WO2022034118A1

WO2022034118A1 - A method of predicting the risk of spontaneous pre-term birth (sptb)

Info

Publication number: WO2022034118A1
Application number: PCT/EP2021/072336
Authority: WO
Inventors: Elizabeth CONSIDINE; Kirsten DOWLING
Original assignee: University College Cork - National University Of Ireland, Cork
Priority date: 2020-08-11
Filing date: 2021-08-11
Publication date: 2022-02-17

Abstract

A method for predicting the risk of spontaneous pre-term birth in a patient comprising assaying a biological sample obtained from the patient (at 20 weeks gestation) for abundance of at least one metabolite biomarker.

Description

Title:

A method of predicting the risk of spontaneous pre-term birth (SPTB)

Field of the Invention

The invention relates to a method of predicting the risk of spontaneous pre-term birth (SPTB). In narrower terms, the invention relates to a method of predicting the risk of SPTB through determining the levels of metabolite biomarkers in a sample from a patient.

Background of Invention:

Preterm birth, defined as birth occurring at less than 37 weeks’ gestation, is an archetype of a complex and heterogeneous disease or syndrome. It is a multifactorial event with a variety of possible initiators and multiple etiologies. Heritability studies with twins show that genes account for approximately 30% of variation in preterm delivery. Spontaneous Preterm Birth (SPTB) is the second leading cause of death in children under 5 in the world, preceded only by pneumonia. It occurs in approximately 10% of all pregnancies worldwide and accounts for approximately 70% of neonatal deaths and almost half of long-term neurological disabilities. Whether SPTB results from the premature activation of the normal labour pathway or labour by distinct means remains to be established. Despite decades of research the field is advancing slowly with yet no reliable prediction method for asymptomatic women, nor any efficacious treatment method. Complications arising from SPTB include acute respiratory, gastrointestinal, immunologic, central nervous system, hearing, and vision problems, as well as longer-term motor, cognitive, visual, hearing, behavioural, emotional, health, and growth problems.

There is currently no clinically useful screening test available to predict SPTB, even though SPTB prediction algorithms based on clinical and demographic factors or measured biomarkers have been developed. One previous study uses Liquid chromatography-mass spectrometry (LCMS) to investigate the serum metabolomic profiles of pregnant women who subsequently gave birth preterm (Heazell, A.E.P., et al., Reproductive Sciences, 2012. 19(8): p. 863-875), although that study only had 3 cases of preterm delivery that were not complicated by other pregnancy disorders (Lizewska, B., et al., Maternal Plasma Metabolomic Profiles in Spontaneous Preterm Birth: Preliminary Results. Mediators of inflammation, 2018). Another study investigated the plasma metabolomic profiles of pregnant women who gave birth preterm, however in that study samples were collected at the time of threatened preterm labour, not at an asymptomatic stage. US Patent Application Number 14/171 ,550 which describes the use of certain metabolic markers isolated from maternal serum (or other samples) at different stages of pregnancy that can be used to diagnose or predict the likelihood of occurrence of PTB or PTB labour of a pregnant subject. The assay described selects at least one marker from each of two panels of markers, the panels comprising small molecules such as amino acids and peptides. Additional biomarkers are also mentioned that can be assayed for in conjunction with the two panels and may be selected from a lipid panel that includes total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL) and triglycerides (TG).

WO 2016/205723 describes compositions and methods for predicting the probability of PTB in a pregnant female. The methods include assaying for markers selected from proteins, peptides and amino acids. Immunoassay and mass spectrometry are used for analysis of the samples being tested.

It is an object of the present invention to overcome at least one of the above-mentioned problems.

Summary of the Invention

The objective is to identify a clinically valuable early pregnancy-screening test for SPTB. Early SPTB (before 37 weeks’ gestation) is particularly associated with high rates of mortality and morbidity, intraventricular haemorrhage, respiratory distress syndrome and neurological deficit. There are many clinical and biochemical risk factors associated with SPTB and it is possible that markers are present in the maternal blood long before the onset of preterm labour.

Broadly, the invention provides a method for predicting the risk of spontaneous pre-term birth (SPTB) in a patient, the method comprising assaying a biological sample obtained from the patient at 20 weeks gestation for the expression of at least one bile acid, and their stereoisomers, and calculating the risk of SPTB based on the abundance levels of the at least one bile acid and the at least one resolvin/neuroprostane metabolite, and their stereoisomers and/or regio-chemistries, when compared to a reference sample from a subject not at risk of SPTB, wherein when the abundance levels of the at least one bile acid and the at least one resolvin/neuroprostane metabolite, and their stereoisomer and/or regio-chemistries, in the sample are increased relative to the abundance of the at least one bile acid and the at least one resolvin/neuroprostane metabolite, and their stereoisomer and/or regio-chemistries, in the reference sample, the patient is at risk of SPTB. Broadly, the invention provides a method for predicting the risk of spontaneous pre-term birth (SPTB) in a patient, the method comprising assaying a biological sample obtained from the patient at 20 weeks gestation for the expression of at least one resolvin/neuroprostane metabolite, and their regio-chemistries, and calculating the risk of SPTB based on the abundance levels the at least one resolvin/neuroprostane metabolite, and their regiochemistries, when compared to a reference sample from a subject not at risk of SPTB, wherein when the abundance levels of the at least one resolvin/neuroprostane metabolite, and their regio-chemistries, in the sample are increased relative to the abundance of the at least one resolvin/neuroprostane metabolite, and their regio-chemistries, in the reference sample, the patient is at risk of SPTB.

Broadly, the invention provides a method for predicting the risk of spontaneous pre-term birth (SPTB) in a patient, the method comprising assaying a biological sample obtained from the patient at 20 weeks gestation for the expression of at least one bile acid and at least one resolvin/neuroprostane metabolite, and their stereoisomers and/or regio-chemisitries, and calculating the risk of SPTB based on the abundance levels of the at least one bile acid and the at least one resolvin/neuroprostane metabolite, and their stereoisomers and/or regio- chemisitries, when compared to a reference sample from a subject not at risk of SPTB, wherein when the abundance levels of the at least one bile acid and the at least one resolvin/neuroprostane metabolite, and their stereoisomer and/or regio-chemisitries, in the sample are increased relative to the abundance of the at least one bile acid and the at least one resolvin/neuroprostane metabolite, and their stereoisomer and/or regio-chemisitries, in the reference sample, the patient is at risk of SPTB.

In one aspect, the bile acid or derivative is selected from 7a,12a-Dihydroxy-3-oxo-4-cholenoic acid, 12a-Dihydroxy-3-oxocholadienic acid, (4R)-4-[(1S,2R,9R,10R,11S,14R,15R,16R)-9,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; 4R)-4-[(1S,2R,9S,10R,11S,14R,15R,16S)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; (4R)-4-

[(1S,2R,9R,10R,11S,14R,15R,16R)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; 4-[(2S,15S)-3,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[( 1 S,2S,7S, 10R, 11 S, 15R)-16-hydroxy-2, 15-dimethyl-5,9- dioxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadecan-14-yl]pentanoic acid; 4-[(1S,2S,7S,11S,15R)-5- hydroxy-2,15-dimethyl-9,16-dioxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadecan-14-yl]pentanoic acid; 4-[(2S,15S)-5,17-dihydroxy-2,15-dimethyl-16-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en- 14-yl]pentanoic acid; 4-[(2R, 15S)-5, 16-di hydroxy-2, 15-dimethyl-8- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[(2R,15S)-8,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[(2S,15S)-3,5-dihydroxy-2,15-dimethyl-9-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14- yl]pentanoic acid; 4-[(2S,15S)-5,9-dihydroxy-2,15-dimethyl-4-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]he heptadec-6-en-14-yl] pentanoic acid; (4R)-4-[(2R,15R,16R)-16-hydroxy-2,15-dimethyl-5- oxotetracyclo-[8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid; 4-[(2R,15R)-9- hydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid, or stereoisomers thereof.

In one aspect, the resolvin/neuroprostane metabolite is selected from Resolvin D series such as RvD1 , RvD2, RvD3, RvD4, AT-RvD1-AT-RvD4, Neuroprostane Series D4, Neuroprostane Series E4, 4-hydroxy-D4-neuroprostane; 4-hydroxy-E4-neuroprostane; 7-hydroxy-D4- neuroprostane; 7-hydroxy-E4-neuroprostane; 10-hydroxy-D4-neuroprostane; 10-hydroxy-E4- neuroprostane; 11-hydroxy-D4-neuroprostane; 11-hydroxy-E4-neuroprostane; 13-hydroxy- D4-neuroprostane; 13-hydroxy-E4-neuroprostane; 14-hydroxy-D4-neuroprostane; 14- hydroxy-E4-neuroprostane; 17-hydroxy-D4-neuroprostane; 17-hydroxy-E4-neuroprostane; 20-hydroxy-D4-neuroprostane; 20-hydroxy-E4-neuroprostane; or regiochemistries thereof.

In one aspect, when the abundance of the at least one bile acid biomarker or stereoisomers thereof, is compared against the abundance of the at least one bile acid biomarker, or stereoisomers thereof, in the reference sample, the risk of SPTB is confirmed if the abundance level of the least one bile acid biomarker, or stereoisomers thereof, in the sample is at least 1.1-times that of the levels in the control sample. Typically, the abundance level is at least 1.5 to twice that of the levels in the control sample.

