WO2023150726A2

WO2023150726A2 - Three-tiered risk stratification for spontaneous preterm birth

Info

Publication number: WO2023150726A2
Application number: PCT/US2023/061998
Authority: WO
Inventors: Brian D. Brohman; Zhen Zhang; Kevin Paul Rosenblatt; Prem P. Gurnani; Robert C. Doss
Original assignee: Nx Prenatal Inc.
Priority date: 2022-02-03
Filing date: 2023-02-03
Publication date: 2023-08-10
Also published as: WO2023150726A3

Abstract

Provided herein are protein biomarkers and methods useful for the prediction of gestational age of a fetus; and also useful for a three-tier clinical stratification of pregnant women for risk of spontaneous preterm birth into lower risk (LR), moderate risk (MR), or higher risk (HR) categories. Such prediction and identification allow for streamlined clinical management of pregnant subjects.

Description

THREE-TIERED RISK STRATIFICATION

FOR SPONTANEOUS PRETERM BIRTH

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority from U.S. Provisional Application No. 63/306,475, filed February 3, 2022. The entire contents of which are incorporated by reference herein.

INCORPORATION OF THE SEQUENCE LISTING

[0002] The contents of the electronic sequence listing (NXPR_010_01WO_SeqList_ST26.xml; Size: 143,242 bytes; and Date of Creation: February 3, 2023) are herein incorporated by reference in its entirety.

BACKGROUND

[0003] Preterm birth is a leading cause of neonatal morbidity and death in children less than 5 years of age, with deliveries at the earlier gestational ages exhibiting a dramatically increased risk (Liu et al., Lancer, 385:61698-61706, 2015; and Katz et al., Lancet, 382:417-425, 2013).

Compared with infants bom after 38 weeks, the composite rate of neonatal morbidity doubles for each earlier gestational week of delivery according to the March of Dimes. Approximately two thirds of spontaneous preterm births (SPTBs) are spontaneous in nature, meaning they are not associated with medical intervention (Goldenberg et al., Lancet, 371 :75-84, 2008; and McElrath et al., Am J Epidemiol, 168:980-989, 2008). Yet, despite the compelling nature of this condition, there has been little recent advancement understanding of the etiology of spontaneous preterm birth (“SPTB”). While there is an increasing consensus that SPTB represents a syndrome rather than a single pathologic entity, it has been both ethically and physically difficult to study the pathophysiology of the utero-placental interface (Romero et al., Science, 345:760-765, 2014).

[0004] Much needed are tools for determining whether a pregnant woman is at an increased risk for premature delivery, including risk stratification tools that will allow to categorize gestational age, and categorize a pregnant woman as having low, moderate, or higher risk, as well as tools for decreasing a pregnant subject’s risk for premature delivery. Provided herein are such tools. SUMMARY

[0005] Provided herein are protein biomarkers and methods useful for the prediction of gestational age of a fetus; and also useful for the three-tiered clinical stratification of pregnant women for risk of spontaneous preterm birth into lower risk (LR), moderate risk (MR), or higher risk (HR) categories. Such prediction and identification may allow for streamlined clinical management of pregnant subjects.

[0006] Accordingly, in one aspect, provided herein is a method of classifying pregnancies as LR, MR, or HR, according to the risk of spontaneous preterm birth for a pregnant subject, wherein the method comprises: (a) providing a microparticle-enriched fraction from a blood sample from the pregnant subject; (b) determining a quantitative measure of at least a first and second panel of microparticle-associated proteins in the fraction; and (c) executing a classification model based on the quantitative measure of the first and second panels to determine whether the pregnant subject (i) is at a lower risk (LR) of spontaneous preterm birth; (ii) is at a moderate risk (MR) of spontaneous preterm birth; or (iii) is at a higher risk (HR) of spontaneous preterm birth.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 shows the gestational age distributions per risk group: LR (lower risk of spontaneous preterm birth); MR (moderate risk of spontaneous preterm birth); and HR (higher risk of spontaneous preterm birth), for one sample set.

[0008] FIG. 2 compares the Kaplan-Meier curves for one simulated sample set.

[0009] FIG. 3 shows the percent of children delivered by gestational age within each risk strata.

[0010] FIG. 4 depicts a schematic illustrating the results of testing that augments care by matching the patient with the appropriate care pathway.

[0011] FIG. 5 shows an exemplary system for predicting the gestational age of a fetus and for assessing the risk of spontaneous preterm birth in a pregnant subject. [0012] FIG. 6 depicts a flow chart of the usage of study samples and distribution of subjects of spontaneous preterm birth /control groups for model derivation (“training”) and validation of the model (“validation”).

[0013] FIG. 7 depicts a graph of the comparison of Kaplan-Meier time-to-birth plots of subjects in model-predicted, 3-tiered SPTB risk categories with training data (on left) and validation data (on right).

[0014] FIG. 8 depicts a graph of the comparison of prevalence-adjusted Kaplan-Meier time- to-birth plots of subjects in model -predicted 3-tiered SPTB risk categories.

[0015] FIGS. 9A-9D depict graphs of composite ROC curves evaluated on validation dataset for SPTBs defined as gestation at delivery less than 32 weeks, 34 weeks, 35 weeks, and 36 weeks, respectively. Point estimate AUCs and bootstrap estimated (stratified by SPTB vs FT) 95% confidence intervals are included.

DETAILED DESCRIPTION

[0016] The disclosure provides statistically significant circulation microparticle-associated - protein biomarkers and methods useful for the prediction of gestational age of a fetus, and for the clinical stratification of pregnant women at risk of spontaneous preterm birth (SPTB) into lower risk (LR), moderate risk (MR), or higher risk (HR) categories, well before any clinical presentation, e.g. as early as in the first trimester of pregnancy. Such methods allow for the improved clinical management of preterm birth risk. Provided herein are systems that can automatically classify pregnant women at risk of SPTB into LR, MR, or HR categories based on an analysis of the one or more of the statistically significant circulation microparticle-associated - protein biomarkers.

[0017] As used herein, the term “about” as used herein in reference to a value refers to 90 to 110% of that value. For instance a diameter of about 1000 nm is a diameter within the range of 900 nm to 1100 nm. Protein Biomarker Panels

[0018] The present disclosure provides tools for prediction of gestational age of a fetus, and for assessing and decreasing risk of SPTB. The methods of the present disclosure include a step of quantifying the levels of a plurality of microparticle-associated proteins in a biological sample.

[0019] A microparticle refers to an extracellular microvesicle or lipid raft protein aggregate having a hydrodynamic diameter of from about 50 to about 5000 nm. As such the term microparticle encompasses exosomes (about 50 to about 100 nm), microvesicles (about 100 to about 300 nm), ectosomes (about 50 to about 1000 nm), apoptotic bodies (about 50 to about 5000 nm) and lipid protein aggregates of the same dimensions. Unless otherwise stated, the term microparticle is a general reference to all of these species, microparticles are recognized as important means of intercellular communication in physiologic, pathophysiologic and apoptotic circumstances. While the contents of different types of microparticles vary with cell type, they can include nuclear, cytosolic and membrane proteins, as well as lipids and messenger and micro RNAs. Information regarding the state of the cell type of origin can be derived from an examination of microparticle contents. Thus, microparticles represent an unique window in realtime into the activities of cells, tissues and organs that may otherwise be difficult to sample.

[0020] A microparticle-associated protein refers to a protein or fragment thereof (e.g., polypeptide) that is detectable in a microparticle-enriched sample from a mammalian (e.g., human) subject.

[0021] Unless otherwise stated, the term protein encompasses polypeptides and fragments thereof. “Fragments” include polypeptides that are shorter in length than the full length or mature protein of interest. If the length of a protein is x amino acids, a fragment is x-1 amino acids of that protein. The fragment may be shorter than this (e.g., x-2, x-3, x-4, . . . ), and is preferably 100 amino acids or less (e.g., 90, 80, 70, 60, 50, 40, 30, 20 or 10 amino acids or less). The fragment may be as short as 4 amino acids, but is preferably longer (e.g., 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, or 100 amino acids). In exemplary embodiments, a plurality of surrogate peptides indicative of the presence of a set of biomarkers are quantified. [0022] The present disclosure provides tools for detecting the level of a plurality of microparticle-associated proteins in a sample, e.g., at least three, four, five, or more proteins. In exemplary embodiments, the disclosure provides for exemplary first and second panels of microparticle-associated proteins that allow for the risk stratification of SPTB.

[0023] As used herein “detecting the level” of microparticle-associated proteins encompasses detecting the expression level of the protein, detecting the absolute concentration of the protein, detecting an increase or decrease of the protein level in relation to a reference standard, detecting an increase or decrease of the protein level in relation to a threshold level, measuring the protein concentration, quantifying the protein concentration, determining a quantitative measure, detecting the presence (e.g., level above a threshold or detectable level) or detecting the absence (e.g., level below a threshold or undetectable level) of at least one microparticle-associated protein in a sample from a pregnant subject. In some embodiments, the quantitative measure can be an absolute value, a ratio, an average, a median, or a range of numbers.

[0024] As used herein, “detection of a protein” and “determining a quantitative measure of one or more proteins” encompasses any means, including, a quantitative ELISA, or detection by an MS method that detects fragments of a protein. The data disclosed in the tables and figures was obtained by MRM-MS, which detects proteins by selecting peptide fragments of a parent protein for detection as surrogates.

[0025] During development of the present disclosure, numerous microparticle-associated proteins were determined to be altered in samples from subjects having spontaneous preterm births at earlier than 32 weeks of gestation, at between 32-37 weeks of gestation, or at greater than 37 weeks of gestation (but not full term) (as compared to samples from subjects have term births), and are therefore termed “preterm birth biomarkers.” Additionally during development of the present disclosure, numerous microparticle-associated proteins were determined to be not altered in samples from subjects having spontaneous preterm births (as compared to samples from subjects have term births), and are therefore termed “term birth biomarkers.”

[0026] Table 1 provides microparticle-associated proteins differentially expressed in preterm births. In the last column of Table 1, (-) indicates the biomarker is downregulated in SPTB cases versus TERM controls; and (+) indicates the biomarker is upregulated in SPTB cases vs TERM controls.

Table 1. Microparticle-Associated Proteins Differentially Expressed in Preterm Birth

[0027] In some embodiments, provided herein are at least two panels of protein biomarkers, a first panel, and a second panel, useful for the risk stratification and gestational age mapping methods of the disclosure. In some embodiments, the risk stratification and gestational age mapping methods of the disclosure may employ greater than two panels, e.g., three, four, five, or more panels.

[0028] In some embodiments, the protein biomarkers of the first and second panels are overlapping. In some embodiments, the protein biomarkers of the first and second panels are non-overlapping. Likewise, if additional panels are utilized, they may contain either overlapping or non-overlapping protein biomarker sets.

[0029] In some embodiments, an analysis of a first panel of biomarkers in a sample from a pregnant subject is used to predict gestational age of a fetus, and to provide an initial risk stratification for the pregnant subject (to rule-out or rule-in the pregnant subject as at being at risk). For example, systems and methods described herein can classify a pregnant subject as a part of a Lower risk (LR) group (classified as LR) based on quantitative measurements of the first panel of biomarkers, thereby indicating that there is a likelihood (e.g., at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, of even at least 100%) that the pregnant subject would give birth at a gestational age of greater than 37 weeks. If the pregnant subject is not classified as LR, then that indicates that there is a likelihood (e.g., at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, of even at least 100%) that the pregnant subject would give birth at a gestational age of 37 weeks 0 days or earlier.

[0030] An exemplary first panel of biomarkers includes at least three, at least four at least five, at least six, or at least seven proteins selected from the group consisting of HEMO, FBLN1, ITIH2, TRFE, IC1, ITIH4, and LCAT. In some embodiments, an exemplary first panel comprises HEMO, FBLN1, and ITIH2. In some embodiments, an exemplary first panel consists of HEMO, FBLN1, and ITIH2. In some embodiments, one, two, three, four, five, or more additional proteins from Table 1 are measured as a part of the first panel.

[0031] If a pregnant subject is classified as LR based on the analysis of the first panel, then the appropriate clinical steps can be taken that are the standard of care.

[0032] If a pregnant subject however is not classified as LR based on the analysis of the first panel, then systems and methods described herein can analyze a second panel of biomarkers in the sample from a pregnant subject. The analysis of the second panel of markers may be used to predict the gestational age of the fetus, and to provide a further risk stratification for the pregnant subject. For example, the pregnant subject may be further classified (e.g., into Moderate Risk group and Higher Risk group as further described herein) based on quantitative measurements of the second panel of markers. It is noted that the quantitative measures from the second panel of biomarkers can be used conditionally dependent on the result from the first panel, or can be ascertained, independent of the result from the first panel, or can be ascertained simultaneously.

