WO2018199849A1 - Characteristic metabolites in miscarriages - Google Patents

Abstract

Disclosed herein are methods of identifying the risk of a miscarriage in a subject comprising detecting and measuring the concentration of at least one metabolite in a sample obtained from the subject. Specifically, an increase in concentration of tetrahydrocortisone, and a decrease in concentration of propionylcarnitine, isovalerylcarnitine, 3- methylglutarylcarnitine, hexanoylcarnitine or 3α,20α-dihydroxy-5β-pregnane-3-glucuronide in a serum or a urine sample are associated with spontaneous miscarriage in first trimester pregnancy. Also disclosed herein are kits for determining the risk of a miscarriage in a subject.

Description

CHARACTERISTIC METABOLITES IN MISCARRIAGES

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority of SG provisional application No. 10201703502T, filed 28 April 2017, the contents of it being hereby incorporated by reference in its entirety for all purposes.

FIELD OF THE INVENTION

[0002] The present invention relates generally to the field of molecular biology. In particular, the present invention relates to the use of biomarkers for the detection of miscarriages and identification of women at risk of miscarriages.

BACKGROUND OF THE INVENTION

[0003] Threatened miscarriage is the most common gynaecological emergency, occurring in about 20% of pregnant women. Approximately one in four of these women go on to have spontaneous miscarriage, whereby the aetiology of miscarriage remains elusive. In a bid to identify possible biomarkers and novel treatment targets, many studies have been undertaken to elucidate the pathways that lead to a miscarriage.

[0004] No study on urine metabolites associated with spontaneous miscarriage has been performed to date. With existing pre-natal care unable to accurately identify women at high risk of spontaneous miscarriage with sufficient accuracy, there is a need to further understand the underlying causes so as to better predict and eventually prevent spontaneous miscarriage. Thus, there is an unmet need for a method of detecting and determining the risk of miscarriage in pregnant women.

SUMMARY OF THE INVENTION

[0005] In one aspect, the present invention refers to a method of identifying the risk of a miscarriage comprising detecting and measuring the concentration of at least one metabolite in a sample obtained from a subject, wherein absence or presence of the metabolite, compared to the control group identifies an increased risk of miscarriage, wherein the metabolite is selected from the group consisting of tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, 3-methylglutarylcarnitine, hexanoylcarnitine and 3α,20α -dihydroxy-5 -pregnane-3-glucuronide.

[0006] In another aspect, the present invention refers to a method of identifying the risk of a miscarriage comprising measuring the concentration of at least one metabolite in a sample obtained from a subject; and comparing the concentration of the at least one metabolite with the concentration of the respective metabolite in a control group; wherein the metabolite is selected from the group consisting of tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, 3-methylglutarylcarnitine, hexanoylcarnitine and 3α,20α -dihydroxy-5 -pregnane-3-glucuronide. [0007] In a further aspect, the present invention refers to a kit for identifying the risk of a spontaneous miscarriage according to the method as described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The invention will be better understood with reference to the detailed description when considered in conjunction with the non-limiting examples and the accompanying drawings, in which:

[0009] FIG. 1 shows a diagram of an exemplary workflow for, in this example, urine metabolite analysis. Samples from both cohorts were randomly injected into the system for separation by ultra- performance liquid chromatography (UPLC) before analysis by mass spectrometry (MS).

[0010] FIG. 2 shows a Schematic of set-up for the identification of metabolites between case and control groups. Quality Control (QC) samples were interspersed between the 80 samples to ensure that the resolution of the column is not compromised.

[0011] FIG. 3 shows scatter plots depicting the results of principle component analysis (PCA) when applied to the metabolomics data after pre-processing. Visual inspection of the clustering of the quality control (QC) samples and drift of the run order QCs in the PCA scored plots were performed to assess the data integrity by tight clustering of the QC samples on the PCA score plots. The QC samples were found to be well clustered near the center of the scores plot for both (A) positive and (B) negative ionization mode. Case and control samples, however, do not exhibit any significant separations. Quality of the dataset is first assessed using principal component analysis (PCA).

[0012] FIG. 4 shows scatter plots depicting the results of a multivariate analysis performed on urine metabolomics data. (A) Scores plot of ESI+ measurements. The urine samples from case and control subjects were found to be well separated along the predictive component axis with an explained variance R2Y = 0.67 and predictability Q2Y = 0.22. (B) Scores plot of the ESI- experiment, with a R2Y = 0.70 and Q2Y = 0.19.

[0013] FIG. 5 shows a schematic pathway showing the intercon version of carnitine and acylcarnitine and their transport from the cytosol into the mitochondria. CPTI: carnitine palmitoyltransferase- 1, CPTII: carnitine palmitoyltransferase-2, CACT: carnitine-acylcarnitine translocase.

[0014] FIG. 6 shows the MS/MS spectra of each of the metabolites identified. MS/MS spectra from samples were compared with that from commercially purchased standards to confirm the identities of the features. 3a, 20a-Dihydroxy-5 -pregnane-3-glucuronide, tetrahyrdocortisone, propionylcarnitine and isovalerycarnitine matched the standards, thus confirming their identities. On the other hand, 3- methylglutarylcarntine and hexanoylcarnitine have additional features and may be structural isomers of the proposed metabolites.

[0015] FIG. 7 shows a schematic of the superhydrophobic SERS chip (a; top) used in the present disclosure, and its analyte concentrating effect (a; bottom), (b) shows SERS spectra of 3a,20a-dihydroxy- 5 -pregnane-3-glucuronide (pregnane) sample measured on the described chip, (c) Standard calibration curve of SERS intensity variation at different 3a,20a-dihydroxy-5 -pregnane-3-glucuronide concentrations based on the 610 cm ¹ peak.

DETAILED DESCRIPTION

[0016] To date, multiple maternal serum biological markers, medical and psychosocial factors have been identified as factors which may have an effect on fetal health. In addition, recent investigations have demonstrated the importance of hormones and endocrine -immune interactions in maintaining early pregnancy. Luteal phase deficiency has been shown to contribute to miscarriages, and the measurement of serum progesterone as a prognostic marker and the prescription of progesterone supplementation have been proposed as possible diagnostic and treatment methods. However, luteal phase deficiency only accounts for 35% of miscarriages.

[0017] One hormone, which has been demonstrated to be important in maintaining early pregnancy, is progesterone, which promotes maternal immune tolerance to the fetal semi-allograft, sustains decidualization and controls uterine contractility. Progesterone triggers the expression of Progesterone Induced Blocking Factor (PIBF) by lymphocytes and decidual cells. PIBF has been shown to exhibit anti- abortive effects in vivo and is a pivotal mediator in progesterone-dependent immunomodulation. Both progesterone and PIBF contribute to the success of early pregnancy, and several earlier studies have reported that the risk of miscarriage is significantly higher in women with lower levels of serum progesterone and PIBF. It has also been shown that women with a serum progesterone higher than (>) 35nmol/L have a relatively low risk of miscarriage, with a corresponding negative predictive value of 92%. However, only about 35% of women with recurrent pregnancy losses are attributed to luteal phase deficiency resulting in inadequate levels of progesterone. Hence, there may be other pathways contributing to a spontaneous miscarriage, and elucidation of these pathways could lead to the development of novel biomarkers and targeted treatment of spontaneous miscarriage.

[0018] Other than serum biomarkers, metabolic profiling has also been used to assess the risk of ectopic pregnancies. Ultra-performance liquid chromatography - mass spectrometry (UPLC-MS) has been utilised to look at plasma metabolites in rats and identified discriminatory metabolites associated with small-for-gestational age syndrome. Other studies were also able to identify signatory metabolic differences between pregnant women with preeclampsia and those who went on to have healthy births. Notably, several metabolites were identified to be associated with prenatal disorders, such as gestational diabetes and pre-term delivery, using urine samples of patients.

[0019] The relative success of urine beta -Human Chorionic Gonadotropin (bHCG) over serum bHCG in diagnosing pregnancy is testament to the utility of urine metabolites as a non-invasive diagnostic or prognostic marker of pregnancy outcomes. However, unlike bHCG, progesterone is metabolized mainly in the liver and its metabolites are excreted in urine. [0020] Thus, in one example, the sample used in the method disclosed herein is a bodily fluid sample. In another example, the sample is a serum or a urine sample. In a further example, the sample is a serum sample. In another example, the sample is a urine sample.