In one aspect, when the abundance of the at least one resolvin/neuroprostane biomarker or regio-chemistries thereof, is compared against the abundance of the at least one resolvin/neuroprostane biomarker or regio-chemistries thereof in the reference sample, the risk of SPTB is confirmed if the abundance level of the least one resolvin/neuroprostane biomarker regio-chemistries thereof in the sample is at least 1.1-times that of the levels in the control sample. Typically, the abundance level is at least 1.5 to twice that of the levels in the control sample.

In one aspect, when the abundance of the at least one bile acid biomarker or derivative, the at least one resolvin/neuroprostane biomarker or stereoisomers or regiochemistries thereof, is compared against the abundance of the at least one bile acid biomarker, the at least one resolvin/neuroprostane biomarker or stereoisomers or regiochemistries thereof in the reference sample, the risk of SPTB is confirmed if the abundance level of the least one bile acid biomarker, the at least one resolvin/neuroprostane biomarker or stereoisomers or regiochemistries thereof in the sample is at least 1.1-times that of the levels in the control sample. Typically, the abundance level is at least 1 .5 to twice that of the levels in the control sample.

In one aspect, the sample is obtained at 20 weeks gestation.

In one aspect, the sample is selected from a blood sample or a blood product, amniotic fluid and cervicovaginal secretions.

In one aspect, the method further comprises assaying the biological sample obtained from the patient for the positive expression of vitamin D and its derivatives, stigmasterol, or combinations thereof, wherein when the abundance levels of the vitamin D and its derivatives, stigmasterol, or a combination thereof in the sample are increased relative to the abundance of vitamin D and its derivatives, stigmasterol, or a combination thereof, in the reference sample, the patient is at risk of SPTB.

In one aspect, there is provided a method of preventing spontaneous pre-term birth (SPTB) in a subject, the method comprising: assaying a biological sample obtained from the patient at 20 weeks gestation for the abundance of at least one bile acid biomarker or derivative detected at 20-weeks’ gestation selected from 7a,12a-Dihydroxy-3-oxo-4-cholenoic acid, 12a- Dihydroxy-3-oxocholadienic acid, (4R)-4-[(1 S,2R,9R, 10R, 11 S, 14R, 15R, 16R)-9, 16-dihydroxy- 2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; 4R)-4- [(1S,2R,9S,10R,11S,14R,15R,16S)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; (4R)-4-

[(1S,2R,9R,10R,11S,14R,15R,16R)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; 4-[(2S,15S)-3,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[( 1 S,2S,7S, 10R, 11 S, 15R)-16-hydroxy-2, 15-dimethyl-5,9- dioxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadecan-14-yl]pentanoic acid; 4-[(1S,2S,7S,11S,15R)-5- hydroxy-2,15-dimethyl-9,16-dioxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadecan-14-yl]pentanoic acid; 4-[(2S,15S)-5,17-dihydroxy-2,15-dimethyl-16-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en- 14-yl]pentanoic acid; 4-[(2R, 15S)-5, 16-di hydroxy-2, 15-dimethyl-8- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[(2R,15S)-8,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[(2S,15S)-3,5-dihydroxy-2,15-dimethyl-9-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14- yl]pentanoic acid; 4-[(2S,15S)-5,9-dihydroxy-2,15-dimethyl-4-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]he heptadec-6-en-14-yl] pentanoic acid; (4R)-4-[(2R,15R,16R)-16-hydroxy-2,15-dimethyl-5- oxotetracyclo-[8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid; 4-[(2R,15R)-9- hydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid, or stereoisomers thereof, and administering a therapy to the subject when the abundance of the at least one bile acid biomarker is increased relative to the abundance of the at least one bile acid biomarker in a reference sample.

In one aspect, there is provided a method of preventing spontaneous pre-term birth (SPTB) in a subject, the method comprising: assaying a biological sample obtained from the patient at 20 weeks gestation for the abundance of at least one resolvin/neuroprostane metabolite biomarker selected from Resolvin D series such as RvD1-RvD4, or AT-RvD1-AT-RvD4, Neuroprostane Series D, Neuroprostane Series E, 4-hydroxy-D4-neuroprostane; 4-hydroxy- E4-neuroprostane; 7-hydroxy-D4-neuroprostane; 7-hydroxy-E4-neuroprostane; 10-hydroxy- D4-neuroprostane; 10-hydroxy-E4-neuroprostane; 11-hydroxy-D4-neuroprostane; 11- hydroxy-E4-neuroprostane; 13-hydroxy-D4-neuroprostane; 13-hydroxy-E4-neuroprostane; 14-hydroxy-D4-neuroprostane; 14-hydroxy-E4-neuroprostane; 17-hydroxy-D4- neuroprostane; 17-hydroxy-E4-neuroprostane; 20-hydroxy-D4-neuroprostane; 20-hydroxy- E4-neuroprostane; or regiochemistries thereof, or a combination thereof, and administering a therapy to the subject when the abundance of the at least one resolvin/neuroprostane metabolite biomarker is increased relative to the abundance of the at least one resolvin/neuroprostane metabolite biomarker in a reference sample.

[(1S,2R,9R,10R,11S,14R,15R,16R)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; 4-[(2S,15S)-3,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[( 1 S,2S,7S, 10R, 11 S, 15R)-16-hydroxy-2, 15-dimethyl-5,9- dioxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadecan-14-yl]pentanoic acid; 4-[(1S,2S,7S,11S,15R)-5- hydroxy-2, 15-dimethyl-9,16-dioxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadecan-14-yl]pentanoic acid; 4-[(2S,15S)-5,17-dihydroxy-2,15-dimethyl-16-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en- 14-yl]pentanoic acid; 4-[(2R, 15S)-5, 16-dihydroxy-2, 15-dimethyl-8- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[(2R,15S)-8,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[(2S,15S)-3,5-dihydroxy-2,15-dimethyl-9-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14- yl]pentanoic acid; 4-[(2S,15S)-5,9-dihydroxy-2,15-dimethyl-4-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]he heptadec-6-en-14-yl] pentanoic acid; (4R)-4-[(2R,15R,16R)-16-hydroxy-2,15-dimethyl-5- oxotetracyclo-[8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid; 4-[(2R,15R)-9- hydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid, or stereoisomers thereof, and at least one resolvin/neuroprostane metabolite biomarker selected from Resolvin D series such as RvD1-RvD4, or AT-RvD1-AT-RvD4, Neuroprostane Series D, Neuroprostane Series E, 4-hydroxy-D4-neuroprostane; 4-hydroxy-E4- neuroprostane; 7-hydroxy-D4-neuroprostane; 7-hydroxy-E4-neuroprostane; 10-hydroxy-D4- neuroprostane; 10-hydroxy-E4-neuroprostane; 11-hydroxy-D4-neuroprostane; 11-hydroxy- E4-neuroprostane; 13-hydroxy-D4-neuroprostane; 13-hydroxy-E4-neuroprostane; 14- hydroxy-D4-neuroprostane; 14-hydroxy-E4-neuroprostane; 17-hydroxy-D4-neuroprostane; 17-hydroxy-E4-neuroprostane; 20-hydroxy-D4-neuroprostane; 20-hydroxy-E4- neuroprostane; or regiochemistries thereof, or a combination thereof, and administering a therapy to the subject when the abundance of the at least one bile acid and the at least one resolvin/neuroprostane metabolite biomarker is increased relative to the abundance of the at least one bile acid and the at least one resolvin/neuroprostane metabolite biomarker in a reference sample.

In one aspect, the sample is obtained at 20 weeks gestation. Preferably, the sample is assayed using liquid chromatography-mass spectrometry.

In one aspect, the therapy is selected from administration of progesterone, cervical cerclage, bed rest and antibiotic use as first line preventative measures, and tocolytic agents and progesterone as second line interventions.

In one aspect the method further comprising assaying the biological sample obtained from the subject for the positive expression of vitamin D and its derivatives, stigmasterol, or combinations thereof, wherein when the abundance levels of the vitamin D and its derivatives, stigmasterol, or a combination thereof in the sample are increased relative to the abundance of vitamin D and its derivatives, stigmasterol, or a combination thereof, in the reference sample, therapy is administered to the subject.

As described herein, levels of bile acids (or derivatives) or resolvin (specialised proresolving mediator) or prostaglandin-related neuroprostane, or combinations thereof, can be increased in SPTB subjects and/or in subjects with a risk of SPTB. In some embodiments of any of the aspects, the level of a bile acid can be increased in SPTB and/or in subjects with risk of SPTB. Accordingly, in one aspect of any of the embodiments, described herein is a method of preventing SPTB in a subject in need thereof, the method comprising administering therapeutic agents used in the prevention of preterm birth to a subject determined to have a serum (or other biological sample) concentration of a bile acid, or a resolvin/neuroprostane, or both, at 19-21 weeks’ gestation_that is increased at least 1.1 to 2-fold relative to a reference concentration for healthy pregnancy at 20 weeks’ gestation. In one aspect of any of the embodiments, described herein is a method of treating SPTB in a subject in need thereof, the method comprising: a) determining the level of a bile acid, or a resolvin/neuroprostane, or a combination thereof, in a sample obtained from a subject; and b) administering therapeutic agents to the subject if the level of the bile acid or the resolvin/neuroprostane, or both, is increased relative to a reference. If there is no increase in the bile acid biomarker, or the resolvin/neuroprostane, no therapeutic agent or treatment is administered to the patient.