[0033] An exemplary second panel of biomarkers includes at least three, at least four at least five, at least six, or at least seven proteins selected from the group consisting of HEMO, FBLN1, ITIH2, TRFE, IC1, ITIH4, and LCAT. In some embodiments, an exemplary second panel comprises TRFE, IC1, ITIH4, and LCAT. In some embodiments, an exemplary second panel consists of TRFE, IC1, ITIH4, and LCAT. In some embodiments, one or more additional proteins from Table 1 are measured as a part of the second panel.

[0034] With the measurements from this second panel, a pregnant subject is further classified as a part of a Moderate Risk (MR) group (classified as MR) or classified as part of a Higher risk (HR) group (classified as HR). If the pregnant subject is classified as MR, this indicates that there is a likelihood (e.g., at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, of even at least 100%) that the pregnant subject would give birth at a gestational age of about 32 weeks to about 37 weeks. If the pregnant subject is classified as HR, this indicates that there is a likelihood (e.g., at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, of even at least 100%) that the pregnant subject would give birth at a gestational age equal to or earlier than 32 weeks 0 days.

[0035] With the measurements from this second panel, if a pregnant subject is classified as MR or even HR, then the appropriate clinical steps can be taken, as further discussed below.

Pregnant Subjects

[0036] The tools and methods provided herein can be used to predict the gestational age of a fetus and assess the risk of SPTB as LR, MR, or HR in a pregnant subject, wherein the subject can be any mammal, of any species. In some embodiments of the present disclosure, the pregnant subject is a human female. In some embodiments, the pregnant human subject is in the first trimester (e.g., weeks 1-12 of gestation), second trimester (e.g., weeks 13-28 of gestation) or third trimester of pregnancy (e.g., weeks 29-37 of gestation). In some embodiments, the pregnant human subject is in early pregnancy (e.g., from 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20, but earlier than 21 weeks of gestation; from 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10 or 9, but later than 8 weeks of gestation). In some embodiments, the pregnant human subject is in mid-pregnancy (e.g., from 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30, but earlier than 31 weeks of gestation; from 30, 29, 28, 27, 26, 25, 24, 23, 22 or 21, but later than 20 weeks of gestation). In some embodiments, the pregnant human subject is in late pregnancy (e.g., from 31, 32, 33, 34, 35, 36 or 37, but earlier than 38 weeks of gestation; from 37, 36, 35, 34, 33, 32 or 31, but later than 30 weeks of gestation). In some embodiments, the pregnant human subject is in less than 17 weeks, less than 16 weeks, less than 15 weeks, less than 14 weeks, or less than 13 weeks of gestation; from 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10 or 9, but later than 8 weeks of gestation). In some embodiments, the pregnant human subject is in about 8-12 weeks of gestation. In some embodiments, the pregnant human subject is in about 18-14 weeks of gestation. In some embodiments, the pregnant human subject is in about 18-24 weeks of gestation. In exemplary embodiments, the pregnant human subject is at 10-12 weeks of gestation. In some embodiments, the pregnant human subject is in about 22-24 weeks of gestation. The stage of pregnancy can be calculated from the first day of the last normal menstrual period of the pregnant subject.

[0037] Pregnant subjects of the methods described herein can belong to one or more classes or status, including primiparous (no previous child brought to delivery) or multiparous (at least one previous child brought to at least 20 weeks of gestation), primigravida (first pregnancy, first time mother) or multigravida (more than one prior pregnancy). A parity status of primiparous can be denoted as parity of 0 (parity = 0); a primiparous status can also be referred to as nulliparous and the terms may be used interchangeably. A parity status of multiparous can be denoted as parity > 1 or parity >0, and the terms may be used interchangeably.

[0038] In some embodiments, the pregnant human subject is primiparous, (i.e., parity =0). In other embodiments, the pregnant subject is multiparous. In some embodiments, the pregnant subject may have brought no previous child to term. In other embodiments, the pregnant subject may have brought at least one previous child to at least 20 weeks of gestation.

[0039] In some embodiments, the pregnant human subject is primigravida. In other embodiments, the pregnant subject is multi gravida. In some embodiments, the pregnant subject may have had at least one prior SPTB (e.g., birth prior to week 38 of gestation). In some embodiments, the pregnant human subject is asymptomatic. In some embodiments, the subject may have a risk factor of PTB such as a history of pre-gestational hypertension, diabetes mellitus, kidney disease, known thrombophilias and/or other significant preexisting medical condition (e.g., short cervical length).

Samples

[0040] A sample for use in the methods of the present disclosure to predict the gestational age of a fetus and assess the risk of SPTB as LR, MR, or HR is a biological sample obtained from a pregnant subject. In preferred embodiments, the sample is collected during a stage of pregnancy described in the preceding section. In some embodiments, the sample is a blood, saliva, tears, sweat, nasal secretions, urine, amniotic fluid or cervicovaginal fluid sample. In some embodiments, the sample is a blood sample, which in preferred embodiments is serum or plasma. In some embodiments, the sample has been stored frozen (e.g., -20°C or -80°C). Detection of Protein Biomarkers

[0041] Biomarkers can be detected and quantified by any method known in the art. This includes, without limitation, immunoassay, chromatography, mass spectrometry, electrophoresis and surface plasmon resonance.

[0042] In some embodiments, detecting the level (e.g., including detecting the presence) of the protein biomarkers is done using an antibody-based method. Suitable antibody-based methods include but are not limited to enzyme linked immunosorbent assay (ELISA), chemiluminescent assay, Western blot, and antibody microarray.

[0043] In some embodiments, detecting the level (e.g., including detecting the presence) of one or both of the protein biomarkers includes detection of an intact protein, or detection of surrogate for the protein, such as a peptide fragment.

[0044] Immunoassay methods include, for example, radioimmunoassay, enzyme-linked immunosorbent assay (ELISA), sandwich assays and Western blot, immunoprecipitation, immunohistochemistry, immunofluorescence, antibody microarray, dot blotting, and FACS.

[0045] Chromatographic methods include, for example, affinity chromatography, ion exchange chromatography, size exclusion chromatography/gel filtration chromatography, hydrophobic interaction chromatography and reverse phase chromatography.

[0046] In some embodiments, detecting the level of a microparticle-associated protein is accomplished using a mass spectrometry (MS)-based proteomic analysis (e.g., liquid chromatography mass spectrometry LC/MS). In exemplary embodiments the method involves subjecting a sample to size exclusion chromatography and collecting the high molecular weight fraction (e.g., by size-exclusion chromatography) to obtain a microparticle-enriched sample. The microparticle-enriched sample is then disrupted (using, for example, chaotropic agents, denaturing agents, reducing agents and/or alkylating agents) and the released contents subjected to proteolysis. In some embodiments, the disrupted preparation contains a plurality of peptides.

[0047] Proteins in a sample can be detected by mass spectrometry. Mass spectrometers typically include an ion source to ionize analytes, and one or more mass analyzers to determine mass. Ionization methods include, among others, electrospray or laser desorption methods. [0048] Selected reaction monitoring is a mass spectrometry method in which a first mass analyzer selects a polypeptide of interest (precursor), a collision cell fragments the polypeptide into product peptide fragments and one or more of the peptide fragments is detected in a second mass analyzer. When multiple fragments of a polypeptide are analyzed, the method is referred to as Multiple Reaction Monitoring Mass Spectrometry (MRM/MS). Typically, protein samples are digested with a proteolytic enzyme, such as trypsin, to produce peptide fragments. Heavy isotope labeled analogs of certain of these peptides are synthesized as isotopic standards. The isotope-labeled reference peptides (interchangeably referred to herein has isotope standards, stable isotope standard peptides, stable isotopic standards, and SIS) are mixed with a protease- treated sample. The mixture is subjected to mass spectrometry. Peptides corresponding to the daughter ions of the stable isotopic standards (SIS) and the target peptides are detected with high accuracy, in either the time domain or the mass domain. Usually, a plurality of the daughter ions is used to unambiguously identify the presence of a parent ion, and one of the daughter ions, usually the most abundant, is used for quantification. SIS peptides can be synthesized to order, or can be available as commercial kits from vendors such as, for example, e.g., ThermoFisher (Waltham, MA) or Biognosys (Zurich, Switzerland).

[0049] The assay can include standards that correspond to the analytes of interest (e.g., peptides having the same amino acid sequence as that of analyte peptides), but differ by the inclusion of stable isotopes. Stable isotopic standards can be incorporated into the assay at precise levels and used to quantify the corresponding unknown analyte. Additional levels of specificity are contributed by the co-elution of the unknown analyte and its corresponding SIS, and by the properties of their transitions (e.g., the similarity in the ratio of the level of two transitions of the analyte and the ratio of the two transitions of its corresponding SIS).

[0050] Accordingly, detection of a protein target by MRM-MS involves detection of one or more peptide fragments of the protein, typically through detection of a stable isotope reference peptide against which the peptide fragment is compared. Typically, an SIS will, itself, be fragmented in a collision cell as will the original digested fragment, and one or more of these fragments is detected by the mass spectrometer.

[0051] Mass spectrometry assays, instruments and systems suitable for biomarker peptide analysis can include, without limitation, matrix-assisted laser desorption/ionization time-of- flight (MALDI-TOF) MS; MALDI-TOF post-source-decay (PSD); MALDI-TOF/TOF; surface- enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF) MS; electrospray ionization mass spectrometry (ESI-MS); ESI-MS/MS; ESI-MS/(MS)n (n is an integer greater than zero); ESI 3D or linear (2D) ion trap MS; ESI triple quadrupole MS; ESI quadrupole orthogonal TOF (Q-TOF); ESI Fourier transform MS systems; desorption/ionization on silicon (DIOS); secondary ion mass spectrometry (SIMS); atmospheric pressure chemical ionization mass spectrometry (APCI-MS); APCI-MS/MS; APCI-(MS)n; ion mobility spectrometry (IMS); inductively coupled plasma mass spectrometry (ICP-MS) atmospheric pressure photoionization mass spectrometry (APPI-MS); APPI-MS/MS; and APPI-(MS)n. Peptide ion fragmentation in tandem MS (MS/MS) arrangements can be achieved using techniques known in the art, such as, e.g., collision induced dissociation (CID). As described herein, detection and quantification of biomarkers by mass spectrometry can involve multiple reaction monitoring (MRM), such as described, inter alia, by Kuhn et al. (2004) Proteomics 4: 1175-1186. Scheduled multiple-reaction-monitoring (Scheduled MRM) mode acquisition during LC-MS/MS analysis enhances the sensitivity and accuracy of peptide quantitation. Anderson and Hunter (2006) Mol. Cell. Proteomics 5(4):573-588. Mass spectrometry-based assays can be advantageously combined with upstream peptide or protein separation or fractionation methods, such as, for example, with the tandem column system described herein.

[0052] In some embodiments, detecting the level (e.g., including detecting the presence) of one or both of SPTB biomarkers and term birth biomarkers is done using a mass spectrometry (MS)-based proteomic analysis (e.g., liquid chromatography-mass spectrometry (LC/MS)-based proteomic analysis). In exemplary embodiments, the method involves subjecting a sample to size exclusion chromatography and collecting the high molecular weight fraction to obtain a microparticle-enriched sample. The microparticle-enriched sample is then extracted before digestion with a proteolytic enzyme (e.g., trypsin) to obtain a digested sample comprising a plurality of peptides. The digested sample can then be subjected to a peptide purification / concentration step before liquid chromatography and mass spectrometry to obtain a proteomic profile of the sample. In some embodiments, the purification / concentration step comprises reverse phase chromatography (e.g., ZIPTIP pipette tip with 0.2 pL Cl 8 resin, from Millipore Corporation, Billerica, MA). [0053] Table A shows exemplary peptides that can be detected to detect an exemplary 4 protein panel of the disclosure (TRFE, ICI, ITIH4, and LCAT) or to detect each protein individually. In some embodiments, the panel is detected using MS/MRM. In some embodiments, the panel is detected using LC-MS/MRM.

[0054] In some embodiments, the panel comprises ICI, ITIH4, TRFE, and LCAT. In some embodiments, peptides of SEQ ID NO: 1, SEQ ID NO:2, SEQ ID NO:3, and SEQ ID NO:4 are detected using MS, MS/MRM, or LC-MS/MRM. In some embodiments, the blood sample is a plasma sample. In some embodiments, the sample is taken from a pregnant subject who is at 8- 14 weeks, or 10-12 weeks, or in her first trimester of gestation. In some embodiments the pregnant subject is primiparous. In some embodiments, the pregnant subject is primigravida.

Table A

[0055] As provided herein, detection of a biomarker by MS, MS/MRM, or LC-MS/MRM involves detection of one or more peptide fragments of the protein, typically through detection of a stable isotope reference peptide against which the peptide fragment is compared.