[0021] A case-control study of eighty patients was conducted, half of whom went on to have healthy births, whilst the other half had spontaneous miscarriages. From the discriminatory metabolites profile, a panel of urine metabolites associated with spontaneous miscarriage, and possible mechanisms responsible for women presenting with threatened miscarriage, progressing to spontaneous miscarriage, was proposed.

[0022] In order to understand the other causes of miscarriage, among others spontaneous miscarriages, and identify biomarkers or metabolites for miscarriage, changes in urinary metabolites in women with threatened miscarriage were analysed. To this end, a case-control study of eighty (80) patients who presented with threatened miscarriage between 6 to 10 weeks gestation was performed.

Urine metabolomics analyses of forty patients with spontaneous miscarriages and forty patients with ongoing pregnancies at 16 weeks gestation point to an impaired placental mitochondrial β-oxidation of fatty acids as the possible cause of spontaneous miscarriage. The results highlighted the use of urine metabolites as a non-invasive screening tool for the risk stratification of women presenting with threatened miscarriage.

[0023] As used herein, the term "spontaneous miscarriage" refers to a non -induced, natural loss of an intrauterine pregnancy before 24 weeks of gestation. As used herein, the term "threatened miscarriage" refers to an ongoing pregnancy associated with vaginal bleeding, with or without abdominal pain, before 24 weeks of gestation.

[0024] Eighty (80) patients who presented with threatened miscarriage between 6 to 10 weeks gestation were recruited. Forty (40) patients had spontaneous miscarriages before 16 weeks gestation (case) and 40 patients had ongoing pregnancies beyond 16 weeks (control). The mean serum progesterone levels were significantly higher in women with ongoing pregnancy (66.5 ± 24.2 nmol/L) than in women with spontaneous miscarriage (32.0 ± 21.0 nmol/L) (P<0.0001) (Table 1). Notably, other than gestation age, there were no significant differences in maternal characteristics between the two groups.

Ongoing pregnancy at 16 Spontaneous miscarriage at

Maternal Characteristics

weeks gestation 16 weeks gestation P value

Cohort (N = 40) Cohort (N = 40)

Serum biological markers

Progesterone, mean ± SD

66.5 ± 24.2 32.0 + 21.0 <0.0001 (nmol/L)

Demographics

Age, mean ± SD (years) 30.8 + 3.9 31.8 + 5.6 n.s.

Health, obstetric and

lifestyle factors Gestation age (GA) at

7.6 + 1.7 6.7 + 1.2 <0.05 recruitment, mean (wks)

Previous miscarriage (%) 35 25 -

BMI, mean ± SD (kg/m²) 22.5 ± 4.4 24.0 ± 4.5 n.s.

Medical comorbidities

Hypertension (%) 0 0 -

Smoking during pregnancy

10 5 n.s.

(%)

Alcohol during pregnancy

0 0 -

(%)

Table 1. Maternal characteristics and serum progesterone levels of women with ongoing pregnancy or with spontaneous miscarriage at 16 weeks gestation.

Urine metabolites analysis

[0025] Ultra-performance liquid chromatography - mass spectrometry (UPLC-MS) was used to analyze the metabolites present in samples, for example urine samples, of both control and case cohorts. Briefly, each sample was first passed through the UPLC column for separation of the various metabolites, before analysis via mass spectrometry (FIG. 1). The UPLC-MS analysis in this study employed a quality control (QC) strategy that was previously described for the purpose of monitoring instrument stability and analyte reproducibility (FIG. 2).

[0026] Next, peak alignment, peak picking, peak deconvolution, median normalization, and log transformation were applied on the raw UPLC-MS data. A total of 1,291 and 1,153 retention time-exact mass pairs (i.e. features) were found, in positive electrospray ionization mode (ESI+) and negative electrospray ionization mode (ESI-), respectively. The stability and the reproducibility of the sample analysis were visualized using Principal Component Analysis (PCA) score plots, as shown in FIG. 3. The quality control samples were found to be tightly clustered in the score plots, and there was no drift in their Principal Component Analysis scores. After removing features that were not present in all the quality control samples, the coefficient of variation (CV) value or relative standard deviation (RSD) value was calculated for all features. In both positive and negative ionization mode, 89% of the features' coefficients of variation were less than 10%.

[0027] Finally, the linearity of the features in the dilution quality control samples was tested. 77% and 87% of features in positive and negative ionization were retained for downstream analysis when the threshold for acceptance was set at a coefficient of determination (R²) value of greater than 0.9 ( R² > 0.9). After these steps, a total of 791 features were obtained from positive ionization, and 1004 features from negative ionization.

[0028] To obtain a list of features capable of defining the variation of the metabolic profiles of urine in case and control subjects, data acquired in both ionization modes were analyzed using OPLS-DA model (FIG. 4). Prior to the analysis, the data were standardized. The OPLS-DA modeling was performed with 5-fold cross-validation adjusted for age and BMI. An explained variance (R2Y) of 0.67 and predictability (Q2Y) of 0.22 was obtained for ESI+. For ESI-, R2Y = 0.70 and Q2Y = 0.19. Finally, the metabolites were selected based on the absolute value of the model coefficients and the p value of the one-way Analysis of variance (ANOVA) after 5% false discovery rate (FDR) correction.

[0029] These short-listed features were further subjected to LC MS/MS and the spectra obtained used for their identification with comparison to open source databases. A total of six metabolites were identified, belonging to three different families, with significant differences in levels between women who went on to have healthy births and those who had spontaneous miscarriages (Table 2).

[0030] Thus, in one example, the method disclosed herein comprises detecting the absence or presence of six metabolites.

[0031] Together, the results presented herein indicate that lower progesterone levels, higher stress and deficiencies in fatty acid metabolism are the reasons behind spontaneous miscarriages in the cohort.

Table 2. Top feature hits with significant differences in levels between women who went on to have healthy births and those with spontaneous miscarriage. The fold change indicates the differences in the mean relative abundance with a negative value indicating lower levels in women who miscarry as compared to women with healthy births. FC - fold change; GA - gestation age.

[0032] Earlier, it was found that other than progesterone levels, gestation age was also significantly different between the two cohorts. Co-variance analysis was then applied to determine if the levels of six metabolites correlated to gestation age differences (Table 2). It was found that only 3a,20a-dihydroxy-5 - pregnane-3-glucuronide was correlated to gestation age. This was due to the fact that progesterone levels change with regard to gestational age and thus, levels of 3a,20a-dihydroxy-5 -pregnane-3-glucuronide, a progesterone metabolite, correlate with gestation age. After adjusting for gestation age differences, the level of 3 a,20a-dihydroxy-5 -pregnane-3 -glucuronide remained significantly different between the two cohorts (Table 2). The remaining five metabolites have been shown to be unaffected by gestation age and therefore were not adjusted for their levels. Notably, all five metabolites were significantly different between the two cohorts (Table 2).

[0033] Thus, in one example, the method is as disclosed herein, wherein an increase or decrease in concentration of the measured values, or absence or presence of a metabolite, compared to the control group identifies an increased risk of miscarriage.

[0034] Two metabolites were found to be in higher levels in women who had spontaneous miscarriage compared to women who had healthy births. These two metabolites are tetrahydrocortisone, a stress-induced hormone, and hexanoylcarnitine, a metabolite belonging to the carnitine family and involved in fatty acid metabolism.

[0035] Thus, in one example, the method disclosed herein comprises detecting the absence or presence of at least two metabolites. In another example, the method disclosed herein comprises detecting the absence or presence of two metabolites. In yet another example, an increase in metabolite concentration or fold change of metabolite concentration indicates an increased risk of miscarriage.

[0036] On the other hand, four metabolites were found to be in lower levels in women who had spontaneous miscarriages. One of them is 3a,20a-dihydroxy-5 -pregnane-3-glucuronide, the glucuronide conjugate of pregnanediol and an inactive metabolic product of progesterone. As such, lower urinary levels of 3 a,20a-dihydroxy-5 -pregnane-3 -glucuronide are reflective of lower progesterone levels in the mother's body, as indeed seen in the maternal characteristics (Table 1).

[0037] Thus, in one example, the method disclosed herein comprises detecting the absence or presence of at least four metabolites. In another example, the method disclosed herein comprises detecting the absence or presence of four metabolites. In yet another example, an increased or a decreased metabolite concentration or fold change of metabolite concentration indicates an increased risk of miscarriage.