In one aspect of any of the embodiments, described herein is a method of prevention of preterm birth in a subject in need thereof, the method comprising: a) determining if the subject has an increased level of a bile acid or a resolvin/neuroprostane, or both; and b) administering a therapeutic agent or agents to the subject if the level of bile acid or resolvin/neuroprostane or both is increased relative to a reference. Therapeutic agents are not administered to the subject if the level of bile acid or resolvin/neuroprostane or both is not increased relative to the reference. In some embodiments of any of the aspects, the step of determining if the subject has an increased level of a bile acid or resolvin/neuroprostane or both can comprise i) obtaining or having obtained a sample from the subject and ii) performing or having performed an assay on the sample obtained from the subject to determine/measure the level of a bile acid or a resolvin/neuroprostane or combination thereof, in the subject. In some embodiments of any of the aspects, the step of determining if the subject has an increased level of a bile acid or a resolvin/neuroprostane metabolite biomarker, or a combination of both, can comprise performing or having performed an assay on a sample obtained from the subject to determine/measure the level of one or both of a bile acid or a resolvin/neuroprostane metabolite biomarker in the subject. In some embodiments of any of the aspects, the step of determining if the subject has an increased level of a bile acid or a resolvin/neuroprostane metabolite biomarker, or a combination of both, can comprise ordering or requesting an assay on a sample obtained from the subject to determine/measure the level of one or all of a bile acid or a resolvin/neuroprostane metabolite biomarker in the subject. In some embodiments of any of the aspects, the step of determining if the subject has an increased level of the bile acid or the resolvin/neuroprostane metabolite biomarker can comprise receiving the results of an assay on a sample obtained from the subject to determine/measure the level of one or both of a bile acid or a resolvin/neuroprostane metabolite biomarker in the subject. In some embodiments of any of the aspects, the step of determining if the subject has an increased level of a bile acid or a resolvin/neuroprostane metabolite biomarker, or both, can comprise receiving a report, results, or other means of identifying the subject as a subject with an increased level of a bile acid or a resolvin/neuroprostane metabolite biomarker, or both.

In one aspect of any of the embodiments, described herein is a method of treating SPTB in a subject in need thereof, the method comprising: a) determining if the subject has an increased level of a bile acid or a resolvin/neuroprostane metabolite biomarker, or a combination thereof; and b) instructing or directing that the subject be administered suitable therapeutic agents or measures, such as of progesterone, cervical cerclage, bed rest and antibiotic use as first line preventative measures and tocolytic agents and progesterone as second line interventions, if the level of the bile acid or the resolvin/neuroprostane metabolite biomarker, or both, is increased relative to a reference level from a subject not suffering from SPTB. In some embodiments of any of the aspects, the step of determining if the subject has an increased level of the bile acid or the resolvin/neuroprostane metabolite biomarker or both can comprise i) obtaining or having obtained a sample from the subject and ii) performing or having performed an assay on the sample obtained from the subject to determine/measure the level of one or all the bile acid or the resolvin/neuroprostane metabolite biomarker, or both, in the subject. In some embodiments of any of the aspects, the step of determining if the subject has an increased level of the bile acid or the resolvin/neuroprostane metabolite biomarker, or both, can comprise performing or having performed an assay on a sample obtained from the subject to determine/measure the level of one or both of the bile acid or the resolvin/neuroprostane metabolite biomarker in the subject. In some embodiments of any of the aspects, the step of determining if the subject has an increased level of the bile acid or the resolvin/neuroprostane metabolite biomarker, or both, can comprise ordering or requesting an assay on a sample obtained from the subject to determine/measure the level of one or both of the bile acid or the resolvin/neuroprostane metabolite biomarker in the subject. In some embodiments of any of the aspects, the step of instructing or directing that the subject be administered a particular treatment can comprise providing a report of the assay results. In some embodiments of any of the aspects, the step of instructing or directing that the subject be administered a particular treatment can comprise providing a report of the assay results and/or treatment recommendations in view of the assay results. If the results of the assay indicate that the levels of the one or both of the bile acid or the resolvin/neuroprostane metabolite biomarker in the subject were unchanged when compared to a reference level, the step of instructing or directing administering a particular treatment involves instructing that the patient does not undergo treatment.

In one aspect of any of the embodiments described above, the bile acid is selected from 7 a

,12 a -Dihydroxy-3-oxo-4-cholenoic acid, and stereoisomers thereof, and 12 a -Dihydroxy-3- oxocholadienic acid and stereoisomers thereof; and the specialised proresolving mediator (SPM) or neuroprostane is selected from Resolvin D series (RvD1-RvD4, or AT-RvD1-AT- RvD4; derived from Docosahexenoic Acid (DHA)) and its regiochemistries; and neuroprostane (specifically, from the D and E series which are defined by the presence of a hydroxypentanone moiety, analogous to D and E series prostaglandins) and its regiochemistries, respectively.

In one aspect of any of the embodiments, the methods can also include determining the levels of additional metabolites selected from a bile acid and/or prostaglandin-related panel.

This discovery allows one to identify in pregnancy, mothers who are at high risk of having a SPTB. A blood test can be taken, biomarkers analysed and a readout taken that will stratify women into high risk and low risk categories. High risk women of having SPTB can then be monitored in a different manner to reduce the risk of SPTB or if birth occurs to better manage the outcome to ensure the baby has the best chance of a positive outcome.

Brief Description of Drawings

The invention will be more clearly understood from the following description of an embodiment thereof, given by way of example only, with reference to the accompanying drawings, in which:-

Figure 1 is a flow chart illustrating the flow of data from raw data stage through data analysis to biomarker candidate lists: 15 weeks dataset: 106 samples (50 cases and 56 controls) x 4055 features.

Figure 2 is a flow chart illustrating the flow of data from raw data stage through data analysis to biomarker candidate lists: 20 weeks dataset: 104 samples (49 cases and 55 controls) x 4055 features. Figure 3A illustrates PLSDA results from 20 weeks dataset following pre-treatment and imputation. Cases are green, controls are red. Earliest delivering cases (circled) are furthest from controls. Figure 3B represents a graph showing a PLS-DA model validation by 1000 permutation tests based on prediction accuracy. The p value based on permutation is p = 0.004 4/1000)

Figure 4 illustrates the bile acid and resolvin/neuroprostane levels, patients are on the x-axis, with cases from 1-49 being the test candidates and the controls or reference samples are the other 104 patients’ numbers 50 to 153 on the x-axis. As can be seen, the levels are very stable in controls. Very interestingly, the very high case (right at the start (point 1)), was born with a urogenital congenital deformity that linked with liver dysfunction.

Figure 5 illustrates the bile acid and resolvin/neuroprostane levels indicated above with the case born with a urogenital congenital deformity removed.

Detailed Description of the Drawings

Definitions

In this specification, the term “preterm birth (PTB)” or “spontaneous preterm birth (SPTB)” should be understood to mean a birth about <37 weeks (for a human) completed gestational age and term birth (TB) was considered about > 37completed weeks gestational age as indicated on the birth certificate.

In this specification, the term “patient” or “subject” should be understood to mean a female mammalian animal, including a human, a veterinary or farm animal, a domestic animal or pet, and animals normally used for clinical research, including non-human primates, dogs and mice. The definitions for preterm birth for non-human mammals include birth less than about 90% term. More specifically, the subject of these methods is a human. In one aspect, the subject undergoing the predictive method is asymptomatic for pre-term birth. In one aspect, the subject undergoing the predictive method shows clinical symptoms, or history, of preterm birth.

In this specification, the term “biological sample” should be understood to mean blood or blood derivatives (serum, plasma, urine, urine derivatives, etc), amniotic fluid and cervicovaginal secretions.

In this specification, the term “control” refers to a subject who experienced a normal, full-term birth (FTB), and who gave birth after 37 weeks. A “case” refers to a patient who experienced SPTB, i.e. that delivered before 37 weeks. “Reference level” is a common term used to refer to the healthy range of any chemical in the blood (commonly used by GPs describing blood test results) from the control subject.

In this specification, the term “treating” and “preventing” refers to administering a suitable therapeutic to a subject who is at risk of sPTB. Suitable therapeutics (and treatments) include administration of progesterone, cervical cerclage, bed rest and antibiotic use as first line preventative measures, and tocolytic agents and progesterone as second line interventions. Since there is limited ability to prevent sPTB, interventions have focused mainly on neonatal survival. The administration of corticosteroids to prepare the foetus’ lungs for delivery has been found to reduce perinatal mortality, respiratory distress syndrome and intraventricular haemorrhage. The term “therapeutically effective amount” refers to the amount of the suitable therapeutic that is required to confer the intended therapeutic effect in the individual, which amount will vary depending on the type of active, route of administration, status of patient, and possible inclusion of other therapeutics or excipients.

Various delivery systems are known and can be used to administer a therapeutic of the invention. Methods of introduction include but are not limited to intradermal, intramuscular, intraperitoneal, intravenous, subcutaneous, intranasal, epidural, intranasal, intracerebral, and oral routes. The compositions may be administered by any convenient route, for example by infusion or bolus injection, by absorption through epithelial or mucocutaneous linings (e.g., oral mucosa, rectal and intestinal mucosa, etc.) and may be administered together with other biologically active agents. Administration can be systemic or local. In addition, it may be desirable to introduce the compositions of the invention into the central nervous system by any suitable route, including intraventricular and intrathecal injection; intraventricular injection may be facilitated by an intraventricular catheter, for example, attached to a reservoir, such as an Ommaya reservoir. Pulmonary administration can also be employed, e.g., by use of an inhaler or nebulizer, and formulation with an aerosolizing agent.

