characterized by having a specificity of at least about 80% using the methods disclosed herein. In some embodiments, the specificity of the methods is at least about 85%. In some embodiments, the specificity of the methods is at least about 90%. In some embodiments, the specificity of the methods is at least about 95%. The specificity of the method can be at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%, or any range in between these values.
  • the present invention provides a method of identifying, classifying (or ruling out a classification) or characterizing a sample that gives a sensitivity of at least about 80% using the methods disclosed herein.
  • the sensitivity of the methods is at least 85%.
  • the sensitivity of the methods is at least 90%.
  • the sensitivity of the methods is at least 95%.
  • the sensitivity of the method can be at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%, or any range in between these values.
  • the methods, kits and systems disclosed herein can improve upon the accuracy of current methods of monitoring or predicting a status or outcome of a pregnancy (e.g. preeclampsia) or identifying or ruling out a classification of a sample.
  • the accuracy of the methods, kits, and systems disclosed herein can be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any range in between these values.
  • the methods, kits, and systems for use in identifying, classifying (or ruling out a classification) or characterizing a sample can be characterized by having a negative predictive value (NPV) greater than or equal to 90%.
  • the NPV can be at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96.7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%, 99.7%, or 99.9%, or any range in between these values.
  • the NPV can be greater than or equal to 95%.
  • the NPV can be greater than or equal to 96%.
  • the NPV can be greater than or equal to 97%.
  • the methods, kits, and/or systems disclosed herein for use in identifying, classifying (or ruling out a classification) or characterizing a sample can be characterized by having a positive predictive value (PPV) of at least about 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, or 57%, or any range in between these values using the methods disclosed herein.
  • PSV positive predictive value
  • the methods, kits and systems disclosed herein can improve upon the AUC of current methods of monitoring or predicting a status or outcome of a pregnancy (e.g. preeclampsia) or identifying or ruling out a classification of a sample.
  • the AUC of the methods, kits, and systems disclosed herein can be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any range in between these values.
  • the methods, kits and systems disclosed herein can improve upon the AUP of current methods of monitoring or predicting a status or outcome of a pregnancy (e.g. preeclampsia) or identifying or ruling out a classification of a sample.
  • the AUP of the methods, kits, and systems disclosed herein can be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any range in between these values.
  • the methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a pregnancy in a subject in need thereof can be characterized by having an accuracy of at least about 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97% or any range in between these values.
  • the methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a pregnancy in a subject in need thereof can be characterized by having a specificity of at least about 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%, or any range in between these values.
  • the methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a pregnancy in a subject in need thereof can be characterized by having a sensitivity of at least about 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%, or any range in between these values.
  • the methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a pregnancy in a subject in need thereof can be characterized by having a negative predictive value (NPV) greater than or equal to 90%.
  • the NPV can be at least about 90%, 91%, 92%, 93%, 94%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96.7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%, 99.7%, or 99.9%, or any range in between these values.
  • the NPV can be greater than or equal to 95%.
  • the NPV can be greater than or equal to 96%.
  • the NPV can be greater than or equal to 97%.
  • the NPV can be greater than or equal to 98%.
  • the methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a pregnancy in a subject in need thereof can be characterized by having a positive predictive value (PPV) of at least about 80%.
  • the methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a pregnancy in a subject in need thereof can be characterized by having a positive predictive value (PPV) of at least about 85%.
  • the methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a pregnancy in a subject in need thereof can be characterized by having a positive predictive value (PPV) of at least about 90%.
  • the PPV can be at least about 80%, 85%, 90%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96.7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%, 99.7%, or 99.9%, or any range in between these values.
  • the PPV can be greater than or equal to 95%.
  • the PPV can be greater than or equal to 98%.
  • disclosure provides a test for confirming preeclampsia in a subject, preferably a pregnant subject, wherein the test is able to discern subjects not having preeclampsia but having one or more symptoms associated with preeclampsia from subjects having by preeclampsia with an NPV of at least about 90%, 91%, 92%, 93%, 94%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96.7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%, 99.7%, or 99.9%, or any range in between these values.
  • the one or more symptoms associated with preeclampsia can be diabetes (e.g. gestational, type I or type II), higher than normal glucose level, hypertension (e.g. chronic or non-chronic), excessive or sudden weight gain, higher than normal weight, obesity, higher than normal body mass index (BMI), abnormal weight gain, abnormal blood pressure, water retention, hereditary factors, abnormal proteinuria, headache, edema, abnormal protein/creatinine ratio, abnormal platelet count, stress, nulliparity, abnormal Papanicolaou test results (Pap smear), prior preeclampsia episodes (e.g., personal history of PreE), familial history of preeclampsia, preeclampsia in prior pregnancies, renal disease, thrombophilia, or any combination thereof. Gestational age may also be used in tests, such as tests for ruling out preeclampsia.
  • diabetes e.g. gestational, type I or type II
  • hypertension e.g. chronic
  • disclosure provides for a method, kit, system, or test that has a sensitivity of at least 79% and a specificity of at least 94%. In some embodiments, a method, kit, system, or test has a sensitivity of at least 82% and a specificity of at least 80%. In some embodiments, a method, kit, system of test has a sensitivity of at least 90% and a specificity of at least 80%.
  • the methods, kits, and systems disclosed herein can include at least one computer program, or use of the same.
  • a computer program can include a sequence of instructions, executable in the digital processing device's CPU (i.e. processor), written to perform a specified task.
  • Computer readable instructions can be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types.
  • APIs Application Programming Interfaces
  • the functionality of the computer readable instructions can be combined or distributed as desired in various environments.
  • the computer program will normally provide a sequence of instructions from one location or a plurality of locations.
  • the system can comprise (a) a digital processing device comprising an operating system configured to perform executable instructions and a memory device; (b) a computer program including instructions executable by the digital processing device to classify a sample from a subject comprising: (i) a first software module configured to receive a biomarker expression profile of one or more biomarkers from the sample from the subject; (ii) a second software module configured to analyze the biomarker expression profile from the subject; and (iii) a third software module configured to classify the sample from the subject based on a classification system.
  • the classification system comprises two classes. In other embodiments, the classification system comprises two or more classes.
  • At least two of the classes can be selected from preeclampsia, non-preeclampsia (e.g., for at least a period of time), normal pregnancy, complicated pregnancy, and gestational hypertension.
  • Analyzing the biomarker expression profile from the subject can comprise applying an algorithm.
  • Analyzing the biomarker expression profile can comprise normalizing the biomarker expression profile from the subject.
  • FIG. 6 shows a computer system (also “system” herein) 401 programmed or otherwise configured for implementing the methods of the disclosure, such as producing a selector set and/or for data analysis.
  • the system 401 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 405 , which can be a single core or multi core processor, or a plurality of processors for parallel processing.
  • the system 401 also includes memory 410 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 415 (e.g., hard disk), communications interface 420 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 425 , such as cache, other memory, data storage and/or electronic display adapters.
  • memory 410 e.g., random-access memory, read-only memory, flash memory
  • electronic storage unit 415 e.g., hard disk
  • communications interface 420 e.g., network adapter
  • peripheral devices 425
  • the memory 410 , storage unit 415 , interface 420 and peripheral devices 425 are in communication with the CPU 405 through a communications bus (solid lines), such as a motherboard.
  • the storage unit 415 can be a data storage unit (or data repository) for storing data.
  • the system 401 is operatively coupled to a computer network (“network”) 430 with the aid of the communications interface 420 .
  • the network 430 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet.
  • the network 430 in some instances is a telecommunication and/or data network.
  • the network 430 can include one or more computer servers, which can enable distributed computing, such as cloud computing.
  • the network 430 in some instances, with the aid of the system 401 , can implement a peer-to-peer network, which can enable devices coupled to the system 401 to behave as a client or a server.
  • the system 401 is in communication with a processing system 435 .
  • the processing system 435 can be configured to implement the methods disclosed herein.
  • the processing system 435 is a microfluidic qPCR system.
  • the processing system 435 is an ALPHA screen or other plate reader.
  • the processing system 435 is a FACS sorter or analyzer.
  • the processing system 435 can be in communication with the system 401 through the network 430 , or by direct (e.g., wired, wireless) connection.
  • raw data from the processing system e.g. a biomarker expression profile
  • This data transfer may be direct (e.g. FTP, TCP, or other direct network connection between the processing system 435 and the system 401 ), or indirect (e.g. transfer to a cloud storage system which can be accessed by the system 401 ).
  • Methods as described herein can be implemented by way of machine (or computer processor) executable code (or software) stored on an electronic storage location of the system 401 , such as, for example, on the memory 410 or electronic storage unit 415 .
  • the code can be executed by the processor 405 .
  • the code can be retrieved from the storage unit 415 and stored on the memory 410 for ready access by the processor 405 .