[0038] Three other carnitine products were also identified. These were propionylcarnitine, isovalerylcarnitine and 3-methylglutarylcarntine, whose levels were lower in mothers who miscarried.

[0039] Thus, in one example, the method disclosed herein comprises detecting the absence or presence of at least three metabolites. In another example, the method disclosed herein comprises detecting the absence or presence of three metabolites. In yet another example, a decreased metabolite concentration or fold change of metabolite concentration indicates an increased risk of miscarriage.

[0040] Thus, in one example, there is disclosed a method of identifying the risk of a miscarriage comprising detecting and measuring the concentration of at least one metabolite in a sample obtained from a subject. In another example, the absence or presence of one or more of the metabolites, compared to the control group, identifies an increased risk of miscarriage. In yet another example, the increase or decrease in the concentration of one or more of the metabolites, compared to the control group, identifies an increased risk of miscarriage.

[0041] In another example, there is disclosed a method of identifying the risk of a miscarriage comprising measuring the concentration of at least one metabolite in a sample obtained from a subject; and comparing the concentration of the at least one metabolite with the concentration of the respective metabolite in a control group.

[0042] In one example, there is disclosed a method of identifying the risk of a miscarriage comprising detecting and measuring the concentration of at least one metabolite in a sample obtained from a subject, wherein absence or presence of the metabolite, compared to the control group identifies an increased risk of miscarriage, wherein the metabolite is selected from the group consisting of tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, 3-methylglutarylcarnitine, hexanoylcarnitine and 3α,20α- dihydroxy-5 -pregnane-3 -glucuronide.

[0043] The changes in the level of concentrations, as disclosed herein, can be depicted and described in several, mathematically accurate ways. For example, a change in the level of a metabolite can be displayed as a change without value, thereby illustrating a deviation from the norm values or the control values. A change can be represented in absolute numbers (for example, concentration of mg/ml), or can be represented in relative numbers, for example as a percentage of change compared to the control, or as fold change compared to the control.

[0044] As used herein, the terms "increase" and "decrease" refer to the relative alteration of the concentration of a chosen metabolite in a subject or a sample obtained from a subject in comparison to the same metabolite in a control subject or a sample obtained therefrom. An increase thus indicates a change on a positive scale, whereas a decrease indicates a change on a negative scale. The term "change", as used herein, also refers to the difference between, for example, the concentration of a metabolite in comparison to the same metabolite in a control sample. However, the term "change" is without any valuation of the difference seen.

[0045] The increase or decrease of a metabolite as disclosed herein, can be disclosed as a fold change. The term "fold change" then refers to a measure describing how much a quantity changes, for example a concentration, going from an initial to a final value. In one example, fold change can be a measure of concentration of a metabolite when comparing said metabolite to a control. For example, an initial value of 30 and a final value of 60 corresponds to a fold change of 1 (or equivalently, a change to 2 times), or in common terms, a one-fold increase. A fold change is calculated as the ratio of the difference between final value and the initial value over the original value. Thus, if the initial value is A and final value is B, the fold change is (B - A)/A or equivalently, B/A - 1. As another example, a change from 80 to 20 would be a fold change of -0.75, while a change from 20 to 80 would be a fold change of 3 (meaning a change of 3 to 4 times the original value). [0046] The changes in concentration as disclosed herein can also be described in percentages. To this end, a change of value from 20 to 10 can be illustrated as a 50% change in value, while a change of value from 20 to 40 can be illustrated as a 100% change in value. The change in percentage can be provided as positive or negative values, whereby a negative percentage denotes a decrease in value over the starting value. A positive percentage thus denotes an increase in value over the starting value.

[0047] In one example, a change in value can be, but is not limited to, a fold change of between -10 to 10, between 0.01 to 10, between 1 to 10, between 0.01 to 1, between 0.1 to 0.2, between 0.2 to 0.3, between 0.3 to 0.4, between 0.4 to 0.5, between 0.5 to 0.6, between 0.6 to 0.7, between 0.7 to 0.8, between 0.8 to 0.9, between 0.9 to 1, between 1 to 5, between 1 to 2, between 1 to 2.5, between 2 to 3.5, between 0.15 to 0.25, between 0.25 to 0.35, between 0.35 to 0.45, between 0.45 to 0.55, between 0.55 to 0.65, between 0.65 to 0.75, between 0.75 to 0.85, between 0.85 to 0.95, between 0.95 to 1, about 0.01, about 0.02, about 0.03, about 0.04, about 0.05, about 0.06, about 0.07, about 0.08, about 0.09, about 0.1, about 0.11, about 0.12, about 0.13, about 0.14, about 0.15, about 0.16, about 0.17, about 0.18, about 0.19, about 0.20, about 0.21, about 0.22, about 0.23, about 0.24, about 0.25, about 0.26, about 0.27, about 0.28, about 0.29, about 0.30, about 0.31, about 0.32, about 0.33, about 0.34, about 0.35, about 0.36, about 0.37, about 0.38, about 0.39, about 0.40, about 0.41, about 0.42, about 0.43, about 0.44, about 0.45, about 0.46, about 0.47, about 0.48, about 0.49, about 0.50, about 0.51, about 0.52, about 0.53, about 0.54, about 0.55, about 0.56, about 0.57, about 0.58, about 0.59, about 0.60, about 0.61, about 0.62, about 0.63, about 0.64, about 0.65, about 0.66, about 0.67, about 0.68, about 0.69, about 0.70, about 0.71, about 0.72, about 0.73, about 0.74, about 0.75, about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, about 0.99, about 1, about 1.5, about 1.75, between -0.01 to -10, between -1 to -10, between -0.01 to -1, between -0.1 to -0.2, between -0.2 to -0.3, between -0.3 to -0.4, between -0.4 to -0.5, between -0.5 to -0.6, between -0.6 to -0.7, between -0.7 to -0.8, between -0.8 to -0.9, between -0.9 to -1, between -1 to -5, between -1 to -2, between -1 to -2.5, between -2 to -3.5, between -0.15 to -0.25, between -0.25 to -0.35, between -0.35 to -0.45, between -0.45 to -0.55, between -0.55 to -0.65, between -0.65 to -0.75, between -0.75 to -0.85, between - 0.85 to -0.95, between -0.95 to -1, about -0.01, about -0.02, about -0.03, about -0.04, about -0.05, about - 0.06, about -0.07, about -0.08, about -0.09, about -0.1, about -0.11, about -0.12, about -0.13, about -0.14, about -0.15, about -0.16, about -0.17, about -0.18, about -0.19, about -0.20, about -0.21, about -0.22, about -0.23, about -0.24, about -0.25, about -0.26, about -0.27, about -0.28, about -0.29, about -0.30, about -0.31, about -0.32, about -0.33, about -0.34, about -0.35, about -0.36, about -0.37, about -0.38, about -0.39, about -0.40, about -0.41, about -0.42, about -0.43, about -0.44, about -0.45, about -0.46, about -0.47, about -0.48, about -0.49, about -0.50, about -0.51, about -0.52, about -0.53, about -0.54, about -0.55, about -0.56, about -0.57, about -0.58, about -0.59, about -0.60, about -0.61, about -0.62, about -0.63, about -0.64, about -0.65, about -0.66, about -0.67, about -0.68, about -0.69, about -0.70, about -0.71, about -0.72, about -0.73, about -0.74, about -0.75, about -0.76, about -0.77, about -0.78, about -0.79, about -0.80, about -0.81, about -0.82, about -0.83, about -0.84, about -0.85, about -0.86, about -0.87, about -0.88, about -0.89, about -0.90, about -0.91, about -0.92, about -0.93, about -0.94, about -0.95, about -0.96, about -0.97, about -0.98, about -0.99, about -1, about -1.5, or about -1.75.