[0056] Table B shows exemplary isotope-labeled reference peptides (isotopic standards) used in the LC-MCS MRM mode for detecting the 4-protein panel (TRFE, ICI, ITH44, and LCAT) of the disclosure.

[0057] In some embodiments, provided herein is a method for measuring a protein panel, comprising: (a) preparing a microparticle-enriched fraction from a blood sample of a subject; and (b) determining a quantitative measure of a panel of microparticle-associated proteins in the fraction, wherein the panel comprises ICI, ITH44, TRFE, and LCAT, and wherein the determining comprises measuring surrogate peptides of the proteins. In some embodiments, peptides of SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, and SEQ ID NO:4 are detected, for example using MS, MS/MRM, or LC-MS/MRM. In some embodiments, the method further comprises using the isotope-labeled reference peptides of SEQ ID NO:8 SEQ ID NO:9, SEQ ID NO: 10, and SEQ ID NO: 11. In some embodiments, the blood sample is a plasma sample. In some embodiments, the sample is taken from a pregnant subject who is at 8-14 weeks, or 10-12 weeks, or in her first trimester of gestation. In some embodiments the pregnant subject is primiparous. In some embodiments, the pregnant subject is primigravida.

[0058] In exemplary embodiments, provided herein is a method for prediction of gestational age of a fetus, or for assessing risk of SPTB for a pregnant subject, the method comprising: (a) preparing a microparticle-enriched fraction from a blood sample from the pregnant subject; and (b) determining a quantitative measure of a panel of microparticle-associated proteins in the fraction, wherein the panel comprises ICI, ITIH4, TRFE, and LCAT and wherein the determining comprises measuring surrogate peptides of the proteins. In some embodiments, peptides of SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, and SEQ ID NO:4 are detected using MS, MS/MRM, or LC-MS/MRM and using the isotope-labeled reference peptides of SEQ ID NO: 8 SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11. In some embodiments, the blood sample is a plasma sample. In some embodiments, the sample is taken from a pregnant subject who is at 8-14 weeks, or 10-12 weeks, or in her first trimester of gestation. In some embodiments the pregnant subject is primiparous. In some embodiments, the pregnant subject is primigravida.

Table B

[0059] In some embodiments, provided herein are kits comprising one or more stable isotope reference peptides corresponding to peptide biomarkers, e.g., peptides produced from protease (e.g., trypsin) digestion of biomarker proteins. [0060] In exemplary embodiments, provided herein is a kit for use in detection of SPTB in a primiparous pregnant subject, wherein the kit comprises the isotope-labeled reference peptides of SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11, and instructions for use.

[0061] In exemplary embodiments, provided herein is a composition comprising a plurality of protein peptides and a plurality of isotope-labeled reference peptides, wherein the protein peptides comprise, or consist of SEQ ID NO: 1, SEQ ID NO:2, SEQ ID NO:3, and SEQ ID NON and the isotope-labeled reference peptides comprise or consist of SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO: 10, and SEQ ID NO: ! !.

[0062] In exemplary embodiments, provided herein is a composition comprising: (i) one or a plurality of peptide fragments of each of one or a plurality of protein biomarkers for preterm birth as disclosed herein and (ii) one or a plurality of isotope-labeled reference peptides (e.g. standard peptides corresponding to SEQ ID NO:8 SEQ ID NO:9, SEQ ID NO: 10, and SEQ ID NO: 11 which correspond in amino acid sequence to each of the one or a plurality of peptide fragments, wherein each peptide fragment and isotope-labeled reference peptide has an amino acid sequence corresponding to a peptide fragment produced by protease digestion of the one or a plurality of protein biomarkers. In some embodiments the composition comprises peptide fragments from a microparticle-enriched, protease-digested sample. In another embodiment, one or more of the isotope-labeled reference peptides are selected from Table B. Further provided are methods (a) comprising providing a sample comprising proteins from a microparticle- enriched fraction of a biological sample; (b) performing protease digestion on the proteins to produce peptide fragments; and (c) contacting the peptide fragments with one or a plurality of isotope-labeled reference peptides (e.g. standard peptides corresponding to SEQ ID NO:8 SEQ ID NO:9, SEQ ID NO: 10, and SEQ ID NO: 11) corresponding in amino acid sequence to each of the one or a plurality of peptide fragments, wherein each isotope-labeled reference peptide has an amino acid sequence corresponding to a peptide fragment produced by protease digestion of the one or a plurality of protein biomarkers for preterm birth as disclosed herein.

[0063] In some embodiments, provided herein is a method for measuring a protein panel, comprising: (a) preparing a sample comprising proteins from a microparticle-enriched fraction of a blood sample; (b) performing protease digestion on the proteins to produce peptide fragments; (c) contacting the peptide fragments with a plurality of isotope-labeled reference peptides; (d) determining a quantitative measure of a first panel of microparticle-associated proteins in the fraction, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2; and (e) optionally determining a quantitative measure of a second panel of microparticle- associated proteins in the fraction, wherein the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, and LCAT. In some embodiments, the method comprises using MS/MRM to perform the method.

[0064] In some embodiments, provided herein is a method for measuring a protein panel, comprising: (a) preparing a microparticle-enriched fraction from a blood sample of a subject; and (b) determining a quantitative measure of a first panel of microparticle-associated proteins in the fraction, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2; and (c) optionally determining a quantitative measure of a second panel of microparticle-associated proteins in the fraction, wherein the second panel comprises at least three protein selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, and LCAT, wherein the determining comprises measuring surrogate peptides of the proteins.

Classification Models

[0065] Systems and methods described herein may execute one or more classification models to predict the gestational age of a fetus and to assess the risk of SPTB as LR, MR, or HR in a pregnant subject. FIG. 5 discussed in further detail below illustrates an example system for generating and executing classification models to predict the gestational age of a fetus and to assess to the risk of SPTB as LR, MR, or HR in a pregnant subject. Some example classification models may include linear and non-linear models, (e.g., processes such as CART - classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (e.g., support vector machines). In some embodiments, the classification models may include classifiers, such as multivariate classifiers. [0066] In some embodiments, classification models can be generated by machine learning techniques that perform analysis of datasets of biomarker measurements derived from subjects classed into one or another group. For example, the classification models can be trained using a training dataset based on machine learning techniques and/or machine learning analysis to predict the gestational age of a fetus and to assess the risk of SPTB as LR, MR, or HR in a pregnant subject. The training dataset may include a plurality of samples obtained from a plurality of pregnant subjects. The training dataset may include the quantitative measures of a first panel and a second panel of proteins in each sample of the plurality of samples. In some embodiments, implementing machine learning analysis and/or machine learning techniques may associate these quantitative measurements of the first panel and the second panel of proteins of each sample in the training dataset with one or more classes such as Low Risk (LR) class that may be indicative LR groups, Moderate Risk (MR) class that may be indicative of MR groups, and High Risk (HR) class that may be indicative of HR groups. The classification models may be trained based on these machine learning analyses and/or machine learning techniques. Training the classification models may generate classification rules that classify a plasma or serum sample from a pregnant subject as belonging to the LR, MR, HR class. In some embodiments, executing and/or implementing the classification model may result in execution of the classification rules that classify the pregnant subject as belonging to the LR, MR, HR class.

[0067] In some embodiments, a classification model of the disclosure is generated by a machine learning method comprising: (a) providing a microparticle-enriched fraction from plasma or serum of a plurality of pregnant subjects obtained at from about 8 to about 14 weeks of gestation, wherein the plurality of subjects include a plurality of subjects that subsequently experienced preterm birth and a plurality of subjects that subsequently experienced term birth; (b) using selected reaction monitoring mass spectrometry, determining a quantitative measure of a first panel and a second panel of proteins in the fraction, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2, and the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, LCAT; (c) generating a training data set indicating, for each sample, values indicating: (i) classification of the sample as belonging to one of lower risk (LR), moderate risk (MR), or higher risk (HR) of birth at about earlier than 37 weeks; and (ii) the quantitative measures of the plurality of protein biomarkers; and (d) training a classification model on the training data set, wherein training generates one or more classification rules that classify a sample as belonging to the LR, MR, HR class. In some embodiments, the quantitative measures of (c)(ii) comprises a quantitative measure of the covariate of maternal body mass index (BMI).

[0068] Diagnostic tests are characterized by sensitivity (percentage classified as positive that are true positives) and specificity (percentage classified as negative that are true negatives). The relative sensitivity and specificity of a diagnostic test can involve a trade-off - higher sensitivity can mean lower specificity, while higher specificity can mean lower sensitivity. These relative values can be displayed on a receiver operating characteristic (ROC) curve. The diagnostic power of a set of variables, such as biomarkers, is reflected by the area under the curve (AUC) of an ROC curve.

[0069] In some embodiments, the classifiers of this disclosure have a sensitivity of at least 85%, at least 90%, at least 95%, at least 98%, or at least 99%. Classifiers of this disclosure have an AUC of at least 0.6, at least 0.7, at least 0.8, at least 0.9 or at least 0.95.

[0070] In some embodiments, provided herein is a computer system capable of executing the classification rule, the system comprising: (a) a processor; and (b) a memory, coupled to the processor, the memory storing a module comprising: (i) test data for a sample from a subject including values indicating a quantitative measure of a first panel and a second panel of protein biomarkers, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2, and the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, LCAT; (ii) a classification rule which, based on values including the measurements, classifies the subject as being at lower risk (LR), moderate risk (MR), or higher risk (HR) for spontaneous preterm birth, wherein the classification rule is configured to have a sensitivity of at least 75%, at least 85% or at least 95% ; and (iii) computer executable instructions for implementing the classification rule on the test data. In some embodiments, the test data of (i) comprises a quantitative measure of the covariate of maternal body mass index (BMI). Methods for Gestational Age Prediction and Risk Stratification

[0071] Accordingly in some embodiments, provided herein are methods of classifying pregnancies as LR, MR, or HR, according to the risk of spontaneous preterm birth for a pregnant subject, wherein the method comprises: (a) providing a microparticle-enriched fraction from a blood sample from the pregnant subject; (b) determining a quantitative measure of at least a first and second panels of microparticle-associated proteins in the fraction; and (c) executing a classification model based on the quantitative measure of the first and second panels to determine whether the pregnant subject (i) is at a lower risk (LR) of spontaneous preterm birth; (ii) is at a moderate risk (MR) of spontaneous preterm birth; or (iii) is at a higher risk (HR) of spontaneous preterm birth.

[0072] In some embodiments, provided herein is a method of classifying pregnancies according to the risk of spontaneous preterm birth for a pregnant subject, wherein the method comprises: (a) providing a microparticle-enriched fraction from a blood sample from the pregnant subject; (b) determining a quantitative measure of at least a first and second panel of microparticle-associated proteins in the fraction; (c) executing a classification model based on the quantitative measure of the first panel to determine whether the pregnant subject (i) is at a lower risk (LR) of spontaneous preterm birth before about 37 weeks of gestation, or (ii) is at an increased risk of spontaneous preterm birth before about 37 weeks of gestation, whereby determining if the subject is at an increased risk of spontaneous preterm birth; and (d) if it is determined in (c)(ii) that there is an increased risk of spontaneous preterm birth before about 37 weeks of gestation, then executing the classification model based on the quantitative measure of the second panel to determine that the pregnant subject either (i) is at a moderate risk (MR) of spontaneous preterm birth before about 37 weeks of gestation, or (ii) is at an higher risk (HR) of spontaneous preterm birth before about 37 weeks of gestation.

[0073] In some embodiments, provided herein is a method of predicting the gestational age at delivery of a fetus of a pregnant subject, wherein the method comprises: (a) providing a microparticle-enriched fraction from a blood sample from the pregnant subject; (b) determining a quantitative measure of at least a first and second panel of microparticle-associated proteins in the fraction; (c) executing a classification model based on the quantitative measure of the first panel to determine whether the gestational age of the fetus (i) will likely be greater than about 37 weeks of gestation, or (ii) will likely be about 37 weeks of gestation or lower; and (d) if it is determined in (c)(ii) that the gestational age of the fetus will likely be 37 weeks of gestation or lower, then executing the classification model based on the quantitative measure of the second panel to determine whether the gestational age of the fetus (i) will likely between about 32 and about 37 weeks of gestation, or (ii) will likely be earlier than about 32 weeks of gestation.

[0074] In some embodiments the first panel comprises 3, 4, 5, or more proteins. In some embodiments, the second panel comprises 3, 4, 5, or more proteins.

[0075] In some embodiments, the first panel comprises any three or more of TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2. In some embodiments, the second panel comprises any three or more of TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2.

[0076] In some embodiments, the first panel comprises HEMO, FBLN1, and ITIH2. In some embodiments, the first panel consists of HEMO, FBLN1, and ITIH2.