  • the electronic storage unit 415 can be precluded, and machine-executable instructions are stored on memory 410 .
  • the digital processing device includes one or more hardware central processing units (CPU) that carry out the device's functions.
  • the digital processing device further comprises an operating system configured to perform executable instructions.
  • the digital processing device is optionally connected a computer network.
  • the digital processing device is optionally connected to the Internet such that it accesses the World Wide Web.
  • the digital processing device is optionally connected to a cloud computing infrastructure.
  • the digital processing device is optionally connected to an intranet.
  • the digital processing device is optionally connected to a data storage device.
  • suitable digital processing devices include, by way of non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles.
  • server computers desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles.
  • smartphones are suitable for use in the system described herein.
  • Suitable tablet computers include those with booklet, slate, and convertible configurations, known to those of skill in the art.
  • the digital processing device will normally include an operating system configured to perform executable instructions.
  • the operating system is, for example, software, including programs and data, which manages the device's hardware and provides services for execution of applications.
  • suitable server operating systems include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®.
  • suitable personal computer operating systems include, by way of non-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU/Linux®.
  • the operating system is provided by cloud computing.
  • suitable mobile smart phone operating systems include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.
  • the device generally includes a storage and/or memory device.
  • the storage and/or memory device is one or more physical apparatuses used to store data or programs on a temporary or permanent basis.
  • the device is volatile memory and requires power to maintain stored information.
  • the device is non-volatile memory and retains stored information when the digital processing device is not powered.
  • the non-volatile memory comprises flash memory.
  • the non-volatile memory comprises dynamic random-access memory (DRAM).
  • the non-volatile memory comprises ferroelectric random access memory (FRAM).
  • the non-volatile memory comprises phase-change random access memory (PRAM).
  • the device is a storage device including, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, magnetic disk drives, magnetic tapes drives, optical disk drives, and cloud computing based storage.
  • the storage and/or memory device is a combination of devices such as those disclosed herein.
  • a display to send visual information to a user will normally be initialized.
  • Examples of displays include a cathode ray tube (CRT, a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD, an organic light emitting diode (OLED) display.
  • CTR cathode ray tube
  • LCD liquid crystal display
  • TFT-LCD thin film transistor liquid crystal display
  • OLED organic light emitting diode
  • on OLED display is a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display.
  • the display can be a plasma display, a video projector or a combination of devices such as those disclosed herein.
  • the digital processing device would normally include an input device to receive information from a user.
  • the input device can be, for example, a keyboard, a pointing device including, by way of non-limiting examples, a mouse, trackball, track pad, joystick, game controller, or stylus; a touch screen, or a multi-touch screen, a microphone to capture voice or other sound input, a video camera to capture motion or visual input or a combination of devices such as those disclosed herein.
  • the methods, kits, and systems disclosed herein can include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system to perform and analyze the test described herein; preferably connected to a networked digital processing device.
  • the computer readable storage medium is a tangible component of a digital device that is optionally removable from the digital processing device.
  • the computer readable storage medium includes, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, cloud computing systems and services, and the like.
  • the program and instructions are permanently, substantially permanently, semi-permanently, or non-transitorily encoded on the media.
  • a non-transitory computer-readable storage media can be encoded with a computer program including instructions executable by a processor to create or use a classification system.
  • the storage media can comprise (a) a database, in a computer memory, of one or more clinical features of two or more control samples, wherein (i) the two or more control samples can be from two or more subjects; and (ii) the two or more control samples can be differentially classified based on a classification system comprising two or more classes; (b) a first software module configured to compare the one or more clinical features of the two or more control samples; and (c) a second software module configured to produce a classifier set based on the comparison of the one or more clinical features.
  • At least two of the classes can be selected from preeclampsia, non-preeclampsia, normal pregnancy, complicated pregnancy, and gestational hypertension.
  • Antigen Detection (E.g., Antibodies)
  • the antigen binding reagent can be an antibody (monoclonal or polyclonal), antigen-binding fragment (e.g. Fab, Fab′, F(ab)2, F(abc)2, or Fv fragment) of an antibody, or an antibody derivative (e.g. diabody, linear antibody, or scFv).
  • the at least one antigen detection moiety is an antibody from FIG. 18 .
  • the antigen binding reagent is an antigen-binding fragment (e.g. Fab, Fab′, F(ab)2, F(abc)2, or Fv fragment) or antibody derivative (e.g. diabody, linear antibody, or scFv) of any of the antibodies provided in FIG. 18 .
  • the disclosure provides assay kits for analysis of any of the sets of biomarkers included herein for the detection of preeclampsia.
  • the assay kits comprise one or more antigen-binding reagents (e.g. monoclonal or polyclonal antibodies provided in FIG. 18 , or antigen-binding fragments or antibody derivatives of antibodies provided in FIG. 18 ).
  • the one or more antigen-binding reagents comprise combinations of antigen-binding reagents with specificities for the antigens/biomarkers presented below.
  • the assay kit comprises at least one antibody, antibody fragment, or antibody derivative specific for each biomarker in one of the following sets: sFlt.1, PlGF, KIM1, and CLEC4A; sFlt.1, PlGF, KIM1, CLEC4A, and FGF21; sFlt.1, PlGF, KIM1, CLEC4A, and CD274; sFlt.1, PlGF, KIM1, CLEC4A, and ENDOGLIN; sFlt.1, PlGF, KIM1, CLEC4A, and DECORIN; sFlt.1, PlGF, KIM1, CLEC4A, FGF21, and ENDOGLIN; sFlt.1, PlGF, KIM1, CLEC4A, FGF21, and CD274; sFlt.1, PlGF, KIM1, CLEC4A, ENDOGLIN, and CD274; sFlt.1, PlGF, KIM1, CLEC4A, ENDOGLIN, and CD274;
  • the assay kit comprises at least one antibody, antibody fragment, or antibody derivative specific for each biomarker in one of the following sets of four optionally in combination with PlGF:SFLT1, KIM1, CLEC4A, FGF21; SFLT1, KIM1, CLEC4A, endoglin; SFLT1, KIM1, CLEC4A, decorin; SFLT1, KIM1, CLEC4A, CD274; SFLT1, KIM1, CLEC4A, HGF; SFLT1, KIM1, CLEC4A, TFF2; SFLT1, KIM1, CLEC4A, PAPP.A2; SFLT1, KIM1, FGF21, endoglin; SFLT1, KIM1, FGF21, decorin; SFLT1, KIM1, FGF21, CD274; SFLT1, KIM1, FGF21, HGF; SFLT1, KIM1, FGF21, TFF2; SFLT1, KIM1, FGF21, PAPP.A2; SFLT1, KIM1, endoglin;
  • the assay kit comprises at least one antibody, antibody fragment, or antibody derivative specific for each biomarker in one of the following sets of three optionally in combination with PlGF: SFLT1, KIM1, CLEC4A; SFLT1, KIM1, FGF21; SFLT1, KIM1, endoglin; SFLT1, KIM1, decorin; SFLT1, KIM1, CD274; SFLT1, KIM1, HGF; SFLT1, KIM1, TFF2; SFLT1, KIM1, PAPP.A2; SFLT1, CLEC4A, FGF21; SFLT1, CLEC4A, endoglin; SFLT1, CLEC4A, decorin; SFLT1, CLEC4A, CD274; SFLT1, CLEC4A, HGF; SFLT1, CLEC4A, TFF2; SFLT1, CLEC4A, PAPP.A2; SFLT1, FGF21, endoglin; SFLT1, FGF21, decorin; SFLT1, FGF21, endo
  • the assay kit comprises at least one antibody, antibody fragment, or antibody derivative specific for each biomarker in one of the following sets of two optionally in combination with PlGF: SFLT1, KIM1; SFLT1, CLEC4A; SFLT1, FGF21; SFLT1, endoglin; SFLT1, decorin; SFLT1, CD274; SFLT1, HGF; SFLT1, TFF2; SFLT1, PAPP.A2; KIM1, CLEC4A; KIM1, FGF21; KIM1, endoglin; KIM1, decorin; KIM1, CD274; KIM1, HGF; KIM1, TFF2; KIM1, PAPP.A2; CLEC4A, FGF21; CLEC4A, endoglin; CLEC4A, decorin; CLEC4A, CD274; CLEC4A, HGF; CLEC4A, TFF2; CLEC4A, PAPP.A2; FGF21, endoglin; CLEC4A, decorin; CLEC4
  • the assay kit comprises at least one antibody, antibody fragment, or antibody derivative specific for each biomarker in one of the following sets: PlGF, sFLT1, KIM1; PlGF, sFLT1, CLEC4A; PlGF, sFLT1, FGF21; PlGF, sFLT1, Decorin; PlGF, sFLT1, CD274; PlGF, sFLT1, HGF; PlGF, sFLT1, TFF2; PlGF, sFLT1, PAPP-A2; PlGF, Endoglin, KIM1; PlGF, Endoglin, CLEC4A; PlGF, Endoglin, FGF21; PlGF, Endoglin, Decorin; PlGF, Endoglin, CD274; PlGF, Endoglin, HGF; PlGF, Endoglin, TFF2; PlGF, Endoglin, PAPP-A2; PlGF, KIM1, CLEC4A; PlGF, KIM1, FGF21; PlGF, KIM1, FGF21;
  • the assay kit provided is suitable for a multiplex homogenous biomarker assay, suitable for detection of all the analytes in a single reaction (e.g. in the same solution compartment).
