Use of circulating microparticles to stratify risk of spontaneous preterm birth

Abstract

The present disclosure relates to proteomic biomarkers of spontaneous preterm birth, proteomic biomarkers of term birth, and methods of use thereof. In particular, the present disclosure provides tools for determining whether a pregnant subject is at an increased risk for premature delivery, as well as tools for decreasing a pregnant subject's risk for premature delivery.

1 - 85 . (canceled) 
     
     
         86 . A computer-implemented method for generating a model to assess a risk of spontaneous preterm birth, the method comprising:
 obtaining a dataset, the dataset comprising measurements associated with a plurality of markers derived from each of a plurality of subjects;   implementing a machine learning analysis to associate a set of markers within the plurality of markers with spontaneous preterm birth, wherein implementing the machine learning analysis generates a model to assess the risk of spontaneous preterm birth.   
     
     
         87 . The computer-implemented method of  claim 86 , wherein assessing the risk comprises classifying a subject as being at one of increased risk or decreased risk of spontaneous preterm birth. 
     
     
         88 . The computer-implemented method of  claim 86 , wherein the model executes at least one classification rule to assess the risk of spontaneous preterm birth, and wherein the at least one classification rule comprises at least one of binary decision trees, artificial neural networks, discriminant analyses, logistic classifiers, and support vector classifiers. 
     
     
         89 . The computer-implemented method of  claim 86 , wherein the model executes at least one classification rule to assess the risk of spontaneous preterm birth, wherein the at least one classification rule produces a receiver operating characteristic (ROC) curve, and wherein the ROC curve has an area under the curve (AUC) of at least 0.6. 
     
     
         90 . The computer-implemented method of  claim 86 , wherein the set of markers comprises at least three markers from the markers of Table 1, Table 2, Table 4, Table 5, Table 7, or Table 8. 
     
     
         91 . The computer-implemented method of  claim 86 , wherein the set of markers comprises at least three markers selected from the group consisting of AACT, A1AG1, A2MG, CBPN, CHLE, C9, F13B, HEMO, IC1, KLKB1, LCAT, PGRP2, PROS, TRFE, A2AP, A2GL, APOL1, APOM, C6, CPN2, FBLNl, ITIH4, KAIN, KNGl, MBL2, SEPPl, THBG, TRY3, AMBP, APOAl, CD5L, C8A, F13A, HPT, ITIH1, and ITIH2. 
     
     
         92 . The computer-implemented method of  claim 86 , wherein the set of markers comprises at least three markers selected from the group consisting of AACT, A1AG1, A2MG, CBPN, CHLE, C9, F13B, HEMO, IC1, KLKB1, LCAT, PGRP2, PROS, and TRFE. 
     
     
         93 . A method for stratifying the risk of spontaneous preterm birth in a subject, the method comprising:
 determining measurements associated with at least three markers in a sample; and   executing a classification rule based on the measurements,   wherein the execution of the classification rule includes performing a receiver-operating-characteristic (ROC) curve analysis on the measurements, and   wherein the execution of the classification rule stratifies the risk of spontaneous preterm birth in the subject.   
     
     
         94 . The method of  claim 93 , wherein the ROC curve analysis produces a ROC curve, wherein the ROC curve has an area under the curve (AUC) of at least 0.6. 
     
     
         95 . The method of  claim 93 , wherein execution of the classification rule stratifies the subject as being at an increased risk of spontaneous preterm birth. 
     
     
         96 . The method of  claim 93 , wherein the classification rule is configured to have a sensitivity of at least 75%, at least 85%, or at least 95%. 
     
     
         97 . The method of  claim 93 , 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, wherein the execution of the classification rule stratifies the subject as being at an increased risk of spontaneous preterm birth. 
     
     
         98 . The method of  claim 93 , wherein the at least three markers are selected from the markers of Table 1, Table 2, Table 4, Table 5, Table 7, or Table 8. 
     