[0077] In some embodiments, the second panel comprises TRFE, IC1, ITIH4, and LCAT. In some embodiments, the second panel consists of TRFE, IC1, ITIH4, and LCAT.

[0078] In some embodiments, the quantitative measure of the first or second panel comprises inclusion of the covariate of maternal body mass index (BMI).

[0079] In some embodiments, the pregnant subject is multiparous. In some embodiments, the pregnant subject is primiparous. In some embodiments, the pregnant subject is multigravida. In some embodiments, the pregnant subject is primigravida.

[0080] In some embodiments, the blood sample is taken from the pregnant subject when the pregnant subject is at about 10 to about 12 weeks of gestation. In some embodiments, the blood sample is taken from the pregnant subject during the first trimester of gestation.

[0081] In some embodiments, the steps of the method are carried out on a first sample taken from the pregnant subject during the first trimester, and the steps of the method are repeated on a second sample taken from the pregnant subject during the second trimester. [0082] In some embodiments, the steps of the method are carried out on a first sample taken from the pregnant subject at about 8, 9, 10, 11, 12, 13, 8-9, 8-10, 8-11, 8-12, 8-13, 9-10, 9-11, 9- 12, 9-13, 10-11, 10-12, 10-13, 11-12, 11-13, or even about 12-13 weeks of gestation, and the steps of the method are repeated on a second sample taken from the pregnant subject at about 18, 19, 20, 21, 22, 23, 24, or at about 18-24 weeks of gestation.

[0083] In some embodiments, the steps of the method are carried out on a first sample taken from the pregnant subject at about 10 to about 12 weeks of gestation, at about 9 to about 12 weeks of gestation, or at about 8 to about 13 weeks of gestation, the steps of the method are repeated on a second sample taken from the pregnant subject during the second trimester.

[0084] In some embodiments, the steps of the method are carried out on a first sample taken from the pregnant subject at about 10 to about 12 weeks of gestation, at about 9 to about 12 weeks of gestation, or at about 8 to about 13 weeks of gestation, and the steps of the method are repeated on a second sample taken from the pregnant subject at about 18 to about 24 weeks of gestation.

Methods For Reducing Risk of Spontaneous Preterm Birth

[0085] In some embodiments, if a pregnant subject is determined to be at increased risk of SPTB (e.g. MR or HR groups), the appropriate treatment plans can be employed.

[0086] In some embodiments, the treatment step comprises the administration of a therapeutic agent selected from the group consisting of low-dose aspirin, tocolytics, a hormone, a complement-inhibitor, and a corticosteroid. In exemplary embodiments, the treatment comprises a hormone, such as progesterone or 17-alpha-hydroxyprogesterone caproate, e.g. a vaginal progesterone or parenteral 17-alpha-hydroxyprogesterone caproate.

[0087] In some embodiments, the treatment step is selected from the group consisting of: (a) increased surveillance by physician and nursing professionals via supplemental office visits and/or telehealth visits; (b) education for the patient regarding risk factors, symptoms, potential behavior and lifestyle modifications, planning for access to neonatal intensive care, usage of remote maternal and fetal monitoring devices, usage of doctor/patient computer/smartphone connectivity applications, and acute-stage decisions and medications; (c) a referral to a Maternal-Fetal Medicine physician that specializes in high-risk pregnancy care; (d) a referral to a preterm birth prevention clinic or provider that offers a holistic array of services for high-risk pregnancies; and (e) follow-up evaluations via cervical length monitoring, fetal fibronectin testing, serial testing, genomic testing, proteomic testing, or metabolomic testing.

[0088] By way of example, a surgical intervention such as cervical cerclage and progesterone supplementation have been shown to be effective in preventing preterm birth (Committee on Practice Bulletins, Obstetrics & Gynecology, 120:964-973, 2012). In some embodiments, other measures are taken by health care professionals, such as switching to an at-risk protocol such as increased office visits and/or tracking the patient to a physician specially trained to deal with higher risk patients. In some embodiments, if a pregnant subject is determined to be at increased risk of SPTB, steps can be taken such that the pregnant subject will have access to NICU facilities and plans for access to such facilities for rural patients. Additionally, the pregnant subject and family members can have better knowledge of acute-phase symptomatic interventions such as fetal fibronectin testing (diagnostic) and corticosteroids (e.g., for baby lung development) and mag sulfate (e.g., for baby neuroprotective purposes). Additionally, the pregnant subject can be monitored such as better adherence to dietary, smoking cessation, and other recommendations from the physician are followed.

[0089] In some embodiments, the pregnant subject is prescribed progesterone supplementation. Currently progesterone supplementation for the prevention of recurrent SPTB is offered to: females with a singleton pregnancy and a prior SPTB; and females with no history of SPTB who have an incidentally detected very short cervix (<15 mm). The present disclosure provides tools to identify additional pregnant subjects that may benefit from progesterone supplementation. These subjects include the following: pregnant females who are primigravidas without a history of risk and without an incidentally detected very short cervix; and pregnant females who are multi gravidas but who did not previously have a SPTB.

[0090] Pregnant subjects determined to be at increased risk for preterm birth are recommended to receive or are administered progesterone until 36 weeks of gestation (e.g., upon identification or between 16 weeks, 0 days and 20 weeks, 6 days gestation until 36 weeks gestation). In some embodiments, progesterone supplementation comprises 250 mg weekly intramuscular injections. In exemplary embodiments, the weekly progesterone supplementation comprises administration of hydroxyprogesterone caproate by injection. In other embodiments, progesterone supplementation comprises vaginal progesterone in doses between 50 and 300 mg daily, between 75 and 200 mg daily or between 90 and 110 mg daily.

[0091] In another embodiment, in females with a singleton pregnancy determined to be at increased risk for preterm birth and who have had a documented prior SPTB at less than 34 weeks of gestation and short cervical length (less than 25 mm) before 24 weeks of gestation, are recommended to receive or are given a cervical cerclage (also known as tracheloplasty or cervical stitch). In some embodiments, the cervical cerclage is a McDonald cerclage, while in other embodiments it is a Shirodkar cerclage or an abdominal cerclage.

[0092] Accordingly, provided herein are methods of decreasing the risk of SPTB for a pregnant subject and/or reducing neonatal complications of SPTB, the method comprising: assessing risk stratification of SPTB for a pregnant subject according to any of the methods provided herein; and administering a therapeutic agent, prescribing a revised care management protocol, carrying out fetal fibronectin testing, administering corticosteroids, administering mag sulfate, or increasing the monitoring and surveillance of the subject in an amount effective to decrease the risk of SPTB and/or reduce neonatal complications of SPTB.

Kits

[0093] In another embodiment, a kit of reagents capable of one or both of SPTB biomarkers and term birth biomarkers in a sample is provided. Reagents capable of detecting protein biomarkers include but are not limited to antibodies. Antibodies capable of detecting protein biomarkers are also typically directly or indirectly linked to a molecule such as a fluorophore or an enzyme, which can catalyze a detectable reaction to indicate the binding of the reagents to their respective targets.

[0094] In some embodiments, the kits further comprise sample processing materials comprising a high molecular gel filtration composition (e.g., agarose such as SEPHAROSE) in a low volume (e.g., 1ml) vertical column for rapid preparation of a microparticle-enriched sample from plasma. For instance, the microparticle-enriched sample can be prepared at the point of care before freezing and shipping to an analytical laboratory for further processing, for example by size exclusion chromatography. [0095] In some embodiments, the kits further comprise instructions for assessing risk of SPTB. As used herein, the term “instructions” refers to directions for using the reagents contained in the kit for detecting the presence (including determining the expression level) of a protein(s) of interest in a sample from a subject. The proteins of interest may comprise one or both of SPTB biomarkers and term birth biomarkers. In some embodiments, the instructions further comprise the statement of intended use required by the U.S. Food and Drug Administration (FDA) in labeling in vitro diagnostic products. The FDA classifies in vitro diagnostics as medical devices and required that they be approved through the 510(k) procedure. Information required in an application under 510(k) includes: 1) The in vitro diagnostic product name, including the trade or proprietary name, the common or usual name, and the classification name of the device; 2) The intended use of the product; 3) The establishment registration number, if applicable, of the owner or operator submitting the 510(k) submission; the class in which the in vitro diagnostic product was placed under section 513 of the FD&C Act, if known, its appropriate panel, or, if the owner or operator determines that the device has not been classified under such section, a statement of that determination and the basis for the determination that the in vitro diagnostic product is not so classified; 4) Proposed labels, labeling and advertisements sufficient to describe the in vitro diagnostic product, its intended use, and directions for use, including photographs or engineering drawings, where applicable; 5) A statement indicating that the device is similar to and/or different from other in vitro diagnostic products of comparable type in commercial distribution in the U.S., accompanied by data to support the statement; 6) A 510(k) summary of the safety and effectiveness data upon which the substantial equivalence determination is based; or a statement that the 510(k) safety and effectiveness information supporting the FDA finding of substantial equivalence will be made available to any person within 30 days of a written request; 7) A statement that the submitter believes, to the best of their knowledge, that all data and information submitted in the premarket notification are truthful and accurate and that no material fact has been omitted; and 8) Any additional information regarding the in vitro diagnostic product requested that is necessary for the FDA to make a substantial equivalency determination. Systems

[0096] FIG. 5 shows an exemplary system for predicting the gestational age of a fetus and for assessing the risk of SPTB as LR, MR, or HR in a pregnant subject. The system may access and/or retrieve data from database 504. A controller 502 may implement machine learning techniques using the data retrieved from the database 504. In some embodiments, the controller may generate one or more classification models described herein using the data retrieved from the database 504. For example, the controller may train the classification models using the data (e.g., training data) retrieved from the database 504. The predictions from the classification models may be transmitted to a health provider application 508 being implemented on a suitable computing device. In some embodiments, the predictions from the classification models (e.g., gestational age of the fetus and/or risk of SPTB outputted from the classification models) may be stored in the database 504. In some embodiments, these predictions may be accessed from the database 504 at a future time to further improve the accuracy of the classification models.

[0097] In some embodiments, the controller 502 may include one or more servers and/or one or more processors running on a cloud platform (e.g., Microsoft Azure®, Amazon® web services, IBM® cloud computing, etc.). The server(s) and/or processor(s) may be any suitable processing device configured to run and/or execute a set of instructions or code, and may include one or more data processors, image processors, graphics processing units, digital signal processors, and/or central processing units. The server(s) and/or processor(s) may be, for example, a general purpose processor, a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), and/or the like.

[0098] In some embodiments, the controller 502 may include a processor (e.g., CPU). The processor may be any suitable processing device configured to run and/or execute a set of instructions or code, and may include one or more data processors, image processors, graphics processing units, physics processing units, digital signal processors, and/or central processing units. The processor may be, for example, a general purpose processor, a Field Programmable Gate Array (FPGA), an application Specific Integrated Circuit (ASIC), and/or the like. The processor may be configured to run and/or execute application processes and/or other modules, processes and/or functions associated with the system and/or a network associated therewith. The underlying device technologies may be provided in a variety of component types (e.g., MOSFET technologies like complementary metal-oxide semiconductor (CMOS), bipolar technologies like emitter-coupled logic (ECL), polymer technologies (e.g., silicon-conjugated polymer and metal-conjugated polymer-metal structures), mixed analog and digital, and/or the like. In some variations, the controller 502 may include one or more modules (e.g., modules in a software code and/or modules stored in a memory) that, when executed by the processor, can be configured to predicting the gestational age of a fetus and to classify the risk of SPTB as LR, MR, or HR in a pregnant subject

[0099] The output of the classification models may be stored in the database 504. The controller 502 can be communicably coupled to the database 504. The database 504 may be accessed at any suitable time to improve the classification models implemented by the controller 502. In some variations, the database 504 may be stored in a memory device such as a randomaccess memory (RAM), a memory buffer, a hard drive, an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), a read-only memory (ROM), Flash memory, and the like. In some variations, the database 604 may be stored on a cloud-based platform such as Amazon web services®.

[0100] In some embodiments, the output of the classification models may be accessible to health care providers via an application software 508 executable on a computing device. Some non-limiting examples of the computing device include computers (e.g., desktops, personal computers, laptops etc.), tablets and e-readers (e.g., Apple iPad®, Samsung Galaxy® Tab, Microsoft Surface®, Amazon Kindle®, etc.), mobile devices and smart phones (e.g., Apple iPhone®, Samsung Galaxy®, Google Pixel®, etc.), etc. In some embodiments, the application software 508 (e.g., web apps, desktop apps, mobile apps, etc.) may be pre-installed on the computing device. Alternatively, the application software 508 may be rendered on the computing device in any suitable way. For example, in some embodiments, the application software 508 (e.g., web apps, desktop apps, mobile apps, etc.) may be downloaded on the computing device from a digital distribution platform such as an app store or application store (e.g., Chrome® web store, Apple® web store, etc.). Additionally or alternatively, the computing device may render a web browser (e.g., Google®, Mozilla®, Safari®, Internet Explorer®, etc.) on the computing device. The web browser may include browser extensions, browser plug-ins, etc. that may render the application software 508 on the computing device. In yet another alternative embodiment, the browser extensions, browser plug-ins, etc. may include installation instructions to install the application software 508 on the computing device.