  • multiple antibodies or antigen detection reagents that bind to separate epitopes are provided against the same analyte/biomarker, and detection of coincident binding/interaction of both antibodies to the same molecule of analyte/biomarker serves to detect the analyte/biomarker in the sample.
  • kits provide two antibodies or antigen-binding reagents against each analyte.
  • kits provide a pair of antibodies or antigen-binding reagents for each analyte conjugated to a complementary pair of FRET dyes, wherein one antibody or antigen-binding reagent of the pair is conjugated to a FRET donor and the other is conjugated to a FRET acceptor.
  • kits provide a pair of antibodies or antigen-binding reagents wherein one antibody or antigen-binding reagent of the pair is conjugated to a photosensitizer and the other antibody or antigen-binding reagent of the pair is conjugated to an oxygen sensitive dye.
  • the assay kit provided is suitable for a multiplex non-homogenous biomarker assay suitable for detection of all the analytes in separate reactions (e.g. in separate solution compartments).
  • assays e.g. sandwich ELISA
  • antibodies or antigen binding reagents against the relevant set of biomarkers are provided attached to a substrate (e.g. in a well of a multiwell plate, or in a lateral flow assay lane).
  • a second free antibody against each of the biomarkers provided attached to the substrate is also provided; this antibody can be labeled (e.g. with a fluorescent dye, with a chemiluminescent enzyme, or a luminescent enzyme) or unlabeled.
  • a secondary labeled (e.g. with a fluorescent dye, with a chemiluminescent enzyme, or a luminescent enzyme) antibody or antigen-binding reagent is provided which has binding specificity against the second free antibody.
  • the kit is for use as an in vitro diagnostic kit that includes one or more cartridges with reagents for testing on third-party platforms. Data collected from third-parties could be uploaded to a server (the cloud), put through a model/algorithm, and results can be shared with a doctor or other medical practitioner. Kits may also include instructions for use.
  • the present disclosure provides for a method for avoiding unnecessary treatment of preeclampsia, the method comprising: (a) contacting a biological sample that has been collected from a pregnant human female with a plurality of different probes, wherein the plurality of different probes comprises probes with specific affinity for four or more proteins selected from proteins listed in Table A or Table B; (b) determining, based on binding of the plurality of different probes to corresponding proteins, an amount or concentration for each of the four or more proteins; and (c) proceeding with treatment of said pregnant human in a manner that avoids unnecessary treatment of preeclampsia based at least in part on the amounts or concentrations of the four or more proteins determined in step (b).
  • the four or more proteins comprise: (a) placental growth factor (PlGF); (b) one or more angiogenesis-associated proteins selected from the group consisting of soluble fms-like tyrosine kinase 1 (sFlt1), endoglin, pappalysin 2 (PAPP-A2), and decorin; and (c) one or more kidney damage associated-proteins selected from the group consisting of (1) kidney injury molecule-1 (KIM1), (2) programmed cell death 1 ligand 1 (CD274), and decorin.
  • PlGF placental growth factor
  • angiogenesis-associated proteins selected from the group consisting of soluble fms-like tyrosine kinase 1 (sFlt1), endoglin, pappalysin 2 (PAPP-A2), and decorin
  • KIM1 kidney injury molecule-1
  • CD274 programmed cell death 1 ligand 1
  • the four or more proteins further comprise one or more proteins selected from the group consisting of C-type lectin domain family 4 member A (CLEC4A), fibroblast growth factor 21 (FGF21), trefoil factor 2 (TFF2), and hepatocyte growth factor (HGF).
  • CLEC4A C-type lectin domain family 4 member A
  • FGF21 fibroblast growth factor 21
  • TGF2 trefoil factor 2
  • HGF hepatocyte growth factor
  • the four or more proteins comprise PlGF, sFlt1, KIM1, and CLEC4A.
  • the plurality of different probes comprises probes with specific affinity to fibroblast growth factor 21 (FGF21), and the four or more proteins comprise FGF21.
  • the plurality of different probes comprises probes with specific affinity for endoglin, and the four or more proteins comprise endoglin.
  • the plurality of different probes comprises probes with specific affinity for decorin, and the four or more proteins comprise decorin. In some embodiments, the plurality of different probes comprises probes with specific affinity for cluster of differentiation 274 (CD274), and the four or more proteins comprise CD274. In some embodiments, the plurality of different probes comprises probes with specific affinity for hepatocyte growth factor (HGF), and the four or more proteins comprise HGF. In some embodiments, the plurality of different probes comprises probes with specific affinity for trefoil factor 2 (TFF2), and the four or more proteins comprises TFF2.
  • CD274 cluster of differentiation 274
  • CD274 hepatocyte growth factor
  • HGF hepatocyte growth factor
  • TFF2 trefoil factor 2
  • the plurality of different probes comprises probes with specific affinity for pappalysin-2 (PAPP-A2), and the four or more proteins comprise PAPP-A2.
  • the biological sample is obtained from the pregnant female after gestational week 20. In some embodiments, the biological sample has been collected from the pregnant female prior to gestational week 30.
  • the method further comprises: applying a classifier algorithm to an expression profile of the four or more proteins, wherein the classifier algorithm calculates an index; and comparing the index to a reference value to determine whether to avoid the unnecessary treatment of preeclampsia.
  • the classifier algorithm further comprises a correction for gestational age.
  • the correction for gestational age comprises a LOESS correction.
  • the classifier algorithm comprises a logistic regression.
  • the classifier algorithm comprises a logistic regression with elastic-net regularization.
  • the classifier algorithm comprises a Random Forest.
  • the biological sample is a urine, blood, amniotic fluid, exosome, plasma, or serum sample.
  • the biological sample is from blood of the pregnant human female.
  • the amount or concentration of no more than 20, no more than 15, nor more than 10, nor more than 9, no more than 8, no more than 7, no more than 6, nor more than 5, or no more than 4 proteins is determined.
  • one or more of the plurality of different probes are antibodies, antibody fragments, or antibody derivatives. In some embodiments, each of the plurality of different probes are antibodies, antibody fragments, or antibody derivatives. In some embodiments, the amount or concentration of at least one of (or all of (e.g., SYND1 and/or CLEC4A)) the four or more proteins is determined using a luminescent oxygen channeling immunoassay. In some embodiments, the amount or concentration of at least one of (or all of) the four or more proteins is determined using a time-resolved fluorescence resonance energy transfer (TR-FRET) assay.
  • TR-FRET time-resolved fluorescence resonance energy transfer
  • the amount or concentration of at least one of (or all of) the four or more proteins is determined using a proximity extension assay. In some embodiments, the amount or concentration of at least one of (or all of) the four or more proteins is determined using an enzyme-linked immunosorbent assay (ELISA). In some embodiments, the amount or concentration of at least one of (or all of) the four or more proteins is determined using an amplified luminescent proximity homogenous assay. In some embodiments, the amount or concentration of at least one of (or all of) the four or more proteins is determined using a lateral flow assay. In some embodiments, the biological sample is obtained from the pregnant human while in a perinatologist's office, a labor and delivery room, or triage (ER).
  • ER enzyme-linked immunosorbent assay
  • the method further comprises separating the biological sample into a plurality of different reaction vessels, the plurality of reaction vessels comprising a first reaction vessel, a second reaction vessel, a third reaction vessel, and a fourth reaction vessel, wherein contacting the biological sample with the plurality of different probes comprises delivering probes with specific affinity for PlGF in a first reaction vessel, delivering probes with specific affinity to sFlt1 to a second reaction vessel, delivering probes with specific affinity to KIM1 to a third reaction vessel, and delivering probes with specific affinity to CLEC4A to a fourth reaction vessel.
  • the step of contacting the biological sample with the plurality of different probes occurs in a single reaction vessel.
  • the biological sample was obtained from the pregnant female after the pregnant female has shown one or more symptoms of preeclampsia, wherein the symptoms of preeclampsia are selected from (1) high blood pressure and (2) proteinuria. In some embodiments, the sample was obtained from the pregnant female after the pregnant female has shown both (1) high blood pressure and (2) proteinuria.
  • the plurality of different probes comprises two sets of probes with specific affinity for each of the four or more proteins, wherein each set of the two sets of probes binds to different epitopes.