In the specification, the term “tocolytic agents” should be understood to mean drugs that prevent preterm labor and immature birth by suppressing uterine contractions (TOCOLYSIS). Agents used to delay premature uterine activity include magnesium sulfate, oxytocin antagonists (such as atosiban), calcium channel inhibitors (such as nifedipine), and betamimetics and adrenergic beta-receptor agonists (such as isoxsuprine, orciprenaline, terbutaline, ritodrine, fenoterol, salbutamol, albuterol, and hexoprenaline).

In the specification, the term “stereoisomers” should be understood to mean where molecules have the same molecular formula and sequence of bonded atoms, but differ in the three- dimensional orientations of their atoms in space and can be any one of the stereoisomers listed in Table 9.

In the specification, the term “regiochemistries” should be understood to mean the atomic arrangement of molecul es with the same chemical formula and functional groups, but with different sequences of bonded atoms and can be any one of the regiochemistries listed in Table 9.

In the specification, the term “bile acid” should be understood to mean acids derived from cholesterol and produced by the liver. Bile acid metabolites or bile acid derivatives result from processing of the bile acids in the intestinal tract and liver.

Embodiments of the invention can be described through functional modules, which are defined by computer executable instructions recorded on computer readable media and which cause a computer to perform method steps when executed. The modules are segregated by function for the sake of clarity. However, the modules/systems need not correspond to discreet blocks of code and the described functions can be carried out by the execution of various code portions stored on various media and executed at various times. Furthermore, it should be appreciated that the modules may perform other functions, thus the modules are not limited to having any particular function or set of functions.

The computer readable storage media can be any available tangible media that can be accessed by a computer. Computer readable storage media includes volatile and non-volatile, removable and non-removable tangible media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer readable storage media includes, but is not limited to, RAM (random access memory), ROM (read only memory), EPROM (erasable programmable read only memory), EEPROM (electrically erasable programmable read only memory), flash memory or other memory technology, CD-ROM (compact disc read only memory), DVDs (digital versatile disks) or other optical storage media, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage media, other types of volatile and non-volatile memory, and any other tangible medium which can be used to store the desired information and which can accessed by a computer including and any suitable combination of the foregoing.

Computer-readable data embodied on one or more computer-readable storage media may define instructions, for example, as part of one or more programs, that, as a result of being executed by a computer, instruct the computer to perform one or more of the functions described herein, and/or various embodiments, variations and combinations thereof. Such instructions may be written in any of a plurality of programming languages, for example, Java, J#, Visual Basic, C, C#, C++, Fortran, Pascal, Eiffel, Basic, COBOL assembly language, and the like, or any of a variety of combinations thereof. The computer-readable storage media on which such instructions are embodied may reside on one or more of the components of either of a system, or a computer readable storage medium described herein, may be distributed across one or more of such components.

The computer-readable storage media may be transportable such that the instructions stored thereon can be loaded onto any computer resource to implement the aspects of the present invention discussed herein. In addition, it should be appreciated that the instructions stored on the computer-readable medium, described above, are not limited to instructions embodied as part of an application program running on a host computer. Rather, the instructions may be embodied as any type of computer code (e.g., software or microcode) that can be employed to program a computer to implement aspects of the present invention. The computer executable instructions may be written in a suitable computer language or combination of several languages. Basic computational biology methods are known to those of ordinary skill in the art and are described in, for example, Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000) and Ouelette and Bzevanis Bioinformatics: A Practical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2nd ed., 2001).

The functional modules of certain embodiments of the invention include at minimum a determination system, a storage device, a comparison module, and a display module. The functional modules can be executed on one, or multiple, computers, or by using one, or multiple, computer networks. The determination system has computer executable instructions to provide e.g., sequence information in computer readable form.

The determination system can comprise any system for that can determine the lipid profile in a biological sample from the patient. Standard procedures may be used.

The information determined in the determination system can be read by the storage device. As used herein the “storage device” is intended to include any suitable computing or processing apparatus or other device configured or adapted for storing data or information. Examples of an electronic apparatus suitable for use with the present invention include a stand-alone computing apparatus, data telecommunications networks, including local area networks (LAN), wide area networks (WAN), Internet, Intranet, and Extranet, and local and distributed computer processing systems. Storage devices also include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage media, magnetic tape, optical storage media such as CD-ROM, DVD, electronic storage media such as RAM, ROM, EPROM, EEPROM and the like, general hard disks and hybrids of these categories such as magnetic/optical storage media. The storage device is adapted or configured for having recorded thereon lipid, nucleic acid or protein/peptide abundance information. Such information may be provided in digital form that can be transmitted and read electronically, e.g., via the Internet, on diskette, via USB (universal serial bus) or via any other suitable mode of communication.

As used herein, "stored" refers to a process for encoding information on the storage device. Those skilled in the art can readily adopt any of the presently known methods for recording information on known media to generate manufactures comprising information relating to peptide, metabolite or nucleic acid abundance information.

In one embodiment the reference data stored in the storage device to be read by the comparison module is compared.

The “comparison module” can use a variety of available software programs and formats for the comparison operative to compare abundance levels of or the bile acid/resolvin/neuroprostane profile relative to reference samples and/or stored reference data. In one embodiment, the comparison module is configured to use pattern recognition techniques to compare information from one or more entries to one or more reference data patterns. The comparison module may be configured using existing commercially available or freely available software for comparing patterns and may be optimized for particular data comparisons that are conducted. The comparison module provides computer readable information related to sample information.

The comparison module, or any other module of the invention, may include an operating system (e.g., UNIX) on which runs a relational database management system, a World Wide Web application, and a World Wide Web server. World Wide Web application includes the executable code necessary for generation of database language statements (e.g., Structured Query Language (SQL) statements). Generally, the executables will include embedded SQL statements. In addition, the World Wide Web application may include a configuration file which contains pointers and addresses to the various software entities that comprise the server as well as the various external and internal databases which must be accessed to service user requests. The Configuration file also directs requests for server resources to the appropriate hardware as may be necessary should the server be distributed over two or more separate computers. In one embodiment, the World Wide Web server supports a TCP/IP protocol. Local networks such as this are sometimes referred to as "Intranets." An advantage of such Intranets is that they allow easy communication with public domain databases residing on the World Wide Web (e.g., the GenBank or Swiss Pro World Wide Web site). Thus, in a particular preferred embodiment of the present invention, users can directly access data (via Hypertext links for example) residing on Internet databases using a HTML interface provided by Web browsers and Web servers.

The comparison module provides a computer readable comparison result that can be processed in computer readable form by predefined criteria, or criteria defined by a user, to provide a content based in part on the comparison result that may be stored and output as requested by a user using a display module.

In one embodiment of the invention, the content based on the comparison result is displayed on a computer monitor. In one embodiment of the invention, the content based on the comparison result is displayed through printable media. The display module can be any suitable device configured to receive from a computer and display computer readable information to a user. Non-limiting examples include, for example, general-purpose computers such as those based on Intel PENTIUM-type processor, Motorola PowerPC, Sun UltraSPARC, Hewlett-Packard PA-RISC processors, any of a variety of processors available from Advanced Micro Devices (AMD) of Sunnyvale, California, or any other type of processor, visual display devices such as flat panel displays, cathode ray tubes and the like, as well as computer printers of various types.

In one embodiment, a World Wide Web browser is used for providing a user interface for display of the content based on the comparison result. It should be understood that other modules of the invention can be adapted to have a web browser interface. Through the Web browser, a user may construct requests for retrieving data from the comparison module. Thus, the user will typically point and click to user interface elements such as buttons, pull down menus, scroll bars and the like conventionally employed in graphical user interfaces.

Systems and computer readable media described herein are merely illustrative embodiments of the invention for performing methods of diagnosis in an individual and are not intended to limit the scope of the invention. Variations of the systems and computer readable media described herein are possible and are intended to fall within the scope of the invention.

The modules of the machine, or those used in the computer readable medium, may assume numerous configurations. For example, function may be provided on a single machine or distributed over multiple machines.

Materials and Methods

Methodology

SPTB Cases (at less than or equal to 37 weeks gestation) were identified along with their matched controls from the SCOPE (Screening fOr Pregnancy Endpoints) biobank cohort (20 weeks). The study cohort consisted of all women who participated in the Screening for Obstetric and Pregnancy Endpoints (SCOPE) study in Cork. Nulliparous healthy women with singleton pregnancies were recruited to the SCOPE study in Cork, Ireland. Enrolment into the study took place between November 2004 and January 2011 , and the last SCOPE baby was delivered in Cork in August 2011. The aim of the SCOPE study was to develop early pregnancy screening tests to predict pre-eclampsia, SGA infants and spontaneous preterm birth. Details of the SCOPE study have previously been provided elsewhere (North, R.A., et al., Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort. BMJ, 2011. 342: p. d1875). In brief, pregnant women attending antenatal care settings such as maternity units, general practitioners, and outreach clinics and early pregnancy ultrasound appointments were invited to participate in the SCOPE study. Women who agreed to take part were interviewed and examined by a SCOPE research midwife at 20 ± 1 (visit 2) weeks’ gestation. Detailed clinical and demographic data were collected at the first visit, including maternal characteristics such as age, body mass index (BMI), education level, ethnic origin, marital status, family income, previous pregnancy loss, participant birth weight and family history of obstetric complications and medical conditions. Women were excluded if they were at high risk of SGA, pre-eclampsia or spontaneous preterm birth because of underlying medical conditions. The data were entered into an internet- accessed central database with a complete audit trail (MedSciNet AB, Sweden).