[0101] The output of the classification models may be accessed by any user (e.g., patient, health care providers, other clinicians, etc.) via the application software 508 in real-time. For example, the health care providers may access the output of the classification models via the application software 508 in real-time. The output of the classification models may be displayed on the display of the computing device.

[0102] Data can be transmitted electronically, e.g., over the Internet. Electronic communication can be, for example, over any communications network include, for example, a high-speed transmission network including, without limitation, Digital Subscriber Line (DSL), Cable Modem, Fiber, Wireless, Satellite and, Broadband over Powerlines (BPL). Information can be transmitted to a modem for transmission, e.g., wireless or wired transmission, to a computer such as a desktop computer. Alternatively, reports can be transmitted to a mobile device. Reports may be accessible through a subscription program in which a user accesses a website which displays the report. Reports can be transmitted to a user interface device accessible by the user. The user interface device could be, for example, a personal computer, a laptop, a smart phone or a wearable device, e.g., a watch, for example worn on the wrist.

[0103] The invention will be more fully understood by reference to the following examples. They should not, however, be construed as limiting the scope of the invention. It is understood that the examples and embodiments described herein are for illustrative purposes only.

EXEMPLARY EMBODIMENTS

[0104] The following non-limiting enumerated exemplary embodiments are provided.

[0105] Embodiment 1-1. A method of classifying pregnancies as low, moderate, or higher risk, according to the risk of spontaneous preterm birth for a pregnant subject, wherein the method comprises:

(a) providing a microparticle-enriched fraction from a blood sample from the pregnant subject; (b) determining a quantitative measure of at least a first and second panels of microparticle-associated proteins in the fraction; and

(c) executing a classification model based on the quantitative measure of the first and second panels to determine whether the pregnant subject (i) is at a lower risk (LR) of spontaneous preterm birth; (ii) is at a moderate risk (MR) of spontaneous preterm birth; or (iii) is at a higher risk (HR) of spontaneous preterm birth.

[0106] Embodiment 1-2. A method of classifying pregnancies according to the risk of spontaneous preterm birth for a pregnant subject, wherein the method comprises:

(a) providing a microparticle-enriched fraction from a blood sample from the pregnant subject;

(b) determining a quantitative measure of at least a first and second panel of microparticle-associated proteins in the fraction;

(c) executing a classification model based on the quantitative measure of the first panel to determine whether the pregnant subject (i) is at a lower risk (LR) of spontaneous preterm birth before about 37 weeks of gestation, or (ii) is at an increased risk of spontaneous preterm birth before about 37 weeks of gestation, whereby determining the subject is at an increased risk of spontaneous preterm birth; and

(d) if it is determined in (c)(ii) that there is an increased risk of spontaneous preterm birth before about 37 weeks of gestation, then executing the classification model based on the quantitative measure of the second panel to determine that the pregnant subject either (i) is at a moderate risk (MR) of spontaneous preterm birth before about 37 weeks of gestation, or (ii) is at an higher risk (HR) of spontaneous preterm birth before about 37 weeks of gestation.

[0107] Embodiment 1-3. A method of predicting the gestational age at delivery of a fetus of a pregnant subject, wherein the method comprises: (a) providing a microparticle-enriched fraction from a blood sample from the pregnant subject;

(c) executing a classification model based on the quantitative measure of the first panel to determine whether the gestational age of the fetus (i) will likely be greater than about 37 weeks of gestation, or (ii) will likely be 37 weeks of gestation or lower; and

(d) if it is determined in (c)(ii) that the gestational age of the fetus will likely be 37 weeks of gestation or lower, then executing the classification model based on the quantitative measure of the second panel to determine whether the gestational age of the fetus (i) will likely between about 32 and about 37 weeks of gestation, or (ii) will likely be lower than about 32 weeks of gestation.

[0108] Embodiment 1-4. The method of any one of embodiments 1-1 to 1-3, wherein the first panel comprises 3, 4, 5, or more proteins.

[0109] Embodiment 1-5. The method of any one of embodiments 1-1 to 1-4, wherein the second panel comprises 3, 4, 5, or more proteins.

[0110] Embodiment 1-6. The method of any one of embodiments 1-1 to 1-5, wherein the first panel comprises any three or more of TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2.

[OHl] Embodiment 1-7. The method of any one of embodiments 1-1 to 1-5, wherein the second panel comprises any three or more of TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2.

[0112] Embodiment 1-8. The method of any one of embodiments 1-1 to 1-5, wherein the first panel comprises HEMO, FBLN1, and ITIH2.

[0113] Embodiment 1-9. The method of any one of embodiments 1-1 to 1-5, wherein the second panel comprises TRFE, IC1, ITIH4, and LCAT. [0114] Embodiment 1-10. The method of any one of embodiments 1-1 to 1-5, wherein the first panel consists of HEMO, FBLN1, and ITIH2.

[0115] Embodiment 1-11. The method of any one of embodiments 1-1 to 1-5, wherein the second panel consists of TRFE, IC1, ITIH4, and LCAT.

[0116] Embodiment 1-12. The method of any one of embodiments 1-1 to 1-11, wherein the quantitative measure of the first or second panel comprises inclusion of the covariate of maternal body mass index (BMI).

[0117] Embodiment 1-13. The method of any one of embodiments 1-1 to 1-12, wherein the pregnant subject is multiparous.

[0118] Embodiment 1-14. The method of any one of embodiments 1-1 to 1-12, wherein the pregnant subject is primiparous.

[0119] Embodiment 1-15. The method of any one of embodiments 1-1 to 1-12, wherein the pregnant subject is multigravida.

[0120] Embodiment 1-16. The method of any one of embodiments 1-1 to 1-12, wherein the pregnant subject is primigravida.

[0121] Embodiment 1-17. The method of any one of embodiments 1-1 to 1-16, wherein the blood sample is taken from the pregnant subject when the pregnant subject is at about 10 to about 12 weeks of gestation.

[0122] Embodiment 1-18. The method of any one of embodiments 1-1 to 1-16, wherein a blood sample is taken from the pregnant subject during the first trimester of gestation.

[0123] Embodiment 1-19. The method of any one of embodiments 1-1 to 1-16, wherein the steps of the method are carried out on a first sample taken from the pregnant subject during the first trimester, and the steps of the method are repeated on a second sample taken from the pregnant subject during the second trimester.

[0124] Embodiment 1-20. The method of any one of embodiments 1-1 to 1-16, wherein the steps of the method are carried out on a first sample taken from the pregnant subject at about 8 to about 12 weeks of gestation, and the steps of the method are repeated on a second sample taken from the pregnant subject at about 18 to about 24 weeks of gestation.

[0125] Embodiment 1-21. The method of any one of embodiments 1-1 to 1-16, wherein the steps of the method are carried out on a first sample taken from the pregnant subject at about 10 to about 12 weeks of gestation, the steps of the method are repeated on a second sample taken from the pregnant subject during the second trimester.

[0126] Embodiment 1-22. The method of any one of embodiments 1-1 to 1-16, wherein the steps of the method are carried out on a first sample taken from the pregnant subject at about 10 to about 12 weeks of gestation, the steps of the method are repeated on a second sample taken from the pregnant subject at about 18 to about 24 weeks of gestation.

[0127] Embodiment 1-23. The method of any one of embodiments 1-1 to 1-22, wherein the blood sample is a serum sample.

[0128] Embodiment 1-24. The method of any one of embodiments 1-1 to 1-22, wherein the blood sample is a plasma sample.

[0129] Embodiment 1-25. The method of any one of embodiments 1-1 to 1-23, wherein the microparticle-enriched fraction is prepared using size-exclusion chromatography.

[0130] Embodiment 1-26. The method of embodiment 1-25, wherein the size-exclusion chromatography comprises elution with water.

[0131] Embodiment 1-27. The method of any one of embodiments 1-25 to 1-26, wherein the size-exclusion chromatography is performed with an agarose solid phase and an aqueous liquid phase.

[0132] Embodiment 1-28. The method of any one of embodiments 1-25 to 1-27, wherein the preparing step further comprises using ultrafiltration or reverse-phase chromatography.

[0133] Embodiment 1-29. The method of any one of embodiments 1-25 to 1-28, wherein the preparing step further comprises denaturation using urea, reduction using dithiothreitol, alkylation using iodoacetamine, and digestion using trypsin prior to the size exclusion chromatography.

[0134] Embodiment 1-30. The method of any one of embodiments 1-1 to 1-29, wherein the determining a quantitative measures of a panel of microparticle-associated proteins in the fraction comprises detection of peptides.

[0135] Embodiment 1-31. The method of any one of embodiments 1-1 to 1-30, wherein the determining a quantitative measure of a panel of microparticle-associated proteins in the fraction comprises mass spectrometry.

[0136] Embodiment 1-32. The method of embodiment 1-31, wherein the determining a quantitative measure of a panel of microparticle-associated proteins in the fraction comprises liquid chromatography/mass spectrometry.

[0137] Embodiment 1-33. The method of embodiment 1-32, wherein the mass spectrometry comprises multiple reaction monitoring, the liquid chromatography is performed using a solvent comprising acetonitrile, and/or the determining step comprises assigning an indexed retention time to the proteins.

[0138] Embodiment 1-34. The method of embodiment 1-31, wherein determining a quantitative measure of a panel of microparticle-associated proteins in the fraction comprises mass spectrometry/multiple reaction monitoring (MS/MRM).

[0139] Embodiment 1-35. The method of embodiment 1-34, wherein the MS/MRM involves the use of a plurality of stable isotope standards.

[0140] Embodiment 1-36. The method of any one of embodiments 1-1 to 1-35, wherein the determining comprises executing a classification rule, which rule classifies the subject at being at risk of spontaneous preterm birth as either lower risk (LR), moderate risk (MR), or higher risk (HR), and wherein execution of the classification rule produces a correlation between preterm birth or term birth with a p value of less than at least 0.05.

[0141] Embodiment 1-37. The method of any one of embodiments 1-1 to 1-36, wherein the method further comprises a treatment step. [0142] Embodiment 1-38. The method of embodiment 1-37, wherein the treatment step comprises the administration of a therapeutic agent selected from the group consisting of low- dose aspirin, tocolytics, a hormone, a complement-inhibitor, and a corticosteroid.

[0143] Embodiment 1-39. The method of embodiment 1-38, wherein the therapeutic agent comprises a hormone, wherein the hormone is optionally progesterone or 17-alpha- hydroxyprogesterone caproate.

[0144] Embodiment 1-40. The method of embodiment 1-37, wherein the treatment step is selected from the group consisting of: (a) increased surveillance by physician and nursing professionals via supplemental office visits and/or telehealth visits; (b) education for the patient regarding risk factors, symptoms, potential behavior and lifestyle modifications, planning for access to neonatal intensive care, usage of remote maternal and fetal monitoring devices, usage of doctor/patient computer/smartphone connectivity applications, and acute-stage decisions and medications; (c) a referral to a Maternal -Fetal Medicine physician that specializes in high-risk pregnancy care; (d) a referral to a preterm birth prevention clinic or provider that offers a holistic array of services for high-risk pregnancies; and (e) follow-up evaluations via cervical length monitoring, fetal fibronectin testing, serial testing, genomic testing, proteomic testing, or metabolomic testing.

[0145] Embodiment 1-41. A method comprising administering to a pregnant subject characterized as having a first panel and a second panel of microparticle-associated proteins indicative of an moderate risk (MR) or higher risk (HR) spontaneous preterm birth, an effective amount of a treatment designed to reduce the risk of spontaneous preterm birth, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2, and the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, LCAT.

[0146] Embodiment 1-42. The method of embodiment 1-41, wherein the treatment is selected from the group consisting of low-dose aspirin, tocolytics, a hormone, a complement-inhibitor, and a corticosteroid. [0147] Embodiment 1-43. The method of embodiment 1-41, wherein the treatment comprises vaginal progesterone or parenteral 17-alpha-hydroxyprogesterone caproate.