[0048] In one example, a change in value can be, but is not limited to, a change of between 1% to 100%, between 1% to 50%, between 50% to 100%, between 25% to 75%, between 75% to 100%, between 30% to 60%, about 1%, about 2%, about 3%, about 4%, about 5%, about 6%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, about 15%, about 16%, about 17%, about 18%, about 19%, about 20%, about 21%, about 22%, about 23%, about 24%, about 25%, about 26%, about 27%, about 28%, about 29%, about 30%, about 31%, about 32%, about 33%, about 34%, about 35%, about 36%, about 37%, about 38%, about 39%, about 40%, about 41%, about 42%, about 43%, about 44%, about 45%, about 46%, about 47%, about 48%, about 49%, about 50%, about 51%, about 52%, about 53%, about 54%, about 55%, about 56%, about 57%, about 58%, about 59%, about 60%, about 61%, about 62%, about 63%, about 64%, about 65%, about 66%, about 67%, about 68%, about 69%, about 70%, about 71%, about 72%, about 73%, about 74%, about 75%, about 76%, about 77%, about 78%, about 79%, about 80%, about 81%, about 82%, about 83%, about 84%, about 85%, about 86%, about 87%, about 88%, about 89%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99%, or about 100%.

[0049] The identities of these metabolites were then confirmed by comparing their MS/MS spectra with that of commercial standards purchased (FIG. 6). Key features in the MS/MS spectra for commercially available 3a,20a-dihydroxy-5 -pregnane-3-glucuronide, tetrahydrocortisone, propionylcarnitine and isovalerylcarnitine matched those seen in the urine samples, thus confirming their identities. However, additional features were seen in the urine samples of 3-methylglutarylcarnitine and hexanoylcarnitine when compared to the commercial standards, indicating that these are possible structural isomers of the metabolites identified in the samples.

[0050] This study outlined herein compares urine metabolites between pregnant women with and without spontaneous miscarriage. These findings contribute to the assembly of metabolic profiles of women with healthy pregnancies and women with spontaneous miscarriages, whereby leading better understanding of the interactions of the derivatives of urine metabolites, thereby improving the understanding of the pathophysiology behind spontaneous miscarriage. It is shown that the metabolites disclosed herein can serve as a predictive and diagnostic tool for the risk and likelihood of miscarriage. Through the screening and comparison of urine metabolite profiles, significant differences in six urine metabolites were found amongst pregnant women with and without spontaneous miscarriages.

Progesterone metabolite

[0051] 3a,20a-dihydroxy-5 -pregnane-3-glucuronide, a progesterone -derived steroid-glucuronide, was found to be significantly lower in women with spontaneous miscarriages. This is in line with previous studies, which found that the same urinary metabolite was significantly lowered in ectopic pregnancies. [0052] Glucuronidation is a metabolic pathway for the degradation of steroids, and is an important step for the conversion of steroids to hydrophilic molecules to facilitate excretion. Since steroid- glucuronides are the metabolites of steroids, their levels in urine are reflective of steroidal levels in the bloodstream. A low 3a,20a-dihydroxy-5 -prenane-3-glucuronide level reflects low progesterone levels in women with spontaneous miscarriages. Progesterone promotes uterine quiescence and has been shown to be critical for the continuation of pregnancy and reduction in uterine contractions. Clinical studies have also shown that women who miscarry have significantly lower serum progesterone.

[0053] Progesterone not only supports endometrial development, it enhances blood flow and oxygen delivery through greater nitric oxide production. The administration of a progesterone receptor antagonist, mifepristone, has also been shown to cause miscarriage, further cementing the pivotal role of progesterone in maintaining pregnancy.

[0054] Progesterone triggers the expression of Progesterone-induced-blocking factor (PIBF). The fetal presence in the maternal body triggers an immune response as it contains cells from the paternal side as well. A successful pregnancy would thus require suppression of the maternal immune response against the fetus. Progesterone-induced-blocking factor (PIBF) has been shown to modulate the cytokine production from a pro-inflammatory Thl response to that of an anti-inflammatory Th2 response. Moreover, PIBF directly modulates the cytotoxic effect of decidual lymphocytes, protecting the fetus from these immune cells. It has been shown that lower PIBF levels result in pathological pregnancies such as pre-term birth or pre -eclampsia. Thus, the indispensability of progesterone in pregnancy aligns well with the finding of low progesterone metabolites in the urine of women who miscarry.

[0055] Thus, urinary 3a,20a-dihydroxy-5 -pregnane-3-glucuronide, a downstream metabolic product of progesterone excreted in urine, has shown to be a metabolite capable of indicating the risk of undergoing a miscarriage.

[0056] Thus, in one example, one of the metabolites is 3a,20a-dihydroxy-5 -pregnane-3-glucuronide. In another example, the metabolite is urinary or serum 3a,20a-dihydroxy-5 -pregnane-3-glucuronide. In yet another example, the metabolite is 3a,20a-dihydroxy-5 -pregnane-3-glucuronide. In a further example, a change in the concentration of 3a,20a-dihydroxy-5 -pregnane-3-glucuronide is indicative of an increased risk of miscarriage. In a yet another example, a decrease in the concentration of 3α,20α- dihydroxy-5 -pregnane-3-glucuronide is indicative of an increased risk of miscarriage. In a further example, a decrease of 3a,20a-dihydroxy-5 -pregnane-3-glucuronide compared to the control is a decrease of at least 0.30 (or -0.31) fold change in concentration compared to the control.

Tetrahydrocortisone

[0057] Based on the analysis performed in the experiments disclosed herein, levels of urinary tetrahydrocortisone were shown to be about 40% higher in women who had spontaneous miscarriage compared to those who went on to have healthy births. [0058] Tetrahydrocortisone is a urinary metabolite of cortisone derived from the reduction of cortisone by 5 -reductase. Cortisone itself is converted from Cortisol via the enzyme 11β -hydroxy steroid dehydrogenase type 2. Cortisol binds to mineralocorticoid and glucocorticoid receptors to regulate homeostasis in several important cellular process such as energy homeostasis, metabolism, triggering adequate responses to stress and limiting inflammation.

[0059] Cortisone, on the other hand, also binds to mineralocorticoid and glucocorticoid receptors, albeit with lower affinity. Thus, with high levels of Cortisol present, cortisone is produced to modulate the activation of these receptors. Cortisol levels increase when stress levels are high.

[0060] Studies in pregnant sheep showed that higher levels of Cortisol led to alterations in uterine blood flow and maternal glucose concentrations. This, in turn, led to changes in uteroplacental metabolism and consequently affected fetal nutrition, leading to impaired fetoplacental growth and poor fetal viability. An increase in Cortisol level has also been shown to reduce the fetal umbilical uptake of glucose due to a larger uptake by the uteroplacental tissue in the maternal uterus in pregnant sheep. In its place, the maternal ewe increases lactate production and switches the fetal metabolism to that of aerobic glycolysis, a metabolic strategy of the early embryo. This metabolic strategy may be detrimental to fetal health as seen in studies with increased Cortisol levels in pregnant ewes. In humans, higher Cortisol levels have also been associated with pregnancy loss, indicative of higher fetal stress in women who eventually miscarry.

[0061] In addition to a direct effect on fetal metabolism, Cortisol affects progesterone levels as well. Cortisol stimulates placental enzymes responsible for the biosynthesis of estradiol from pregnenolone, causing an increase in estrogen secretion and a subsequent decrease in progesterone production. This might explain the decrease in progesterone levels observed in the women who miscarried. In addition, higher estrogen levels induce the release of prostaglandin F2a from the placenta, causing enhanced myometrial responses to oxytocin and stimulating contractions. This may cause unfavorable pregnancy conditions.

[0062] Thus, in one example, one of the metabolites is tetrahydrocortisone. In another example, the metabolite is urinary or serum tetrahydrocortisone. In yet another example, the metabolite is tetrahydrocortisone. In a further example, a change in the concentration of tetrahydrocortisone is indicative of an increased risk of miscarriage. In a yet another example, an increase in the concentration of tetrahydrocortisone is indicative of an increased risk of miscarriage. In a further example, an increase of tetrahydrocortisone compared to the control is an increase of at least 0.40 fold change in concentration compared to the control.

Carnitine family

[0063] Carnitine biosynthesis occurs mainly in the liver and kidneys from the amino acids lysine and methionine. Carnitines play an important role in fatty acid metabolism. They are obligatory co-factors in the transport of fatty acids with long chain acyl groups into the mitochondria. These fatty acids are then broken down via fatty acid oxidation to produce energy. In the present study, it was seen that a higher level of hexanoylcarnitine (C6) was present in women who miscarry when compared to women who went on to have healthy births. The increase in carnitine levels in urine in the form of acylcarnitine is characteristic of secondary carnitine deficiency and is most likely caused by an accumulation of organic acids. Secondary carnitine deficiency can occur due to, or in association with, defects in fatty acid oxidation metabolism.