This was a nested case-control study design with approximately equal numbers of cases and controls. Cases were individually matched to controls according to age within 5 years. Cases were also individually matched to a different set of controls to age within 5 years and BMI within 3 points. Table 1 : Demographic and clinical data of the study population

Sample Collection and Bio-banking procedures

Blood was processed within 3 hours of collection to serum and stored in aliquots at -80 degrees Celsius for future analysis. No freeze thaw cycles were employed.

LCMS analysis

LCMS analysis was carried out in both negative and positive ion modes at 2 time points: 20 weeks as described by Dunn et al. (Nat Protoc, 2011. 6(7): p. 1060-83). Deproteinized samples were prepared for UPLC-MS analysis by reconstitution in 70 L of high-performance liquid chromatography grade water followed by vortex mixing (15 seconds), centrifugation (11 337 g, 15 minutes), and transfer to vials. Samples were analyzed by an Acquity LIPLC (Waters Corp) coupled to a hybrid LTQ-Orbitrap mass spectrometry system (Thermo Fisher Scientific) operating in electrospray ionization mode (Manchester University, 2012). All data related to technical or biological replicates are acquired by analysis of the Quality Control (QC) samples.

These QC samples are prepared from a pooled sample and so multiple samples are prepared which provides information on both variations associated with multiple preparation of the same sample but also technical variation through analysis of the same sample. Data were acquired over 3 analytical batches each consisting of 145 sample injections. The biologically identical QC samples were created to assess analytical precision and model systematic measurement bias. These samples were periodically injected throughout the analytical run following standard practice. At the start of each analytical batch, a solvent blank, matrix blank and ten conditioning QC samples were analysed. A pooled QC was then analysed every 5th sample. After all the complete set of raw Thermo MS files had been acquired, they were grouped into metabolite features, and peak areas quantified for each sample, using the XCMS algorithm using nonlinear peak alignment, matching, and identification. XCMS peak detection and alignment were completed using the following parameters: method = ‘centWave’, ppm = 10, peak width = 5 - 20, snthresh = 6, mzdiff = 0.01 , retention time correction method = ‘obiwarp’, and mzdiff = 0.01. For each metabolite feature in turn, any within or between batch systematic bias in peak area w.r.t. injection order, was corrected using the Quality Control-Robust Spline Correction (QC-RSC) algorithm (Kirwan, J. A., et al., Anal Bioanal Chem, 2013. 405(15): p. 5147-57). All metabolite features were annotated according to level 2 of the MSI reporting standards applying PUTMEDID_LCMS, as previously described (Brown, M., et al., Bioinformatics, 2011. 27(8): p. 1108-12.).

Data analysis

Biomarker discovery was carried out on cases matched only on age with controls. Controls matched on age and BMI were not used for biomarker discovery for the following reasons:

1 . There was a risk of over matching due to BMI’s relationship with the outcome (SPTB) being controversial despite many investigations not established as a true confounder as opposed to a mediator.

2. Matching can lead to bias that is offset by appropriate analysis, i.e. conditional logistic regression. However in this study the low stringency analysis is unlikely to be appropriate for matching.

3. If the control group is too similar to the case group, the study may fail to detect the difference even if one exists. Since over 40% of the cases give birth very close to term from 36 weeks on, metabolomically speaking it is likely that they are very similar to controls. Matching to controls on age and BMI in this situation leads to overmatching as a risk.

However, not wanting to take advantage of this extra group of controls composed of an entirely different set of individuals, a dataset was created with the same cases and the 2 sets of controls combined, so 49 cases and 104 controls to assess the performance of the candidates. The dataset analysed for biomarker discovery Is:

1 . 20 weeks SPTB vs Age Matched controls; 49 cases and 55 controls

One dataset was used to check the performance of the candidate biomarkers

2. 20 weeks SPTB vs Age Matched case controls combined with BMI and Age matched cases and controls. 49 cases and 104 controls.

and rationale

Considering the extreme heterogeneity of the SPTB case group, but also the small size of the sample group, an unbiased approach (untargeted LCMS) was combined with a knowledgebased approach in the search for biomarker candidates. Candidate biomarker selection guided by expert knowledge is especially important in a disease such as SPTB, as potential therapies can be indicated by the biomarkers that give clues to disease aetiologies.

The knowledge-based approach involved the following steps on the dataset generated in an unbiased way:

• Before data analysis begins obvious xenobiotics and exogenous metabolites (drugs, plant metabolites and others) and unidentified metabolites were removed from the unbiased and untargeted metabolomics dataset as these features would not form part of the biomarker panel.

• After data analysis domain knowledge was employed to guide the selection of plausible biomarker candidates from the top ranked features by: o selecting those features that have supporting literature of their biological relevance and involvement in the disease process; o visually investigating the behaviour of each of the top ranked features across cases and controls according to the clinical requirements of a suitable biomarker candidate (stable in controls, perturbed in at least some cases) via scatter plots, (box plots were not used as measures of central tendency were not of interest). o Selecting only those top ranked features with low or zero missing values for assessment in Panelomix to avoid the caveats of the imputation method (identification of false positives),

Pretreatment -Explanation and Rationale

Let Xtj be the measurements of levels for features (metabolites) i = 1,2, , m and samples (patients) j = 1,2, . , n. The samples fall into two groups, Group 1 is the case group and Group 2 is the control group. Let C_k be the set of indices of the observations in group k for k = 1,2.

• Quality Control (QC) correction consisted of calculating the Relative Standard Deviation (RSD) of every feature using QC samples and removing from the dataset all those features with RSD>20%.

^RSDt⁼ i^s (¹ )

Here s_t is the pooled standard deviation of metabolite i and x_t is the mean of metabolite i across both groups.

• Filtering of all unidentified or exogenous features before pretreatment was carried out as screening biomarker candidates will consist of endogenous metabolites. Filtering was performed after QC correction and prior to any pretreatment so that only endogenous compounds remained that either had complete identification or identification by biological group.

• Normalisation was carried out by dividing every row by the row mean. Where x represents the normalised values of the dataset, let

Where T, is the mean of each row, all metabolite measurements in a sample i

• Scaling was carried out by dividing every column by the mean of controls. This was performed on the normalised dataset.

Where x_l2 represents the mean of metabolite i in controls.

Imputation of cases and controls was performed separately. This was done based on their respective means for each feature in each group. The imputation method performed here is a modification of a previous method. Where the previous method performed separate imputation of cases and controls using k-nearest neighbours, here the arithmetic mean of each feature, in each group, is used. Imputing case and control features separately according to their respective means serves to emphasise subtle differences in metabolite levels between the two groups, for example present only in a subset of the case group, rather than mask them. In the situation that that the missing value proportion for a particular feature is high, this approach has the potential to exaggerate the separation between the two groups and also exaggerate the within group similarity. For such instances, after data analysis, the missing data proportions of top ranked candidate features can be carried out so that these features, which have the possibility of being false positives, can be identified and scrutinized and removed from further analysis if necessary. In the situation that the missing value proportion for a feature is low, this imputation approach will preserve a subtle signal, such as coming only from a subset of the case group. This imputation method also prevents the contamination of a feature’s vector that has a very low variance in controls. If such a feature has a low proportion of missing values, their imputation based only on their group mean for that feature will preserve this low variance. Low variance in controls is a major requirement of our desired, ideal biomarker, that is, stable levels in healthy individuals.

For all values of i, for every missing value in column x_t for Group 1 (cases) mv_ilt let

(4) where x_£1is the average measurement of metabolite i in Group 1 (cases).

And for all values of i, for every missing value in column x_t for Group 2 (controls) mv_l2, let mv_i2=x_i2 (5) where x_l2 is the average measurement of metabolite i in Group 2 (controls).

Finally, the order in which the pretreatment steps are carried out is shown to influence the biomarker candidate lists produced in downstream analysis. It has been recommended that normalisation proceed imputation to reduce bias. Many normalisation methods require a complete dataset with no missing values, the simple method that was used here however does not require a complete dataset. So, the pretreatment steps were carried out in the following order: After QC adjustment normalisation was performed first, then scaling and finally imputation.

Steps were carried out in the following order:

QC Correction -^Filtering - N ormalisation -^Scaling -^Imputation

All steps were carried out using simple R commands and short R programs.

Univariate Analysis (Fold Chanqej-Explanation and Rationale

The Fold Change (FC) for each feature (metabolite) i was calculated as the absolute value of the Iog2 of the ratio of averages of cases and controls and thus the features were ranked according to decreasing FC

FCj = log₂ =^

The question of which univariate statistic to use should be based on the biological question of interest. The absolute changes in levels of a metabolite are what of interest for a clinically transferable combinatorial panel biomarker panel. When absolute changes in levels of a feature are of interest, the fold change is superior to the t-test or its variations. Fold change is the simplest method for ranking differentially expressed features and was often the first method used in microarray data analysis. Fold change has been criticised as it does not control the variance as much as the t-test and related tests do, and as such is susceptible to outliers. However, this criticism is not an issue for this study as features exhibiting outlier behaviour among the case samples are actively sought in this instance. In this heterogeneous dataset, a uniform shift among SPTB cases of a potentially informative feature was not imagined to be likely to exist. It is far more likely that for a particular feature associated with disease some cases will exhibit a shift (dysregulated level), therefore, features with large variances in cases were potentially of interest. Fold change ranking ranks those features with the largest variance in cases relative to the variance in controls. Fold change is also preferable when stringency is to be relaxed and has been associated with high concordance and reproducibility. Thus, for a starting point of feature selection for highly complex and heterogeneous disease dataset its advantages are intuitively obvious.