  • the present disclosure provides for a method, such as a laboratory method, for detecting and/or quantifying a plurality of proteins in a sample from a pregnant human female, the method comprising: contacting a biological sample from a pregnant human female with a plurality of probes, wherein the plurality of probes comprises probes with specific affinity for four or more proteins selected from the proteins listed in Table A or Table B and detecting the presence and/or quantity of the four or more proteins based on binding of the plurality of different probes to corresponding proteins.
  • the four or more proteins comprise: (a) placental growth factor; (b) one or more angiogenesis-associated proteins selected from the group consisting of soluble fms-like tyrosine kinase 1 (sFlt1), endoglin, pappalysin 2 (PAPP-A2), and decorin; and (c) one or more kidney damage associated-proteins selected from the group consisting of (1) kidney injury molecule-1 (KIM1), (2) programmed cell death 1 ligand 1 (CD274), and decorin.
  • KIM1 kidney injury molecule-1
  • CD274 programmed cell death 1 ligand 1
  • the four or more proteins further comprise one or more proteins selected from the group consisting of C-type lectin domain family 4 member A (CLEC4A), fibroblast growth factor 21 (FGF21), trefoil factor 2 (TFF2), and hepatocyte growth factor (HGF).
  • CLEC4A C-type lectin domain family 4 member A
  • FGF21 fibroblast growth factor 21
  • TGF2 trefoil factor 2
  • HGF hepatocyte growth factor
  • the four or more proteins comprise PlGF, sFlt1, KIM1, and CLEC4A.
  • the plurality of different probes comprises probes with specific affinity to fibroblast growth factor 21 (FGF21), and the four or more proteins comprise FGF21.
  • the plurality of different probes comprises probes with specific affinity for endoglin, and the four or more proteins comprise endoglin.
  • the plurality of different probes comprises probes with specific affinity for decorin, and the four or more proteins comprise decorin. In some embodiments, the plurality of different probes comprises probes with specific affinity for cluster of differentiation 274 (CD274), and the four or more proteins comprise CD274. In some embodiments, the plurality of different probes comprises probes with specific affinity for hepatocyte growth factor (HGF), and the four or more proteins comprise HGF. In some embodiments, the plurality of different probes comprises probes with specific affinity for trefoil factor 2 (TFF2), and the four or more proteins comprises TFF2.
  • CD274 cluster of differentiation 274
  • CD274 hepatocyte growth factor
  • HGF hepatocyte growth factor
  • TFF2 trefoil factor 2
  • the plurality of different probes comprises probes with specific affinity for pappalysin-2 (PAPP-A2), and the four or more proteins comprise PAPP-A2.
  • the biological sample has been collected from the pregnant human female after gestational week 20. In some embodiments, the biological sample has been collected from the pregnant female prior to gestational week 30. In some embodiments, the biological sample is a urine, blood, amniotic fluid, exosome, plasma, or serum sample. In some embodiments, the biological sample is from blood of the pregnant human female. In some embodiments, the amount or concentration of no more than 20, no more than 15, nor more than 10, nor more than 9, no more than 8, no more than 7, no more than 6, nor more than 5, or no more than 4 proteins is determined.
  • one or more of the plurality of different probes are antibodies, antibody fragments, or antibody derivatives. In some embodiments, each of the plurality of different probes are antibodies, antibody fragments, or antibody derivatives. In some embodiments, the plurality of probes contact the biological sample or a fraction thereof in a single reaction vessel. In some embodiments, the plurality of probes contact the biological sample or a fraction thereof in separate reaction vessels for each protein of the four or more proteins.
  • the method further comprises a second plurality of probes, wherein the second plurality of probes comprises a probe set that is specific for binding to PlGF, a probe set that is specific for binding to sFlt1, a probe set that is specific for binding to KIM1, and a probe set that is specific for binding to CLEC4A, wherein the second plurality of probes binds to its corresponding protein at an epitope that differs from the epitope to which each of the first plurality of probes binds.
  • coincident binding of at least one pair of the first and the second plurality of probes to the same protein molecules in the sample is detected by a luminescent oxygen channeling immunoassay (LOCI), a time-resolved fluorescence resonance energy transfer (TR-FRET) assay, an amplified luminescent proximity homogenous assay, an enzyme-linked immunosorbent assay, a proximity extension assay, or a lateral flow assay.
  • LOCI luminescent oxygen channeling immunoassay
  • TR-FRET time-resolved fluorescence resonance energy transfer
  • the present disclosure provides for a method for managing a pregnant human subject and identifying the pregnant human subject as not at risk for preeclampsia for a specified period of time, the method comprising: (a) identifying, via a test having (1) a specificity of greater than 80% and (2) a sensitivity of greater than 85%, the pregnant subject as not at risk for developing preeclampsia within the specified period of time, wherein the specified period of time is between one week and six weeks; and (b) managing the pregnant human subject identified as not at risk for developing preeclampsia within the specified period of time by proceeding with ambulant monitoring treatment of said pregnant patient without treating the patient for preeclampsia.
  • the test has a specificity of at least 82.0%, at least 84.0%, at least 85.0%, at least 87.0%, at least 88.0%, at least 89.0%, at least 90.0%, at least 90.5%, at least 91.0%, or at least 91.5%. In some embodiments, the test has a sensitivity of at least 86%, at least 87%, at least 88%, at least 89%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, or at least 95%.
  • the test has a negative predictive value of at least about 95.0%, 96.0%, 97.0%, 98.0%, 98.2%, 98.4%, 98.5%, 98.7%, 99%, 99.2%, or 99.5% when applied to a random population of ethnically diverse pregnant women after gestational week 20 that exhibit one or more of (1) high blood pressure and (2) proteinuria.
  • the test has a positive predictive value of at least about 30%, at least about 32%, at least about 35%, at least about 37%; at least about 40%, at least about 42%, at least about 45%, at least about 50%, at least about 55%, or at least about 57% when applied to a random population of ethnically diverse pregnant women after gestational week 20 that exhibit one or more of (1) high blood pressure and (2) proteinuria.
  • the negative predictive value of the test is higher than the positive predictive value of the test.
  • the specified period of time is between one week and four weeks, one week and three weeks, or one week and two weeks.
  • the test comprises determining an amount or concentration of each of four or more proteins selected from proteins listed in Table A or Table B.
  • the four or more proteins comprise PlGF, sFlt-1, KIM1, and CLEC4A.
  • the four or more proteins further comprise FGF21.
  • the present disclosure provides for a method of treating a pregnant human subject, the method comprising: obtaining an indicium generated, at least in part, from a determination of levels for four or more proteins listed in Table A or Table B; and changing a clinical regimen for the pregnant human subject based, at least in part, on the obtained indicium.
  • obtaining the indicium comprises: determining the levels for PlGF, sFlt1, KIM1, and CLEC4A, wherein the levels are determined from binding of each of PlGF, sFlt1, KIM1, and CLEC4A to corresponding probes.
  • the obtaining the indicium further comprises determining the level for FGF21, wherein the level of FGF21 is determined from binding of FGF21 to corresponding probes.
  • the method further comprises classifying (or ruling out a classification) the pregnant subject as having a low risk of having preeclampsia or developing preeclampsia within a specified period of time.
  • the pregnant subject is classified by any method described herein.
  • the method further comprises administering an antihypertensive drug to the patient.
  • the antihypertensive drug is a central alpha agonist, a vasodilator, a calcium-channel blocker, an alpha-blocker or a beta-blocker.
  • the antihypertensive drug is methyldopa, labetalol, nifedipine, verapamil, clonidine, hydralazine, diazoxide, prazosin, or oxprenolol.
  • the present disclosure provides for a mixture comprising: a fluid sample from a pregnant female subject; a first plurality of different probes, wherein the first plurality of different probes comprises different probes, each with specific affinity for four or more proteins selected from proteins listed in Table A or Table B.
  • the four or more proteins comprise: (a) placental growth factor (PlGF) (b) one or more angiogenesis-associated proteins selected from the group consisting of soluble fms-like tyrosine kinase 1 (sFlt1), endoglin, pappalysin 2 (PAPP-A2), and decorin; and (c) one or more kidney damage associated-proteins selected from the group consisting of (1) kidney injury molecule-1 (KIM1), (2) programmed cell death 1 ligand 1 (CD274), and decorin.
  • PlGF placental growth factor
  • angiogenesis-associated proteins selected from the group consisting of soluble fms-like tyrosine kinase 1 (sFlt1), endoglin, pappalysin 2 (PAPP-A2), and decorin
  • KIM1 kidney injury molecule-1
  • CD274 programmed cell death 1 ligand 1
  • the four or more proteins further comprise one or more proteins selected from the group consisting of C-type lectin domain family 4 member A (CLEC4A), fibroblast growth factor 21 (FGF21), trefoil factor 2 (TFF2), and hepatocyte growth factor (HGF).