[0064] Hexanoylcarnitine is converted from the long chain acyl coenzyme A (acyl-CoA) via carnitine palmitoyltransferase- 1 (CPTl), which swaps the CoA moiety for Carnitine. Acylcarnitine from the cytosol is then exchanged with carnitine from the mitochondria using carnitine -acylcarnitine translocase (CACT). After acylcarnitine is successfully transported into the mitochondria, acylcarnitine is converted back to acyl-CoA and carnitine, catalyzed by carnitine palmitoyltransferase-2 (CPT2). Subsequently, acyl-CoA can proceed to participate in β-oxidation and the citric acid cycle (FIG. 5).

[0065] Accumulation of acylcarnitines has been linked to fatty acid oxidation deficiency due to a defect in carnitine-acylcarnitine translocase, and acylcarnitines directly reflect the oxidation rate of fatty acid. A clinical report of a mother who has had previous miscarriages, but gave birth to a baby with a lethal deficiency of CACT, indicates that maternal heterozygosity for CACT deficiency with fetal homozygosity for the same deficiency can contribute to impaired metabolism and toxic metabolites formation in both fetus and placenta. Thus, fatty acid oxidation is understood to play a major role in pregnancy maintenance.

[0066] Thus, in one example, one of the metabolites is hexanoylcarnitine. In another example, the metabolite is urinary or serum hexanoylcarnitine. In yet another example, the metabolite is hexanoylcarnitine. In a further example, a change in the concentration of hexanoylcarnitine is indicative of an increased risk of miscarriage. In a yet another example, an increase in the concentration of hexanoylcarnitine is indicative of an increased risk of miscarriage. In a further example, an increase of hexanoylcarnitine compared to the control is an increase of at least 0.38 fold change in concentration compared to the control.

[0067] It was also found that propionylcarnitine (C3) levels were about 40% lower in women who miscarried compared to those who went on to have healthy births. This implies a lower level of carnitine and thus, lower levels of fatty acid metabolism in women who miscarry.

[0068] Thus, in one example, one of the metabolites is propionylcarnitine. In another example, the metabolite is urinary or serum propionylcarnitine. In yet another example, the metabolite is propionylcarnitine. In a further example, a change in the concentration of propionylcarnitine is indicative of an increased risk of miscarriage. In a yet another example, a decrease in the concentration of propionylcarnitine is indicative of an increased risk of miscarriage. In a further example, a decrease of propionylcarnitine compared to the control is a decrease of at least 0.41 (or -0.41) fold change in concentration compared to the control. [0069] Previous studies showed that enzymes involved in β-oxidation exhibited increased activities in the placenta early in gestation and were less active nearing delivery, indicating the importance of fatty acid oxidation to the placental provision of energy. Since the fetus draws considerable energy from the placenta during development, lower levels of carnitine imply less available energy for the fetal growth, survival and metabolic processes, leading to unfavorable pregnancy conditions. This is supported by previous studies showing that fatty acid oxidation plays a major role in energy generation by the placenta.

[0070] In addition, impairment in mitochondrial β-oxidation of fatty acids can cause fasting induced hypoglycemia and cardiovascular collapse. In mice, deficiency of the mitochondrial trifunctional protein (MTP) responsible for catalysis in the last three steps of fatty acid β-oxidation causes stunting of fetal development, hypoglycemia and sudden death in neonates, further illustrating the importance of unimpeded β-oxidation in ensuring fetal survival.

[0071] Studies in mice further reveal that β-oxidation enzymes are present in trophoblast cells, and defects in the OCTN2 transporter result in reduced carnitine accumulation in both fetus and placenta, adversely affecting fatty acid metabolism important to fetal and placental development. Carnitines are also understood to play important roles in preventing over-accumulation of acyl compounds occurring in organic academia, including propionic acidemia and isovaleric acidemia, any of which would be toxic to the cell.

[0072] Lower levels of isovalerylcarnitine in women who miscarry may also indicate lower isovaleryl-CoA, a metabolite of leucine, and correspondingly lower levels of leucine. Leucine, an essential amino acid, is important for protein synthesis and a significant decrease in the rate of transamination of leucine contributes to maternal protein and nitrogen accretion. Thus, lower levels of leucine may adversely affect fetal development and contribute to eventual miscarriage.

[0073] Thus, in one example, one of the metabolites is isovalerylcarnitine. In another example, the metabolite is urinary or serum isovalerylcarnitine. In yet another example, the metabolite is isovalerylcarnitine. In a further example, a change in the concentration of isovalerylcarnitine is indicative of an increased risk of miscarriage. In a yet another example, a decrease in the concentration of isovalerylcarnitine is indicative of an increased risk of miscarriage. In a further example, a decrease of isovalerylcarnitine compared to the control is a decrease of at least 0.32 (or -0.32) fold change in concentration compared to the control.

[0074] Reduced levels of 3-methylglutarylcarnitine, a urine metabolite of 3 -hydroxy-3 -methyl - glutaryl-coenzyme A (HMG-CoA), signal lower HMG-CoA levels in women who miscarry.

[0075] HMG-CoA is converted to mevalonate, under catalysis by HMG-CoA reductase. Mevalonate is a precursor molecule to many biologically important molecules, including cholesterol and steroid hormones. Reduced HMG-CoA levels are understood to be a direct cause of lower progesterone levels, since pregnenolone, synthesized from cholesterol, is an important precursor for progesterone. In addition, possible shunting of pregnenolone to produce Cortisol through the glucocorticoid pathway, at the expense of progesterone, may contribute to the raised Cortisol levels and reduced progesterone levels in women who miscarry as well.

[0076] Thus, in one example, one of the metabolites is 3-methylglutarylcarnitine. In another example, the metabolite is urinary or serum 3-methylglutarylcarnitine. In yet another example, the metabolite is 3- methylglutarylcarnitine. In a further example, a change in the concentration of 3-methylglutarylcarnitine is indicative of an increased risk of miscarriage. In a yet another example, a decrease in the concentration of 3-methylglutarylcarnitine is indicative of an increased risk of miscarriage. In a further example, a decrease of 3-methylglutarylcarnitine compared to the control is a decrease of at least 0.59 (or -0.59) fold change in concentration compared to the control.

[0077] Together, these changes in carnitine metabolite levels indicate defects in amino acid oxidation and/or fatty acid oxidation process and can be caused by any one or more of the several players involved in the pathway. This reduces the energy output by the maternal placenta and directly affects the fetus negatively. In addition, with unmet fetal metabolic demands, fetal stress will likely be induced, contributing to raised tetrahydrocortisone levels and the eventual miscarriage of the fetus.

[0078] Thus, in one example, the method comprises measuring at least two, at least three, at least four, at least five, or six metabolites. In another example, the method comprises measuring one, two, three, four, five or six metabolites, as described herein.

[0079] In another example, the one or more metabolites used in the method disclosed herein is/are selected from the group consisting of tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, 3- methylglutarylcarnitine, hexanoylcarnitine, 3a,20a-dihydroxy-5 -pregnane-3-glucuronide and combinations thereof.

[0080] In another example, two metabolites are used in the method as disclosed herein, wherein two metabolites are selected from the group consisting of tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, 3-methylglutarylcarnitine, hexanoylcarnitine and 3a,20a-dihydroxy-5 -pregnane-3- glucuronide. In yet another example, the two metabolites are selected from the group consisting of tetrahydrocortisone and propionylcarnitine; tetrahydrocortisone and isovalerylcarnitine; tetrahydrocortisone and 3-methylglutarylcarnitine; tetrahydrocortisone and hexanoylcarnitine; tetrahydrocortisone and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; propionylcarnitine and isovalerylcarnitine; propionylcarnitine and 3-methylglutarylcarnitine; propionylcarnitine and hexanoylcarnitine; propionylcarnitine and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; isovalerylcarnitine and 3-methylglutarylcarnitine; isovalerylcarnitine and hexanoylcarnitine; isovalerylcarnitine and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; 3-methylglutarylcarnitine and hexanoylcarnitine; 3-methylglutarylcarnitine and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; and hexanoylcarnitine and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide.