Multivariate analysis-Explanation and Rationale

The data was then imputed as described in the pretreatment section above to deliberately amplify differences between cases and controls and at the same time amplify intra group similarities (to approximate homogeneity in the groups). Following imputation, multivariate analyses PLSDA + VIP were carried out using Metaboanalyst 4.0. Since the outcome variable (disease state) was used to impute the data, therefore deliberately incorporating bias, a predictive model was not derived. PLSDA was utiliesd however to orient the imputed data in multidimensional space to observe which features were most discriminating between the cases and controls. To assess the validity of the PLSDA model and the features selected according to it, permutation testing was performed (repeated 2000 times). Parameters employed for PLSDA and VIP analysis were the default settings of Metaboanalyst 4.0. Since data already had undergone pretreatment (normalisation, scaling and imputation) the normalisation and imputation steps were skipped in Metaboanalyst 4.0.

Knowledge based approach in selecting plausible candidates

The top 20 metabolites found by both univariate and multivariate analysis were assessed visually, via line graphs, for their distribution across case and control groups in the original dataset before any imputation was applied. Box-and-whisker plots were deliberately not used for this purpose as the behaviour of the feature across individual samples was of interest and measures of central tendency were not of interest. This assessment was performed to check if the features uncovered corresponded to or approximated the clinical reguirements for a biomarker (stable in controls, perturbed in at least some cases) even before the deliberately biased imputation method had been applied.

Missing value assessment

Missing value proportions were assessed for 20-week datasets for cases and controls. Missing value proportions for top ranked features by each method were also calculated.

Panel development

From the top ranked features, those that had low or zero missing values were chosen to assess their performance as a predictive combinatorial panel. This was to avoid bias that the imputation method would have towards features with high levels of missing-ness. Panel experiments were performed using the PanelomiX toolbox, which uses the iterative combination of biomarkers and thresholds (ICBT) method. Panelomix selects cut-offs for each biomarker to create the optimal panel performance. Panelomix features a cross-validation procedure for panel verification and performs and shows the ROC curves of both the individual biomarkers and the panel using the pROC tool. The ROC curves of the cross-validation are built as the mean of centered predictions over the 10 CV folds.

Alternative biomarker panels

For comparison purposes, the top ranked features from the 20 week dataset according to the Mann Whitney II test were also tested in Panelomix for their ability to build a biomarker panel that performed well upon cross validation.

Results

Analysis

PCA score plot of samples and QC samples shows that QC samples cluster tightly together indicating the reliability and stability of the data. QC treatment resulted in the removal of 192 features from the negative mode dataset and therefore the number of features (columns) of the dataset was reduced from 4055 to 3863 (see Figure 1 and Figure 2). Filtering of exogenous and unidentified compounds further resulted in the number of features of the dataset being reduced from 3863 to 1348. (Identified compounds were defined as compounds where the biological group was putatively identified at least even if the compound was not absolutely and uniquely identified.) The 20 weeks dataset contained 11.8% missing values. The 20 weeks dataset matched on age and BMI contained 10.1 % missing values. Normalisation, scaling and imputation were carried out as described in the methods section in that order.

Results of univariate and multivariate analysis on 20-week dataset matched by age only The top 20 metabolites ranked according to Fold Change are presented in Table 2 for the 20- week dataset. Non-parametric analysis (MWU) was also carried out for the purposes of comparison. Table 2. Ranked results of univariate and multivariate analysis on 20 weeks GA dataset.

The top 20 features ranked according to this analysis are presented in Table 3.

Table 3: non-parametric test MWU ranked top 20 features from 20-week dataset.

Abbreviations for all tables are as follows: BA: Bile Acid; PG: Prostaglandin-related (includes neuroprostane, resolvin); FA: Fatty Acid; VITD: Vitamin D2 and D3 and derivatives; PUR: Purines; STR: Steroids; NUC: Nucleotides; BH4: Tetrahdrobiopterin; DA: Dicarboxylic Acids; PL: Phospholipds; HF A: Hydroxy Fatty Acids; OCT: Octadecanoids; ALD: Aldehydes; IND: Indoles; ISO: Isoprenoids; A G: Acyl Glycines; DCGP: Diacylglycerophosphoinositols; AA: Amino Acids; NA: Nucleic Acids; GL: Glycerolipids; ARA: Aromatic Acids PLSDA analysis was carried out on pretreated data using Metaboanalyst 4.0. A 2000 time’s permutation assessed the validity of the models produced from the 20 weeks dataset. VIP scores for the 20 week dataset are presented in Table 3.

The results of the permutation test on the 20-week dataset (p=0.004) showed that the model produced by PLSDA was valid (Figure 4) and no over-fitting was observed. Therefore, features ranked highest by VIP score according to this model are reliable. (Table 3). Therefore, the potential for predictive biomarker discovery was strong at 20 weeks gestational age. For this reason, only those features from the 20 weeks dataset were investigated further for suitability as potential biomarker candidates.

There is a high level (85%) of concordance between the lists generated by univariate (fold change) and multivariate (PLS-DA) analysis from the 20 weeks dataset. Out of the top 20 features extracted by univariate and multivariate analysis 17 are in common (Table 3).

Two features showed distributions across the samples that are most consistent with an ideal biomarker candidate: (mostly) stable concentration in controls and perturbed in (at least some) cases. These are a bile acid (Figure 4) and a prostaglandin-related compound (which includes neuroprostane and resolvin) (Figure 5).

Biomarker Panel assessment with Panelomix

PanelomiX (http://www.panelomix.net/) allows ROC analysis of panels by testing various combinations of markers based on the iterative combination of biomarkers and thresholds (ICBT) method. Panelomix selects cut-offs for each biomarker to create the optimal panel performance. Panelomix has been previously used in metabolomics to find panels of discriminatory biomarker panels for lung cancer and mild traumatic brain injury and in proteomics to identify biomarker panels for bladder cancer and mild traumatic brain injury.

Panel A: 4 features from 20-week dataset ranked highly by Fold Change/PLSDA & VIP preceded by separate imputation

For assessment of the predictive ability of the top ranked candidates from the 20-week dataset in Panelomix only those features from Table 3 that had low or zero missing values were chosen, to offset the limitation of the imputation method being biased towards discovering features with high levels of missing-ness. Those features that had evidence in the literature of the involvement of their biological grouping in preterm labour, preterm birth or term parturition were included in the selection (if their level of missing values was low). This selection method at the biomarker panel assessment stage was to demonstrate the efficacy of the biomarker discovery method by eliminating bias towards features of high missing-ness and also to produce a panel that would make biological sense and could be potentially easily abstractable to medical community. This selection amounted to 4 features (Panel A) indicated with an asterisk in the penultimate column of Table 3. This 4-candidate feature panel had 9% missing values in cases and 4% missing values in controls. Since six biomarker candidates are being evaluated the panels’ maximum size was set to six so that all possible combinations of biomarker candidates and all sizes of panels between 1 and 6 were tested. PanelomiX features a cross-validation procedure for panel verification. 10- fold cross-validation was selected. PanelomiX performs and shows the ROC curves of both the individual biomarkers and the panel using the pROC tool. The ROC curves of the cross- validation are built as the mean of centered predictions over the 10 CV folds.

Panelomix selected a panel of 4 features with the highest performance in terms of sensitivity/specificity and pAUC. This final panel contained a bile acid, a resolvin/neuroprostane, a Vitamin D and derivatives and a fatty acid and conjugates and had a sensitivity 83.7%, a specificity of 49.9% and a p-value of 0.04125 upon 10-fold cross validation (Table 4).

Panel B: 6 features from 20-week dataset ranked by Mann- Whitney U test

For comparison purposes the top ranked 6 features according to the non-parametric Mann Whitney II test (also with low missing values) from the 20-week dataset were assessed in Panelomix for their ability to create a biomarker panel. The best panel produced by these features performed reasonably well with a pAUC of 11.9 (95%CI: 8.5-15.8) a sensitivity of 93.9 (85.7-100.0) and a specificity of 47.3 (34.5-61.8) and a p-value of 0.0426 upon cross validation. (Table 4)

Panel C: 4 features from 15-week dataset ranked in top 20 by Fold Change

A panel that performed well from the 15-week dataset was not expected. Nonetheless for the purposes of comparison, the features ranked highest according to fold change from the 15- week dataset were analysed fortheir ability to form a biomarker panel. This resulted in a panel that did not withstand cross validation (p-value =0.24057). (Table 4)

Panel D: 4 features from 15-week dataset ranked by Mann-Whitney U test

The top ranked features by MWU test from the 15-week datastet were also assessed for their ability to form a biomarker panel. Again, this analysis resulted in a panel that did not withstand cross validation (p-value =0.59576). (Table 4) Table 4: Panelomix Results Summary

Panelomix Results summary (Table 4):

The results of Panelomix analysis show that the 15-week dataset does not produce panels that perform well upon cross validation whether the features are derived from fold change analysis or non-parametric univariate analysis, (as shown earlier in the results section there are no candidate features to assess from PLSDA and VIP analysis from the 15-week dataset as the PLSDA model was proven invalid upon permutation). Panel D did not produce panels that withstood cross validation.