[0148] Embodiment 1-44. The method of embodiment 1-41, wherein the treatment step is selected from the group consisting of: (a) increased surveillance by physician and nursing professionals via supplemental office visits and/or telehealth visits; (b) education for the patient regarding risk factors, symptoms, potential behavior and lifestyle modifications, planning for access to neonatal intensive care, usage of remote maternal and fetal monitoring devices, usage of doctor/patient computer/smartphone connectivity applications, and acute-stage decisions and medications; (c) a referral to a Maternal -Fetal Medicine physician that specializes in high-risk pregnancy care; (d) a referral to a preterm birth prevention clinic or provider that offers a holistic array of services for high-risk pregnancies; and (e) follow-up evaluations via cervical length monitoring, fetal fibronectin testing, serial testing, genomic testing, proteomic testing, or metabolomic testing.

[0149] Embodiment 1-45. The method of any one of embodiments 1-41 to 1-44, wherein the pregnant subject is primiparous.

[0150] Embodiment 1-46. The method of any one of embodiments 1-41 to 1-45, wherein the blood sample is taken from the pregnant subject when the pregnant human subject is at about 10 to about 12 weeks of gestation.

[0151] Embodiment 1-47. The method of any one of embodiments 1-41 to 1-46, wherein method comprises measuring the covariate of maternal body mass index (BMI).

[0152] Embodiment 1-48. A method for measuring a protein panel, comprising: a. preparing a sample comprising proteins from a microparticle-enriched fraction of a blood sample; b. performing protease digestion on the proteins to produce peptide fragments; c. contacting the peptide fragments with a plurality of isotope-labeled reference peptides; d. determining a quantitative measure of a first panel of microparticle-associated proteins in the fraction, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2; and e. optionally determining a quantitative measure of a second panel of microparticle- associated proteins in the fraction, wherein the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, and LCAT.

[0153] Embodiment 1-49. The method of any of embodiments 1-48 comprising using MS/MRM to perform the method.

[0154] Embodiment 1-50. The method of any of embodiments 1-48 to 1-49, wherein the blood sample comprises a plasma sample.

[0155] Embodiment 1-51. The method of any of embodiments 1-48 to 1-49, wherein the blood sample comprises a serum sample.

[0156] Embodiment 1-52. The method of any of embodiments 1-48 to 1-51, wherein the blood sample is from a subject, and the subject is a pregnant subject who is at about 8 to about 14 weeks of gestation.

[0157] Embodiment 1-53. The method of any of embodiments 1-48 to 1-51, wherein the blood sample is from a subject, and the subject is a pregnant subject who is at about 10 to about 12 weeks of gestation.

[0158] Embodiment 1-54. The method of any of embodiments 1-48 to 1-51, wherein the blood sample is from a subject, and the subject is a pregnant subject who is primiparous.

[0159] Embodiment 1-55. A method for measuring a protein panel, comprising: a. preparing a microparticle-enriched fraction from a blood sample of a subject; and b. determining a quantitative measure of a first panel of microparticle-associated proteins in the fraction, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2; and c. optionally determining a quantitative measure of a second panel of microparticle- associated proteins in the fraction, wherein the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, and LCAT, and wherein the determining comprises measuring surrogate peptides of the proteins.

[0160] Embodiment 1-56. The method of embodiment 1-55, wherein the blood sample comprises a plasma sample.

[0161] Embodiment 1-57. The method of embodiment 1-55, wherein the blood sample comprises a serum sample.

[0162] Embodiment 1-58. The method of any of embodiments 1-55 to 1-57, wherein the subject is a pregnant subject who is at about 8 to about 14 weeks of gestation.

[0163] Embodiment 1-59. The method of any of embodiments 1-55 to 1-57, wherein the subject is a pregnant subject who is at about 10 to about 12 weeks of gestation.

[0164] Embodiment 1-60. The method of any of embodiments 1-55 to 1-59, wherein the subject is a pregnant subject who is primiparous.

[0165] Embodiment 1-61. The method of any of embodiments 1-55 to 1-59, wherein the subject is a pregnant subject who is multiparous.

[0166] Embodiment 1-62. A computer system comprising: a. a processor; and b. a memory, coupled to the processor, the memory storing a module comprising: (i) test data for a sample from a subject including values indicating a quantitative measure of a first panel and a second panel of protein biomarkers, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2, and the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, LCAT;

(ii) a classification rule which, based on values including the measurements, classifies the subject as being at lower risk (LR), moderate risk (MR), or higher risk (HR) for spontaneous preterm birth, wherein the classification rule is configured to have a sensitivity of at least 75%, at least 85% or at least 95% ; and

(iii) computer executable instructions for implementing the classification rule on the test data.

[0167] Embodiment 1-63. The computer system of embodiment 1-62, wherein the test data of (i) comprises a quantitative measure of the covariate of maternal body mass index (BMI).

[0168] Embodiment 1-64. A machine learning method comprising: a. providing a microparticle-enriched fraction from plasma or serum of a plurality of pregnant subjects obtained at from about 8 to about 14 weeks of gestation, wherein the plurality of subjects include a plurality of subjects that subsequently experienced preterm birth and a plurality of subjects that subsequently experienced term birth; b. using selected reaction monitoring mass spectrometry, determining a quantitative measure of a first panel and a second panel of proteins in the fraction, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2, and the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, LCAT; c. generating a training data set indicating, for each sample, values indicating:

(i) classification of the sample as belonging to one of lower risk (LR), moderate risk (MR), or higher risk (HR) of birth at about earlier than 37 weeks; and

(ii) the quantitative measures of the plurality of protein biomarkers; and d. training a classification model on the training data set, wherein training generates one or more classification rules that classify a sample as belonging to the LR, MR, HR class.

[0169] Embodiment 1-65. The method of embodiment 1-64, wherein the quantitative measures of (c)(ii) comprises a quantitative measure of the covariate of maternal body mass index (BMI).

EXAMPLES

Example 1: First Trimester Preterm Birth Risk - Three Tier Risk Stratification

Materials and Methods

[0170] CMP Enrichment.. Circulating microparticles (“CMP”) were enriched by Size Exclusion Chromatography (SEC). The columns were packed with 4% Sepharose 4B Crosslinked (bead size Bead size range 45-165 um, pore size range ~42-70nm) from Cytiva (Marlborough, MA) (or 2% is for 2B-CL beads) to a total packed volume of lOmL. The columns were equilibrated with distilled, deionized water (“ddH2O”). 0.5 mL of plasma was applied and allowed to incorporate into the column. The plasma samples were not filtered, diluted, or pretreated prior to application to the columns. Following the incorporation of the sample into the column, ddH2O was added and 0.5 mL column fractions were collected. The eluted fractions yielded two peaks. The CMPs were captured in the column void volume and resolved from the high abundant soluble protein peak.

[0171] Liquid Chromatography-Mass Spectrometry. Quantitative proteomic liquid chromatography-mass spectrometry (LC-MS) analysis was performed. Briefly, for each sample 50 pg of total protein was denatured with 8M urea, reduced using dithiothreitol, alkylated with iodoacetamide, and digested overnight with trypsin (ThermoFisher Scientific, Waltham, MA)). 10 pL of acetic acid was added to each sample to quench further trypsin digestion. Resulting samples were cleaned using low protein binding, filter tube, spin columns by spinning them for 30 minutes at 15000 RPM in a centrifuge. 1.5 pL Internal Standard (heavy isotope-labeled synthetic tryptic peptides) was then added to each eluent sample from a spin column, and then 200 pL of that spin column sample was added to injection glass vials for LC-MSMS analysis.

[0172] Then 40 pL of sample was injected into a Reverse Phased Column C-18 (2.1 mm diameter and 25 cm column length) from ThermoFisher Scientific, Waltham, MA. The column packing material was Ultrapure Silica, 2.2 pm particle size, 120 A pore size). Before the samples were injected into the C-18 Reverse Phased Column, they were cleaned using the Trap Column Online Solid Phase Extraction System. LC-MS-MRM assays analysis was done on Thermo Scientific Vanquish UHPLC system connected to TSQ Altis™ triple quadrupole mass spectrometer equipped with electrospray ionization source. The LC-MS-MRM analysis was done with linear gradient using Solvent A (LC-MS Grade Water with 0.1% Formic Acid) and Solvent B (LC-MS Grade Acetonitrile with 0.1% Formic Acid). The total LC-MSMS method was for 13 minutes. Signal processing and data analysis was carried out using ThermoFisher’s TraceFinder™ software.

Study Design

[0173] This study used a nested case-control set of 240 plasma samples from subjects included in the NICHD-supported, multi-center nuMoM2b Study who had blood draws between 10 - 12 weeks’ gestation. The characteristics of the pregnant subjects of the nuMoM2b can be found in Haas et al. (American Journal of Obstetrics and Gynecology, April 2015; 212:539. el-24). The NICHD “nuMoM2b” cohort of 9,559 subjects is one of the largest prospectively collected biorepository of pregnancy samples known; these are all “first time moms,” (nulliparous) representing a difficult group to risk stratify.

[0174] Nulliparous women with singleton pregnancies were included for the study. Among them, 120 had a final gestational age (FGA) < 37 weeks. The SPTB markers comprise the IC1, ITIH2, ITIH4, LCAT, TRFE, FBLN1, and HEMO proteins. Top performing models in Monte Carlo (MC) cross-validation were combined into a single “rule-out” model and reapplied to the whole sample set to separate a lower-risk (LR) group. Similarly, multivariate models including models previously trained (prior cohort) were used in conjunction with BMI to further divide the remaining samples into higher risk (HR) and moderate risk (MR) groups. The results were evaluated by time-to-event (SPTB) analysis adjusted for weekly SPTB prevalence base on the nuMoM2b cohort.

Results

[0175] FIG. 1 lists the fetal gestational age (FGA) distributions per risk groups among the 240 subjects in the study. At 20 weeks, the 240 subjects were classified into lower risk (n=84), moderate risk (n=105), and higher risk (n=51). None of them has had any preterm birth event yet. The HR and MR groups captured 18 and 15 of the 36 very preterm cases (FGA < 32 weeks), respectively. To estimate the prevalence-adjusted performance of the rule-out and rule-in models in real clinical setting, the time-to-event Kaplan-Meier curves from the 240 subjects were projected onto the nuMoM2b cohort through Monte-Carlo simulation using the weekly FGA data of births of the nuMoM2b cohort. This was done by repeatedly sampling the 240 samples based on distribution of weekly SPTBs in the nuMoM2b cohort. FIG. 2 compares the Kaplan- Meier curves for one simulated sample set. The proportions of the LR, MR, and HR groups were 49.4%, 36.4%, and 14.2%. Monte Carlo-simulation estimated hazard ratios of the HR or MR group over the LR group were 16.8 or 7.2, respectively, for SPTB < 32 weeks’ gestation, and 5.0 or 3.2, respectively, for SPTB < 37 weeks’ gestation.

Conclusion

[0176] The circulating microparticle biomarker models demonstrated continued potential as a first-trimester, risk stratification tool to predict risk of SPTBs using circulating microparticles collected between 10 - 12 weeks’ gestation. As indicated in FIG. 4, the segregation of pregnant women into the three risk groups with striking difference in rate of pre-term births, whether defined at <32 weeks, or <37 weeks, will indicated clinically actionable interventions to improve overall outcome of pregnancies. For example, as applied to pregnant patients, the strategy could be used to personalize care plans for the relatively common great diseases of obstetrics, such as preterm labor and preeclampsia, by segmenting the patients into a high-risk level and a rising (medium) risk level, while distinguishing a significant proportion of patients that are at lower risk who do not require a high-intensity support. Example 2: First Trimester Preterm Birth Risk - Three Tier Risk Stratification

[0177] This study was repeated using a 2:3 nested case-control set of 400 plasma samples from the nuMoM2b Study, who had blood draws between 10 - 12 weeks gestation. Cases (n = 160) were defined as SPTB < 37 weeks gestation, the remaining subjects (n = 240) were used as controls. By design, the cases were enriched for low gestation SPTBs with block-randomized selection, including 40 cases of SPTB at < 32 weeks, 40 cases of SPTB between 32 and 34 weeks inclusively, and 80 cases of SPTB at 35 or 36 weeks. The controls were matched by gestational age at sampling time, maternal age, and race/ethnicity. According to the World Health Organization (WHO) defined sub-categories 15, the selected cases in the sample set included 40 extremely or very preterm and 120 moderate-to-late preterm cases.

[0178] The selection of cases was block-randomized to satisfy a pre-defined number of cases in SPTB subcategories and matched for gestational age at sampling time points (+/- 2 weeks), maternal age (+/- 2 years), and race/ethnicity. Individual sites were required to contribute both cases and controls to avoid site biases confounding with sample phenotypes. CMPs were enriched by size exclusion Chromatography as described above in Example 1. Liquid Chromatograph-Mass Spectrometry was performed on the samples as described above in Example 1.