  • CLEC4A C-type lectin domain family 4 member A
  • FGF21 fibroblast growth factor 21
  • TGF2 trefoil factor 2
  • HGF hepatocyte growth factor
  • the four or more proteins comprise PlGF, sFlt1, KIM1, and CLEC4A.
  • the four or more proteins comprise FGF 21.
  • the four or more proteins comprise endoglin.
  • the four or more proteins comprise decorin.
  • the four or more proteins comprise CD274.
  • the four or more proteins comprise HGF.
  • the four or more proteins comprise TFF2.
  • the four or more proteins comprise PAPP-A2.
  • the fluid sample has been collected after gestational week 20. In some embodiments, the fluid sample has been collected from the pregnant female prior to gestational week 30. In some embodiments, the mixture includes no more than 20, no more than 16, no more than 14, no more than 12, no more than 10, no more than eight, no more than seven, no more than 6, no more than 5, or nor more than 4 sets of probes that are designed to bind to different proteins in the fluid sample. In some embodiments, the fluid sample is from a blood, plasma, serum, or exosome sample.
  • the fluid sample was obtained from the subject after the subject has shown one or more symptoms of preeclampsia, wherein the symptoms of preeclampsia are selected from (1) high blood pressure and (2) proteinuria. In some embodiments, the fluid sample was obtained from the subject after the subject has shown both (1) high blood pressure and (2) proteinuria.
  • the first plurality of different probes comprises a first set of probes with specific affinity for each of the four or more proteins and a second set of probes with specific affinity for each of the four or more proteins, wherein each set of the two sets of probes binds to different epitopes.
  • the first set of probes and the second set of probes are conjugated to pairs of oligonucleotides containing complementary hybridization regions for each protein-specific probe pair. In some embodiments, the first set of probes and the second set of probes are conjugated to unique FRET pairs of fluorophores for each protein-specific probe pair. In some embodiments, for each protein-specific probe pair, one probe is conjugated to biotin or a streptavidin-binding analog thereof. In some embodiments, the mixture further comprises (a) a photosensitizer; and (b) an oxygen-sensitive dye, wherein one of (a) and (b) is capable of binding the first set of probes, and the other is capable of binding the second set of probes. In some embodiments, one or more of the first plurality of different probes are antibodies, antibody fragments, or antibody derivatives. In some embodiments, each of the first plurality of different probes are antibodies, antibody fragments, or antibody derivatives.
  • the present disclosure provides for a reaction plate comprising a plurality of reaction wells, wherein the plurality of reaction wells comprises: a first well comprising (1) a first portion of a biological sample from a pregnant human subject, wherein the biological sample was obtained from a pregnant human subject after 20 weeks of gestation, and (2) a first set of probes for binding to PlGF; a second well comprising (1) a second portion of the biological sample from the pregnant human subject, and (2) a second set of probes for binding to sFlt1, endoglin, pappalysin 2 (PAPP-A2), or decorin; a third well comprising (1) a third portion of the biological sample from the pregnant human subject, and (2) a third set of probes for binding to KIM1, (2) programmed cell death 1 ligand 1 (CD274), or decorin; and a fourth well comprising (1) a fourth portion of the biological sample from the pregnant human subject, and (2) a fourth set of probes for binding to CLEC4A, FGF21, T
  • the second set of probes in the second well are configured to bind to sFlt1.
  • the third set of probes in the third well are configured to bind to KIM1.
  • the fourth set of probes in the fourth well are configured to bind to CLEC4A.
  • the plurality of wells comprises a fifth well, the fifth well comprising (1) a fifth portion of the biological sample from the pregnant human subject and (2) a fifth set of probes for binding to FGF21.
  • the plurality of reaction wells comprises a well, the well comprising a portion of the biological sample and a set of probes for binding to endoglin.
  • the plurality of reaction wells comprises a well, the well comprising a portion of the biological sample and a set of probes for binding to decorin. In some embodiments, the plurality of reaction wells comprises a well, the well comprising a portion of the biological sample and a set of probes for binding to CD274. In some embodiments, the plurality of reaction wells comprises a well, the well comprising a portion of the biological sample and a set of probes for binding to HGF. In some embodiments, the plurality of reaction wells comprises a well, the well comprising a portion of the biological sample and a set of probes for binding to TFF2.
  • the plurality of reaction wells comprises a well, the well comprising a portion of the biological sample and a set of probes for binding to PAPP-A2.
  • the biological sample has been collected from the pregnant female prior to gestational week 30.
  • the biological sample is from blood of the pregnant human female.
  • the plurality of wells of the reaction include probes for specific binding to no more than 20, no more than 15, no more than 12, no more than 10, no more than 8, no more than 7, no more than 6, no more than 5, or no more than 4 proteins.
  • the probes comprise antibodies, antibody fragments, or antibody derivatives.
  • the biological sample is from the subject after the subject has shown one or more symptoms of preeclampsia, wherein the symptoms of preeclampsia are selected from (1) high blood pressure and (2) proteinuria.
  • the sample is from the subject after the subject has shown both (1) high blood pressure and (2) proteinuria.
  • each set of probes comprises probes that specifically bind to different epitopes.
  • the present disclosure provides for a kit for ruling out preeclampsia in a pregnant female subject, the kit comprising: (a) probes for determining the levels of four or more proteins selected from the proteins listed in Tables A and B; (b) wherein the kit is designed to measure the levels of no more than 20, no more than 15, no more than 10, nor more than 8, no more than 7, no more than 6, nor more than 5, or no more than 4 proteins.
  • the four or more proteins comprises: (a) placental growth factor (PlGF); (b) one or more angiogenesis-associated proteins selected from the group consisting of soluble fms-like tyrosine kinase 1 (sFlt1), endoglin, pappalysin 2 (PAPP-A2), and decorin; and (c) one or more kidney damage associated-proteins selected from the group consisting of (1) kidney injury molecule-1 (KIM1), (2) programmed cell death 1 ligand 1 (CD274), and decorin.
  • PlGF placental growth factor
  • angiogenesis-associated proteins selected from the group consisting of soluble fms-like tyrosine kinase 1 (sFlt1), endoglin, pappalysin 2 (PAPP-A2), and decorin
  • KIM1 kidney injury molecule-1
  • CD274 programmed cell death 1 ligand 1
  • the four or more proteins comprise one or more proteins selected from the group consisting of C-type lectin domain family 4 member A (CLEC4A), fibroblast growth factor 21 (FGF21), trefoil factor 2 (TFF2), and hepatocyte growth factor (HGF).
  • CLEC4A C-type lectin domain family 4 member A
  • FGF21 fibroblast growth factor 21
  • TGF2 trefoil factor 2
  • HGF hepatocyte growth factor
  • the four or more proteins comprise PlGF, sFlt1, KIM1, and CLEC4A.
  • four or more proteins comprise FGF21.
  • the four or more proteins comprise endoglin.
  • the four or more proteins comprise decorin.
  • the four or more proteins comprise CD274.
  • the four or more proteins comprise HGF.
  • the four or more proteins comprise TFF2.
  • the four or more proteins comprise PAPP-A2.
  • the kit further comprises instructions for carrying out an immunoassay.
  • the kit is an enzyme-linked immunosorbent assay kit.
  • one or more of the probes are attached to substrate.
  • kit is for a lateral flow immunoassay.
  • the present disclosure provides for a system for ruling out preeclampsia in a pregnant female subject for a specified period of time, the system comprising: a processor; an input module for inputting levels of at least four proteins in a biological sample, wherein the at least four proteins are selected from Tables A and B; a computer readable medium containing instructions that, when executed by the processor, perform a first algorithm on the input levels of the at least four proteins; and an output module providing one or more indicia based on the input levels of the at least four proteins, wherein the one or more indicia are indicative of the subject not having preeclampsia for at least a specified period of time.
  • the system further comprises probes to each the at least four proteins.
  • the system is designed to output the one or more indicia based on input levels for no more than 15, no more than 10, no more than 8, no more than 7, nor more than 6, no more than 5, or no more than 4 proteins.
  • the at least four proteins comprise: (a) placental growth factor (PlGF); (b) one or more angiogenesis-associated proteins selected from the group consisting of soluble fms-like tyrosine kinase 1 (sFlt1), endoglin, pappalysin 2 (PAPP-A2), and decorin; and (c) one or more kidney damage associated-proteins selected from the group consisting of (1) kidney injury molecule-1 (KIM1), (2) programmed cell death 1 ligand 1 (CD274), and decorin.
  • KIM1 kidney injury molecule-1
  • CD274 programmed cell death 1 ligand 1
  • the at least four proteins comprise one or more proteins selected from the group consisting of C-type lectin domain family 4 member A (CLEC4A), fibroblast growth factor 21 (FGF21), trefoil factor 2 (TFF2), and hepatocyte growth factor (HGF).