Features from the 20-week dataset however do perform well upon analysis by Panelomix. Pane A from the 20-week dataset produced a panel that withstood cross validation with p- values less than 0.05. Features found through FC/ PLSDA & VIP preceded by separate imputation produce panels with high sensitivity and specificity that perform well upon cross validation. Features from the 20-week dataset ranked highly by non-parametric testing (MWU test) also perform well but not as well as those found from FC/ PLSDA & VIP preceded by separate imputation.

This best performing panel (Panel A) consisted of features from the biological groups of bile acids, Vitamin D and derivatives, prostaglandin-related and fatty acids and conjugates (Table 5). The best performing candidate biomarker of this Panel according to Panelomix was the bile acid.

As a further test of the performance of Panels A and B, their performances were tested on the 20-week dataset that consisted of cases matched with both sets of controls (49 cases and 104 controls).

Table 5: Panelomix results summary on 20-week dataset with both sets of controls combined.

Panel A performed very well upon cross validation with a pAUC of 12.2, a sensitivity of 87.8%, a specificity 57.7% and a p-value 0.0013. Panel B performed less well, with a pAUC of 10.3, a specificity of 57.7%, a sensitivity of 77.6% and a p-value of 0.09158.

From this, it was concluded that the features found through both FC ranking and PLSDA and VIP ranking preceded by separate imputation outperform features found by Mann Whitney II test ranking. Also, they not only outperform but produce a valid biomarker panel that stands up to cross validation.

Table 6: Table of features of the top performing panel found by FC and PLSDA/VIP ranking preceded by separate imputation.

Table 7: Suitable Bile Acids for determining risk of SPTB

Table 8: Suitable resolvin/neuroprostanes for determining risk of SPTB

Bile acid Markers

Experimental mass spectrometry: m/z = 385.23704

Probable identity if m/z = M - H2O- H

7 ct ,12 ct -Dihydroxy-3-oxo-4-cholenoic acid; Formula: C24H36O5; Average Molecular Weight:

404.5396; IUPAC name: (4R)-4-[(1S,2R,9R,10R,11S,14R,15R,16S)-9,16-dihydroxy-2,15- dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; CAS number: 13587-11-6.

Alternate regio- and stereoisomer possibilities: See Table 7

Identity if m/z = M - H

12 a -Dihydroxy-3-oxocholadienic acid; Formula: C24H34O4; Average Molecular Weight:

386.5244; IUPAC name: (4R)-4-[(2R,15R,16S)-16-hydroxy-2,15-dimethyl-5-oxotetracyclo- [8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid; CAS number: 13535-96-1.

Alternate regio- and stereoisomer possibilities: See Table 7 Prostaglandin-related Markers Neuroprostane or Resolvin

Experimental mass spectrometry: m/z = 357.20508 Possible Identities, m/z = M - H2O- H

Formula: C22H32O5; Average Molecular Weight: 376.4865

A. Resolvin D series: RvD1-RvD4, Derived from Docosahexenoic Acid (DHA)

B. Neuroprostane, Series D and Series E, Oxidative product of DHA Neuroprostanes from the D and E series with possible regiochemistries: 4-hydroxy-D4/E4-neuroprostane;

7-hydroxy- D4/E4-neuroprostane; 10-hydroxy- D4/E4-neuroprostane;

11 -hydroxy- D4/E4-neuroprostane;

20-Hydroxy- D4/E4-neuroprostane;

17-Hydroxy- D4/E4-neuroprostane;

13-Hydroxy- D4/E4-neuroprostane; and 14-Hydroxy- D4/E4-neuroprostane.

D4 neuroprostane E4 neuroprostane

The invention comprises a simple biochemical test for detecting two species of biomarkers/metabolites, which if present above a reference level from a healthy subject, would identify a pregnant woman at high risk of giving birth prematurely. This test is capable of predicting a woman’s premature birth up to 16 weeks in advance. A negative test would not rule out an imminent preterm birth. However, a positive one would be a highly accurate indicator of premature birth. The advantages of this test are its simplicity, and its ease of interpretability for clinicians, which makes it a more readily adoptable test by clinicians as it indicates the underlying pathology. At present, it is known that pre-eclampsia, obstetric cholestasis and other pregnancy-related conditions or diseases increase the risk of preterm birth. While these diseases have symptoms, they are usually detected in antenatal care. But for the majority of preterm birth it is unknown why it has occurred. The claimed inveniton would elucidate an asymptomatic disease of pregnancy that has previously gone undeteced. Also the biomarkers themselves could potentially be therapeutic targets or point to other therapeutic targets in their biochemical pathway.

In the specification the terms "comprise, comprises, comprised and comprising" or any variation thereof and the terms “include, includes, included and including" or any variation thereof are considered to be totally interchangeable and they should all be afforded the widest possible interpretation and vice versa.

The invention is not limited to the embodiments hereinbefore described but may be varied in both construction and detail.

Claims

1 . A method for predicting the risk of spontaneous pre-term birth (SPTB) in a patient, the method comprising assaying a biological sample obtained from the patient at 20 weeks gestation for the expression of at least one resolvin/neuroprostane metabolite, and their regiochemistries, and calculating the risk of SPTB based on the abundance levels the at least one resolvin/neuroprostane metabolite, and their regio-chemistries, when compared to a reference sample from a subject not at risk of SPTB, wherein when the abundance levels of the at least one resolvin/neuroprostane metabolite, and their regio-chemistries, in the sample are increased relative to the abundance of the at least one resolvin/neuroprostane metabolite, and their regio-chemistries, in the reference sample, the patient is at risk of SPTB.

2. A method for predicting the risk of spontaneous pre-term birth (SPTB) in a patient, the method comprising assaying a biological sample obtained from the patient at 20 weeks gestation for the expression of at least one bile acid, and their stereoisomers, and calculating the risk of SPTB based on the abundance levels of the at least one bile acid, and their stereoisomers, when compared to a reference sample from a subject not at risk of SPTB, wherein when the abundance levels of the at least one bile acid, and their stereoisomers, in the sample are increased relative to the abundance of the at least one bile acid, and their stereoisomers, in the reference sample, the patient is at risk of SPTB.

3. A method for predicting the risk of spontaneous pre-term birth (SPTB) in a patient, the method comprising the step of assaying a biological sample obtained from the patient at 20 weeks gestation for the expression of at least one bile acid and at least one resolvin/neuroprostane metabolite, and their stereoisomers and/or regio-chemistries, and calculating the risk of SPTB based on the abundance levels of the at least one bile acid and the at least one resolvin/neuroprostane metabolite, and their stereoisomers and/or regiochemistries, when compared to a reference sample from a subject not at risk of SPTB, wherein when the abundance levels of the at least one bile acid and the at least one resolvin/neuroprostane metabolite, and their stereoisomer and/or regio-chemistries, in the sample are increased relative to the abundance of the at least one bile acid and the at least one resolvin/neuroprostane metabolite, and their stereoisomer and/or regio-chemistries, in the reference sample, the patient is at risk of SPTB.

4. The method of Claim 2 or Claim 3, wherein the bile acid metabolite is selected from 7a,12a-Dihydroxy-3-oxo-4-cholenoic acid, 12a-Dihydroxy-3-oxocholadienic acid, (4R)-4- [(1S,2R,9R,10R,11S,14R,15R,16R)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; 4R)-4-

[(1S,2R,9S,10R,11S,14R,15R,16S)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; (4R)-4-

5. The method of Claim 1 or Claim 3, wherein the resolvin/neuroprostane metabolite is selected from Resolvin D series such as RvD1 , RvD2, RvD3, RvD4, AT-RvD1-AT-RvD4, Neuroprostane Series D4, Neuroprostane Series E4, 4-hydroxy-D4-neuroprostane; 4- hydroxy-E4-neuroprostane; 7-hydroxy-D4-neuroprostane; 7-hydroxy-E4-neuroprostane; 10- hydroxy-D4-neuroprostane; 10-hydroxy-E4-neuroprostane; 11-hydroxy-D4-neuroprostane; 11-hydroxy-E4-neuroprostane; 13-hydroxy-D4-neuroprostane; 13-hydroxy-E4- neuroprostane; 14-hydroxy-D4-neuroprostane; 14-hydroxy-E4-neuroprostane; 17-hydroxy- D4-neuroprostane; 17-hydroxy-E4-neuroprostane; 20-hydroxy-D4-neuroprostane; 20- hydroxy-E4-neuroprostane; or regio-chemistries thereof.

6. The method of any one of Claims 1 to 5, wherein the abundance of the at least one bile acid biomarker or stereoisomers thereof, is compared against the abundance of the at least one bile acid biomarker, or stereoisomers thereof, in the reference sample, the risk of SPTB is confirmed if the abundance level of the least one bile acid biomarker, or stereoisomers thereof, in the sample is at least 1.1-times that of the levels in the control sample.