[0179] The 400 samples were processed and analyzed in block-randomized order stratified for cases and controls. Plasma CMP protein biomarkers comprising HEMO, FBLN1, ITIH2, TRFE, IC1, ITIH4, and LCAT were measured by targeted selected reaction monitoring mass spectrometry (SRM-MS) in two steps, each included 160 and 240 samples, respectively. The 160 subjects in step 1 samples were originally planned for model development. An additional 81 samples randomly selected from step 2 with stratification on gestation at delivery were added to the step 1 samples to enhance the coverage and statistical power of a final training sample set for IVDMIA model derivation. The remaining 159 subjects in Step 2 were used as an independent validation sample set.

Model Derivation and Statistical Analysis

[0180] Input variables to the in vitro diagnostic, multivariate index assay (“IVDMIA”) model included HEMO, FBLN1, ITIH2, TRFE, IC1, ITIH4, and LCAT, and BMI, as described above. Missing BMI values for 11 out of the 400 subjects were imputed with the constant 25 kg/m2. Protein biomarker measurements were rescaled by median absolute deviation normalization. BMI was normalized by division by 25.

[0181] Optimal model structures and training hyperparameters were determined through extensive Monte Carlo cross-validation within the training dataset. The final derived IVDMIA included two multivariate models that are applied sequentially to stratify the test populations into 3-tiered risk categories: a rule-out model identifies a subset of test population as low-risk (LR), a second rule-in model identifies a small portion of the remaining test population as high-risk (HR). The remaining subjects are labeled as moderate risk (MR). The rule-out model was by design trained to achieve a high-sensitivity and hence a high negative predictive value (NPV) for subjects classified as LR. On the other hand, the rule-in model was aimed to capture a clinically meaning proportion of the SPTBs while maintaining a high specificity and hence a high positive predictive value (PPV) for subjects classified as HR.

[0182] Clinical performances of the IVDMIA were evaluated by time-to-events (births) analysis with comparison among the 3-tiered SPTB risk categories. Similar to that of survival analysis used to compare multiple treatment arms with respect to overall survival time, Kaplan- Meier plots from time-to-events analysis and corresponding risk tables were compared among the validation samples in the three IVDMIA predicted SPTB risk categories and tested for statistical significance by Log Rank test. However, the validation sample set itself was a casecontrol set where the distribution of gestation at delivery does not represent a true test population intended for the IVDMIA. In order to predict the clinical performance of the IVDMIA for its intended test population, the validation sample set was repeatedly sampled 9,559 times (the number of subjects in the nuMoM2b cohort) with replacement by probability sampling using the actual distribution of gestation at delivery of the entire nuMoM2b cohort. Results from Kaplan- Meier plots and risk tables from this Monte Carlo (MC) simulation sample set were then used to predict the clinical performance of the IVDMIA. Risk tables from 500 replicates of the same MC simulation analysis were aggregated to compute point estimates and confidence intervals of the risk table entries and additional calculated performance metrics. Among them, percent cumulative events represent the proportion of subjects in an IVDMIA-predicted SPTB risk group who had a SPTB during or before a given gestation week. It is therefore also the post-test prevalence or positive predictive value of SPTB during or before a given gestation week. Other clinically meaningful performance metrics are the risk ratios between HR and LR, or MR and LR of SPTB at or before a given gestation week.

[0183] Clinical tests are typically evaluated by receiver-operating characteristic (ROC) curve analysis for their overall diagnostic performance. However, SPTB is not a typical clinical classification with binary clinicopathologic features. It is rather a continuum of events prior to a cutoff on gestation at delivery. The shape and area-under-curve (AUC) from ROC analysis will change with SPTB defined at varying cutoffs on gestation weeks. ROC analysis therefore is not always necessarily appropriate for assessing a test for SPTB.

[0184] Technically, a 3-tiered stratification IVDMIA with two internal models is not very conducive to ROC analysis. However, as the rule-out and rule-in models in the IVDMIA are applied sequentially, ROC analysis was possible by performing ROC analysis using the rule-out model first, and a second ROC analysis was using only the samples that were not assigned to LR. The ROC curves were then “fused” together by using only the portion of the first ROC curve corresponding to the rule-out portion of the samples, and the second ROC curve rescaled based on the sensitivity and specificity of the rule-out model at its cutoff point. Figures 9A-9D show four fused ROC curves with different SPTB gestation definitions including pre-term birth of less than 32 weeks, pre-term birth of less than 34 weeks, pre-term birth of less than 35 weeks, and pre-term birth of less than 36 weeks. Statistical and model development calculations were carried out in the R statistical computational environment (version 2021.9.0.351)

Results

[0185] The demographic and clinical characteristics of the study population are tabulated by SPTB groups in Table 2.

Table 2. Demographic and clinical characteristics of study population. Spontaneous preterm birth (“SPTB”) is defined below and Full-term (FT) defined as gestation at delivery > 37 weeks.

[0186] All mothers from the cohort had a parity of 0 as per the design of the nuMoM2b study; the variables of maternal age, gestational age at sample collection, race, maternal BMI, smoking, and fetal sex did not differ with significance among SPTB/control groups as they were controlled for by study inclusion and exclusion criteria and/or covariate stratification during selection. As expected, the difference in gestation at delivery was extremely significant (p < 0.0001). The distribution of SPTB groups and controls between the training and validation sample sets are described in Figure 6. It should be noted that the two potential risk factors of maternal BMI and ever-smoker status, were somewhat associated with SPTB/control grouping in the training sample set (p values = 0.0181 and 0.0489, respectively). The associations were, however, not significant in the validation set (p values = 0.5005 and 0.2523, respectively).

[0187] The effectiveness of this 3-tiered stratification was visualized using Kaplan-Meier time-to-event plots as depicted in Figure 7. The differences among the three risk categories were statistically significant for both training data (p < 0.0001, Log Rank test) (FIG. 7 left) and validation data (p = 0.0032) (FIG. 7 right).

[0188] The risk table of Table 3 A provides actual counts of SPTB events over selected gestation weeks at delivery. N at Risk indicates the number of subjects in a risk group at the beginning of a particular gestation week, yet Cumulative Events is the group’s cumulative number of births at the end of the week Table 3A. Risk table of validation sample set from time-to-event (birth) analysis comparing birth events among model-predicted 3-tiered risk categories.

[0189] It was observed that for both the training sample set and the validation sample set, a significant portion of extreme or very SPTBs at gestation < 28 weeks were captured by HR.

[0190] In Table 3B, the birth events in the validation samples among the IVDMIA assigned risk groups are tabulated according to WHO pre-term birth sub-categories show the correlation between IVDMIA risk categories and WHO SPTB subcategories (p = 0.098, Fisher exact test) and an even stronger correlation when the full-term birth samples are included in analysis (p = 0.010).

Table 3B. Birth events in validation samples tabulated according to WHO pre-term birth sub- categories and full-term birth (> 37 weeks).

[0191] To estimate the performance of this 3-tiered, stratification IVDMIA in its intended population, the results from the validation sample set needed to be adjusted according to the actual SPTB incidence rate per final gestation week at delivery of the nuMoM2b cohort. This was done through Monte Carlo (MC) simulation in which the validation samples were resampled with replacement at a fixed gestation week at delivery-dependent probability estimated from the nuMoM2b cohort. Figure 8 shows the prevalence-adjusted Kaplan-Meier plots of the three risk categories using the mean event counts from 500 MC simulations. The differences among the three risk categories remain statistically significant (p < 0.0001, Log Rank test).

[0192] Table 4A is the risk table aggregated from the 500 MC simulations with estimated means and 2.5 and 97.5 percentiles for the predicted SPTB risk categories. In the MC analysis, the validation sample set was repeatedly resampled based on gestation at delivery and the nuMoM2b cohort week-by-week SPTB prevalence data, the selected samples - with distribution of weekly prevalence of SPTBs adjusted to follow that of the nuMoM2b cohort — are used to estimate the projected model performance onto the nuMoM2b cohort. Mean and percentiles were estimated through 500 Monte Carlo simulations.

Table 4A. Monte Carlo (MC)-simulation estimated risk table comparing time-to-events (births) among model-predicted 3-tiered risk categories using the validation sample set adjusted for nuMoM2b cohort birth rate per gestation week at delivery.

[0193] Table 4B lists the estimated mean and (2.5, 97.5) percentiles of percentage cumulative events among the IVDMIA assigned risk groups, along with risk ratios of HR over LR, MR over LR. In order compare with other clinical risk factors, risk ratios of HR over (LR + MR) and LR over (MR + HR) were also included, representing the positive likelihood ratio of HR and the negative likelihood ratio of LR, respectively. Table 4B. Results from Monte Carlo (MC)-simulation using validation data with adjustment for nuMoM2b cohort prevalence in model-predicted 3-tiered risk categories tabulated according to the WHO defined preterm birth subcategories and full-term births (> 37 weeks).

Table 4C (both panels). Percentage cumulative events, risk ratios of HR/LR, MR/LR, HR/(LR + MR), and LR/(MR + HR) estimated based on risk table from 500 Monte Carlo simulations. The latter two risk ratios are equivalent to positive likelihood ratio of HR, and negative likelihood ratio of LR.

*Equivalent to positive likelihood ratio for higher risk; **Equivalent to negative likelihood ratio for lower risk.

[0194] Finally, Table 4C tubulates the mean event counts from MC simulations among the three IVDMIA risk groups according to WHO SPTB subcategories, again showing significant correlation in both SPTBs only (p < 0.0005) and in all samples (p < 0.0005).

[0195] The incidence rate-adjusted proportions of HR, MR, and LR in Table 4A were 8.6%, 59.8%, and 31.6% respectively. The small proportion of subjects in HR was able to capture 70.0% (28/40) of the extremely preterm cases (< 28 weeks). Considering the clinically critical gestation time of < 35 weeks, the nuMoM2b cohort had a pre-test baseline incidence rate of 5.6%. In comparison, the post-test risks of SPTB < 35 weeks for the HR, MR, and LR risk groups were 13.5% (95% CI: 11.4-15.8%), 6.0% (95% CI: 5.4-6.7%), and 1.4% (95% CI: 1.0- 1.8%), respectively. The corresponding risk ratios of HR/LR and MR/LR were 9.65 (95% CI: 6.95-13.36), 4.31 (95% CI: 3.19-6.00), respectively. The rule-out LR group had a negative likelihood ratio of 0.21 (95% CL 0.15-0.27) and the rule-in HR group had a positive likelihood ratio of 3.06 (95% CI: 2.50-3.72).

[0196] Due to the non-binary nature of SPTB clinically, the three-tiered IVDMIA model was not designed to optimize the area-under-curve (AUC) of a traditional single receiver-operating characteristic (ROC) curve with a binary outcome. It was instead designed to first establish a “rule-out low risk” cutoff point with high sensitivity, and then to employ a second cutoff point to effectively “rule-in high risk” patients with high specificity. In FIGS. 9A-9D, we nevertheless constructed ROC curves with respect to “cases” and “controls” using cutoffs at gestation weeks at delivery of < 32 weeks, <34 weeks, < 35 weeks, and < 36 weeks (in FIGS. 9A-9D respectively). The corresponding AUCs were 69.7% (95% CI: 61.7-79.6%), 71.2% (95% CI: 68.6-77.3%), 69.2% (95% CI: 68.7-74.6%), and 68.4% (95% CI: 68.7-73.0%), respectively. Each AUC curve includes an upper “Rule Out” marker and a lower “Rule In” marker, wherein each include a specificity value (“ Sp”) and a sensitivity value (“Se”). The “Rule Out” marker indicates that the patients with the lowest risk are ruled out with approximately between 86.7% sensitivity with gestation weeks at delivery of < 32 weeks (FIG. 9A) to the highest of 90.5 % sensitivity with gestation weeks at delivery of < 35 weeks (FIG. 9C). The “Rule In” marker indicates that the patients with the highest risk are ruled in from the remainder of the group with approximately between 91.0% specificity with gestation weeks at delivery of < 32 weeks (FIG. 9A) and 92.6% specificity with gestation weeks at delivery of < 36 weeks (FIG. 9D). The “Rule Out” marker and “Rule In” marker for each AUC curve correspond to a high negative predictive value for LR and a higher positive predictive value for HR. These results, without excluding any subjects from analysis, should be viewed in context of the clinical observation that the continuum of gestation time for SPTBs does not resemble any bimodal distributions.