  • CLEC4A C-type lectin domain family 4 member A
  • FGF21 fibroblast growth factor 21
  • TFF2 trefoil factor 2
  • HGF hepatocyte growth factor
  • the at least four proteins comprise PlGF, sFlt1, KIM1, and CLEC4A.
  • the at least four proteins comprise FGF21.
  • the at least four proteins comprise decorin.
  • the at least four proteins comprise CD274.
  • the at least four proteins comprise HGF.
  • the at least four proteins comprise TFF2.
  • the at least four proteins comprise PAPP-A2.
  • the algorithm comprises a correction based on gestational age. In some embodiments, the algorithm comprises a logistic regression.
  • the disclosure provides for a method for assessing a risk of a female subject having or developing preeclampsia within a specified time period, the method comprising: (a) obtaining a sample from a pregnant female subject; (b) measuring the levels of a plurality of proteins from the sample derived from a pregnant female subject, wherein at least two of the plurality of proteins is selected from the group (“Group 2”) consisting of Tables 2, 3, 4, and 5; (c) calculating an index based, at least in part, on the levels of the plurality of proteins; and (d) determining a risk of having or developing preeclampsia in the female subject based on the index; wherein the specified period of time is at least one week.
  • the sample is a urine, blood, amniotic fluid, cervical-vaginal, exosome, plasma, or serum sample.
  • the sample is a blood sample.
  • the sample is a serum sample.
  • the sample is a plasma sample.
  • measuring the levels of a plurality of proteins from the sample comprises contacting the proteins with a plurality of probes specific for each protein.
  • the probes may comprise antibodies.
  • at least two of the plurality of proteins are selected from Group 2.
  • at least three of the plurality of proteins are selected from Group 2.
  • at least four of the plurality of proteins are selected from Group 2.
  • At least five of the plurality of proteins are selected from Group 2. In some embodiments, at least six of the plurality of proteins are selected from Group 2. In some embodiments, at least seven of the plurality of proteins are selected from Group 2. In some embodiments, levels are measured for no more than ten proteins. In some embodiments, levels are measured for no more than nine proteins. In some embodiments, levels are measured for no more than eight proteins. In some embodiments, levels are measured for no more than seven proteins. In some embodiments, levels are measured for no more than six proteins. In some embodiments, levels are measured for no more than five proteins. In some embodiments, levels are measured for no more than four proteins. In some embodiments, levels are measured for no more than three proteins.
  • the sample was obtained from the subject after the subject has shown one or more symptoms of preeclampsia, wherein the symptoms of preeclampsia are selected from (1) high blood pressure and (2) proteinuria. In some embodiments, the sample was obtained from the subject after the subject has shown both (1) high blood pressure and (2) proteinuria. In some embodiments, the sample is obtained from the subject after week 20 of the pregnancy. In some embodiments, determining the risk of preeclampsia in the female subject comprises comparing the index to a threshold value.
  • a predefined relationship between the index and the threshold value is indicative, with a negative predictive value of at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 97.5%, at least 98%, at least 98.5%, or at least 99%, of the subject not having or developing preeclampsia when used on an unbiased population of pregnant women that have both high blood pressure and proteinuria.
  • a predefined relationship between the index and the threshold value is indicative, with a negative predictive value of at least 90%, of the subject not having or developing preeclampsia.
  • the negative predictive value is higher than any negative predictive value of any test that measures only one or more of sFLT-1, PlGF, endoglin, and PAPP-A.
  • the plurality of proteins comprises soluble fms-like tyrosine kinase-1 (“sFLT-1”), and the index is calculated, in part, based on the level of sFLT-1 in the sample.
  • the plurality of proteins comprises placental growth factor (“PlGF”), and the index is calculated, in part, based on the level of PlGF in the sample.
  • the index is calculated, at least in part, based on the levels of both sFLT-1 and PlGF.
  • the plurality of proteins comprises pregnancy-associated plasma protein A (PAPP-A), and the index is calculated, in part, based on the levels of PAPP-A in the sample. In some embodiments, the index is calculated based on the levels of no protein some than the proteins of Group 1, sFLT-1, PlGF, endoglin, and PAPP-A. In some embodiments, the levels of the plurality of proteins are determined via an immunoassay. In some embodiments, the levels of the at least two proteins are determined by one or more enzyme-linked immunosorbent assays. In some embodiments, the levels of the at least two proteins are determined by one or more luminescent oxygen channeling immunoassays (LOCI).
  • LOCI luminescent oxygen channeling immunoassays
  • the levels of the at least two proteins are determined by one or more of mass spectrometry, ELISPOT, nanoparticles, or radioimmunoassays.
  • the sample is obtained from the subject in a perinatologist's office, a labor and delivery room, or triage (ER).
  • the disclosure provides for a method for assessing whether or not a pregnant female subject currently has or will develop (or will not develop) preeclampsia within a specified period of time, the method comprising: performing a binary classification test on a first sample derived from a pregnant female subject who has both high blood pressure and proteinuria, wherein the test comprises measuring the levels of one or more proteins in the first sample, wherein the one or more proteins are selected from the group consisting of Tables 2, 3, 4, and 5; wherein the specified time period is between one week and six weeks; and wherein the test has a negative predictive value of greater than 90% when used on an unbiased population of pregnant women that have both high blood pressure and proteinuria.
  • the binary classification test is a computer implemented two-way classification algorithm. In some embodiments, the binary classification test is a decision tree, random forest, Bayesian network, support vector machine, neural network, linear discriminant analysis (LDA), gradient boosting method (GBM), elastic-net logistic regression, or logistic regression test. In some embodiments, the method further comprises obtaining a sample from the subject. In some embodiments, the sample is a urine, blood, amniotic fluid, exosome, plasma, or serum sample. In some embodiments, the sample is a serum sample. In some embodiments, the sample is a plasma sample. In some embodiments, the binary classification test has a positive predictive power of at least 85% when used on an unbiased population of pregnant women that have both high blood pressure and proteinuria.
  • the binary classification test has a negative predictive value that is greater than its positive predictive value. In some embodiments, the binary classification test has a specificity of at least 85%. In some embodiments, the binary classification test has a sensitivity of at least 85%. In some embodiments, the specified time period is between 10 days and three weeks.
  • the disclosure provides for a method of classifying a pregnant human subject as having a low risk of having or developing preeclampsia within a specified time period, the method comprising: (a) obtaining a sample from a pregnant human subject who has been identified as having high blood pressure or proteinuria; (b) running a test to obtain a protein expression profile, wherein the protein expression profile includes levels of two or more proteins from Tables 2, 3, 4, and 5; (c) applying a classifier algorithm to the expression profile, wherein the classifier algorithm calculates an index; and comparing the index to a reference value to determine whether the pregnant human subject has a low risk of having or developing preeclampsia.
  • the disclosure provides for a method of treatment comprising: (a) classifying a pregnant subject as having a low risk of having or developing preeclampsia according to any method described herein; and (b) changing a therapeutic regimen for the subject based on the classification.
  • the method further comprises administering an antihypertensive drug to the patient if the test indicates that the pregnant female subject will not develop preeclampsia within the specified time period.
  • the antihypertensive drug is a central alpha agonist, a vasodilator, a calcium-channel blocker, an alpha-blocker or a beta-blocker.
  • the antihypertensive drug is methyldopa, labetalol, nifedipine, verapamil, clonidine, hydralazine, diazoxide, prazosin, or oxprenolol.
  • the disclosure provides for a kit for confirming the presence or absence of preeclampsia in a female subject, the kit comprising: (a) reagents for detecting one or more protein selected from the group consisting of Tables 2, 3, 4, and 5; and optionally (b) reagents for detecting PlGF and/or sFLT-1, wherein the kit is designed to measure the levels of not more than 10 proteins.
  • the kit further comprises reagents for measuring the levels of PAPP-A.
  • the kit is an enzyme-linked immunosorbent assay kit.
  • the disclosure provides for a system for assessing the likelihood that a pregnant subject has or will develop preeclampsia within a specified period of time, the system comprising: (a) a first agent that selectively binds to the one or more proteins in a sample from a pregnant subject, wherein the one or more proteins are selected from the group consisting of Group 2; (b) optionally a second agent that selectively binds to placental growth factor (PlGF); (c) optionally a third agent that selectively bind to soluble fms-like tyrosine kinase 1 (sFlt-1); (d) an input module for inputting levels of optionally PlGF, optionally sFlt-1, and the one or more proteins from the group consisting of Tables 2, 3, 4, and 5; (d) a processor; (e) a computer readable medium containing instructions which, when executed by the processor, performs a first algorithm on the input levels of optionally PlGF, optionally sFlt-1
  • the disclosure provides for a computer-implemented method of assessing the likelihood a pregnant subject has or will develop preeclampsia within a specified period of time, comprising: (a) receiving, at a computer, expression level data derived from a plasma or serum sample from the pregnant subject; (b) applying, by the computer, a classifier algorithm to the expression level data derived from the plasma or serum sample from the pregnant subject using a classification rule or a class probability equation; and (c) using, by the computer, the classification rule or class probability equation to output a classification for the sample, wherein the classification classifies the sample as a having a probability of having preeclampsia with a negative predictive value of greater than 80 percent, wherein the pregnant subject has hypertension or proteinuria.