[0081] In one example, three metabolites are used in the method as disclosed herein, wherein three metabolites are selected from the group consisting of tetrahydrocortisone, propionylcarnitine, and isovalerylcarnitine; tetrahydrocortisone, propionylcarnitine, and 3 -methylglutarylcarnitine; tetrahydrocortisone, propionylcarnitine, and hexanoylcarnitine; tetrahydrocortisone, propionylcarnitine, and 3 a,20a-dihydroxy-5 -pregnane-3 -glucuronide; tetrahydrocortisone, isovalerylcarnitine, and 3- methylglutarylcarnitine; tetrahydrocortisone, isovalerylcarnitine, and hexanoylcarnitine; tetrahydrocortisone, isovalerylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; tetrahydrocortisone, 3-methylglutarylcarnitine, and hexanoylcarnitine; tetrahydrocortisone, 3- methylglutarylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; tetrahydrocortisone, hexanoylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; propionylcarnitine, isovalerylcarnitine, and 3-methylglutarylcarnitine; propionylcarnitine, isovalerylcarnitine, and hexanoylcarnitine; propionylcarnitine, isovalerylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3- glucuronide; propionylcarnitine, 3-methylglutarylcarnitine, and hexanoylcarnitine; propionylcarnitine, 3- methylglutarylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; propionylcarnitine, hexanoylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; isovalerylcarnitine, 3- methylglutarylcarnitine, and hexanoylcarnitine; isovalerylcarnitine, 3-methylglutarylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; isovalerylcarnitine, hexanoylcarnitine, and 3α,20α- dihydroxy-5 -pregnane-3-glucuronide; and 3-methylglutarylcarnitine, hexanoylcarnitine, and 3α,20α- dihydroxy-5 -pregnane-3 -glucuronide.

[0082] In one example, four metabolites are used in the method as disclosed herein, wherein four metabolites are selected from the group consisting of tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, and 3-methylglutarylcarnitine; tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, and hexanoylcarnitine; tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; tetrahydrocortisone, propionylcarnitine, 3- methylglutarylcarnitine, and hexanoylcarnitine; tetrahydrocortisone, propionylcarnitine, 3- methylglutarylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; tetrahydrocortisone, propionylcarnitine, hexanoylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; tetrahydrocortisone, isovalerylcarnitine, 3-methylglutarylcarnitine, and hexanoylcarnitine; tetrahydrocortisone, isovalerylcarnitine, 3-methylglutarylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3- glucuronide; tetrahydrocortisone, isovalerylcarnitine, hexanoylcarnitine, and 3a,20a-dihydroxy-5 - pregnane-3-glucuronide; tetrahydrocortisone, 3-methylglutarylcarnitine, hexanoylcarnitine, and 3α,20α- dihydroxy-5 -pregnane-3-glucuronide; propionylcarnitine, isovalerylcarnitine, 3 methylglutarylcarnitine, and hexanoylcarnitine; propionylcarnitine, isovalerylcarnitine, 3 methylglutarylcarnitine, and 3α,20α- dihydroxy-5 -pregnane-3-glucuronide; propionylcarnitine, isovalerylcarnitine, hexanoylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; propionylcarnitine, 3-methylglutarylcarnitine, hexanoylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; and isovalerylcarnitine, 3- methylglutarylcarnitine, hexanoylcarnitine, and 3 a,20a-dihydroxy-5 -pregnane-3 -glucuronide.

[0083] In one example, five metabolites are used in the method as disclosed herein, wherein five metabolites are selected from the group consisting of tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, and 3-methylglutarylcarnitine, hexanoylcarnitine; tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, and 3-methylglutarylcarnitine, 3a,20a-dihydroxy-5 -pregnane-3- glucuronide; tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, hexanoylcarnitine, and 3α,20α- dihydroxy-5 -pregnane-3-glucuronide; tetrahydrocortisone, propionylcarnitine, 3- methylglutarylcarnitine, hexanoylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide; tetrahydrocortisone, isovalerylcarnitine, 3-methylglutarylcarnitine, hexanoylcarnitine, and 3α,20α- dihydroxy-5 -pregnane-3-glucuronide; and propionylcarnitine, isovalerylcarnitine, 3- methylglutarylcarnitine, hexanoylcarnitine, and 3a,20a-dihydroxy-5 -pregnane-3-glucuronide.

[0084] In one example, six metabolites are used in the method as disclosed herein, wherein the six metabolites are tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, 3-methylglutarylcarnitine, hexanoylcarnitine and 3α,20α -dihydroxy-5 -pregnane-3-glucuronide.

[0085] As disclosed herein, in some examples, the method as disclosed herein refers to a control group or a sample obtained from a control subject. As used herein, the term "control", when used in conjunction with a sample, a reference, or a reference group, refers to a subject having undergone a full- term birth, a group of subjects having undergone full-term births, or one or more samples obtained from a subject having undergone a full-term birth.

[0086] Detection of the metabolites, the levels of said metabolites or concentration of said metabolites can be detected and/or measured using a method selected from the group consisting of chromatography, antibody-based assays, quantitative assays, semi-quantitative assays, qualitative assays, mass spectrometry, spectroscopy, multiplex assays, immunoprecipitation, electrophoresis, nuclear magnetic resonance (NMR), NMR spectroscopy, proton nuclear magnetic resonance ('H-NMR) and combinations thereof. Based on the underlying fundamental principles required in order to use any of the detection methods mentioned herein, a person skilled in the art would be able to determine the appropriate conditions and processes required by each of the detection methods disclosed herein. For example, if one were to use an antibody-based assay, such as, for example an enzyme -linked immunosorbent assay (ELISA) to detect the metabolites disclosed herein, it is understood that antibodies capable of fulfilling ELISA-dependent criteria (for example, sensitivity, coupling to a fluorescent or colorimetric agent) are to be used. When using chromatography, for example, a person skilled in the art would be aware to choose appropriate immobilised and mobile phase for one or more analytes in question.

[0087] Examples of chromatographic or separation methods are, but are not limited to, thin layer chromatography (TLC), column chromatography, gas chromatography, liquid chromatography, affinity chromatography, size -exclusion chromatography, two-dimensional chromatography, fast protein liquid chromatography, and the like. Combinations of such chromatographic methods are, but are not limited to liquid chromatography/ mass spectrometry (LC/MS), gas chromatography/mass spectrometry (GC/MS) and the like.

[0088] Examples of semi-quantitative methods are, but are not limited to, antibody-conjugated methods. Examples of antibody-conjugated methods are, but are not limited to, enzyme -linked immunosorbent assay (ELISA), direct ELISA, sandwich ELISA, immunoprecipitation (IP) methods, antibody-conjugated detection methods, antigen-affinity chromatography, immunohistochemistry and immunofluorescence.

[0089] Examples of quantitative methods are, but are not limited to, enzyme -linked immunosorbent assay (ELISA), liquid chromatograph, liquid chromatography/mass spectrometry, and surface plasmon resonance (SPR).

[0090] Examples of qualitative methods are, but are not limited to, liquid chromatography, immunohistochemistry, immunofluorescence, thin layer chromatography (TLC), column chromatography, gas chromatography, affinity chromatography, size-exclusion chromatography, two- dimensional chromatography, fast protein liquid chromatography, and the like.

[0091] Examples of mass spectrometry -based methods are, but are not limited to, a matrix-assisted laser desorption/ionization source with a time-of-flight mass analyser (MALDI-TOF), capillary electrophoresis-mass spectrometry and the like.

[0092] Examples of spectroscopy-based assay are, but are not limited to, surface enhanced Raman spectroscopy (SERS), nuclear magnetic resonance (NMR), NMR spectroscopy, proton nuclear magnetic resonance ('H-NMR) and Raman spectroscopy.

[0093] Where the method used is surface enhanced Raman spectroscopy (SERS), the spectra generated for the sample metabolites are compared to, for example, reference spectra generated using standard calibration curves. These may be established using commercially available versions of the metabolites disclosed herein. In one example, a standard calibration curve for the pure metabolites is established. These metabolites can include, but are not limited to, 3a,20a-dihydroxy-5 -pregnane-3- glucuronide (pregnane) and tetrahydrocortisone, and is done by measuring the SERS responses of each of the metabolites at different concentrations. Quantitative information on the amount of biomarkers present can then be derived based on the changes in SERS intensity. As a proof -of -concept, preliminary data using 3a,20a-dihydroxy-5 -pregnane-3-glucuronid on superhydrophobic SERS chip is shown in FIG 7a. Strong SERS signals showing detection limits at concentrations of 1 nM are achieved (FIG. 7b), along with a linearity range spanning concentrations of 1 μΜ to 1 nM (FIG. 7c). This data is a clear indication that the superhydrophobic SERS chips used herein can detect the metabolites of interest.