7. The method of any one of Claims 1 to 5, wherein the abundance of the at least one resolvin/neuroprostane biomarker or regio-chemistries thereof, is compared against the abundance of the at least one resolvin/neuroprostane biomarker or regio-chemistries thereof in the reference sample, the risk of SPTB is confirmed if the abundance level of the least one resolvin/neuroprostane biomarker regio-chemistries thereof in the sample is at least 1.1 -times that of the levels in the control sample.

8. The method of any one of Claims 1 to 5, when the abundance of the at least one bile acid biomarker, the at least one resolvin/neuroprostane biomarker or stereoisomers or regiochemistries thereof, is compared against the abundance of the at least one bile acid biomarker, the at least one resolvin/neuroprostane biomarker or stereoisomers or regiochemistries thereof in the reference sample, the risk of SPTB is confirmed if the abundance level of the least one bile acid biomarker, the at least one resolvin/neuroprostane biomarker or stereoisomers or regiochemistries thereof in the sample is at least 1.1 -times that of the levels in the control sample.

9. The method of any one of Claims 6 to 8, wherein the abundance level is at least 1.5 to twice that of the levels in the control sample.

10. The method of any one of the preceding claims, wherein the sample is obtained at 20 weeks gestation.

11. The method of any one of the preceding claims, wherein the sample is selected from a blood sample or a blood product, amniotic fluid and cervicovaginal secretions.

12. The method of any one of the preceding claims further comprising assaying the biological sample obtained from the patient for the positive expression of vitamin D and its derivatives, stigmasterol, or combinations thereof, wherein when the abundance levels of the vitamin D and its derivatives, stigmasterol, or a combination thereof in the sample are increased relative to the abundance of vitamin D and its derivatives, stigmasterol, or a combination thereof, in the reference sample, the patient is at risk of SPTB.

13. A method of preventing spontaneous pre-term birth (SPTB) in a subject, the method comprising: assaying a biological sample obtained from the patient at 20 weeks gestation for the abundance of at least one bile acid biomarker detected at 20-weeks’ gestation selected from 7a,12a-Dihydroxy-3-oxo-4-cholenoic acid, 12a-Dihydroxy-3-oxocholadienic acid, (4R)- 4-[(1S,2R,9R,10R,11S,14R,15R,16R)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; 4R)-4- [(1S,2R,9S,10R,11S,14R,15R,16S)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; (4R)-4-

[(1S,2R,9R,10R,11S,14R,15R,16R)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; 4-[(2S,15S)-3,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[( 1 S,2S,7S, 10R, 11 S, 15R)-16-hydroxy-2, 15-dimethyl-5,9- dioxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadecan-14-yl]pentanoic acid; 4-[(1S,2S,7S,11S,15R)-5- hydroxy-2,15-dimethyl-9,16-dioxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadecan-14-yl]pentanoic acid; 4-[(2S,15S)-5,17-dihydroxy-2,15-dimethyl-16-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en- 14-yl]pentanoic acid; 4-[(2R, 15S)-5, 16-di hydroxy-2, 15-dimethyl-8- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[(2R,15S)-8,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[(2S,15S)-3,5-dihydroxy-2,15-dimethyl-9-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14- yl]pentanoic acid; 4-[(2S,15S)-5,9-dihydroxy-2,15-dimethyl-4-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]he heptadec-6-en-14-yl] pentanoic acid; (4R)-4-[(2R,15R,16R)-16-hydroxy-2,15-dimethyl-5- oxotetracyclo-[8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid; 4-[(2R,15R)-9- hydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid, or stereoisomers thereof, and when the abundance of the at least one bile acid is increased relative to the abundance of the at least one bile acid in a reference sample, the subject is suitable for receiving a therapy.

14. A method of preventing spontaneous pre-term birth (SPTB) in a subject, the method comprising: assaying a biological sample obtained from the patient at 20 weeks gestation for the abundance of at least one resolvin/neuroprostane metabolite biomarker selected from Resolvin D series such as RvD1-RvD4, or AT-RvD1-AT-RvD4, Neuroprostane Series D, Neuroprostane Series E, 4-hydroxy-D4-neuroprostane; 4-hydroxy-E4-neuroprostane; 7- hydroxy-D4-neuroprostane; 7-hydroxy-E4-neuroprostane; 10-hydroxy-D4-neuroprostane; 10- hydroxy-E4-neuroprostane; 11-hydroxy-D4-neuroprostane; 11-hydroxy-E4-neuroprostane; 13-hydroxy-D4-neuroprostane; 13-hydroxy-E4-neuroprostane; 14-hydroxy-D4- neuroprostane; 14-hydroxy-E4-neuroprostane; 17-hydroxy-D4-neuroprostane; 17-hydroxy- E4-neuroprostane; 20-hydroxy-D4-neuroprostane; 20-hydroxy-E4-neuroprostane; or regiochemistries thereof, and when the abundance of the at least one resolvin/neuroprostane metabolite biomarker is increased relative to the abundance of the at least one resolvin/neuroprostane metabolite biomarker in a reference sample, the subject is suitable for receiving a therapy.

15. A method of preventing spontaneous pre-term birth (SPTB) in a subject, the method comprising: assaying a biological sample obtained from the patient at 20 weeks gestation for the abundance of at least one bile acid biomarker detected at 20-weeks’ gestation selected from 7a,12a-Dihydroxy-3-oxo-4-cholenoic acid, 12a-Dihydroxy-3-oxocholadienic acid, (4R)- 4-[(1S,2R,9R,10R,11S,14R,15R,16R)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; 4R)-4-

[(1S,2R,9R,10R,11S,14R,15R,16R)-9,16-dihydroxy-2,15-dimethyl-5- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadec-6-en-14-yl]pentanoic acid; 4-[(2S,15S)-3,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[( 1 S,2S,7S, 10R, 11 S, 15R)-16-hydroxy-2, 15-dimethyl-5,9- dioxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadecan-14-yl]pentanoic acid; 4-[(1S,2S,7S,11S,15R)-5- hydroxy-2,15-dimethyl-9,16-dioxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadecan-14-yl]pentanoic acid; 4-[(2S,15S)-5,17-dihydroxy-2,15-dimethyl-16-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en- 14-yl]pentanoic acid; 4-[(2R, 15S)-5, 16-di hydroxy-2, 15-dimethyl-8- oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[(2R,15S)-8,16- dihydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14-yl]pentanoic acid; 4-[(2S,15S)-3,5-dihydroxy-2,15-dimethyl-9-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵] heptadec-6-en-14- yl]pentanoic acid; 4-[(2S,15S)-5,9-dihydroxy-2,15-dimethyl-4-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]he heptadec-6-en-14-yl] pentanoic acid; (4R)-4-[(2R,15R,16R)-16-hydroxy-2,15-dimethyl-5- oxotetracyclo-[8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid; 4-[(2R,15R)-9- hydroxy-2,15-dimethyl-5-oxotetracyclo[8.7.0.0²,⁷.0¹¹,¹⁵]heptadeca-6,8-dien-14-yl]pentanoic acid, or stereoisomers thereof, and at least one resolvin/neuroprostane metabolite biomarker selected from Resolvin D series such as RvD1-RvD4, or AT-RvD1-AT-RvD4, Neuroprostane Series D, Neuroprostane Series E, 4-hydroxy-D4-neuroprostane; 4-hydroxy-E4- neuroprostane; 7-hydroxy-D4-neuroprostane; 7-hydroxy-E4-neuroprostane; 10-hydroxy-D4- neuroprostane; 10-hydroxy-E4-neuroprostane; 11-hydroxy-D4-neuroprostane; 11-hydroxy- E4-neuroprostane; 13-hydroxy-D4-neuroprostane; 13-hydroxy-E4-neuroprostane; 14- hydroxy-D4-neuroprostane; 14-hydroxy-E4-neuroprostane; 17-hydroxy-D4-neuroprostane; 17-hydroxy-E4-neuroprostane; 20-hydroxy-D4-neuroprostane; 20-hydroxy-E4- neuroprostane; or regio-chemistries thereof, or a combination thereof, and when the abundance of the at least one bile acid and the at least one resolvin/neuroprostane metabolite biomarker is increased relative to the abundance of the at least one bile acid and the at least one resolvin/neuroprostane metabolite biomarker in a reference sample, the subject is suitable for receiving a therapy.

16. The method of any one of Claims 13 to 15, wherein the therapy is selected from administration of progesterone, cervical cerclage, bed rest and antibiotic use as first line preventative measures, and tocolytic agents and progesterone as second line interventions.

17. The method of any one of Claims 13 to 16, wherein the sample is selected from a blood sample or a blood product, amniotic fluid and cervicovaginal secretions.

18. The method of any one of Claims 13 to 17, further comprising assaying the biological sample obtained from the subject for the positive expression of vitamin D and its derivatives, stigmasterol, or combinations thereof, wherein when the abundance levels of the vitamin D and its derivatives, stigmasterol, or a combination thereof in the sample are increased relative to the abundance of vitamin D and its derivatives, stigmasterol, or a combination thereof, in the reference sample, therapy is administered to the subject.

19. The method of any one of Claims 1 to 18, wherein the sample is assayed using liquid chromatography-mass spectrometry.

20. A method of preventing preterm birth in a subject in need thereof, the method comprising: a) determining if the subject has an increased level of a bile acid or a resolvin/neuroprostane, or both; and b) administering a therapeutic agent to the subject if the level of bile acid or resolvin/neuroprostane or both is increased relative to a reference.