[0197] In the current study, it was demonstrated that the previously reported, CMP-derived set of biomarkers, collected from blood samples as early as 9-13 weeks, continue to show a potential as a first-trimester, risk stratification tool to predict the risk of SPTB in first-time and primiparous mothers. This biomarker set was developed as an in vitro diagnostic, multivariate index assay (IVDMIA) based on 7 CMP protein biomarkers that comprise two multivariate models working in tandem to first “rule-out” low-risk SPTB subjects followed by a “rule-in” step to identify patients at the highest risk for SPTB, especially very early SPTB. This strategy results in a three-tiered clinical stratification of pregnant women for risk of SPTB into LR, MR, or HR categories. As shown in FIG. 8 and Tables 4A and 4C, the segregation of pregnant women into the three risk groups with statistically significant difference even after adjustment for prevalence in Kaplan-Meier curves representing time-to-events (births) cumulative distribution patterns. The striking differences in post-test rate of pre-term births among the three- tied risk groups with SPTBs defined at multiple critical gestation weeks at delivery indicates that clinically actionable interventions are possible to potentially improve overall outcome of pregnancies and enable the streamlined clinical management of pregnant subjects. The negative likelihood ratio of LR for rule-out and the positive likelihood ratio of HR for rule-in at gestation weeks at delivery for SPTB case defined as <32 weeks or <35 weeks represent clinically meaningful decrease or increase in SPTB risks. These likelihood ratios were in general higher than those reported in the literature for many of the general maternal health factors, obstetric history, and anatomy/biomarkers. Even more differentiating likelihood ratios were observed for the extreme preterm births. However, with its very low prevalence, further validation with larger studies will be needed to assess the stability of the results and the net clinical implication.

Claims

1. A method of classifying pregnancies as lower, moderate, or higher risk, according to the risk of spontaneous preterm birth for a pregnant subject, wherein the method comprises:

(b) determining a quantitative measure of at least a first and second panels of microparticle-associated proteins in the fraction; and

2. A method of classifying pregnancies according to the risk of spontaneous preterm birth for a pregnant subject, wherein the method comprises:

3. A method of predicting the gestational age at delivery of a fetus of a pregnant subject, wherein the method comprises:

4. The method of any one of claims 1 to 3, wherein the first panel comprises 3, 4, 5, or more proteins.

5. The method of any one of claims 1 to 4, wherein the second panel comprises 3, 4, 5, or more proteins.

6. The method of any one of claims 1 to 5, wherein the first panel comprises any three or more of TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2.

7. The method of any one of claims 1 to 5, wherein the second panel comprises any three or more of TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2.

8. The method of any one of claims 1 to 5, wherein the first panel comprises HEMO, FBLN1, and ITIH2.

9. The method of any one of claims 1 to 5, wherein the second panel comprises TRFE, IC1, ITIH4, and LCAT.

10. The method of any one of claims 1 to 5, wherein the first panel consists of HEMO, FBLN1, and ITIH2.

11. The method of any one of claims 1 to 5, wherein the second panel consists of TRFE, IC1, ITIH4, and LCAT.

12. The method of any one of claims 1 to 11, wherein the quantitative measure of the first or second panel comprises inclusion of the covariate of maternal body mass index (BMI).

13. The method of any one of claims 1 to 12, wherein the pregnant subject is multiparous.

14. The method of any one of claims 1 to 12, wherein the pregnant subject is primiparous.

15. The method of any one of claims 1 to 12, wherein the pregnant subject is multigravida.

16. The method of any one of claims 1 to 12, wherein the pregnant subject is primigravida.

17. The method of any one of claims 1 to 16, wherein the blood sample is taken from the pregnant subject when the pregnant subject is at about 10 to about 12 weeks of gestation.

18. The method of any one of claims 1 to 16, wherein a blood sample is taken from the pregnant subject during the first trimester of gestation.

19. The method of any one of claims 1 to 16, wherein the steps of the method are carried out on a first sample taken from the pregnant subject during the first trimester, and the steps of the method are repeated on a second sample taken from the pregnant subject during the second trimester.

20. The method of any one of claims 1 to 16, wherein the steps of the method are carried out on a first sample taken from the pregnant subject at about 8 to about 12 weeks of gestation, and the steps of the method are repeated on a second sample taken from the pregnant subject at about 18 to about 24 weeks of gestation.

21. The method of any one of claims 1 to 16, wherein the steps of the method are carried out on a first sample taken from the pregnant subject at about 10 to about 12 weeks of gestation, the steps of the method are repeated on a second sample taken from the pregnant subject during the second trimester.

22. The method of any one of claims 1 to 16, wherein the steps of the method are carried out on a first sample taken from the pregnant subject at about 10 to about 12 weeks of gestation, the steps of the method are repeated on a second sample taken from the pregnant subject at about 18 to about 24 weeks of gestation.

23. The method of any one of claims 1 to 22, wherein the blood sample is a serum sample.

24. The method of any one of claims 1 to 22, wherein the blood sample is a plasma sample.

25. The method of any one of claims 1 to 23, wherein the microparticle-enriched fraction is prepared using size-exclusion chromatography.

26. The method of claim 25, wherein the size-exclusion chromatography comprises elution with water.

27. The method of any one of claims 25 to 26, wherein the size-exclusion chromatography is performed with an agarose solid phase and an aqueous liquid phase.

28. The method of any one of claims 25 to 27, wherein the preparing step further comprises using ultrafiltration or reverse-phase chromatography.

29. The method of any one of claims 25 to 28, wherein the preparing step further comprises denaturation using urea, reduction using dithiothreitol, alkylation using iodoacetamine, and digestion using trypsin prior to the size exclusion chromatography.

30. The method of any one of claims 1 to 29, wherein the determining a quantitative measures of a panel of microparticle-associated proteins in the fraction comprises detection of peptides.

31. The method of any one of claims 1 to 30, wherein the determining a quantitative measure of a panel of microparticle-associated proteins in the fraction comprises mass spectrometry.

32. The method of claim 31, wherein the determining a quantitative measure of a panel of microparticle-associated proteins in the fraction comprises liquid chromatography/mass spectrometry.

33. The method of claim 32, wherein the mass spectrometry comprises multiple reaction monitoring, the liquid chromatography is performed using a solvent comprising acetonitrile, and/or the determining step comprises assigning an indexed retention time to the proteins.

34. The method of claim 31, wherein determining a quantitative measure of a panel of microparticle-associated proteins in the fraction comprises mass spectrometry/multiple reaction monitoring (MS/MRM).

35. The method of claim 34, wherein the MS/MRM involves the use of a plurality of stable isotope standards.

36. The method of any one of claims 1 to 35, wherein the determining comprises executing a classification rule, which rule classifies the subject at being at risk of spontaneous preterm birth as either lower risk (LR), moderate risk (MR), or higher risk (HR), and wherein execution of the classification rule produces a correlation between preterm birth or term birth with a p value of less than at least 0.05.

37. The method of any one of claims 1 to 36, wherein the method further comprises a treatment step.

38. The method of claim 37, wherein the treatment step comprises the administration of a therapeutic agent selected from the group consisting of low-dose aspirin, tocolytics, a hormone, a complement-inhibitor, and a corticosteroid.

39. The method of claim 38, wherein the therapeutic agent comprises a hormone, wherein the hormone is optionally progesterone or 17-alpha-hydroxyprogesterone caproate.

40. The method of claim 37, wherein the treatment step is selected from the group consisting of: (a) increased surveillance by physician and nursing professionals via supplemental office visits and/or telehealth visits; (b) education for the patient regarding risk factors, symptoms, potential behavior and lifestyle modifications, planning for access to neonatal intensive care, usage of remote maternal and fetal monitoring devices, usage of doctor/patient computer/ smartphone connectivity applications, and acute-stage decisions and medications; (c) a referral to a Maternal-Fetal Medicine physician that specializes in high-risk pregnancy care; (d) a referral to a preterm birth prevention clinic or provider that offers a holistic array of services for high-risk pregnancies; and (e) follow-up evaluations via cervical length monitoring, fetal fibronectin testing, serial testing, genomic testing, proteomic testing, or metabolomic testing.

41. A method comprising administering to a pregnant subject characterized as having a first panel and a second panel of microparticle-associated proteins indicative of an moderate risk (MR) or higher risk (HR) spontaneous preterm birth, an effective amount of a treatment designed to reduce the risk of spontaneous preterm birth, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2, and the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, LCAT.

42. The method of claim 41, wherein the treatment is selected from the group consisting of low-dose aspirin, tocolytics, a hormone, a complement-inhibitor, and a corticosteroid.

43. The method of claim 41, wherein the treatment comprises vaginal progesterone or parenteral 17-alpha-hydroxyprogesterone caproate.

44. The method of claim 41, wherein the treatment step is selected from the group consisting of (a) increased surveillance by physician and nursing professionals via supplemental office visits and/or telehealth visits; (b) education for the patient regarding risk factors, symptoms, potential behavior and lifestyle modifications, planning for access to neonatal intensive care, usage of remote maternal and fetal monitoring devices, usage of doctor/patient computer/ smartphone connectivity applications, and acute-stage decisions and medications; (c) a referral to a Maternal-Fetal Medicine physician that specializes in high-risk pregnancy care; (d) a referral to a preterm birth prevention clinic or provider that offers a holistic array of services for high-risk pregnancies; and (e) follow-up evaluations via cervical length monitoring, fetal fibronectin testing, serial testing, genomic testing, proteomic testing, or metabolomic testing.

45. The method of any one of claims 41 to 44, wherein the pregnant subject is primiparous.

46. The method of any one of claims 41 to 45, wherein the blood sample is taken from the pregnant subject when the pregnant human subject is at about 10 to about 12 weeks of gestation.

47. The method of any one of claims 41 to 46, wherein method comprises measuring the covariate of maternal body mass index (BMI).

48. A method for measuring a protein panel, comprising: a. preparing a sample comprising proteins from a microparticle-enriched fraction of a blood sample; b. performing protease digestion on the proteins to produce peptide fragments; c. contacting the peptide fragments with a plurality of isotope-labeled reference peptides; d. determining a quantitative measure of a first panel of microparticle-associated proteins in the fraction, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2; and e. optionally determining a quantitative measure of a second panel of microparticle- associated proteins in the fraction, wherein the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, and LCAT.

49. The method of claim 48 comprising using MS/MRM to perform the method.

50. The method of any of claims 48 to 49, wherein the blood sample comprises a plasma sample.

51. The method of any of claims 48 to 49, wherein the blood sample comprises a serum sample.

52. The method of any of claims 48 to 51, wherein the blood sample is from a subject, and the subject is a pregnant subject who is at about 8 to about 14 weeks of gestation.

53. The method of any of claims 48 to 51, wherein the blood sample is from a subject, and the subject is a pregnant subject who is at about 10 to about 12 weeks of gestation.

54. The method of any of claims 48 to 51, wherein the blood sample is from a subject, and the subject is a pregnant subject who is primiparous.

55. A method for measuring a protein panel, comprising: a. preparing a microparticle-enriched fraction from a blood sample of a subject; and b. determining a quantitative measure of a first panel of microparticle-associated proteins in the fraction, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2; and c. optionally determining a quantitative measure of a second panel of microparticle- associated proteins in the fraction, wherein the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, and LCAT, and wherein the determining comprises measuring surrogate peptides of the proteins.

56. The method of claim 55, wherein the blood sample comprises a plasma sample.

57. The method of claim 55, wherein the blood sample comprises a serum sample.

58. The method of any of claims 55 to 57, wherein the subject is a pregnant subject who is at about 8 to about 14 weeks of gestation.

59. The method of any of claims 55 to 57, wherein the subject is a pregnant subject who is at about 10 to about 12 weeks of gestation.

60. The method of any of claims 55 to 59, wherein the subject is a pregnant subject who is primiparous.

61. The method of any of claims 55 to 59, wherein the subject is a pregnant subject who is multiparous.

62. A computer system comprising: a. a processor; and b. a memory, coupled to the processor, the memory storing a module comprising:

(i) test data for a sample from a subject including values indicating a quantitative measure of a first panel and a second panel of protein biomarkers, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2, and the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, LCAT;

63. The computer system of claim 62, wherein the test data of (i) comprises a quantitative measure of the covariate of maternal body mass index (BMI).

64. A machine learning method comprising: a. providing a microparticle-enriched fraction from plasma or serum of a plurality of pregnant subjects obtained at from about 8 to about 14 weeks of gestation, wherein the plurality of subjects include a plurality of subjects that subsequently experienced preterm birth and a plurality of subjects that subsequently experienced term birth; b. using selected reaction monitoring mass spectrometry, determining a quantitative measure of a first panel and a second panel of proteins in the fraction, wherein the first panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally HEMO, FBLN1, and ITIH2, and the second panel comprises at least three proteins selected from TRFE, IC1, ITIH4, LCAT, HEMO, FBLN1, and ITIH2, optionally TRFE, IC1, ITIH4, LCAT; c. generating a training data set indicating, for each sample, values indicating:

65. The method of claim 64, wherein the quantitative measures of (c)(ii) comprises a quantitative measure of the covariate of maternal body mass index (BMI).