[0094] Examples of immunoprecipitation-based methods are, but are not limited to, individual protein immunoprecipitation (IP), tagged proteins, and combinations thereof.

[0095] Also disclosed herein is a kit for identifying and/or determining the risk of a miscarriage according to the method as disclosed herein. Examples of such a kit are, but are not limited to, an enzyme -linked immunosorbent assay (ELISA) kit, a test strip kit, a dip stick kit, and a microchip test kit. In one example, a kit as disclosed herein comprises one metabolite standard for each of the metabolites as described herein. Such a metabolite standard can be, for example, an isotope -tagged metabolite [0096] At present, there is no triage for subjects at risk of miscarriage. It is understood that one of the etiologies of miscarriage is luteal phase deficiency. Therefore, supplementing subjects with compounds which have an effect on luteal phase deficiency, for example but not limited to, progestogen, or derivatives thereof, is understood to reduce the risk of miscarriage in such subjects. For example, progestogen causes a shift in T-lymphocyte -dependent immune response in the subject from Thl to Th2, thereby ameliorating the risk of rejection of the fetal allograft in the pregnant subject.

[0097] Thus, after having been identified as being at risk of miscarriage, in one example, the subject is to be administered dydrogesterone (also known as duphaston, isopregnenone, dehydroprogesterone, didrogesteron, 6-dehydroretroprogesterone, or 9 , 10a-pregna-4,6-diene-3,20-dione) or progesterone (also known as pregn-4-ene-3,20-dione, prometrium, utrogestan, endometrin or crinone).

[0098] A person skilled in the art would appreciate that such a drug administration may vary depending on various factors, for example, the subject's weight, physiological condition, urgency of treatment required and overall medical condition. Thus, in one example, the subject can be required to be administered a treatment once, twice, or three times a day. In another example, the subject can be administered a drug immediately after having been determined to be at risk of miscarriage. In another example, the drug can be administered within 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 18 hours, or 24 hours after having been determined to be at risk of a miscarriage. In another example, the drug is to be administered during the day. In a further example, the drug is to be administered at night.

[0099] In another example, the subject is to be administered the drug three times a day. In another example, the subject is to be administered the drug three times a day for at least one week. In another example, the subject is to be administered the drug three times a day for two weeks in full. In another example, the subject is to be administered the drug three times a day for at least two weeks. In another example, the subject is to be administered the drug once a day, at night, for two weeks.

[00100] In another example, the subject is to be administered dydrogesterone once, immediately, followed by three times daily for 2 weeks. In another example, the subject is to be administered dydrogesterone orally, once, immediately, followed by three times daily for 2 weeks.

[00101] In another example, the subject is to be administered progesterone once a day, at night, for 2 weeks. In a further example, the subject is to be administered progesterone vaginally, once a day, at night, for 2 weeks. In another example, the subject is to be administered progesterone orally, once a day, at night, for 2 weeks.

[00102] In one example, the subject is to be treated with between 5 mg to 250 mg, between 8 mg to 12 mg, between 10 mg to 20 mg, 30 mg to 50 mg, between 45 mg to 85 mg, between 75 mg to 100 mg, between 80 mg to 120 mg, between 150 mg to 200 mg, between 180 mg to 250 mg, between 75 mg to 100 mg 18 mg to 26 mg, between 38 mg to 42 mg, about 5 mg, about 10 mg, about 15 mg, about 20 mg, about 25 mg, about 30 mg, 35 mg, 40 mg, 45 mg, about 50 mg, about 60 mg, about 65 mg, about 70 mg, about 75 mg, about 80 mg, about 85 mg, about 90 mg, about 95 mg, about 100 mg, about 105 mg, about 110 mg, about 115 mg, about 120 mg, about 125 mg, about 130 mg, about 135 mg, about 140 mg, about 145 mg, about 150 mg, about 155 mg, about 160 mg, about 165 mg, about 170 mg, about 175 mg, about 180 mg, about 185 mg, about 190 mg, about 195 mg, about 196 mg, about 197 mg, about 198 mg, about 199 mg, about 200 mg, about 201 mg, about 202 mg, about 203 mg, about 204 mg, about 205 mg, about 225 mg, or about 250 mg of the drug or compound. This amount can be per administration, or as a treatment total over a specific period of time. In another example, the subject is to be administered 10 mg of the drug. In another example, the subject is to be administered 40 mg of the drug. In another example, the subject is to be administered 200 mg of the drug.

[00103] In one example, the subject is to be administered 40 mg of the drug, followed by 10 mg of the drug three times a day for two weeks. In another example, the subject is to be administered 40 mg of the drug immediately or within 24 hours, followed by 10 mg of the drug three times a day for two weeks. In yet example, the subject is to be administered 40 mg dydrogesterone immediately or within 24 hours, followed by 10 mg dydrogesterone three times a day for two weeks. In a further example, the subject is to be administered 200 mg of progesterone vaginally, once a day, at night, for 2 weeks. In another example, the subject is to be administered 200 mg of progesterone orally, once a day, at night, for 2 weeks.

[00104] In one example, the drug can be administered orally, vaginally, subcutaneously or intravenously.

[00105] The invention illustratively described herein may suitably be practiced in the absence of any element or elements, limitation or limitations, not specifically disclosed herein. Thus, for example, the terms "comprising", "including", "containing", etc. shall be read expansively and without limitation. Additionally, the terms and expressions employed herein have been used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the inventions embodied therein herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention.

[00106] As used in this application, the singular form "a," "an," and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a genetic marker" includes a plurality of genetic markers, including mixtures and combinations thereof.

[00107] As used herein, the term "about", in the context of concentrations of components of the formulations, typically means +/- 5% of the stated value, more typically +/- 4% of the stated value, more typically +/- 3% of the stated value, more typically, +/- 2% of the stated value, even more typically +/- 1% of the stated value, and even more typically +/- 0.5% of the stated value. [00108] The word "substantially" does not exclude "completely" e.g. a composition which is "substantially free" from Y may be completely free from Y. Where necessary, the word "substantially" may be omitted from the definition of the invention.

[00109] Throughout this disclosure, certain embodiments may be disclosed in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosed ranges. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[00110] Certain embodiments may also be described broadly and generically herein. Each of the narrower species and sub-generic groupings falling within the generic disclosure also form part of the disclosure. This includes the generic description of the embodiments with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein.

[00111] The invention has been described broadly and generically herein. Each of the narrower species and sub-generic groupings falling within the generic disclosure also form part of the invention. This includes the generic description of the invention with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein.

[00112] Other embodiments are within the following claims and non- limiting examples. In addition, where features or aspects of the invention are described in terms of Markush groups, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.

EXPERIMENTAL SECTION

Patient Recruitment

[00113] A case-control study was performed on 80 pregnant women, aged 21 years and above. Patients presenting at the KK Women's and Children's Hospital (KKH) Singapore, 24-hour Women's Clinic from September 2013 to June 2015 were recruited. Inclusion criteria were (i) patients with a single intrauterine pregnancy between 6 to 10 weeks of gestation (confirmed and dated by ultrasonography) and (ii) patients presenting with pregnancy-related per vaginam bleeding. Women with previous episodes of per vaginam bleeding or women treated with progesterone for previous per vaginam bleeding in the current pregnancy, or women diagnosed with inevitable miscarriage, missed miscarriage, blighted ovum or women who are planning to terminate the pregnancy were excluded. [00114] Maternal blood samples were taken to measure serum progesterone level at presentation. Blood was collected in plain tubes and centrifuged for 10 minutes at 3000 g within 2 hours of collection. Serum progesterone level was measured in the KKH clinical laboratory using a commercial ARCHITECT progesterone kit (Abbott, Ireland). Urine samples were collected at presentation for metabolite analysis. Covariates for the analysis were maternal demographics, health, obstetric and lifestyle factors collected by an investigator administered questionnaire in either English or Mandarin.

Outcome measures and follow-up

[00115] The primary outcome measured was spontaneous miscarriage, defined as self -reported uterine evacuation after inevitable or incomplete miscarriage, or complete miscarriage with an empty uterus, by the 16^th week of gestation. All participants were contacted at the 16^th week of pregnancy to verify their pregnancy status. 40 patients experienced spontaneous miscarriage whilst pregnancy was ongoing in 40 patients.