  • the expression level data comprises levels of proteins selected from the group consisting of Tables 2, 3, 4, and 5.
  • the classifier algorithm is logistic regression. In some embodiments, the classifier algorithm is a decision tree, random forest, Bayesian network, support vector machine, neural network, or logistic regression algorithm.
  • Examples 1-7 describe an initial, smaller study design to identify markers that can be used to rule out the need for treatment of preeclampsia.
  • Examples 8-16 describe follow-on, larger studies that provided additional information regarding the discriminatory of biomarkers and combinations of biomarkers in the context of assessing risks associated with, for example, women with one or more symptoms of preeclampsia.
  • Samples of serum and urine for biomarker analysis were obtained from a Progenity Multicenter specimen procurement study, which was designed to, inter alia, identify biomarkers that improve the detection or ruling out of preeclampsia with improved performance relative to the sFlt/PlGF ratio method described by Zeisler et al. (NEJM 274(2017):13-22). More particularly, pregnant women who were 18 years or older (20 weeks to 39 weeks of gestation at first visit) with suspected preeclampsia (based on new onset symptoms, elevated blood pressure, proteinuria, edema or others) were selected for participation.
  • Baseline procedures were performed, including collection of demographic, medical and obstetric histories, list of concomitant medications, weight, height, blood pressure, and other clinical information, as well as obtaining blood and urine samples for use in biomarker assays. After discharge, all patients in the study were followed by interim research visits every 14 days (+/ ⁇ 3 days). For patients who developed PreE, the time (in days) from baseline sampling, the gestational age at diagnosis, and the severity of the disease was recorded. Patients who did not develop preeclampsia before or at delivery were included in the NEGATIVE-PRE-E CONTROL (NonPreE) group. For these NEGATIVE PRE-E CONTROLS, the time from baseline sampling (in days) to either delivery or loss to follow-up was recorded.
  • Delivery outcomes were collected on all subjects enrolled in the study. Additionally, if possible during admission for delivery, blood and urine samples were collected for analysis at delivery.
  • the discovery set of samples for further analysis consisted of a total of 70 samples that were separated into non-preeclampsia or preeclampsia based on whether they delivered pre-37 weeks gestation: 40 non-preeclampsia (NonPreE) and 30 with preeclampsia (PreE).
  • the 70 samples were grouped into four further subcohorts based on preeclampsia status and whether or not sFlt/PlGF ratio was predictive: (A) control patients who delivered at term and were not diagnosed with preeclampsia.
  • sSBP>140 mmHg or sDBP>90 mmHg or both patients were diagnosed with suspected preeclampsia based on new onset of hypertension (sSBP>140 mmHg or sDBP>90 mmHg or both) with accompanying proteinuria (defined with the cutoffs of 2+ protein by dipstick, >300 mg of protein per 24-hour urine collection, >30 mg of protein per deciliter in a spot urine sample, or a ratio of protein to creatinine of >30 mg per millimole).
  • sFlt/PlGF criteria for suspected preeclampsia were an sFlt/PlGF ratio greater than or equal to 38.
  • patients diagnosed with suspected preeclampsia who delivered preterm were classified as actual preeclampsia (true positive).
  • patients diagnosed with suspected preeclampsia who did not deliver preterm were classified as complicated pregnancy (false positive).
  • Exclusion criteria for patients from the study included male, not pregnant, age less than 18 or greater than 45 years, pregnancy with multiple gestation, pregnancy with gestational age less than 20 or greater than 39 weeks, or pregnancy with known fetal abnormalities.
  • Serum samples collected according to a standard procedure from 68 patients were analyzed retrospectively. Briefly, filled red top blood collection tubes were allowed to clot at room temperature for 30-60 minutes, were centrifuged 20 minutes at 1300 g to remove the clot, and were then aliquoted for long term storage below ⁇ 80° C. Hemolysis, date and time of blood collection, and date and time of freezing were recorded.
  • sFlt1, PAPP-A, PlGF, and Fibronectin single analytes were measured by a biotin/fluorescein-based AlphaScreenTM assay.
  • Antibodies labeled with biotin and fluorescein was prepared fresh each time by combining 2.5 Antibody mix with 125 ⁇ l dilution buffer and placing the mixture on ice. This proximity mix was placed in a single well of a standard white 96 well plate (Biorad, Hemel Hempstead, UK) followed by 2 ⁇ l of target antigen or sample, which was appropriately diluted withlx serum dilution buffer (SDB II, 4483013, Life Technologies) if needed.
  • No protein controls consisted of 2 ⁇ l of proximity mix and 2 ⁇ l of 1 ⁇ SDB II.
  • the plate was sealed using an optically clear heat seal with a PX1 PCR plate sealer (Biorad, Hemel Hempstead, UK), centrifuged at 780 g for 2 min (Rotina 380R Hettich Zentrifuge, Germany) and incubated for 1 h at 20° C. Following removal of the seal, 16 ⁇ l of anti-fluorescein acceptor beads (10 ⁇ gs) and St/.AV sensitizer beads (2 ⁇ gs) (Perkin Elmer) was added to each well, the plate was sealed again, spun as before and the incubation was performed at 37° C. for 60 min. The assay was read on a standard ALPHA screen reader.
  • proximity extension assays were used. A pair of oligonucleotide-conjugated antibodies specific to each panel protein was added to 1 ⁇ L of serum. Antibody-protein antibody sandwiches were detected by the hybridization of the nucleotide pairs in close proximity, followed by an extension reaction to generate a unique sequence product. These sequences were then quantitated by microfluidic qPCR. A total of 552 distinct marker levels including markers implicated in inflammation, immune response, oncology, organ damage, immuno-oncology, and metabolism were measured in this assay.
  • a response screening of non-preeclampsia vs preeclampsia using ANOVA for each of the 552 markers was performed, defining a FDR LogWorth >2 as significance.
  • a plot of FDR LogWorth vs Effect Size ( FIG. 1A ) was generated to analyze the value of single biomarkers for distinguishing Nonpreeclampsia vs PreE.
  • Three biomarkers (CLEC4A, SYND1, and PlGF) meet the FDR LogWorth criteria for significance (>2), while 6 additional biomarkers (PGF, FES, TGF-alpha 2, APLP1, KIM1, and NOS32) show an FDR LogWorth more significant than most of the biomarkers.
  • a summary of these top distinguishing markers is provided in Table 2; while visual representations of the data spread for each of the top 3 biomarkers for non-preeclampsia vs preeclampsia is shown in FIG. 1B .
  • Example 4 Subcohort Analysis of Nine Biomarkers from Example 3
  • Example 3 random bootstrap forest predictor screening was run on the expression level data from the 552 unbiased biomarkers assessed in multiplex screening.
  • the top 20 predictors discovered by this method ranked by contribution are shown in Table 3.
  • Random bootstrap forest predictor screening (10 rounds) was applied to the 552 biomarkers expression levels analyzed in the multiplex analysis combined with the four candidate biomarker expression levels into a single comprehensive data set (556 markers).
  • the top 50 markers resulting from this analysis are displayed in Table 3 by median rank, where the top row represents the top 10 markers; the second row represents the second 10 markers, and so on.
  • markers were then fit to a graded response (GR) model, followed by 250 bootstrap fits. This resulted in nine markers (CAPG, ZBTB16, SYND1, CLEC4C, TGF alpha 2, uPA, CLEC4A, PGF, and AMN) that had a p-value ⁇ 1 (meaning they had non-zero coefficients in >50% of the 250 models built from bootstrapped samples). These markers are presented in Table 5.
  • Example 2 An expanded study using the same inclusion/exclusion criteria as Example 1 was conducted to further identify and validate biomarkers for preeclampsia.
  • An overview of the process used is set forth in the flow diagram of FIG. 17 , which shows a method 100 , in which the levels of biomarkers are determined 101 , the resulting data undergoes a log transformation 102 and a Loess correction for gestational age 103 . Then machine learning 104 is used to determine an algorithm suitable for identifying a subject who does not need to be treated for preeclampsia.
  • a breakdown of the samples collected from patients is detailed in Table 6.
  • Bona fide PreE positive (+) samples were from patients diagnosed clinically using 2013 ACOG criteria who delivered preterm (i.e. in less than 37 weeks gestational age), where the sample was collected after clinical diagnosis and before labor, and where the sample was collected within 2 weeks of preeclampsia diagnosis.