     
         99 . The method of  claim 93 , wherein the at least three markers are selected from the group consisting of AACT, A1AG1, A2MG, CBPN, CHLE, C9, F13B, HEMO, IC1, KLKB1, LCAT, PGRP2, PROS, TRFE, A2AP, A2GL, APOL1, APOM, C6, CPN2, FBLNl, ITIH4, KAIN, KNGl, MBL2, SEPPl, THBG, TRY3, AMBP, APOAl, CD5L, C8A, F13A, HPT, ITIH1, and ITIH2. 
     
     
         100 . The method of  claim 93 , wherein the at least three markers are selected from the group consisting of AACT, A1AG1, A2MG, CBPN, CHLE, C9, F13B, HEMO, IC1, KLKB1, LCAT, PGRP2, PROS, and TRFE. 
     
     
         101 . A computer-implemented method for training a machine learning model, the method comprising:
 obtaining a dataset, the dataset comprising measurements associated with a plurality of markers derived from each of a plurality of subjects;   performing a receiver-operating-characteristic (ROC) analysis on the dataset;   extracting co-expression patterns among at least two markers in the plurality of markers using a differential dependency network (DDN); and   training a machine learning model using the ROC analysis and the co-expression patterns.   
     
     
         102 . The computer-implemented method of  claim 101 , wherein the machine learning model is a linear model. 
     
     
         103 . The computer-implemented method of  claim 101 , wherein implementing the machine learning model classifies a subject as belonging to at least one of a first class or a second class, wherein the first class is associated with preterm birth and the second class is associated with term birth. 
     
     
         104 . The computer-implemented method of  claim 101 , wherein the at least one classification rule produces a receiver operating characteristic (ROC) curve, and wherein the ROC curve has an area under the curve (AUC) of at least 0.6. 
     
     
         105 . The computer-implemented method of  claim 101 , wherein the machine learning model associates a set of markers within the plurality of markers with spontaneous preterm birth. 
     
     
         106 . The computer-implemented method of  claim 105 , wherein the set of markers comprises at least three markers of Table 1, Table 2, Table 4, Table 5, Table 7, or Table 8. 
     
     
         107 . The computer-implemented method of  claim 105 , wherein the set of markers comprises at least three markers selected from the group consisting of AACT, A1AG1, A2MG, CBPN, CHLE, C9, F13B, HEMO, IC1, KLKB1, LCAT, PGRP2, PROS, TRFE, A2AP, A2GL, APOL1, APOM, C6, CPN2, FBLNl, ITIH4, KAIN, KNGl, MBL2, SEPPl, THBG, TRY3, AMBP, APOAl, CD5L, C8A, F13A, HPT, ITIH1, and ITIH2. 
     
     
         108 . The computer-implemented method of  claim 105 , wherein the set of markers comprises at least three markers selected from the group consisting of AACT, A1AG1, A2MG, CBPN, CHLE, C9, F13B, HEMO, IC1, KLKB1, LCAT, PGRP2, PROS, and TRFE. 
     
     
         109 . A system to assess risk in a subject, the system comprising:
 (a) a processor; and   (b) memory coupled to the processor, the memory to store:
 (i) a first dataset comprising a first plurality of measurements associated with a plurality of markers derived from each of a plurality of subjects; 
 (ii) a second dataset comprising a second plurality of measurements associated with the plurality of markers derived from another subject; and 
 (iii) computer-readable instructions to:
 (1) implement a machine learning analysis to associate a set of markers within the plurality of markers within the first dataset, wherein the machine learning analysis generates a model to assess the risk of spontaneous preterm birth; and 
 (2) execute a classification rule based on the second plurality of measurements from the other subject, wherein the execution of the classification rule assesses the risk of spontaneous preterm birth in the other subject. 
 
   
     
     
         110 . A system to assess a risk of spontaneous preterm birth in a subject, the system comprising:
 (a) a processor; and   (b) memory coupled to the processor, the memory to store:
 (i) a dataset comprising measurements associated with a plurality of markers derived from a subject; and 
 (ii) computer-readable instructions to execute a classification rule based on the measurements from the subject, wherein the execution of the classification rule assesses the risk of spontaneous preterm birth in the subject.