Urine Metabolite Profiling using UPLC-MS

[00116] Methods of urine metabolite profiling were adapted from a previously published protocol, and performed on ACQUITY UPLC/Xevo G2-XS QTof (Waters, Manchester, UK) equipped with an electrospray source operating at either positive (ESI+) or negative ionization mode (ESI-). The source temperature was set at 120 °C with a cone gas flow of 50 L/h and a desolvation gas temperature of 450 °C with a desolvation gas flow of 1000 L/h. The capillary voltage was set to 2 kV in the positive ionization mode, and 1.8 kV in the negative ionization mode. The cone voltage was set at 30 V. 3 μΐ of sample was injected into 100 mm x 2.1 mm, 1.7 μιη HSS T3 column (Waters) held at 40°C using the ACQUITY UPLC system from Waters. Elution was performed with a linear gradient of 1 -15% B over 1-3 minutes, 15-50% B over 3-6 minutes, 50-95% B over 6-9 minutes, and finally the gradient was held at 95% for 1.1 minutes. In both the positive and negative ionization modes, mobile phase A was water with 0.1% formic acid and mobile phase B was acetonitrile with 0.1% formic acid. The column flow rate was 0.5 ml/min. Profile data were collected from 50 to 1 ,200 m/z for both the positive and negative ionization mode with a scan time of 0.15 seconds over a 12 minute analysis. Leucine enkephalin, at a concentration of 200 ng/ml, was used as the lock mass with a flow rate of 5 μΐ/min. It had a m/z of 556.2771 and 554.2615 in the positive and negative ionization mode respectively. MassLynx software from Waters was used to control the system and data acquisition. The UPLC-MS analysis in this study employed a QC strategy that was previously described. Firstly, to condition the column, QC sample was run 10 times before initiating the runs for the actual samples. Next, the QC sample was injected every time after the injection of 5 samples, and at the start and end of the analysis run. During the sample analysis, a total of 17 QC samples were injected, for the purpose of monitoring instrument stability and analyte reproducibility. After sample analysis, a series of diluted QC samples (1 :9, 1 :4, 1 :2, 1 : 1) in the reconstitution solvent mixture was injected. Finally, a blank sample was injected at the start and end of the analysis. Data Pre-processing

[00117] Pre-processing of MS data (in RAW format), which includes automatic alignment using retention time, peak picking, and deconvolution, was performed using Progenesis QI v2.0 (Nonlinear Dynamics, Newcastle, UK). Samples were median normalized and log transformed. Features near the solvent front, with a retention time less than 0.55 minute, and chromatographic peak width less than 0.03 minute were not included for further analysis. Features with an intensity of less than 3,000 were also discarded. A data matrix containing the samples analysed versus detected features and their corresponding raw and normalized abundance values was produced for downstream analysis and processing in Python and MATLAB (Mathworks, Natick, MA). Using the quality control (QC) samples, the unreliable features were removed following the procedures outlined in a previous publication. Features were only accepted if they were present in all of the quality control (QC) samples, and revealed a coefficient of variation (COV) less than 10%. Finally, raw abundance of features that did not display good linearity in the dilution quality control (QC) samples, as defined by R² < 0.9 and p value > 0.05, were also excluded from downstream analysis.

Multivariate Data Analysis

[00118] Principal component analysis (PCA) was performed in MATLAB and Python to visualize clustering and identify outliers. Orthogonal projection to latent structure (OPLS) analysis was performed to maximize separation between case and control samples while minimizing variability unrelated to the separation using the "ropls" package implemented in R. The measurement values were standardized prior to OPLS analysis and adjusted for age and body mass index (BMI). The optimal number of orthogonal components was determined using 5 -fold cross validation. The R2Y parameter was calculated to provide an indication of the variability explained by the model and the cross validated Q2Y parameter was calculated to indicate the model performance in cross validation datasets. After statistical modelling, top ranking features with false discovery rate (FDR) adjusted p-value <0.05, were selected for further downstream identification.

Statistical Analysis

[00119] T-tests were used for statistical comparison of differences in maternal characteristics and metabolite levels between the miscarriage and full-term birth groups. Analysis of co-variances was used to determine if metabolite levels were linked to gestation age.

Claims

A method of identifying the risk of a miscarriage comprising detecting and measuring the concentration of at least one metabolite in a sample obtained from a subject, wherein absence or presence of the metabolite, compared to the control group identifies an increased risk of miscarriage, wherein the metabolite is selected from the group consisting of tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, 3-methylglutarylcarnitine, hexanoylcarnitine and 3α,20α - dihydroxy-5 -pregnane-3 -glucuronide.

A method of identifying the risk of a miscarriage comprising:

measuring the concentration of at least one metabolite in a sample obtained from a subject; and comparing the concentration of the at least one metabolite with the concentration of the respective metabolite in a control group;

wherein the metabolite is selected from the group consisting of tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, 3-methylglutarylcarnitine, hexanoylcarnitine, 3α,20α - dihydroxy-5 -pregnane-3 -glucuronide and combinations thereof.

The method of any of the preceding claims, wherein an increase or decrease in concentration of the measured values, or absence or presence of a metabolite, compared to the control group identifies an increased risk of miscarriage.

The method of any of the preceding claims, wherein the control group consists of subjects having undergone a full-term birth.

The method of any of the preceding claims, wherein the method comprises measuring at least two, at least three, at least four metabolites, at least five metabolites, or six metabolites.

The method of any of the preceding claims, wherein the metabolites are tetrahydrocortisone, propionylcarnitine, isovalerylcarnitine, 3-methylglutarylcarnitine, hexanoylcarnitine and 3α,20α - dihydroxy-5 -pregnane-3 -glucuronide.

The method of any of the preceding claims, wherein a decrease of 3α,20α -dihydroxy-5 - pregnane-3 -glucuronide is indicative of an increased risk of miscarriage.

The method of claim 7, wherein the decrease of 3a,20a-dihydroxy-5 -pregnane-3-glucuronide compared to the control is a decrease of at least 0.30 fold change (-0.30) in concentration compared to the control.

The method of any of the preceding claims, wherein an increase of tetrahydrocortisone is indicative of an increased risk of miscarriage.

The method of claim 9, wherein an increase of tetrahydrocortisone compared to the control is an increase of at least 0.40 fold change in concentration compared to the control.

The method of any of the preceding claims, wherein a decrease of propionylcarnitine is indicative of an increased risk of miscarriage.

The method of claim 11 , wherein a decrease of propionylcarnitine compared to the control is a decrease of at least 0.41 fold change (-0.41) in concentration compared to the control

The method of any of the preceding claims, wherein a decrease of isovalerylcarnitme is indicative of an increased risk of miscarriage.

The method of claim 13, wherein a decrease of isovalerylcarnitme compared to the control is a decrease of at least 0.32 fold change (-0.32) in concentration compared to the control

The method of any of the preceding claims, wherein a decrease of 3-methylglutarylcarnitine is indicative of an increased risk of miscarriage.

The method of claim 15, wherein a decrease of 3-methylglutarylcarnitine compared to the control is a decrease of at least 0.59 fold change (-0.59) in concentration compared to the control

The method of any of the preceding claims, wherein an increase of hexanoylcarnitine is indicative of an increased risk of miscarriage.

The method of claim 17, wherein an increase of hexanoylcarnitine compared to the control is an increase of at least 0.38 fold change in concentration compared to the control

The method of any of the preceding claims, wherein the miscarriage is selected from the group consisting of spontaneous miscarriage, first-trimester miscarriage, second-trimester miscarriage, blighted ovum, and missed miscarriage.

The method of any of the preceding claims, wherein the metabolites are detected using methods selected from the group consisting of chromatography, antibody-based assays, mass spectrometry, spectroscopy, immunoprecipitation and combinations thereof.

The method of any of the preceding claims, wherein the sample is a serum or a urine sample.

22. The method of any of the preceding claims, wherein the subject is to be administered dydrogesterone if identified to be at risk of miscarriage.

23. A kit for identifying the risk of a miscarriage according to the method as described in claims 1 to 21.

24. The kit according to claim 23, wherein the kit is one selected from the group consisting of an ELISA kit, a test strip kit, and a microchip test kit.

25. The kit according to claim 23, wherein the kit comprises one metabolite standard for each metabolite as described in any one of claims 1 to 21.