  • Bona fide PreE negative ( ⁇ ) samples were from patients having at least one of the preeclampsia symptoms as defined by the ACOG 2013 guidelines who gave birth at full-term (i.e. delivery at 37 weeks gestational age or later), who had no clinical diagnostics of preeclampsia in the current pregnancy, and wherein the sample was collected before week 38 of gestational age.
  • Bona fide PreE positive (+) samples were from patients diagnosed clinically using 2013 ACOG criteria who delivered preterm.
  • Bona fide PreE negative ( ⁇ ) samples displayed at least 1 symptom according to the 2013 ACOG criteria but who delivered at term. Samples that did not fit into (+) or ( ⁇ ) categories were excluded from algorithm development and testing. Ethnicity/race information for this cohort is provided in Table 6A.
  • a series of 16 markers (CLEC4A, HGF, PlGF, KIM1, FGF-21, FN, DCN, SYND1, CD-274, TFF-2, PAPP-A, ADAM-12, sFLT1, PAPP-A2, ENG, and UPA) was selected by hierarchical clustering of high-throughput protein expression on the bona fide (+) and ( ⁇ ) samples of Example 9 (wherein the bona fide criteria are the same as in Example 9). Following selection, assays for protein level of each analyte were developed and log (protein level) or ratios of log(protein level) with and without their bivariate interaction terms were used for these markers as features to build na ⁇ ve multivariate models predicting preeclampsia.
  • the new models all showed improvements in specificity and PPV.
  • a “stacked” structure involving a combination of Elastic net logistic regression and Random forest was investigated as a method to reduce false negatives.
  • the performance of the stacked model is shown in Table 8.
  • An exemplary description of a stacked model for use in diagnosing or ruling out preeclampsia is shown in FIG. 19 .
  • the stacked model structure improved sensitivity, NPV, and AUC versus the individual models and the sFlt1/PlGF model.
  • the first applied approach sought to more rationally choose genes as features to improve detection.
  • the fold upregulation/downregulation of each biomarker feature was analyzed graphically in the “false negative samples”. This analysis is illustrated in FIG. 20 .
  • PlGF, END, PAPP-A2, and sFlt1 all had low signal (in terms of changes of fold expression), suggesting that they are insufficient for the detection of a subset of preeclampsia samples.
  • KIM-1, FGF-21, and CLEC4A had signal in both false negative and true negative samples, suggesting they may broadly improve detection of all subsets of preeclampsia samples.
  • both a regular Enet and “stacked” RF/Enet model was constructed using just KIM-1, FGF-21, and CLEC4A as features (see Table 8).
  • This model had marked improvements in NPV and some improvement in sensitivity versus the sFlt1/PlGF baseline model, confirming that models using KIM-1, FGF-21, and CLEC4A improve sensitivity of detection of preeclampsia.
  • the second applied approach examined principal components (from a PCA performed on the expression data) as features, and examined the signal of each principal component in the bona fide positive samples (wherein the bona fide criteria are the same as in Example 9).
  • This analysis showed that the first 4 principal components (PC1, PC2, PC4, and PC9) explain 61.5% of the variance; and that PC4 in particular showed signal in the false negative samples PC1 and PC2 did not.
  • Most of this signal appeared to originate from CLEC4A, HGF, FGF21, KIM1, and TFF2, which are contributors to PC4.
  • models was generated using the top four principal components using the same algorithms used above. The performance of models built using these algorithms is presented in Table 9.
  • the stacked model using the top four principal components as features showed improved characteristics versus the sFlt1/PlGF model in NPV, PPV, specificity, sensitivity, and AUC.
  • FIG. 13 Graphs of expression level in preeclampsia versus non-preeclampsia samples for the top 11 of these markers is presented in FIG. 13 .
  • the top 2-10 features from this ranking were used to generate LR models, the performance of which are presented in Table 10.
  • models using the 4-marker combination PlGF/sFLT1/KIM1/CLEC4A
  • all combinations of 5 th markers CD274 or TFF2 or ADAM12 or DCN or END or HGF or FGF21 or PAPP-A1 or FN or SYND1 or UPA or PAPP-A
  • FIG. 14 depicts an exemplary procedure wherein a Loess model is used to perform gestational-age correction of biomarker (PlGF) expression levels
  • FIG. 17 illustrates a procedure where this can be incorporated into the model-building workflow.
  • Table 12 depicts performance parameters of the models with and without the Loess gestational age (GA) correction, wherein “Loess GA Removal” corresponds to models that account for a gestational age and “No GA Removal” corresponds to models that do not account for gestational age.
  • GA Loess gestational age
  • the sensitivity threshold was adjusted using several values to see if the specificity threshold of the top-performing models could be optimized.
  • the results of the model with several different threshold values are presented in Table 15. The results demonstrate that specificity can be traded down to 81.7% to increase sensitivity up to 94.77%.
  • Performance of the Enet-LR model using the top 5 or 4 biomarkers as features was evaluated in samples from pregnant patients that were 1, 2, 4, or 6 weeks out from delivery.
  • the performance characteristics for the model in each scenario are presented in Table 12.
  • sensitivity and PPV decreased according to increased time to delivery, whereas specificity and NPV increased with time to delivery.
  • Model 1_4 Features - SFLT.1, PlGF, 99.10% 40.43% 88.70% 90.00% 81.41% 94.21% KIM1, CLEC4A, 4features, PlGF, KIM, CLEC4A, 98.99% 43.13% 90.02% 88.64% 79.05% 93.71% ENDOGLIN_LR_noSFLT 5features, PlGF, KIM, CLEC4A, 99.06% 39.81% 88.52% 89.55% 76.83% 93.57% ENDOGLIN, PAPP.A2_LR_noSFLT 5features, PlGF, KIM, CLEC4A, 98.97% 41.94% 89.59% 88.41% 78.56% 93.59% ENDOGLIN, DECORIN_LR_noSFLT
  • Example 1 An independent cohort of patient samples collected as described in Example 1 and Example 9 was used to validate the performance of the high-performance models developed in the previous examples. These originally consisted of 451 samples, which was reduced to 342 bona fide positive or negative for preeclampsia samples (308 bona fide negative and 34 bona fide positive). After adjudication procedures in which a group of independent-specialist physicians were employed to adjudicate and affirm or modify the initial expanded study classification status of bona fide PreE positive and PreE negative samples (as in Example 12) this cohort was reduced to a final set of 331 patient samples, with 221 being bona-fide negative for preeclampsia and 32 being bona-fide positive for preeclampsia.
  • the validation data for the performance parameters of the models built in Table 20 is shown in Table 21.
  • the 4.1 model Enet logistic regression using sFLT-1, PlGF, KIM1, and END using a cutoff of 0.3, with exclusion of false positive and negative samples, and using loess correction for gestational age
  • the AUC of the top 3 models on this independent cohort is consistent with (equivalent or better than) the performance seen in the previous examples (e.g. Example 14).
  • Model1_LR 99.20% 48.85% 91.78% 90.91% 83.47% 94.70%
  • Model2_LR 99.14% 48.79% 91.91% 90.23% 82.77% 94.95%
  • Model3_LR 99.22% 48.56% 91.78% 91.14% 83.87% 94.69%
  • Model4_LR 99.18% 47.54% 91.39% 90.68%
  • Model5_LR 99.10% 47.57% 91.59% 89.77% 81.92%
  • Model6_LR 99.21% 49.67% 92.17% 90.91% 81.66% 95.23%
  • Model8_LR 99.20% 46.22% 90.96% 90.91% 83.11% 94.42%
  • Model9_LR 99.08% 45.85% 91.07% 89.55% 81.77% 94.31%
  • Model (Intercept) PLGF SFLT.1
  • Model2_LR 3.64 2.22 ⁇ 1.41 ⁇ 0.52 NA NA NA NA Model3_LR 3.11 1.78 ⁇ 1.31 ⁇ 0.44 0.37
  • NA NA Model4_LR 4.41 2.82 ⁇ 1.42 ⁇ 0.64 NA ⁇ 0.62 NA
  • Model5_LR 4.09 2.36 ⁇ 1.45 ⁇ 0.52 NA NA ⁇ 0.90
  • Model6_LR 3.92 2.08 ⁇ 1.11 ⁇ 0.67 NA ⁇ 0.77 NA
  • Model7_LR 3.75 2.20 ⁇ 1.44 ⁇ 0.49 0.36 NA NA
  • Model8_LR 4.81 2.67 ⁇ 1.67 ⁇ 0.77 0.69 ⁇ 0.74
  • Model9_LR 5.06 2.85 ⁇ 1.53 ⁇ 0.75 NA ⁇ 0.59 ⁇ 1.14
  • Model10_LR 4.06 1.95 ⁇ 1.18 ⁇ 0.67 0.50 ⁇
  • Ethnicity information for the 331 patient sample set used for validation in this example is provided in Table 24.

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